Daily Peak Load Forecasting of Next Day using Weather Distribution and Comparison Value of Each Nearby Date Data

NASA Astrophysics Data System (ADS)

Ito, Shigenobu; Yukita, Kazuto; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Nakano, Hiroyuki

By the development of industry, in recent years; dependence to electric energy is growing year by year. Therefore, reliable electric power supply is in need. However, to stock a huge amount of electric energy is very difficult. Also, there is a necessity to keep balance between the demand and supply, which changes hour after hour. Consequently, to supply the high quality and highly dependable electric power supply, economically, and with high efficiency, there is a need to forecast the movement of the electric power demand carefully in advance. And using that forecast as the source, supply and demand management plan should be made. Thus load forecasting is said to be an important job among demand investment of electric power companies. So far, forecasting method using Fuzzy logic, Neural Net Work, Regression model has been suggested for the development of forecasting accuracy. Those forecasting accuracy is in a high level. But to invest electric power in higher accuracy more economically, a new forecasting method with higher accuracy is needed. In this paper, to develop the forecasting accuracy of the former methods, the daily peak load forecasting method using the weather distribution of highest and lowest temperatures, and comparison value of each nearby date data is suggested.

The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city

NASA Astrophysics Data System (ADS)

Qiu, Yunfei; Li, Xizhong; Zheng, Wei; Hu, Qinghe; Wei, Zhanmeng; Yue, Yaqin

2017-08-01

The climate changes have great impact on the residents’ electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.

Short-term load and wind power forecasting using neural network-based prediction intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

A Gaussian Processes Technique for Short-term Load Forecasting with Considerations of Uncertainty

NASA Astrophysics Data System (ADS)

Ohmi, Masataro; Mori, Hiroyuki

In this paper, an efficient method is proposed to deal with short-term load forecasting with the Gaussian Processes. Short-term load forecasting plays a key role to smooth power system operation such as economic load dispatching, unit commitment, etc. Recently, the deregulated and competitive power market increases the degree of uncertainty. As a result, it is more important to obtain better prediction results to save the cost. One of the most important aspects is that power system operator needs the upper and lower bounds of the predicted load to deal with the uncertainty while they require more accurate predicted values. The proposed method is based on the Bayes model in which output is expressed in a distribution rather than a point. To realize the model efficiently, this paper proposes the Gaussian Processes that consists of the Bayes linear model and kernel machine to obtain the distribution of the predicted value. The proposed method is successively applied to real data of daily maximum load forecasting.

7 CFR 1710.202 - Requirement to prepare a load forecast-power supply borrowers.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Requirement to prepare a load forecast-power supply borrowers. 1710.202 Section 1710.202 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load...

Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoff, Thomas Hoff; Kankiewicz, Adam

Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP)more » forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest uncertainties. This work culminated in a GO decision being made by the California ISO to include zonal BTM forecasts into its operational load forecasting system. The California ISO’s Manager of Short Term Forecasting, Jim Blatchford, summarized the research performed in this project with the following quote: “The behind-the-meter (BTM) California ISO region forecasting research performed by Clean Power Research and sponsored by the Department of Energy’s SUNRISE program was an opportunity to verify value and demonstrate improved load forecast capability. In 2016, the California ISO will be incorporating the BTM forecast into the Hour Ahead and Day Ahead load models to look for improvements in the overall load forecast accuracy as BTM PV capacity continues to grow.”« less

Supplier Short Term Load Forecasting Using Support Vector Regression and Exogenous Input

NASA Astrophysics Data System (ADS)

Matijaš, Marin; Vukićcević, Milan; Krajcar, Slavko

2011-09-01

In power systems, task of load forecasting is important for keeping equilibrium between production and consumption. With liberalization of electricity markets, task of load forecasting changed because each market participant has to forecast their own load. Consumption of end-consumers is stochastic in nature. Due to competition, suppliers are not in a position to transfer their costs to end-consumers; therefore it is essential to keep forecasting error as low as possible. Numerous papers are investigating load forecasting from the perspective of the grid or production planning. We research forecasting models from the perspective of a supplier. In this paper, we investigate different combinations of exogenous input on the simulated supplier loads and show that using points of delivery as a feature for Support Vector Regression leads to lower forecasting error, while adding customer number in different datasets does the opposite.

A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

PubMed

Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias

2013-10-01

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, themore » proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.« less

Forecasting of hourly load by pattern recognition in a small area power system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dehdashti-Shahrokh, A.

1982-01-01

An intuitive, logical, simple and efficient method of forecasting hourly load in a small area power system is presented. A pattern recognition approach is used in developing the forecasting model. Pattern recognition techniques are powerful tools in the field of artificial intelligence (cybernetics) and simulate the way the human brain operates to make decisions. Pattern recognition is generally used in analysis of processes where the total physical nature behind the process variation is unkown but specific kinds of measurements explain their behavior. In this research basic multivariate analyses, in conjunction with pattern recognition techniques, are used to develop a linearmore » deterministic model to forecast hourly load. This method assumes that load patterns in the same geographical area are direct results of climatological changes (weather sensitive load), and have occurred in the past as a result of similar climatic conditions. The algorithm described in here searches for the best possible pattern from a seasonal library of load and weather data in forecasting hourly load. To accommodate the unpredictability of weather and the resulting load, the basic twenty-four load pattern was divided into eight three-hour intervals. This division was made to make the model adaptive to sudden climatic changes. The proposed method offers flexible lead times of one to twenty-four hours. The results of actual data testing had indicated that this proposed method is computationally efficient, highly adaptive, with acceptable data storage size and accuracy that is comparable to many other existing methods.« less

A Load-Based Temperature Prediction Model for Anomaly Detection

NASA Astrophysics Data System (ADS)

Sobhani, Masoud

Electric load forecasting, as a basic requirement for the decision-making in power utilities, has been improved in various aspects in the past decades. Many factors may affect the accuracy of the load forecasts, such as data quality, goodness of the underlying model and load composition. Due to the strong correlation between the input variables (e.g., weather and calendar variables) and the load, the quality of input data plays a vital role in forecasting practices. Even if the forecasting model were able to capture most of the salient features of the load, a low quality input data may result in inaccurate forecasts. Most of the data cleansing efforts in the load forecasting literature have been devoted to the load data. Few studies focused on weather data cleansing for load forecasting. This research proposes an anomaly detection method for the temperature data. The method consists of two components: a load-based temperature prediction model and a detection technique. The effectiveness of the proposed method is demonstrated through two case studies: one based on the data from the Global Energy Forecasting Competition 2014, and the other based on the data published by ISO New England. The results show that by removing the detected observations from the original input data, the final load forecast accuracy is enhanced.

A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

PubMed Central

Moriano, Javier; Rodríguez, Francisco Javier; Martín, Pedro; Jiménez, Jose Antonio; Vuksanovic, Branislav

2016-01-01

In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF) allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected. PMID:26771613

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

PubMed

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs

PubMed Central

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network. PMID:26571042

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution systemmore » operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-01-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the loadmore » and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.« less

Short term load forecasting of anomalous load using hybrid soft computing methods

NASA Astrophysics Data System (ADS)

Rasyid, S. A.; Abdullah, A. G.; Mulyadi, Y.

2016-04-01

Load forecast accuracy will have an impact on the generation cost is more economical. The use of electrical energy by consumers on holiday, show the tendency of the load patterns are not identical, it is different from the pattern of the load on a normal day. It is then defined as a anomalous load. In this paper, the method of hybrid ANN-Particle Swarm proposed to improve the accuracy of anomalous load forecasting that often occur on holidays. The proposed methodology has been used to forecast the half-hourly electricity demand for power systems in the Indonesia National Electricity Market in West Java region. Experiments were conducted by testing various of learning rate and learning data input. Performance of this methodology will be validated with real data from the national of electricity company. The result of observations show that the proposed formula is very effective to short-term load forecasting in the case of anomalous load. Hybrid ANN-Swarm Particle relatively simple and easy as a analysis tool by engineers.

[Demography perspectives and forecasts of the demand for electricity].

PubMed

Roy, L; Guimond, E

1995-01-01

"Demographic perspectives form an integral part in the development of electric load forecasts. These forecasts in turn are used to justify the addition and repair of generating facilities that will supply power in the coming decades. The goal of this article is to present how demographic perspectives are incorporated into the electric load forecasting in Quebec. The first part presents the methods, hypotheses and results of population and household projections used by Hydro-Quebec in updating its latest development plan. The second section demonstrates applications of such demographic projections for forecasting the electric load, with a focus on the residential sector." (SUMMARY IN ENG AND SPA) excerpt

Improved Neural Networks with Random Weights for Short-Term Load Forecasting

PubMed Central

Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo

2015-01-01

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting. PMID:26629825

Improved Neural Networks with Random Weights for Short-Term Load Forecasting.

PubMed

Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo

2015-01-01

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.

Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steckler, N.; Florita, A.; Zhang, J.

2013-11-01

As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecastsmore » relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.« less

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abbas, Nikhar; Tom, Nathan M

2017-06-03

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abbas, Nikhar; Tom, Nathan

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

Quantifying and Reducing Uncertainty in Correlated Multi-Area Short-Term Load Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Yannan; Hou, Zhangshuan; Meng, Da

2016-07-17

In this study, we represent and reduce the uncertainties in short-term electric load forecasting by integrating time series analysis tools including ARIMA modeling, sequential Gaussian simulation, and principal component analysis. The approaches are mainly focusing on maintaining the inter-dependency between multiple geographically related areas. These approaches are applied onto cross-correlated load time series as well as their forecast errors. Multiple short-term prediction realizations are then generated from the reduced uncertainty ranges, which are useful for power system risk analyses.

Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mbamalu, G.A.N.; El-Hawary, M.E.

The authors propose suboptimal least squares or IRWLS procedures for estimating the parameters of a seasonal multiplicative AR model encountered during power system load forecasting. The proposed method involves using an interactive computer environment to estimate the parameters of a seasonal multiplicative AR process. The method comprises five major computational steps. The first determines the order of the seasonal multiplicative AR process, and the second uses the least squares or the IRWLS to estimate the optimal nonseasonal AR model parameters. In the third step one obtains the intermediate series by back forecast, which is followed by using the least squaresmore » or the IRWLS to estimate the optimal season AR parameters. The final step uses the estimated parameters to forecast future load. The method is applied to predict the Nova Scotia Power Corporation's 168 lead time hourly load. The results obtained are documented and compared with results based on the Box and Jenkins method.« less

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE PAGES

Buitrago, Jaime; Asfour, Shihab

2017-01-01

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buitrago, Jaime; Asfour, Shihab

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Bulk electric system reliability evaluation incorporating wind power and demand side management

NASA Astrophysics Data System (ADS)

Huang, Dange

Electric power systems are experiencing dramatic changes with respect to structure, operation and regulation and are facing increasing pressure due to environmental and societal constraints. Bulk electric system reliability is an important consideration in power system planning, design and operation particularly in the new competitive environment. A wide range of methods have been developed to perform bulk electric system reliability evaluation. Theoretically, sequential Monte Carlo simulation can include all aspects and contingencies in a power system and can be used to produce an informative set of reliability indices. It has become a practical and viable tool for large system reliability assessment technique due to the development of computing power and is used in the studies described in this thesis. The well-being approach used in this research provides the opportunity to integrate an accepted deterministic criterion into a probabilistic framework. This research work includes the investigation of important factors that impact bulk electric system adequacy evaluation and security constrained adequacy assessment using the well-being analysis framework. Load forecast uncertainty is an important consideration in an electrical power system. This research includes load forecast uncertainty considerations in bulk electric system reliability assessment and the effects on system, load point and well-being indices and reliability index probability distributions are examined. There has been increasing worldwide interest in the utilization of wind power as a renewable energy source over the last two decades due to enhanced public awareness of the environment. Increasing penetration of wind power has significant impacts on power system reliability, and security analyses become more uncertain due to the unpredictable nature of wind power. The effects of wind power additions in generating and bulk electric system reliability assessment considering site wind speed correlations and the interactive effects of wind power and load forecast uncertainty on system reliability are examined. The concept of the security cost associated with operating in the marginal state in the well-being framework is incorporated in the economic analyses associated with system expansion planning including wind power and load forecast uncertainty. Overall reliability cost/worth analyses including security cost concepts are applied to select an optimal wind power injection strategy in a bulk electric system. The effects of the various demand side management measures on system reliability are illustrated using the system, load point, and well-being indices, and the reliability index probability distributions. The reliability effects of demand side management procedures in a bulk electric system including wind power and load forecast uncertainty considerations are also investigated. The system reliability effects due to specific demand side management programs are quantified and examined in terms of their reliability benefits.

Residential Saudi load forecasting using analytical model and Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Al-Harbi, Ahmad Abdulaziz

In recent years, load forecasting has become one of the main fields of study and research. Short Term Load Forecasting (STLF) is an important part of electrical power system operation and planning. This work investigates the applicability of different approaches; Artificial Neural Networks (ANNs) and hybrid analytical models to forecast residential load in Kingdom of Saudi Arabia (KSA). These two techniques are based on model human modes behavior formulation. These human modes represent social, religious, official occasions and environmental parameters impact. The analysis is carried out on residential areas for three regions in two countries exposed to distinct people activities and weather conditions. The collected data are for Al-Khubar and Yanbu industrial city in KSA, in addition to Seattle, USA to show the validity of the proposed models applied on residential load. For each region, two models are proposed. First model is next hour load forecasting while second model is next day load forecasting. Both models are analyzed using the two techniques. The obtained results for ANN next hour models yield very accurate results for all areas while relatively reasonable results are achieved when using hybrid analytical model. For next day load forecasting, the two approaches yield satisfactory results. Comparative studies were conducted to prove the effectiveness of the models proposed.

Short term load forecasting using a self-supervised adaptive neural network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, H.; Pimmel, R.L.

The authors developed a self-supervised adaptive neural network to perform short term load forecasts (STLF) for a large power system covering a wide service area with several heavy load centers. They used the self-supervised network to extract correlational features from temperature and load data. In using data from the calendar year 1993 as a test case, they found a 0.90 percent error for hour-ahead forecasting and 1.92 percent error for day-ahead forecasting. These levels of error compare favorably with those obtained by other techniques. The algorithm ran in a couple of minutes on a PC containing an Intel Pentium --more » 120 MHz CPU. Since the algorithm included searching the historical database, training the network, and actually performing the forecasts, this approach provides a real-time, portable, and adaptable STLF.« less

7 CFR 1710.203 - Requirement to prepare a load forecast-distribution borrowers.

Code of Federal Regulations, 2011 CFR

2011-01-01

...—distribution borrowers. (a) A distribution borrower that is a member of a power supply borrower with a total... forecast work plan of its power supply borrower. (b) A distribution borrower that is a member of a power supply borrower which is itself a member of another power supply borrower that has a total utility plant...

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-07-25

This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load valuesmore » to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-09-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and windmore » forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system “breaking points”, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.« less

Short-term load forecasting using neural network for future smart grid application

NASA Astrophysics Data System (ADS)

Zennamo, Joseph Anthony, III

Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.

An econometric simulation model of income and electricity demand in Alaska's Railbelt, 1982-2022

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maddigan, R.J.; Hill, L.J.; Hamblin, D.M.

1987-01-01

This report describes the specification of-and forecasts derived from-the Alaska Railbelt Electricity Load, Macroeconomic (ARELM) model. ARELM was developed as an independent, modeling tool for the evaluation of the need for power from the Susitna Hydroelectric Project which has been proposed by the Alaska Power Authority. ARELM is an econometric simulation model consisting of 61 equations - 46 behavioral equations and 15 identities. The system includes two components: (1) ARELM-MACRO which is a system of equations that simulates the performance of both the total Alaskan and Railbelt macroeconomies and (2) ARELM-LOAD which projects electricity-related activity in the Alaskan Railbelt region.more » The modeling system is block recursive in the sense that forecasts of population, personal income, and employment in the Railbelt derived from ARELM-MACRO are used as explanatory variables in ARELM-LOAD to simulate electricity demand, the real average price of electricity, and the number of customers in the Railbelt. Three scenarios based on assumptions about the future price of crude oil are simulated and documented in the report. The simulations, which do not include the cost-of-power impacts of Susitna-based generation, show that the growth rate in Railbelt electricity load is between 2.5 and 2.7% over the 1982 to 2022 forecast period. The forecasting results are consistent with other projections of load growth in the region using different modeling approaches.« less

Analyzing Effect of System Inertia on Grid Frequency Forecasting Usnig Two Stage Neuro-Fuzzy System

NASA Astrophysics Data System (ADS)

Chourey, Divyansh R.; Gupta, Himanshu; Kumar, Amit; Kumar, Jitesh; Kumar, Anand; Mishra, Anup

2018-04-01

Frequency forecasting is an important aspect of power system operation. The system frequency varies with load-generation imbalance. Frequency variation depends upon various parameters including system inertia. System inertia determines the rate of fall of frequency after the disturbance in the grid. Though, inertia of the system is not considered while forecasting the frequency of power system during planning and operation. This leads to significant errors in forecasting. In this paper, the effect of inertia on frequency forecasting is analysed for a particular grid system. In this paper, a parameter equivalent to system inertia is introduced. This parameter is used to forecast the frequency of a typical power grid for any instant of time. The system gives appreciable result with reduced error.

Application of Classification Methods for Forecasting Mid-Term Power Load Patterns

NASA Astrophysics Data System (ADS)

Piao, Minghao; Lee, Heon Gyu; Park, Jin Hyoung; Ryu, Keun Ho

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed approach in this paper consists of three stages: (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

Integrating Solar Power onto the Electric Grid - Bridging the Gap between Atmospheric Science, Engineering and Economics

NASA Astrophysics Data System (ADS)

Ghonima, M. S.; Yang, H.; Zhong, X.; Ozge, B.; Sahu, D. K.; Kim, C. K.; Babacan, O.; Hanna, R.; Kurtz, B.; Mejia, F. A.; Nguyen, A.; Urquhart, B.; Chow, C. W.; Mathiesen, P.; Bosch, J.; Wang, G.

2015-12-01

One of the main obstacles to high penetrations of solar power is the variable nature of solar power generation. To mitigate variability, grid operators have to schedule additional reliability resources, at considerable expense, to ensure that load requirements are met by generation. Thus despite the cost of solar PV decreasing, the cost of integrating solar power will increase as penetration of solar resources onto the electric grid increases. There are three principal tools currently available to mitigate variability impacts: (i) flexible generation, (ii) storage, either virtual (demand response) or physical devices and (iii) solar forecasting. Storage devices are a powerful tool capable of ensuring smooth power output from renewable resources. However, the high cost of storage is prohibitive and markets are still being designed to leverage their full potential and mitigate their limitation (e.g. empty storage). Solar forecasting provides valuable information on the daily net load profile and upcoming ramps (increasing or decreasing solar power output) thereby providing the grid advance warning to schedule ancillary generation more accurately, or curtail solar power output. In order to develop solar forecasting as a tool that can be utilized by the grid operators we identified two focus areas: (i) develop solar forecast technology and improve solar forecast accuracy and (ii) develop forecasts that can be incorporated within existing grid planning and operation infrastructure. The first issue required atmospheric science and engineering research, while the second required detailed knowledge of energy markets, and power engineering. Motivated by this background we will emphasize area (i) in this talk and provide an overview of recent advancements in solar forecasting especially in two areas: (a) Numerical modeling tools for coastal stratocumulus to improve scheduling in the day-ahead California energy market. (b) Development of a sky imager to provide short term forecasts (0-20 min ahead) to improve optimization and control of equipment on distribution feeders with high penetration of solar. Leveraging such tools that have seen extensive use in the atmospheric sciences supports the development of accurate physics-based solar forecast models. Directions for future research are also provided.

A Solar Time-Based Analog Ensemble Method for Regional Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Zhang, Xinmin; Li, Yuan

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, formore » both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. The normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.« less

Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

NASA Astrophysics Data System (ADS)

Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

2010-02-01

This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2014-04-14

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation.more » We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.« less

Carbon-Carbon Recuperators in Closed-Brayton-Cycle Space Power Systems

NASA Technical Reports Server (NTRS)

Barrett, Michael J.; Johnson, Paul K.

2006-01-01

The use of carbon-carbon (C-C) recuperators in closed-Brayton-cycle space power conversion systems was assessed. Recuperator performance was forecast based on notional thermodynamic cycle state values for planetary missions. Resulting thermal performance, mass and volume for plate-fin C-C recuperators were estimated and quantitatively compared with values for conventional offset-strip-fin metallic designs. Mass savings of 40-55% were projected for C-C recuperators with effectiveness greater than 0.9 and thermal loads from 25-1400 kWt. The smaller thermal loads corresponded with lower mass savings; however, at least 50% savings were forecast for all loads above 300 kWt. System-related material challenges and compatibility issues were also discussed.

Study on power grid characteristics in summer based on Linear regression analysis

NASA Astrophysics Data System (ADS)

Tang, Jin-hui; Liu, You-fei; Liu, Juan; Liu, Qiang; Liu, Zhuan; Xu, Xi

2018-05-01

The correlation analysis of power load and temperature is the precondition and foundation for accurate load prediction, and a great deal of research has been made. This paper constructed the linear correlation model between temperature and power load, then the correlation of fault maintenance work orders with the power load is researched. Data details of Jiangxi province in 2017 summer such as temperature, power load, fault maintenance work orders were adopted in this paper to develop data analysis and mining. Linear regression models established in this paper will promote electricity load growth forecast, fault repair work order review, distribution network operation weakness analysis and other work to further deepen the refinement.

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

NASA Astrophysics Data System (ADS)

Singh, Navneet K.; Singh, Asheesh K.; Tripathy, Manoj

2012-05-01

For power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planning etc. A new technique for long term load forecasting (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study. The prediction performance of proposed technique is evaluated using mean absolute percentage error (MAPE) of Thailand private electricity consumption and forecasted data. The results obtained are compared with the results of classical auto-regressive (AR) and moving average (MA) methods. It is, in general, observed that the proposed method is prediction wise more accurate.

Carbon-Carbon Recuperators in Closed-Brayton-Cycle Space Power Systems

NASA Technical Reports Server (NTRS)

Barrett, Michael J.; Johnson, Paul K.; Naples, Andrew G.

2006-01-01

The feasibility of using carbon-carbon (C-C) recuperators in conceptual closed-Brayton-cycle space power conversion systems was assessed. Recuperator performance expectations were forecast based on notional thermodynamic cycle state values for potential planetary missions. Resulting thermal performance, mass and volume for plate-fin C-C recuperators were estimated and quantitatively compared with values for conventional offset-strip-fin metallic designs. Mass savings of 30 to 60 percent were projected for C-C recuperators with effectiveness greater than 0.9 and thermal loads from 25 to 1400 kWt. The smaller thermal loads corresponded with lower mass savings; however, 60 percent savings were forecast for all loads above 300 kWt. System-related material challenges and compatibility issues were also discussed.

Hybrid robust predictive optimization method of power system dispatch

DOEpatents

Chandra, Ramu Sharat [Niskayuna, NY; Liu, Yan [Ballston Lake, NY; Bose, Sumit [Niskayuna, NY; de Bedout, Juan Manuel [West Glenville, NY

2011-08-02

A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

Wet snow hazard for power lines: a forecast and alert system applied in Italy

NASA Astrophysics Data System (ADS)

Bonelli, P.; Lacavalla, M.; Marcacci, P.; Mariani, G.; Stella, G.

2011-09-01

Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly. The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast), developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.

Real-time anomaly detection for very short-term load forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Jian; Hong, Tao; Yue, Meng

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

Real-time anomaly detection for very short-term load forecasting

DOE PAGES

Luo, Jian; Hong, Tao; Yue, Meng

2018-01-06

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

Network-Cognizant Voltage Droop Control for Distribution Grids

DOE PAGES

Baker, Kyri; Bernstein, Andrey; Dall'Anese, Emiliano; ...

2017-08-07

Our paper examines distribution systems with a high integration of distributed energy resources (DERs) and addresses the design of local control methods for real-time voltage regulation. Particularly, the paper focuses on proportional control strategies where the active and reactive output-powers of DERs are adjusted in response to (and proportionally to) local changes in voltage levels. The design of the voltage-active power and voltage-reactive power characteristics leverages suitable linear approximation of the AC power-flow equations and is network-cognizant; that is, the coefficients of the controllers embed information on the location of the DERs and forecasted non-controllable loads/injections and, consequently, on themore » effect of DER power adjustments on the overall voltage profile. We pursued a robust approach to cope with uncertainty in the forecasted non-controllable loads/power injections. Stability of the proposed local controllers is analytically assessed and numerically corroborated.« less

Network-Cognizant Voltage Droop Control for Distribution Grids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Kyri; Bernstein, Andrey; Dall'Anese, Emiliano

Our paper examines distribution systems with a high integration of distributed energy resources (DERs) and addresses the design of local control methods for real-time voltage regulation. Particularly, the paper focuses on proportional control strategies where the active and reactive output-powers of DERs are adjusted in response to (and proportionally to) local changes in voltage levels. The design of the voltage-active power and voltage-reactive power characteristics leverages suitable linear approximation of the AC power-flow equations and is network-cognizant; that is, the coefficients of the controllers embed information on the location of the DERs and forecasted non-controllable loads/injections and, consequently, on themore » effect of DER power adjustments on the overall voltage profile. We pursued a robust approach to cope with uncertainty in the forecasted non-controllable loads/power injections. Stability of the proposed local controllers is analytically assessed and numerically corroborated.« less

Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

PubMed Central

Hu, Zhongyi; Xiong, Tao

2013-01-01

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature. PMID:24459425

Electricity load forecasting using support vector regression with memetic algorithms.

PubMed

Hu, Zhongyi; Bao, Yukun; Xiong, Tao

2013-01-01

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.

7 CFR 1710.300 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

... forecast. The forecast should be used by the board of directors and the manager to guide the system towards... projected results of future actions planned by the borrower's board of directors; (2) The financial goals... type of large power loads, projections of future borrowings and the associated interest, projected...

7 CFR 1710.300 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

... forecast. The forecast should be used by the board of directors and the manager to guide the system towards... projected results of future actions planned by the borrower's board of directors; (2) The financial goals... type of large power loads, projections of future borrowings and the associated interest, projected...

Power Flow Simulations of a More Renewable California Grid Utilizing Wind and Solar Insolation Forecasting

NASA Astrophysics Data System (ADS)

Hart, E. K.; Jacobson, M. Z.; Dvorak, M. J.

2008-12-01

Time series power flow analyses of the California electricity grid are performed with extensive addition of intermittent renewable power. The study focuses on the effects of replacing non-renewable and imported (out-of-state) electricity with wind and solar power on the reliability of the transmission grid. Simulations are performed for specific days chosen throughout the year to capture seasonal fluctuations in load, wind, and insolation. Wind farm expansions and new wind farms are proposed based on regional wind resources and time-dependent wind power output is calculated using a meteorological model and the power curves of specific wind turbines. Solar power is incorporated both as centralized and distributed generation. Concentrating solar thermal plants are modeled using local insolation data and the efficiencies of pre-existing plants. Distributed generation from rooftop PV systems is included using regional insolation data, efficiencies of common PV systems, and census data. The additional power output of these technologies offsets power from large natural gas plants and is balanced for the purposes of load matching largely with hydroelectric power and by curtailment when necessary. A quantitative analysis of the effects of this significant shift in the electricity portfolio of the state of California on power availability and transmission line congestion, using a transmission load-flow model, is presented. A sensitivity analysis is also performed to determine the effects of forecasting errors in wind and insolation on load-matching and transmission line congestion.

Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid (Spanish Version)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tian, Tian; Chernyakhovskiy, Ilya; Brancucci Martinez-Anido, Carlo

This document is the Spanish version of 'Greening the Grid- Forecasting Wind and Solar Generation Improving System Operations'. It discusses improving system operations with forecasting with and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

Intra-Hour Dispatch and Automatic Generator Control Demonstration with Solar Forecasting - Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coimbra, Carlos F. M.

2016-02-25

In this project we address multiple resource integration challenges associated with increasing levels of solar penetration that arise from the variability and uncertainty in solar irradiance. We will model the SMUD service region as its own balancing region, and develop an integrated, real-time operational tool that takes solar-load forecast uncertainties into consideration and commits optimal energy resources and reserves for intra-hour and intra-day decisions. The primary objectives of this effort are to reduce power system operation cost by committing appropriate amount of energy resources and reserves, as well as to provide operators a prediction of the generation fleet’s behavior inmore » real time for realistic PV penetration scenarios. The proposed methodology includes the following steps: clustering analysis on the expected solar variability per region for the SMUD system, Day-ahead (DA) and real-time (RT) load forecasts for the entire service areas, 1-year of intra-hour CPR forecasts for cluster centers, 1-year of smart re-forecasting CPR forecasts in real-time for determination of irreducible errors, and uncertainty quantification for integrated solar-load for both distributed and central stations (selected locations within service region) PV generation.« less

Day-Ahead Short-Term Forecasting Electricity Load via Approximation

NASA Astrophysics Data System (ADS)

Khamitov, R. N.; Gritsay, A. S.; Tyunkov, D. A.; E Sinitsin, G.

2017-04-01

The method of short-term forecasting of a power consumption which can be applied to short-term forecasting of power consumption is offered. The offered model is based on sinusoidal function for the description of day and night cycles of power consumption. Function coefficients - the period and amplitude are set up is adaptive, considering dynamics of power consumption with use of an artificial neural network. The presented results are tested on real retrospective data of power supply company. The offered method can be especially useful if there are no opportunities of collection of interval indications of metering devices of consumers, and the power supply company operates with electrical supply points. The offered method can be used by any power supply company upon purchase of the electric power in the wholesale market. For this purpose, it is necessary to receive coefficients of approximation of sinusoidal function and to have retrospective data on power consumption on an interval not less than one year.

Analysis of rock mass dynamic impact influence on the operation of a powered roof support control system

NASA Astrophysics Data System (ADS)

Szurgacz, Dawid; Brodny, Jaroław

2018-01-01

A powered roof support is a machine responsible for protection of an underground excavation against deformation generated by rock mass. In the case of dynamic impact of rock mass, the proper level of protection is hard to achieve. Therefore, the units of the roof support and its components are subject to detailed tests aimed at acquiring greater reliability, efficiency and efficacy. In the course of such test, however, it is not always possible to foresee values of load that may occur in actual conditions. The article presents a case of a dynamic load impacting the powered roof support during a high-energy tremor in an underground hard coal mine. The authors discuss the method for selecting powered roof support units proper for specific forecasted load conditions. The method takes into account the construction of the support and mining and geological conditions of an excavation. Moreover, the paper includes tests carried out on hydraulic legs and yield valves which were responsible for additional yielding of the support. Real loads impacting the support unit during tremors are analysed. The results indicated that the real registered values of the load were significantly greater than the forecasted values. The analysis results of roof support operation during dynamic impact generated by the rock mass (real life conditions) prompted the authors to develop a set of recommendations for manufacturers and users of powered roof supports. These include, inter alia, the need for innovative solutions for testing hydraulic section systems.

Price elasticity matrix of demand in power system considering demand response programs

NASA Astrophysics Data System (ADS)

Qu, Xinyao; Hui, Hongxun; Yang, Shengchun; Li, Yaping; Ding, Yi

2018-02-01

The increasing renewable energy power generations have brought more intermittency and volatility to the electric power system. Demand-side resources can improve the consumption of renewable energy by demand response (DR), which becomes one of the important means to improve the reliability of power system. In price-based DR, the sensitivity analysis of customer’s power demand to the changing electricity prices is pivotal for setting reasonable prices and forecasting loads of power system. This paper studies the price elasticity matrix of demand (PEMD). An improved PEMD model is proposed based on elasticity effect weight, which can unify the rigid loads and flexible loads. Moreover, the structure of PEMD, which is decided by price policies and load types, and the calculation method of PEMD are also proposed. Several cases are studied to prove the effectiveness of this method.

Neural network based load and price forecasting and confidence interval estimation in deregulated power markets

NASA Astrophysics Data System (ADS)

Zhang, Li

With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman filter (EKF) can be used as an integrated adaptive learning and confidence interval estimation algorithm for neural networks, with fast convergence and small confidence intervals. However, EKF learning is computationally expensive because it involves high dimensional matrix manipulations. A modified U-D factorization within the decoupled EKF (DEKF-UD) framework is developed in this research. The computational efficiency and numerical stability are significantly improved.

Load research manual. Volume 2: Fundamentals of implementing load research procedures

NASA Astrophysics Data System (ADS)

1980-11-01

This manual will assist electric utilities and state regulatory authorities in investigating customer electricity demand as part of cost-of-service studies, rate design, marketing research, system design, load forecasting, rate reform analysis, and load management research. Load research procedures are described in detail. Research programs at three utilities are compared: Carolina Power and Light Company, Long Island Lighting Company, and Southern California Edison Company. A load research bibliography and glossaries of load research and statistical terms are also included.

76 FR 72203 - Voltage Coordination on High Voltage Grids; Notice of Reliability Workshop Agenda

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-22

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. AD12-5-000] Voltage... currently coordinate the dispatch of reactive resources to support forecasted loads, generation and... reactive power needs of the distribution system or loads are coordinated or optimized. Panelists: Khaled...

7 CFR 1710.300 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

... the forecast, including the methodology used to project loads, rates, revenue, power costs, operating expenses, plant additions, and other factors having a material effect on the balance sheet and on financial... regional office will consult with the Power Supply Division in the case of generation projects for...

Research on Operation Strategy for Bundled Wind-thermal Generation Power Systems Based on Two-Stage Optimization Model

NASA Astrophysics Data System (ADS)

Sun, Congcong; Wang, Zhijie; Liu, Sanming; Jiang, Xiuchen; Sheng, Gehao; Liu, Tianyu

2017-05-01

Wind power has the advantages of being clean and non-polluting and the development of bundled wind-thermal generation power systems (BWTGSs) is one of the important means to improve wind power accommodation rate and implement “clean alternative” on generation side. A two-stage optimization strategy for BWTGSs considering wind speed forecasting results and load characteristics is proposed. By taking short-term wind speed forecasting results of generation side and load characteristics of demand side into account, a two-stage optimization model for BWTGSs is formulated. By using the environmental benefit index of BWTGSs as the objective function, supply-demand balance and generator operation as the constraints, the first-stage optimization model is developed with the chance-constrained programming theory. By using the operation cost for BWTGSs as the objective function, the second-stage optimization model is developed with the greedy algorithm. The improved PSO algorithm is employed to solve the model and numerical test verifies the effectiveness of the proposed strategy.

Loss of Load Probability Calculation for West Java Power System with Nuclear Power Plant Scenario

NASA Astrophysics Data System (ADS)

Azizah, I. D.; Abdullah, A. G.; Purnama, W.; Nandiyanto, A. B. D.; Shafii, M. A.

2017-03-01

Loss of Load Probability (LOLP) index showing the quality and performance of an electrical system. LOLP value is affected by load growth, the load duration curve, forced outage rate of the plant, number and capacity of generating units. This reliability index calculation begins with load forecasting to 2018 using multiple regression method. Scenario 1 with compositions of conventional plants produce the largest LOLP in 2017 amounted to 71.609 days / year. While the best reliability index generated in scenario 2 with the NPP amounted to 6.941 days / year in 2015. Improved reliability of systems using nuclear power more efficiently when compared to conventional plants because it also has advantages such as emission-free, inexpensive fuel costs, as well as high level of plant availability.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

Advanced Cloud Forecasting for Solar Energy Production

NASA Astrophysics Data System (ADS)

Werth, D. W.; Parker, M. J.

2017-12-01

A power utility must decide days in advance how it will allocate projected loads among its various generating sources. If the latter includes solar plants, the utility must predict how much energy the plants will produce - any shortfall will have to be compensated for by purchasing power as it is needed, when it is more expensive. To avoid this, utilities often err on the side of caution and assume that a relatively small amount of solar energy will be available, and allocate correspondingly more load to coal-fired plants. If solar irradiance can be predicted more accurately, utilities can be more confident that the predicted solar energy will indeed be available when needed, and assign solar plants a larger share of the future load. Solar power production is increasing in the Southeast, but is often hampered by irregular cloud fields, especially during high-pressure periods when rapid afternoon thunderstorm development can occur during what was predicted to be a clear day. We are currently developing an analog forecasting system to predict solar irradiance at the surface at the Savannah River Site in South Carolina, with the goal of improving predictions of available solar energy. Analog forecasting is based on the assumption that similar initial conditions will lead to similar outcomes, and involves the use of an algorithm to look through the weather patterns of the past to identify previous conditions (the analogs) similar to those of today. For our application, we select three predictor variables - sea-level pressure, 700mb geopotential, and 700mb humidity. These fields for the current day are compared to those from past days, and a weighted combination of the differences (defined by a cost function) is used to select the five best analog days. The observed solar irradiance values subsequent to the dates of those analogs are then combined to represent the forecast for the next day. We will explain how we apply the analog process, and compare it to existing solar forecasts.

Low Probability Tail Event Analysis and Mitigation in BPA Control Area: Task 2 Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Shuai; Makarov, Yuri V.; McKinstry, Craig A.

Task report detailing low probability tail event analysis and mitigation in BPA control area. Tail event refers to the situation in a power system when unfavorable forecast errors of load and wind are superposed onto fast load and wind ramps, or non-wind generators falling short of scheduled output, causing the imbalance between generation and load to become very significant.

NASA Products to Enhance Energy Utility Load Forecasting

NASA Technical Reports Server (NTRS)

Lough, G.; Zell, E.; Engel-Cox, J.; Fungard, Y.; Jedlovec, G.; Stackhouse, P.; Homer, R.; Biley, S.

2012-01-01

Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts.

7 CFR 1710.302 - Financial forecasts-power supply borrowers.

Code of Federal Regulations, 2013 CFR

2013-01-01

... facilities; (3) Provide an in-depth analysis of the regional markets for power if loan feasibility depends to any degree on a borrower's ability to sell surplus power while its system loads grow to meet the... sensitivity analysis if required by RUS pursuant to § 1710.300(d)(5). (e) The projections shall be coordinated...

The effect of real-time pricing on load shifting in a highly renewable power system dominated by generation from the renewable sources of wind and photovoltaics

NASA Astrophysics Data System (ADS)

Kies, Alexander; Brown, Tom; Schlachtberger, David; Schramm, Stefan

2017-04-01

The supply-demand imbalance is a major concern in the presence of large shares of highly variable renewable generation from sources like wind and photovoltaics (PV) in power systems. Other than the measures on the generation side, such as flexible backup generation or energy storage, sector coupling or demand side management are the most likely option to counter imbalances, therefore to ease the integration of renewable generation. Demand side management usually refers to load shifting, which comprises the reaction of electricity consumers to price fluctuations. In this work, we derive a novel methodology to model the interplay of load shifting and provided incentives via real-time pricing in highly renewable power systems. We use weather data to simulate generation from the renewable sources of wind and photovoltaics, as well as historical load data, split into different consumption categories, such as, heating, cooling, domestic, etc., to model a simplified power system. Together with renewable power forecast data, a simple market model and approaches to incorporate sector coupling [1] and load shifting [2,3], we model the interplay of incentives and load shifting for different scenarios (e.g., in dependency of the risk-aversion of consumers or the forecast horizon) and demonstrate the practical benefits of load shifting. First, we introduce the novel methodology and compare it with existing approaches. Secondly, we show results of numerical simulations on the effects of load shifting: It supports the integration of PV power by providing a storage, which characteristics can be described as "daily" and provides a significant amount of balancing potential. Lastly, we propose an experimental setup to obtain empirical data on end-consumer load-shifting behaviour in response to price incentives. References [1] Brown, T., Schlachtberger, D., Kies. A., Greiner, M., Sector coupling in a highly renewable European energy system, Proc. of the 15th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants, Vienna, Austria, 15.-17. November 2016 [2] Kleinhans, D.: Towards a systematic characterization of the potential of demand side management, arXiv preprint arXiv:1401.4121, 2014 [3] Kies, A., Schyska, B. U., von Bremen, L., The Demand Side Management Potential to Balance a Highly Renewable European Power System. Energies, 9(11), 955, 2016

Uses and Applications of Climate Forecasts for Power Utilities.

NASA Astrophysics Data System (ADS)

Changnon, Stanley A.; Changnon, Joyce M.; Changnon, David

1995-05-01

The uses and potential applications of climate forecasts for electric and gas utilities were assessed 1) to discern needs for improving climate forecasts and guiding future research, and 2) to assist utilities in making wise use of forecasts. In-depth structured interviews were conducted with 56 decision makers in six utilities to assess existing and potential uses of climate forecasts. Only 3 of the 56 use forecasts. Eighty percent of those sampled envisioned applications of climate forecasts, given certain changes and additional information. Primary applications exist in power trading, load forecasting, fuel acquisition, and systems planning, with slight differences in interests between utilities. Utility staff understand probability-based forecasts but desire climatological information related to forecasted outcomes, including analogs similar to the forecasts, and explanations of the forecasts. Desired lead times vary from a week to three months, along with forecasts of up to four seasons ahead. The new NOAA forecasts initiated in 1995 provide the lead times and longer-term forecasts desired. Major hindrances to use of forecasts are hard-to-understand formats, lack of corporate acceptance, and lack of access to expertise. Recent changes in government regulations altered the utility industry, leading to a more competitive world wherein information about future weather conditions assumes much more value. Outreach efforts by government forecast agencies appear valuable to help achieve the appropriate and enhanced use of climate forecasts by the utility industry. An opportunity for service exists also for the private weather sector.

Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market

PubMed Central

2017-01-01

Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load. PMID:28426739

Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market.

PubMed

Bozkurt, Ömer Özgür; Biricik, Göksel; Tayşi, Ziya Cihan

2017-01-01

Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load.

NREL Integrate: RCS-4-42326

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hudgins, Andrew P.; Waight, Jim; Grover, Shailendra

OMNETRIC Corp., Duke Energy, CPS Energy, and the University of Texas at San Antonio (UTSA) created a project team to execute the project 'OpenFMB Reference Architecture Demonstration.' The project included development and demonstration of concepts that will enable the electric utility grid to host larger penetrations of renewable resources. The project concept calls for the aggregation of renewable resources and loads into microgrids and the control of these microgrids with an implementation of the OpenFMB Reference Architecture. The production of power from the renewable resources that are appearing on the grid today is very closely linked to the weather. Themore » difficulty of forecasting the weather, which is well understood, leads to difficulty in forecasting the production of renewable resources. The current state of the art in forecasting the power production from renewables (solar PV and wind) are accuracies in the range of 12-25 percent NMAE. In contrast the demand for electricity aggregated to the system level, is easier to predict. The state of the art of demand forecasting done, 24 hours ahead, is about 2-3% MAPE. Forecasting the load to be supplied from conventional resources (demand minus generation from renewable resources) is thus very hard to forecast. This means that even a few hours before the time of consumption, there can be considerable uncertainty over what must be done to balance supply and demand. Adding to the problem of difficulty of forecasting, is the reality of the variability of the actual production of power from renewables. Due to the variability of wind speeds and solar insolation, the actual output of power from renewable resources can vary significantly over a short period of time. Gusts of winds result is variation of power output of wind turbines. The shadows of clouds moving over solar PV arrays result in the variation of power production of the array. This compounds the problem of balancing supply and demand in real time. Establishing a control system that can manage distribution systems with large penetrations of renewable resources is difficult due to two major issues: (1) the lack of standardization and interoperability between the vast array of equipment in operation and on the market, most of which use different and proprietary means of communication and (2) the magnitude of the network and the information it generates and consumes. The objective of this project is to provide the industry with a design concept and tools that will enable the electric power grid to overcome these barriers and support a larger penetration of clean energy from renewable resources.« less

Low Probability Tail Event Analysis and Mitigation in BPA Control Area: Task One Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Shuai; Makarov, Yuri V.

This is a report for task one of the tail event analysis project for BPA. Tail event refers to the situation in a power system when unfavorable forecast errors of load and wind are superposed onto fast load and wind ramps, or non-wind generators falling short of scheduled output, the imbalance between generation and load becomes very significant. This type of events occurs infrequently and appears on the tails of the distribution of system power imbalance; therefore, is referred to as tail events. This report analyzes what happened during the Electric Reliability Council of Texas (ERCOT) reliability event on Februarymore » 26, 2008, which was widely reported because of the involvement of wind generation. The objective is to identify sources of the problem, solutions to it and potential improvements that can be made to the system. Lessons learned from the analysis include the following: (1) Large mismatch between generation and load can be caused by load forecast error, wind forecast error and generation scheduling control error on traditional generators, or a combination of all of the above; (2) The capability of system balancing resources should be evaluated both in capacity (MW) and in ramp rate (MW/min), and be procured accordingly to meet both requirements. The resources need to be able to cover a range corresponding to the variability of load and wind in the system, additional to other uncertainties; (3) Unexpected ramps caused by load and wind can both become the cause leading to serious issues; (4) A look-ahead tool evaluating system balancing requirement during real-time operations and comparing that with available system resources should be very helpful to system operators in predicting the forthcoming of similar events and planning ahead; and (5) Demand response (only load reduction in ERCOT event) can effectively reduce load-generation mismatch and terminate frequency deviation in an emergency situation.« less

Planning a Target Renewable Portfolio using Atmospheric Modeling and Stochastic Optimization

NASA Astrophysics Data System (ADS)

Hart, E.; Jacobson, M. Z.

2009-12-01

A number of organizations have suggested that an 80% reduction in carbon emissions by 2050 is a necessary step to mitigate climate change and that decarbonization of the electricity sector is a crucial component of any strategy to meet this target. Integration of large renewable and intermittent generators poses many new problems in power system planning. In this study, we attempt to determine an optimal portfolio of renewable resources to meet best the fluctuating California load while also meeting an 80% carbon emissions reduction requirement. A stochastic optimization scheme is proposed that is based on a simplified model of the California electricity grid. In this single-busbar power system model, the load is met with generation from wind, solar thermal, photovoltaic, hydroelectric, geothermal, and natural gas plants. Wind speeds and insolation are calculated using GATOR-GCMOM, a global-through-urban climate-weather-air pollution model. Fields were produced for California and Nevada at 21km SN by 14 km WE spatial resolution every 15 minutes for the year 2006. Load data for 2006 were obtained from the California ISO OASIS database. Maximum installed capacities for wind and solar thermal generation were determined using a GIS analysis of potential development sites throughout the state. The stochastic optimization scheme requires that power balance be achieved in a number of meteorological and load scenarios that deviate from the forecasted (or modeled) data. By adjusting the error distributions of the forecasts, the model describes how improvements in wind speed and insolation forecasting may affect the optimal renewable portfolio. Using a simple model, we describe the diversity, size, and sensitivities of a renewable portfolio that is best suited to the resources and needs of California and that contributes significantly to reduction of the state’s carbon emissions.

Forecasting Strategies for Predicting Peak Electric Load Days

NASA Astrophysics Data System (ADS)

Saxena, Harshit

Academic institutions spend thousands of dollars every month on their electric power consumption. Some of these institutions follow a demand charges pricing structure; here the amount a customer pays to the utility is decided based on the total energy consumed during the month, with an additional charge based on the highest average power load required by the customer over a moving window of time as decided by the utility. Therefore, it is crucial for these institutions to minimize the time periods where a high amount of electric load is demanded over a short duration of time. In order to reduce the peak loads and have more uniform energy consumption, it is imperative to predict when these peaks occur, so that appropriate mitigation strategies can be developed. The research work presented in this thesis has been conducted for Rochester Institute of Technology (RIT), where the demand charges are decided based on a 15 minute sliding window panned over the entire month. This case study makes use of different statistical and machine learning algorithms to develop a forecasting strategy for predicting the peak electric load days of the month. The proposed strategy was tested for a whole year starting May 2015 to April 2016 during which a total of 57 peak days were observed. The model predicted a total of 74 peak days during this period, 40 of these cases were true positives, hence achieving an accuracy level of 70 percent. The results obtained with the proposed forecasting strategy are promising and demonstrate an annual savings potential worth about $80,000 for a single submeter of RIT.

Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

2016-09-01

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

Modeling spot markets for electricity and pricing electricity derivatives

NASA Astrophysics Data System (ADS)

Ning, Yumei

Spot prices for electricity have been very volatile with dramatic price spikes occurring in restructured market. The task of forecasting electricity prices and managing price risk presents a new challenge for market players. The objectives of this dissertation are: (1) to develop a stochastic model of price behavior and predict price spikes; (2) to examine the effect of weather forecasts on forecasted prices; (3) to price electricity options and value generation capacity. The volatile behavior of prices can be represented by a stochastic regime-switching model. In the model, the means of the high-price and low-price regimes and the probabilities of switching from one regime to the other are specified as functions of daily peak load. The probability of switching to the high-price regime is positively related to load, but is still not high enough at the highest loads to predict price spikes accurately. An application of this model shows how the structure of the Pennsylvania-New Jersey-Maryland market changed when market-based offers were allowed, resulting in higher price spikes. An ARIMA model including temperature, seasonal, and weekly effects is estimated to forecast daily peak load. Forecasts of load under different assumptions about weather patterns are used to predict changes of price behavior given the regime-switching model of prices. Results show that the range of temperature forecasts from a normal summer to an extremely warm summer cause relatively small increases in temperature (+1.5%) and load (+3.0%). In contrast, the increases in prices are large (+20%). The conclusion is that the seasonal outlook forecasts provided by NOAA are potentially valuable for predicting prices in electricity markets. The traditional option models, based on Geometric Brownian Motion are not appropriate for electricity prices. An option model using the regime-switching framework is developed to value a European call option. The model includes volatility risk and allows changes in prices and volatility to be correlated. The results show that the value of a power plant is much higher using the financial option model than using traditional discounted cash flow.

Using Information Processing Techniques to Forecast, Schedule, and Deliver Sustainable Energy to Electric Vehicles

NASA Astrophysics Data System (ADS)

Pulusani, Praneeth R.

As the number of electric vehicles on the road increases, current power grid infrastructure will not be able to handle the additional load. Some approaches in the area of Smart Grid research attempt to mitigate this, but those approaches alone will not be sufficient. Those approaches and traditional solution of increased power production can result in an insufficient and imbalanced power grid. It can lead to transformer blowouts, blackouts and blown fuses, etc. The proposed solution will supplement the ``Smart Grid'' to create a more sustainable power grid. To solve or mitigate the magnitude of the problem, measures can be taken that depend on weather forecast models. For instance, wind and solar forecasts can be used to create first order Markov chain models that will help predict the availability of additional power at certain times. These models will be used in conjunction with the information processing layer and bidirectional signal processing components of electric vehicle charging systems, to schedule the amount of energy transferred per time interval at various times. The research was divided into three distinct components: (1) Renewable Energy Supply Forecast Model, (2) Energy Demand Forecast from PEVs, and (3) Renewable Energy Resource Estimation. For the first component, power data from a local wind turbine, and weather forecast data from NOAA were used to develop a wind energy forecast model, using a first order Markov chain model as the foundation. In the second component, additional macro energy demand from PEVs in the Greater Rochester Area was forecasted by simulating concurrent driving routes. In the third component, historical data from renewable energy sources was analyzed to estimate the renewable resources needed to offset the energy demand from PEVs. The results from these models and components can be used in the smart grid applications for scheduling and delivering energy. Several solutions are discussed to mitigate the problem of overloading transformers, lack of energy supply, and higher utility costs.

7 CFR 1710.207 - RUS criteria for approval of load forecasts by distribution borrowers not required to maintain an...

Code of Federal Regulations, 2011 CFR

2011-01-01

... distribution borrowers that are unaffiliated with a power supply borrower, or by distribution borrowers that are members of a power supply borrower that has a total utility plant less than $500 million and that is not itself a member of another power supply borrower with a total utility plant of $500 million or...

Modeling and Analysis of Commercial Building Electrical Loads for Demand Side Management

NASA Astrophysics Data System (ADS)

Berardino, Jonathan

In recent years there has been a push in the electric power industry for more customer involvement in the electricity markets. Traditionally the end user has played a passive role in the planning and operation of the power grid. However, many energy markets have begun opening up opportunities to consumers who wish to commit a certain amount of their electrical load under various demand side management programs. The potential benefits of more demand participation include reduced operating costs and new revenue opportunities for the consumer, as well as more reliable and secure operations for the utilities. The management of these load resources creates challenges and opportunities to the end user that were not present in previous market structures. This work examines the behavior of commercial-type building electrical loads and their capacity for supporting demand side management actions. This work is motivated by the need for accurate and dynamic tools to aid in the advancement of demand side operations. A dynamic load model is proposed for capturing the response of controllable building loads. Building-specific load forecasting techniques are developed, with particular focus paid to the integration of building management system (BMS) information. These approaches are tested using Drexel University building data. The application of building-specific load forecasts and dynamic load modeling to the optimal scheduling of multi-building systems in the energy market is proposed. Sources of potential load uncertainty are introduced in the proposed energy management problem formulation in order to investigate the impact on the resulting load schedule.

Final Technical Report for Contract No. DE-EE0006332, "Integrated Simulation Development and Decision Support Tool-Set for Utility Market and Distributed Solar Power Generation"

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cormier, Dallas; Edra, Sherwin; Espinoza, Michael

This project will enable utilities to develop long-term strategic plans that integrate high levels of renewable energy generation, and to better plan power system operations under high renewable penetration. The program developed forecast data streams for decision support and effective integration of centralized and distributed solar power generation in utility operations. This toolset focused on real time simulation of distributed power generation within utility grids with the emphasis on potential applications in day ahead (market) and real time (reliability) utility operations. The project team developed and demonstrated methodologies for quantifying the impact of distributed solar generation on core utility operations,more » identified protocols for internal data communication requirements, and worked with utility personnel to adapt the new distributed generation (DG) forecasts seamlessly within existing Load and Generation procedures through a sophisticated DMS. This project supported the objectives of the SunShot Initiative and SUNRISE by enabling core utility operations to enhance their simulation capability to analyze and prepare for the impacts of high penetrations of solar on the power grid. The impact of high penetration solar PV on utility operations is not only limited to control centers, but across many core operations. Benefits of an enhanced DMS using state-of-the-art solar forecast data were demonstrated within this project and have had an immediate direct operational cost savings for Energy Marketing for Day Ahead generation commitments, Real Time Operations, Load Forecasting (at an aggregate system level for Day Ahead), Demand Response, Long term Planning (asset management), Distribution Operations, and core ancillary services as required for balancing and reliability. This provided power system operators with the necessary tools and processes to operate the grid in a reliable manner under high renewable penetration.« less

Power control and management of the grid containing largescale wind power systems

NASA Astrophysics Data System (ADS)

Aula, Fadhil Toufick

The ever increasing demand for electricity has driven many countries toward the installation of new generation facilities. However, concerns such as environmental pollution and global warming issues, clean energy sources, high costs associated with installation of new conventional power plants, and fossil fuels depletion have created many interests in finding alternatives to conventional fossil fuels for generating electricity. Wind energy is one of the most rapidly growing renewable power sources and wind power generations have been increasingly demanded as an alternative to the conventional fossil fuels. However, wind power fluctuates due to variation of wind speed. Therefore, large-scale integration of wind energy conversion systems is a threat to the stability and reliability of utility grids containing these systems. They disturb the balance between power generation and consumption, affect the quality of the electricity, and complicate load sharing and load distribution managing and planning. Overall, wind power systems do not help in providing any services such as operating and regulating reserves to the power grid. In order to resolve these issues, research has been conducted in utilizing weather forecasting data to improve the performance of the wind power system, reduce the influence of the fluctuations, and plan power management of the grid containing large-scale wind power systems which consist of doubly-fed induction generator based energy conversion system. The aims of this research, my dissertation, are to provide new methods for: smoothing the output power of the wind power systems and reducing the influence of their fluctuations, power managing and planning of a grid containing these systems and other conventional power plants, and providing a new structure of implementing of latest microprocessor technology for controlling and managing the operation of the wind power system. In this research, in order to reduce and smooth the fluctuations, two methods are presented. The first method is based on a de-loaded technique while the other method is based on utilizing multiple storage facilities. The de-loaded technique is based on characteristics of the power of a wind turbine and estimation of the generated power according to weather forecasting data. The technique provides a reference power by which the wind power system will operate and generate a smooth power. In contrast, utilizing storage facilities will allow the wind power system to operate at its maximum tracking power points' strategy. Two types of energy storages are considered in this research, battery energy storage system (BESS) and pumped-hydropower storage system (PHSS), to suppress the output fluctuations and to support the wind power system to follow the system load demands. Furthermore, this method provides the ability to store energy when there is a surplus of the generated power and to reuse it when there is a shortage of power generation from wind power systems. Both methods are new in terms of utilizing of the techniques and wind speed data. A microprocessor embedded system using an IntelRTM Atom(TM) processor is presented for controlling the wind power system and for providing the remote communication for enhancing the operation of the individual wind power system in a wind farm. The embedded system helps the wind power system to respond and to follow the commands of the central control of the power system. Moreover, it enhances the performance of the wind power system through self-managing, self-functioning, and self-correcting. Finally, a method of system power management and planning is modeled and studied for a grid containing large-scale wind power systems. The method is based on a new technique through constructing a new load demand curve (NLDC) from merging the estimation of generated power from wind power systems and forecasting of the load. To summarize, the methods and their results presented in this dissertation, enhance the operation of the large-scale wind power systems and reduce their drawbacks on the operation of the power grid.

Regional PV power estimation and forecast to mitigate the impact of high photovoltaic penetration on electric grid.

NASA Astrophysics Data System (ADS)

Pierro, Marco; De Felice, Matteo; Maggioni, Enrico; Moser, David; Perotto, Alessandro; Spada, Francesco; Cornaro, Cristina

2017-04-01

The growing photovoltaic generation results in a stochastic variability of the electric demand that could compromise the stability of the grid and increase the amount of energy reserve and the energy imbalance cost. On regional scale, solar power estimation and forecast is becoming essential for Distribution System Operators, Transmission System Operator, energy traders, and aggregators of generation. Indeed the estimation of regional PV power can be used for PV power supervision and real time control of residual load. Mid-term PV power forecast can be employed for transmission scheduling to reduce energy imbalance and related cost of penalties, residual load tracking, trading optimization, secondary energy reserve assessment. In this context, a new upscaling method was developed and used for estimation and mid-term forecast of the photovoltaic distributed generation in a small area in the north of Italy under the control of a local DSO. The method was based on spatial clustering of the PV fleet and neural networks models that input satellite or numerical weather prediction data (centered on cluster centroids) to estimate or predict the regional solar generation. It requires a low computational effort and very few input information should be provided by users. The power estimation model achieved a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 hours) obtained a RMSE of 5% - 7% while the one and two days forecast achieve to a RMSE of 7% and 7.5%. A model to estimate the forecast error and the prediction intervals was also developed. The photovoltaic production in the considered region provided the 6.9% of the electric consumption in 2015. Since the PV penetration is very similar to the one observed at national level (7.9%), this is a good case study to analyse the impact of PV generation on the electric grid and the effects of PV power forecast on transmission scheduling and on secondary reserve estimation. It appears that, already with 7% of PV penetration, the distributed PV generation could have a great impact both on the DSO energy need and on the transmission scheduling capability. Indeed, for some hours of the days in summer time, the photovoltaic generation can provide from 50% to 75% of the energy that the local DSO should buy from Italian TSO to cover the electrical demand. Moreover, mid-term forecast can reduce the annual energy imbalance between the scheduled transmission and the actual one from 10% of the TSO energy supply (without considering the PV forecast) to 2%. Furthermore, it was shown that prediction intervals could be used not only to estimate the probability of a specific PV generation bid on the energy market, but also to reduce the energy reserve predicted for the next day. Two different methods for energy reserve estimation were developed and tested. The first is based on a clear sky model while the second makes use of the PV prediction intervals with the 95% of confidence level. The latter reduces the amount of the day-ahead energy reserve of 36% with respect the clear sky method.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, Martin; Giebel, Gregor; Skov Nielsen, Torben; Hahmann, Andrea; Sørensen, Poul; Madsen, Henrik

2013-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the title "Integrated Wind Power Planning Tool". The goal is to integrate a mesoscale numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. Using the state-of-the-art mesoscale NWP model Weather Research & Forecasting model (WRF) the forecast error is sought quantified in dependence of the time scale involved. This task constitutes a preparative study for later implementation of features accounting for NWP forecast errors in the DTU Wind Energy maintained Corwind code - a long term wind power planning tool. Within the framework of PSO 10464 research related to operational short term wind power prediction will be carried out, including a comparison of forecast quality at different mesoscale NWP model resolutions and development of a statistical wind power prediction tool taking input from WRF. The short term prediction part of the project is carried out in collaboration with ENFOR A/S; a Danish company that specialises in forecasting and optimisation for the energy sector. The integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated short term prediction tool constitutes scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator. The need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2025 from the current 20%.

Application of global weather and climate model output to the design and operation of wind-energy systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curry, Judith

This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatorymore » environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.« less

Neural network based short-term load forecasting using weather compensation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chow, T.W.S.; Leung, C.T.

This paper presents a novel technique for electric load forecasting based on neural weather compensation. The proposed method is a nonlinear generalization of Box and Jenkins approach for nonstationary time-series prediction. A weather compensation neural network is implemented for one-day ahead electric load forecasting. The weather compensation neural network can accurately predict the change of actual electric load consumption from the previous day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing a more accurate load forecast with a 0.9% reduction in forecast error.

7 CFR 1710.208 - RUS criteria for approval of all load forecasts by power supply borrowers and by distribution...

Code of Federal Regulations, 2010 CFR

2010-01-01

... borrower developed an adequate supporting database and analyzed a reasonable range of relevant assumptions and alternative futures; (d) The borrower adopted methods and procedures in general use by the...

Load Modeling and Forecasting | Grid Modernization | NREL

Science.gov Websites

Load Modeling and Forecasting Load Modeling and Forecasting NREL's work in load modeling is focused resources (such as rooftop photovoltaic systems) and changing customer energy use profiles, new load models distribution system. In addition, NREL researchers are developing load models for individual appliances and

Systematic Evaluation of Stochastic Methods in Power System Scheduling and Dispatch with Renewable Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Yishen; Zhou, Zhi; Liu, Cong

2016-08-01

As more wind power and other renewable resources are being integrated into the electric power grid, the forecast uncertainty brings operational challenges for the power system operators. In this report, different operational strategies for uncertainty management are presented and evaluated. A comprehensive and consistent simulation framework is developed to analyze the performance of different reserve policies and scheduling techniques under uncertainty in wind power. Numerical simulations are conducted on a modified version of the IEEE 118-bus system with a 20% wind penetration level, comparing deterministic, interval, and stochastic unit commitment strategies. The results show that stochastic unit commitment provides amore » reliable schedule without large increases in operational costs. Moreover, decomposition techniques, such as load shift factor and Benders decomposition, can help in overcoming the computational obstacles to stochastic unit commitment and enable the use of a larger scenario set to represent forecast uncertainty. In contrast, deterministic and interval unit commitment tend to give higher system costs as more reserves are being scheduled to address forecast uncertainty. However, these approaches require a much lower computational effort Choosing a proper lower bound for the forecast uncertainty is important for balancing reliability and system operational cost in deterministic and interval unit commitment. Finally, we find that the introduction of zonal reserve requirements improves reliability, but at the expense of higher operational costs.« less

Ramping and Uncertainty Prediction Tool - Analysis and Visualization of Wind Generation Impact on Electrical Grid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Etingov, Pavel; Makarov, PNNL Yuri; Subbarao, PNNL Kris

RUT software is designed for use by the Balancing Authorities to predict and display additional requirements caused by the variability and uncertainty in load and generation. The prediction is made for the next operating hours as well as for the next day. The tool predicts possible deficiencies in generation capability and ramping capability. This deficiency of balancing resources can cause serious risks to power system stability and also impact real-time market energy prices. The tool dynamically and adaptively correlates changing system conditions with the additional balancing needs triggered by the interplay between forecasted and actual load and output of variablemore » resources. The assessment is performed using a specially developed probabilistic algorithm incorporating multiple sources of uncertainty including wind, solar and load forecast errors. The tool evaluates required generation for a worst case scenario, with a user-specified confidence level.« less

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-12-18

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and comparesmore » the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Load Forecasting in Electric Utility Integrated Resource Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carvallo, Juan Pablo; Larsen, Peter H.; Sanstad, Alan H

Integrated resource planning (IRP) is a process used by many vertically-integrated U.S. electric utilities to determine least-cost/risk supply and demand-side resources that meet government policy objectives and future obligations to customers and, in many cases, shareholders. Forecasts of energy and peak demand are a critical component of the IRP process. There have been few, if any, quantitative studies of IRP long-run (planning horizons of two decades) load forecast performance and its relationship to resource planning and actual procurement decisions. In this paper, we evaluate load forecasting methods, assumptions, and outcomes for 12 Western U.S. utilities by examining and comparing plansmore » filed in the early 2000s against recent plans, up to year 2014. We find a convergence in the methods and data sources used. We also find that forecasts in more recent IRPs generally took account of new information, but that there continued to be a systematic over-estimation of load growth rates during the period studied. We compare planned and procured resource expansion against customer load and year-to-year load growth rates, but do not find a direct relationship. Load sensitivities performed in resource plans do not appear to be related to later procurement strategies even in the presence of large forecast errors. These findings suggest that resource procurement decisions may be driven by other factors than customer load growth. Our results have important implications for the integrated resource planning process, namely that load forecast accuracy may not be as important for resource procurement as is generally believed, that load forecast sensitivities could be used to improve the procurement process, and that management of load uncertainty should be prioritized over more complex forecasting techniques.« less

Bi-Level Arbitrage Potential Evaluation for Grid-Scale Energy Storage Considering Wind Power and LMP Smoothing Effect

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Hantao; Li, Fangxing; Fang, Xin

Our paper deals with extended-term energy storage (ES) arbitrage problems to maximize the annual revenue in deregulated power systems with high penetration wind power. The conventional ES arbitrage model takes the locational marginal prices (LMP) as an input and is unable to account for the impacts of ES operations on system LMPs. This paper proposes a bi-level ES arbitrage model, where the upper level maximizes the ES arbitrage revenue and the lower level simulates the market clearing process considering wind power and ES. The bi-level model is formulated as a mathematical program with equilibrium constraints (MPEC) and then recast intomore » a mixed-integer linear programming (MILP) using strong duality theory. Wind power fluctuations are characterized by the GARCH forecast model and the forecast error is modeled by forecast-bin based Beta distributions. Case studies are performed on a modified PJM 5-bus system and an IEEE 118-bus system with a weekly time horizon over an annual term to show the validity of the proposed bi-level model. The results from the conventional model and the bi-level model are compared under different ES power and energy ratings, and also various load and wind penetration levels.« less

Bi-Level Arbitrage Potential Evaluation for Grid-Scale Energy Storage Considering Wind Power and LMP Smoothing Effect

DOE PAGES

Cui, Hantao; Li, Fangxing; Fang, Xin; ...

2017-10-04

Our paper deals with extended-term energy storage (ES) arbitrage problems to maximize the annual revenue in deregulated power systems with high penetration wind power. The conventional ES arbitrage model takes the locational marginal prices (LMP) as an input and is unable to account for the impacts of ES operations on system LMPs. This paper proposes a bi-level ES arbitrage model, where the upper level maximizes the ES arbitrage revenue and the lower level simulates the market clearing process considering wind power and ES. The bi-level model is formulated as a mathematical program with equilibrium constraints (MPEC) and then recast intomore » a mixed-integer linear programming (MILP) using strong duality theory. Wind power fluctuations are characterized by the GARCH forecast model and the forecast error is modeled by forecast-bin based Beta distributions. Case studies are performed on a modified PJM 5-bus system and an IEEE 118-bus system with a weekly time horizon over an annual term to show the validity of the proposed bi-level model. The results from the conventional model and the bi-level model are compared under different ES power and energy ratings, and also various load and wind penetration levels.« less

A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang

This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.« less

Advanced Intelligent System Application to Load Forecasting and Control for Hybrid Electric Bus

NASA Technical Reports Server (NTRS)

Momoh, James; Chattopadhyay, Deb; Elfayoumy, Mahmoud

1996-01-01

The primary motivation for this research emanates from providing a decision support system to the electric bus operators in the municipal and urban localities which will guide the operators to maintain an optimal compromise among the noise level, pollution level, fuel usage etc. This study is backed up by our previous studies on study of battery characteristics, permanent magnet DC motor studies and electric traction motor size studies completed in the first year. The operator of the Hybrid Electric Car must determine optimal power management schedule to meet a given load demand for different weather and road conditions. The decision support system for the bus operator comprises three sub-tasks viz. forecast of the electrical load for the route to be traversed divided into specified time periods (few minutes); deriving an optimal 'plan' or 'preschedule' based on the load forecast for the entire time-horizon (i.e., for all time periods) ahead of time; and finally employing corrective control action to monitor and modify the optimal plan in real-time. A fully connected artificial neural network (ANN) model is developed for forecasting the kW requirement for hybrid electric bus based on inputs like climatic conditions, passenger load, road inclination, etc. The ANN model is trained using back-propagation algorithm employing improved optimization techniques like projected Lagrangian technique. The pre-scheduler is based on a Goal-Programming (GP) optimization model with noise, pollution and fuel usage as the three objectives. GP has the capability of analyzing the trade-off among the conflicting objectives and arriving at the optimal activity levels, e.g., throttle settings. The corrective control action or the third sub-task is formulated as an optimal control model with inputs from the real-time data base as well as the GP model to minimize the error (or deviation) from the optimal plan. These three activities linked with the ANN forecaster proving the output to the GP model which in turn produces the pre-schedule of the optimal control model. Some preliminary results based on a hypothetical test case will be presented for the load forecasting module. The computer codes for the three modules will be made available fe adoption by bus operating agencies. Sample results will be provided using these models. The software will be a useful tool for supporting the control systems for the Electric Bus project of NASA.

Forecasting giant, catastrophic slope collapse: lessons from Vajont, Northern Italy

NASA Astrophysics Data System (ADS)

Kilburn, Christopher R. J.; Petley, David N.

2003-08-01

Rapid, giant landslides, or sturzstroms, are among the most powerful natural hazards on Earth. They have minimum volumes of ˜10 6-10 7 m 3 and, normally preceded by prolonged intervals of accelerating creep, are produced by catastrophic and deep-seated slope collapse (loads ˜1-10 MPa). Conventional analyses attribute rapid collapse to unusual mechanisms, such as the vaporization of ground water during sliding. Here, catastrophic collapse is related to self-accelerating rock fracture, common in crustal rocks at loads ˜1-10 MPa and readily catalysed by circulating fluids. Fracturing produces an abrupt drop in resisting stress. Measured stress drops in crustal rock account for minimum sturzstrom volumes and rapid collapse accelerations. Fracturing also provides a physical basis for quantitatively forecasting catastrophic slope failure.

Long term load forecasting accuracy in electric utility integrated resource planning

DOE PAGES

Carvallo, Juan Pablo; Larsen, Peter H.; Sanstad, Alan H.; ...

2018-05-23

Forecasts of electricity consumption and peak demand over time horizons of one or two decades are a key element in electric utilities’ meeting their core objective and obligation to ensure reliable and affordable electricity supplies for their customers while complying with a range of energy and environmental regulations and policies. These forecasts are an important input to integrated resource planning (IRP) processes involving utilities, regulators, and other stake-holders. Despite their importance, however, there has been little analysis of long term utility load forecasting accuracy. We conduct a retrospective analysis of long term load forecasts on twelve Western U. S. electricmore » utilities in the mid-2000s to find that most overestimated both energy consumption and peak demand growth. A key reason for this was the use of assumptions that led to an overestimation of economic growth. We find that the complexity of forecast methods and the accuracy of these forecasts are mildly correlated. In addition, sensitivity and risk analysis of load growth and its implications for capacity expansion were not well integrated with subsequent implementation. As a result, we review changes in the utilities load forecasting methods over the subsequent decade, and discuss the policy implications of long term load forecast inaccuracy and its underlying causes.« less

Long term load forecasting accuracy in electric utility integrated resource planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carvallo, Juan Pablo; Larsen, Peter H.; Sanstad, Alan H.

Forecasts of electricity consumption and peak demand over time horizons of one or two decades are a key element in electric utilities’ meeting their core objective and obligation to ensure reliable and affordable electricity supplies for their customers while complying with a range of energy and environmental regulations and policies. These forecasts are an important input to integrated resource planning (IRP) processes involving utilities, regulators, and other stake-holders. Despite their importance, however, there has been little analysis of long term utility load forecasting accuracy. We conduct a retrospective analysis of long term load forecasts on twelve Western U. S. electricmore » utilities in the mid-2000s to find that most overestimated both energy consumption and peak demand growth. A key reason for this was the use of assumptions that led to an overestimation of economic growth. We find that the complexity of forecast methods and the accuracy of these forecasts are mildly correlated. In addition, sensitivity and risk analysis of load growth and its implications for capacity expansion were not well integrated with subsequent implementation. As a result, we review changes in the utilities load forecasting methods over the subsequent decade, and discuss the policy implications of long term load forecast inaccuracy and its underlying causes.« less

Feasibility study for the Swaziland/Mozambique interconnector. Final report. Export trade information

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1997-11-01

This study, conducted by Black & Veatch, was funded by the U.S. Trade and Development Agency. The report, produced for the Ministry of National Resources, Energy and Environment (MNRE) of Swaziland, determines the least cost capacity expansion option to meet the future power demand and system reliability criteria of Swaziland, with particular emphasis on the proposed interconnector between Swaziland and Mozambique. Volume 2, the Final Report, contains the following sections: (1.0) Introduction; (2.0) Review of SEB Power System; (3.0) SEB Load Forecast and Review; (4.0) SEB Load Forecast Revision; (5.0) The SEB Need for Power; (6.0) SEB System Development Planmore » Review; (7.0) Southern Mozambique EdM power System Review; (8.0) Southern Mozambique EdM Energy and Demand; (9.0) Supply Side Capacity Options for Swaziland and Mozambique; (10.0) SEB Expansion Plan Development; (11.0) EdM Expansion Plan Development; (12.0) Cost Sharing of the Interconnector; (13.0) Enviroinmental Evaluation of Interconnector Options; (14.0) Generation/Transmission Trade Offs; (15.0) Draft Interconnection Agreement and Contract Packages; (16.0) Transmission System Study; (17.0) Automatic General Control; (18.0) Automatic Startup and Shutdown of Hydro Electric Power Plants; (19.0) Communications and Metering; (20.0) Conclusions and Recommendations; Appendix A: Demand Side Management Primer; Appendix B. PURPA and Avoided Cost Calculations.« less

System load forecasts for an electric utility. [Hourly loads using Box-Jenkins method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Uri, N.D.

This paper discusses forecasting hourly system load for an electric utility using Box-Jenkins time-series analysis. The results indicate that a model based on the method of Box and Jenkins, given its simplicity, gives excellent results over the forecast horizon.

Characterizing and analyzing ramping events in wind power, solar power, load, and netload

DOE PAGES

Cui, Mingjian; Zhang, Jie; Feng, Cong; ...

2017-04-07

Here, one of the biggest concerns associated with integrating a large amount of renewable energy into the power grid is the ability to handle large ramps in the renewable power output. For the sake of system reliability and economics, it is essential for power system operators to better understand the ramping features of renewable, load, and netload. An optimized swinging door algorithm (OpSDA) is used and extended to accurately and efficiently detect ramping events. For wind power ramps detection, a process of merging 'bumps' (that have a different changing direction) into adjacent ramping segments is included to improve the performancemore » of the OpSDA method. For solar ramps detection, ramping events that occur in both clear-sky and measured (or forecasted) solar power are removed to account for the diurnal pattern of solar generation. Ramping features are extracted and extensively compared between load and netload under different renewable penetration levels (9.77%, 15.85%, and 51.38%). Comparison results show that (i) netload ramp events with shorter durations and smaller magnitudes occur more frequently when renewable penetration level increases, and the total number of ramping events also increases; and (ii) different ramping characteristics are observed in load and netload even with a low renewable penetration level.« less

An Overview of ANN Application in the Power Industry

NASA Technical Reports Server (NTRS)

Niebur, D.

1995-01-01

The paper presents a survey on the development and experience with artificial neural net (ANN) applications for electric power systems, with emphasis on operational systems. The organization and constraints of electric utilities are reviewed, motivations for investigating ANN are identified, and a current assessment is given from the experience of 2400 projects using ANN for load forecasting, alarm processing, fault detection, component fault diagnosis, static and dynamic security analysis, system planning, and operation planning.

Steam-load-forecasting technique for central-heating plants. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, M.C.; Carnahan, J.V.

Because boilers generally are most efficient at full loads, the Army could achieve significant savings by running fewer boilers at high loads rather than more boilers at low loads. A reliable load prediction technique could help ensure that only those boilers required to meet demand are on line. This report presents the results of an investigation into the feasibility of forecasting heat plant steam loads from historical patterns and weather information. Using steam flow data collected at Fort Benjamin Harrison, IN, a Box-Jenkins transfer function model with an acceptably small prediction error was initially identified. Initial investigation of forecast modelmore » development appeared successful. Dynamic regression methods using actual ambient temperatures yielded the best results. Box-Jenkins univariate models' results appeared slightly less accurate. Since temperature information was not needed for model building and forecasting, however, it is recommended that Box-Jenkins models be considered prime candidates for load forecasting due to their simpler mathematics.« less

7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Minimum approval requirements for all load forecasts. 1710.205 Section 1710.205 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load Forecasts §...

The research and application of the power big data

NASA Astrophysics Data System (ADS)

Zhang, Suxiang; Zhang, Dong; Zhang, Yaping; Cao, Jinping; Xu, Huiming

2017-01-01

Facing the increasing environment crisis, how to improve energy efficiency is the important problem. Power big data is main support tool to realize demand side management and response. With the promotion of smart power consumption, distributed clean energy and electric vehicles etc get wide application; meanwhile, the continuous development of the Internet of things technology, more applications access the endings in the grid power link, which leads to that a large number of electric terminal equipment, new energy access smart grid, and it will produce massive heterogeneous and multi-state electricity data. These data produce the power grid enterprise's precious wealth, as the power big data. How to transform it into valuable knowledge and effective operation becomes an important problem, it needs to interoperate in the smart grid. In this paper, we had researched the various applications of power big data and integrate the cloud computing and big data technology, which include electricity consumption online monitoring, the short-term power load forecasting and the analysis of the energy efficiency. Based on Hadoop, HBase and Hive etc., we realize the ETL and OLAP functions; and we also adopt the parallel computing framework to achieve the power load forecasting algorithms and propose a parallel locally weighted linear regression model; we study on energy efficiency rating model to comprehensive evaluate the level of energy consumption of electricity users, which allows users to understand their real-time energy consumption situation, adjust their electricity behavior to reduce energy consumption, it provides decision-making basis for the user. With an intelligent industrial park as example, this paper complete electricity management. Therefore, in the future, power big data will provide decision-making support tools for energy conservation and emissions reduction.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks.

PubMed

Liu, Da; Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks

PubMed Central

Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012. PMID:27281032

A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization

DOE PAGES

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard; ...

2016-01-01

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of the paper.« less

Volcanic Eruption Forecasts From Accelerating Rates of Drumbeat Long-Period Earthquakes

NASA Astrophysics Data System (ADS)

Bell, Andrew F.; Naylor, Mark; Hernandez, Stephen; Main, Ian G.; Gaunt, H. Elizabeth; Mothes, Patricia; Ruiz, Mario

2018-02-01

Accelerating rates of quasiperiodic "drumbeat" long-period earthquakes (LPs) are commonly reported before eruptions at andesite and dacite volcanoes, and promise insights into the nature of fundamental preeruptive processes and improved eruption forecasts. Here we apply a new Bayesian Markov chain Monte Carlo gamma point process methodology to investigate an exceptionally well-developed sequence of drumbeat LPs preceding a recent large vulcanian explosion at Tungurahua volcano, Ecuador. For more than 24 hr, LP rates increased according to the inverse power law trend predicted by material failure theory, and with a retrospectively forecast failure time that agrees with the eruption onset within error. LPs resulted from repeated activation of a single characteristic source driven by accelerating loading, rather than a distributed failure process, showing that similar precursory trends can emerge from quite different underlying physics. Nevertheless, such sequences have clear potential for improving forecasts of eruptions at Tungurahua and analogous volcanoes.

The Wind Integration National Dataset (WIND) toolkit (Presentation)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caroline Draxl: NREL

2014-01-01

Regional wind integration studies require detailed wind power output data at many locations to perform simulations of how the power system will operate under high penetration scenarios. The wind datasets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as being time synchronized with available load profiles.As described in this presentation, the WIND Toolkit fulfills these requirements by providing a state-of-the-art national (US) wind resource, power production and forecast dataset.

Compensated Box-Jenkins transfer function for short term load forecast

DOE Office of Scientific and Technical Information (OSTI.GOV)

Breipohl, A.; Yu, Z.; Lee, F.N.

In the past years, the Box-Jenkins ARIMA method and the Box-Jenkins transfer function method (BJTF) have been among the most commonly used methods for short term electrical load forecasting. But when there exists a sudden change in the temperature, both methods tend to exhibit larger errors in the forecast. This paper demonstrates that the load forecasting errors resulting from either the BJ ARIMA model or the BJTF model are not simply white noise, but rather well-patterned noise, and the patterns in the noise can be used to improve the forecasts. Thus a compensated Box-Jenkins transfer method (CBJTF) is proposed tomore » improve the accuracy of the load prediction. Some case studies have been made which result in about a 14-33% reduction of the root mean square (RMS) errors of the forecasts, depending on the compensation time period as well as the compensation method used.« less

Load forecast method of electric vehicle charging station using SVR based on GA-PSO

NASA Astrophysics Data System (ADS)

Lu, Kuan; Sun, Wenxue; Ma, Changhui; Yang, Shenquan; Zhu, Zijian; Zhao, Pengfei; Zhao, Xin; Xu, Nan

2017-06-01

This paper presents a Support Vector Regression (SVR) method for electric vehicle (EV) charging station load forecast based on genetic algorithm (GA) and particle swarm optimization (PSO). Fuzzy C-Means (FCM) clustering is used to establish similar day samples. GA is used for global parameter searching and PSO is used for a more accurately local searching. Load forecast is then regressed using SVR. The practical load data of an EV charging station were taken to illustrate the proposed method. The result indicates an obvious improvement in the forecasting accuracy compared with SVRs based on PSO and GA exclusively.

Support vector machine for day ahead electricity price forecasting

NASA Astrophysics Data System (ADS)

Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti

2015-05-01

Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.

Studies of air traffic forecasts, airspace load and the effect of ADS-B via satellites on flight times

NASA Astrophysics Data System (ADS)

Zhong, Z. W.; Ridhwan Salleh, Saiful; Chow, W. X.; Ong, Z. M.

2016-10-01

Air traffic forecasting is important as it helps stakeholders to plan their budgets and facilities. Thus, three most commonly used forecasting models were compared to see which model suited the air passenger traffic the best. General forecasting equations were also created to forecast the passenger traffic. The equations could forecast around 6.0% growth from 2015 onwards. Another study sought to provide an initial work for determining a theoretical airspace load with relevant calculations. The air traffic was simulated to investigate the current airspace load. Logical and reasonable results were obtained from the modelling and simulations. The current utilization percentages for airspace load per hour and the static airspace load in the interested airspace were found to be 6.64% and 11.21% respectively. Our research also studied how ADS-B would affect the time taken for aircraft to travel. 6000 flights departing from and landing at the airport were studied. New flight plans were simulated with improved flight paths due to the implementation of ADS-B, and flight times of all studied flights could be improved.

How to Integrate Variable Power Source into a Power Grid

NASA Astrophysics Data System (ADS)

Asano, Hiroshi

This paper discusses how to integrate variable power source such as wind power and photovoltaic generation into a power grid. The intermittent renewable generation is expected to penetrate for less carbon intensive power supply system, but it causes voltage control problem in the distribution system, and supply-demand imbalance problem in a whole power system. Cooperative control of customers' energy storage equipment such as water heater with storage tank for reducing inverse power flow from the roof-top PV system, the operation technique using a battery system and the solar radiation forecast for stabilizing output of variable generation, smart charging of plug-in hybrid electric vehicles for load frequency control (LFC), and other methods to integrate variable power source with improving social benefits are surveyed.

Proceedings of the American Power Conference. Volume 58-I

DOE Office of Scientific and Technical Information (OSTI.GOV)

McBride, A.E.

1996-10-01

This is volume 58-I of the proceedings of the American Power Conference, 1996, Technology for Competition and Globalization. The topics of the papers include power plant DC issues; cost of environmental compliance; advanced coal systems -- environmental performance; technology for competition in dispersed generation; superconductivity technologies for electric utility applications; power generation trends and challenges in China; aging in nuclear power plants; innovative and competitive repowering options; structural examinations, modifications and repairs; electric load forecasting; distribution planning; EMF effects; fuzzy logic and neural networks for power plant applications; electrokinetic decontamination of soils; integrated gasification combined cycle; advances in fusion; coolingmore » towers; relays; plant controls; flue gas desulfurization; waste product utilization; and improved technologies.« less

7 CFR 1710.207 - RUS criteria for approval of load forecasts by distribution borrowers not required to maintain an...

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false RUS criteria for approval of load forecasts by distribution borrowers not required to maintain an approved load forecast on an ongoing basis. 1710.207 Section 1710.207 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PR...

Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

NASA Astrophysics Data System (ADS)

Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

2016-12-01

Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

Energy management of a university campus utilizing short-term load forecasting with an artificial neural network

NASA Astrophysics Data System (ADS)

Palchak, David

Electrical load forecasting is a tool that has been utilized by distribution designers and operators as a means for resource planning and generation dispatch. The techniques employed in these predictions are proving useful in the growing market of consumer, or end-user, participation in electrical energy consumption. These predictions are based on exogenous variables, such as weather, and time variables, such as day of week and time of day as well as prior energy consumption patterns. The participation of the end-user is a cornerstone of the Smart Grid initiative presented in the Energy Independence and Security Act of 2007, and is being made possible by the emergence of enabling technologies such as advanced metering infrastructure. The optimal application of the data provided by an advanced metering infrastructure is the primary motivation for the work done in this thesis. The methodology for using this data in an energy management scheme that utilizes a short-term load forecast is presented. The objective of this research is to quantify opportunities for a range of energy management and operation cost savings of a university campus through the use of a forecasted daily electrical load profile. The proposed algorithm for short-term load forecasting is optimized for Colorado State University's main campus, and utilizes an artificial neural network that accepts weather and time variables as inputs. The performance of the predicted daily electrical load is evaluated using a number of error measurements that seek to quantify the best application of the forecast. The energy management presented utilizes historical electrical load data from the local service provider to optimize the time of day that electrical loads are being managed. Finally, the utilization of forecasts in the presented energy management scenario is evaluated based on cost and energy savings.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Mingjian; Zhang, Jie; Feng, Cong

Here, one of the biggest concerns associated with integrating a large amount of renewable energy into the power grid is the ability to handle large ramps in the renewable power output. For the sake of system reliability and economics, it is essential for power system operators to better understand the ramping features of renewable, load, and netload. An optimized swinging door algorithm (OpSDA) is used and extended to accurately and efficiently detect ramping events. For wind power ramps detection, a process of merging 'bumps' (that have a different changing direction) into adjacent ramping segments is included to improve the performancemore » of the OpSDA method. For solar ramps detection, ramping events that occur in both clear-sky and measured (or forecasted) solar power are removed to account for the diurnal pattern of solar generation. Ramping features are extracted and extensively compared between load and netload under different renewable penetration levels (9.77%, 15.85%, and 51.38%). Comparison results show that (i) netload ramp events with shorter durations and smaller magnitudes occur more frequently when renewable penetration level increases, and the total number of ramping events also increases; and (ii) different ramping characteristics are observed in load and netload even with a low renewable penetration level.« less

The transport forecast - an important stage of transport management

NASA Astrophysics Data System (ADS)

Dragu, Vasile; Dinu, Oana; Oprea, Cristina; Alina Roman, Eugenia

2017-10-01

The transport system is a powerful system with varying loads in operation coming from changes in freight and passenger traffic in different time periods. The variations are due to the specific conditions of organization and development of socio-economic activities. The causes of varying loads can be included in three groups: economic, technical and organizational. The assessing of transport demand variability leads to proper forecast and development of the transport system, knowing that the market price is determined on equilibrium between supply and demand. The reduction of transport demand variability through different technical solutions, organizational, administrative, legislative leads to an increase in the efficiency and effectiveness of transport. The paper presents a new way of assessing the future needs of transport through dynamic series. Both researchers and practitioners in transport planning can benefit from the research results. This paper aims to analyze in an original approach how a good transport forecast can lead to a better management in transport, with significant effects on transport demand full meeting in quality terms. The case study shows how dynamic series of statistics can be used to identify the size of future demand addressed to the transport system.

Assessment of the Charging Policy in Energy Efficiency of the Enterprise

NASA Astrophysics Data System (ADS)

Shutov, E. A.; E Turukina, T.; Anisimov, T. S.

2017-04-01

The forecasting problem for energy facilities with a power exceeding 670 kW is currently one of the main. In connection with rules of the retail electricity market such customers also pay for actual energy consumption deviations from plan value. In compliance with the hierarchical stages of the electricity market a guaranteeing supplier is to respect the interests of distribution and generation companies that require load leveling. The answer to this question for industrial enterprise is possible only within technological process through implementation of energy-efficient processing chains with the adaptive function and forecasting tool. In such a circumstance the primary objective of a forecasting is reduce the energy consumption costs by taking account of the energy cost correlation for 24 hours for forming of pumping unit work schedule. The pumping unit virtual model with the variable frequency drive is considered. The forecasting tool and the optimizer are integrated into typical control circuit. Economic assessment of the optimization method was estimated.

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finley, Cathy

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less

Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon

NASA Astrophysics Data System (ADS)

Yildiz, Baran; Bilbao, Jose I.; Dore, Jonathon; Sproul, Alistair B.

2018-05-01

Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance.

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

PubMed

Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

2016-01-01

Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

A stochastic post-processing method for solar irradiance forecasts derived from NWPs models

NASA Astrophysics Data System (ADS)

Lara-Fanego, V.; Pozo-Vazquez, D.; Ruiz-Arias, J. A.; Santos-Alamillos, F. J.; Tovar-Pescador, J.

2010-09-01

Solar irradiance forecast is an important area of research for the future of the solar-based renewable energy systems. Numerical Weather Prediction models (NWPs) have proved to be a valuable tool for solar irradiance forecasting with lead time up to a few days. Nevertheless, these models show low skill in forecasting the solar irradiance under cloudy conditions. Additionally, climatic (averaged over seasons) aerosol loading are usually considered in these models, leading to considerable errors for the Direct Normal Irradiance (DNI) forecasts during high aerosols load conditions. In this work we propose a post-processing method for the Global Irradiance (GHI) and DNI forecasts derived from NWPs. Particularly, the methods is based on the use of Autoregressive Moving Average with External Explanatory Variables (ARMAX) stochastic models. These models are applied to the residuals of the NWPs forecasts and uses as external variables the measured cloud fraction and aerosol loading of the day previous to the forecast. The method is evaluated for a set one-moth length three-days-ahead forecast of the GHI and DNI, obtained based on the WRF mesoscale atmospheric model, for several locations in Andalusia (Southern Spain). The Cloud fraction is derived from MSG satellite estimates and the aerosol loading from the MODIS platform estimates. Both sources of information are readily available at the time of the forecast. Results showed a considerable improvement of the forecasting skill of the WRF model using the proposed post-processing method. Particularly, relative improvement (in terms of the RMSE) for the DNI during summer is about 20%. A similar value is obtained for the GHI during the winter.

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2010 CFR

2010-01-01

... narrative shall address the overall approach, time periods, and expected internal and external uses of the forecast. Examples of internal uses include providing information for developing or monitoring demand side... suppliers. Examples of external uses include meeting state and Federal regulatory requirements, obtaining...

Multi-Temporal Decomposed Wind and Load Power Models for Electric Energy Systems

NASA Astrophysics Data System (ADS)

Abdel-Karim, Noha

This thesis is motivated by the recognition that sources of uncertainties in electric power systems are multifold and may have potentially far-reaching effects. In the past, only system load forecast was considered to be the main challenge. More recently, however, the uncertain price of electricity and hard-to-predict power produced by renewable resources, such as wind and solar, are making the operating and planning environment much more challenging. The near-real-time power imbalances are compensated by means of frequency regulation and generally require fast-responding costly resources. Because of this, a more accurate forecast and look-ahead scheduling would result in a reduced need for expensive power balancing. Similarly, long-term planning and seasonal maintenance need to take into account long-term demand forecast as well as how the short-term generation scheduling is done. The better the demand forecast, the more efficient planning will be as well. Moreover, computer algorithms for scheduling and planning are essential in helping the system operators decide what to schedule and planners what to build. This is needed given the overall complexity created by different abilities to adjust the power output of generation technologies, demand uncertainties and by the network delivery constraints. Given the growing presence of major uncertainties, it is likely that the main control applications will use more probabilistic approaches. Today's predominantly deterministic methods will be replaced by methods which account for key uncertainties as decisions are made. It is well-understood that although demand and wind power cannot be predicted at very high accuracy, taking into consideration predictions and scheduling in a look-ahead way over several time horizons generally results in more efficient and reliable utilization, than when decisions are made assuming deterministic, often worst-case scenarios. This change is in approach is going to ultimately require new electricity market rules capable of providing the right incentives to manage uncertainties and of differentiating various technologies according to the rate at which they can respond to ever changing conditions. Given the overall need for modeling uncertainties in electric energy systems, we consider in this thesis the problem of multi-temporal modeling of wind and demand power, in particular. Historic data is used to derive prediction models for several future time horizons. Short-term prediction models derived can be used for look-ahead economic dispatch and unit commitment, while the long-term annual predictive models can be used for investment planning. As expected, the accuracy of such predictive models depends on the time horizons over which the predictions are made, as well as on the nature of uncertain signals. It is shown that predictive models obtained using the same general modeling approaches result in different accuracy for wind than for demand power. In what follows, we introduce several models which have qualitatively different patterns, ranging from hourly to annual. We first transform historic time-stamped data into the Fourier Transform (Fr) representation. The frequency domain data representation is used to decompose the wind and load power signals and to derive predictive models relevant for short-term and long-term predictions using extracted spectral techniques. The short-term results are interpreted next as a Linear Prediction Coding Model (LPC) and its accuracy is analyzed. Next, a new Markov-Based Sensitivity Model (MBSM) for short term prediction has been proposed and the dispatched costs of uncertainties for different predictive models with comparisons have been developed. Moreover, the Discrete Markov Process (DMP) representation is applied to help assess probabilities of most likely short-, medium- and long-term states and the related multi-temporal risks. In addition, this thesis discusses operational impacts of wind power integration in different scenario levels by performing more than 9,000 AC Optimal Power Flow runs. The effects of both wind and load variations on system constraints and costs are presented. The limitations of DC Optimal Power Flow (DCOPF) vs. ACOPF are emphasized by means of system convergence problems due to the effect of wind power on changing line flows and net power injections. By studying the effect of having wind power on line flows, we found that the divergence problem applies in areas with high wind and hydro generation capacity share (cheap generations). (Abstract shortened by UMI.).

Performance of fuzzy approach in Malaysia short-term electricity load forecasting

NASA Astrophysics Data System (ADS)

Mansor, Rosnalini; Zulkifli, Malina; Yusof, Muhammad Mat; Ismail, Mohd Isfahani; Ismail, Suzilah; Yin, Yip Chee

2014-12-01

Many activities such as economic, education and manafucturing would paralyse with limited supply of electricity but surplus contribute to high operating cost. Therefore electricity load forecasting is important in order to avoid shortage or excess. Previous finding showed festive celebration has effect on short-term electricity load forecasting. Being a multi culture country Malaysia has many major festive celebrations such as Eidul Fitri, Chinese New Year and Deepavali but they are moving holidays due to non-fixed dates on the Gregorian calendar. This study emphasis on the performance of fuzzy approach in forecasting electricity load when considering the presence of moving holidays. Autoregressive Distributed Lag model was estimated using simulated data by including model simplification concept (manual or automatic), day types (weekdays or weekend), public holidays and lags of electricity load. The result indicated that day types, public holidays and several lags of electricity load were significant in the model. Overall, model simplification improves fuzzy performance due to less variables and rules.

Application of Hybrid Optimization-Expert System for Optimal Power Management on Board Space Power Station

NASA Technical Reports Server (NTRS)

Momoh, James; Chattopadhyay, Deb; Basheer, Omar Ali AL

1996-01-01

The space power system has two sources of energy: photo-voltaic blankets and batteries. The optimal power management problem on-board has two broad operations: off-line power scheduling to determine the load allocation schedule of the next several hours based on the forecast of load and solar power availability. The nature of this study puts less emphasis on speed requirement for computation and more importance on the optimality of the solution. The second category problem, on-line power rescheduling, is needed in the event of occurrence of a contingency to optimally reschedule the loads to minimize the 'unused' or 'wasted' energy while keeping the priority on certain type of load and minimum disturbance of the original optimal schedule determined in the first-stage off-line study. The computational performance of the on-line 'rescheduler' is an important criterion and plays a critical role in the selection of the appropriate tool. The Howard University Center for Energy Systems and Control has developed a hybrid optimization-expert systems based power management program. The pre-scheduler has been developed using a non-linear multi-objective optimization technique called the Outer Approximation method and implemented using the General Algebraic Modeling System (GAMS). The optimization model has the capability of dealing with multiple conflicting objectives viz. maximizing energy utilization, minimizing the variation of load over a day, etc. and incorporates several complex interaction between the loads in a space system. The rescheduling is performed using an expert system developed in PROLOG which utilizes a rule-base for reallocation of the loads in an emergency condition viz. shortage of power due to solar array failure, increase of base load, addition of new activity, repetition of old activity etc. Both the modules handle decision making on battery charging and discharging and allocation of loads over a time-horizon of a day divided into intervals of 10 minutes. The models have been extensively tested using a case study for the Space Station Freedom and the results for the case study will be presented. Several future enhancements of the pre-scheduler and the 'rescheduler' have been outlined which include graphic analyzer for the on-line module, incorporating probabilistic considerations, including spatial location of the loads and the connectivity using a direct current (DC) load flow model.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Cui, Mingjian; Hodge, Bri-Mathias

The large variability and uncertainty in wind power generation present a concern to power system operators, especially given the increasing amounts of wind power being integrated into the electric power system. Large ramps, one of the biggest concerns, can significantly influence system economics and reliability. The Wind Forecast Improvement Project (WFIP) was to improve the accuracy of forecasts and to evaluate the economic benefits of these improvements to grid operators. This paper evaluates the ramp forecasting accuracy gained by improving the performance of short-term wind power forecasting. This study focuses on the WFIP southern study region, which encompasses most ofmore » the Electric Reliability Council of Texas (ERCOT) territory, to compare the experimental WFIP forecasts to the existing short-term wind power forecasts (used at ERCOT) at multiple spatial and temporal scales. The study employs four significant wind power ramping definitions according to the power change magnitude, direction, and duration. The optimized swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental WFIP forecasts improve the accuracy of the wind power ramp forecasting. This improvement can result in substantial costs savings and power system reliability enhancements.« less

Prediction-based manufacturing center self-adaptive demand side energy optimization in cyber physical systems

NASA Astrophysics Data System (ADS)

Sun, Xinyao; Wang, Xue; Wu, Jiangwei; Liu, Youda

2014-05-01

Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufacturing center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2012 CFR

2012-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2014 CFR

2014-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2011 CFR

2011-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.206 - Approval requirements for load forecasts prepared pursuant to approved load forecast work plans.

Code of Federal Regulations, 2013 CFR

2013-01-01

... effects on electric revenues caused by competition from alternative energy sources or other electric... uncertainty or alternative futures that may determine the borrower's actual loads. Examples of economic... basis. Include alternative futures, as applicable. This summary shall be designed to accommodate the...

7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

Code of Federal Regulations, 2013 CFR

2013-01-01

... electronically to RUS computer software applications. RUS will evaluate borrower load forecasts for readability...'s engineering planning documents, such as the construction work plan, incorporate consumer and usage...

7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

Code of Federal Regulations, 2011 CFR

2011-01-01

... electronically to RUS computer software applications. RUS will evaluate borrower load forecasts for readability...'s engineering planning documents, such as the construction work plan, incorporate consumer and usage...

7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

Code of Federal Regulations, 2014 CFR

2014-01-01

... computer software applications. RUS will evaluate borrower load forecasts for readability, understanding..., distribution costs, other systems costs, average revenue per kWh, and inflation. Also, a borrower's engineering...

7 CFR 1710.205 - Minimum approval requirements for all load forecasts.

Code of Federal Regulations, 2012 CFR

2012-01-01

... electronically to RUS computer software applications. RUS will evaluate borrower load forecasts for readability...'s engineering planning documents, such as the construction work plan, incorporate consumer and usage...

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced.more » The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

2017-08-31

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) ismore » analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

Recent Trends in Variable Generation Forecasting and Its Value to the Power System

DOE PAGES

Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; ...

2014-12-23

We report that the rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value ofmore » adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.« less

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, M. H.; Giebel, G.; Nielsen, T. S.; Hahmann, A.; Sørensen, P.; Madsen, H.

2012-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the working title "Integrated Wind Power Planning Tool". The project commenced October 1, 2011, and the goal is to integrate a numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. With regard to the latter, one such simulation tool has been developed at the Wind Energy Division, Risø DTU, intended for long term power system planning. As part of the PSO project the inferior NWP model used at present will be replaced by the state-of-the-art Weather Research & Forecasting (WRF) model. Furthermore, the integrated simulation tool will be improved so it can handle simultaneously 10-50 times more turbines than the present ~ 300, as well as additional atmospheric parameters will be included in the model. The WRF data will also be input for a statistical short term prediction model to be developed in collaboration with ENFOR A/S; a danish company that specialises in forecasting and optimisation for the energy sector. This integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated prediction tool constitute scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator, and the need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2020, from the current 20%.

Operation and Management of Thermostatically Controlled Loads for Providing Regulation Services to Power Grids

NASA Astrophysics Data System (ADS)

Vanouni, Maziar

The notion of demand-side participation in power systems operation and control is on the verge of realization because of the advancement in the required technologies an tools like communications, smart meters, sensor networks, large data management techniques, large scale optimization method, etc. Therefore, demand-response (DR) programs can be one of the prosperous solutions to accommodate part of the increasing demand for load balancing services which is brought about by the high penetration of intermittent renewable energies in power systems. This dissertation studies different aspects of the DR programs that utilized the thermostatically controlled loads (TCLs) to provide load balancing services. The importance of TCLs among the other loads lie on their flexibility in power consumption pattern while the customer/end-user comfort is not (or minimally) impacted. Chapter 2 discussed a previously presented direct load control (DLC) to control the power consumption of aggregated TCLs. The DLC method performs a power tracking control and based on central approach where a central controller broadcasts the control command to the dispersed TCLs to toggle them on/off. The central controller receives measurement feedback from the TCLs once per couple of minutes to run a successful forecast process. The performance evaluation criteria to evaluate the load balancing service provided by the TCLs are presented. The results are discussed under different scenarios and situation. The numerical results show the proper performance of the DLC method. This DLC method is used as the control method in all the studies in this dissertation. Chapter 3 presents performance improvements for the original method in Chapter 2 by communicating two more pieces of information called forecast parameters (FPs). Communicating improves the forecast process in the DLC and hence, both performance accuracy and the amount of tear-and-wear imposed on the TCLs. Chapter 4 formulates a stochastic optimization model for a load aggregator (LA) to participate in the performance-based regulation markets (PBRM). PBRMs are the recently developed and practiced regulation market structure recommended by Federal Energy Regulatory Commission (FERC) in 2011. In PBRMs, regulation resources are paid based on both regulation capacity bids and the regulation performance including the provided mileage and the performance accuracy. In order to develop the income from the PBRM, the convention of California Independent System Operator (CAISO) is used. In the presented optimization model, the amount of tear-and-wear imposed on the TCLs are confined to prevent abrupt switching of TCLs. In Chapter 5, a two-stage reward allocation mechanism is developed for a LA recruiting TCLs for regulation service provision. The mechanism helps the LA to distribute the total reward (earned from regulation service provision) among the TCLs according to their contribution in the whole provided service. In the first stage, TCLs are prioritized based on their service provision capability. In order to do so, an index called SPCI is presented to quantify TCLs capability/flexibility and therefore, prioritize them. After prioritization TCLs a priority list is constructed in the first stage. In the second stage, a reward curve is constructed representing the functionality of the possible total reward with respect to the number top TCLs in the priority list. Then, the allocated reward to individual TCLs is calculated by applying the incremental method on the constructed reward curve. This presented reward allocation mechanism is based on the definition of maximum service capacity (MSC) for a control group including TCLs. MSC is defined and its calculation method is presented before discussing the two stages of the reward allocation mechanism. The numerical results proves the suitability of the proposed prioritization method as it is observed the TCLs with higher rankings can contribute more to the total reward in comparison to the TCLs with lower rankings in the priority list.

7 CFR 1710.302 - Financial forecasts-power supply borrowers.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 11 2011-01-01 2011-01-01 false Financial forecasts-power supply borrowers. 1710.302... AND GUARANTEES Long-Range Financial Forecasts § 1710.302 Financial forecasts—power supply borrowers. (a) The requirements of this section apply only to financial forecasts submitted by power supply...

Integrating Solar PV in Utility System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mills, A.; Botterud, A.; Wu, J.

2013-10-31

This study develops a systematic framework for estimating the increase in operating costs due to uncertainty and variability in renewable resources, uses the framework to quantify the integration costs associated with sub-hourly solar power variability and uncertainty, and shows how changes in system operations may affect these costs. Toward this end, we present a statistical method for estimating the required balancing reserves to maintain system reliability along with a model for commitment and dispatch of the portfolio of thermal and renewable resources at different stages of system operations. We estimate the costs of sub-hourly solar variability, short-term forecast errors, andmore » day-ahead (DA) forecast errors as the difference in production costs between a case with “realistic” PV (i.e., subhourly solar variability and uncertainty are fully included in the modeling) and a case with “well behaved” PV (i.e., PV is assumed to have no sub-hourly variability and can be perfectly forecasted). In addition, we highlight current practices that allow utilities to compensate for the issues encountered at the sub-hourly time frame with increased levels of PV penetration. In this analysis we use the analytical framework to simulate utility operations with increasing deployment of PV in a case study of Arizona Public Service Company (APS), a utility in the southwestern United States. In our analysis, we focus on three processes that are important in understanding the management of PV variability and uncertainty in power system operations. First, we represent the decisions made the day before the operating day through a DA commitment model that relies on imperfect DA forecasts of load and wind as well as PV generation. Second, we represent the decisions made by schedulers in the operating day through hour-ahead (HA) scheduling. Peaking units can be committed or decommitted in the HA schedules and online units can be redispatched using forecasts that are improved relative to DA forecasts, but still imperfect. Finally, we represent decisions within the operating hour by schedulers and transmission system operators as real-time (RT) balancing. We simulate the DA and HA scheduling processes with a detailed unit-commitment (UC) and economic dispatch (ED) optimization model. This model creates a least-cost dispatch and commitment plan for the conventional generating units using forecasts and reserve requirements as inputs. We consider only the generation units and load of the utility in this analysis; we do not consider opportunities to trade power with neighboring utilities. We also do not consider provision of reserves from renewables or from demand-side options. We estimate dynamic reserve requirements in order to meet reliability requirements in the RT operations, considering the uncertainty and variability in load, solar PV, and wind resources. Balancing reserve requirements are based on the 2.5th and 97.5th percentile of 1-min deviations from the HA schedule in a previous year. We then simulate RT deployment of balancing reserves using a separate minute-by-minute simulation of deviations from the HA schedules in the operating year. In the simulations we assume that balancing reserves can be fully deployed in 10 min. The minute-by-minute deviations account for HA forecasting errors and the actual variability of the load, wind, and solar generation. Using these minute-by-minute deviations and deployment of balancing reserves, we evaluate the impact of PV on system reliability through the calculation of the standard reliability metric called Control Performance Standard 2 (CPS2). Broadly speaking, the CPS2 score measures the percentage of 10-min periods in which a balancing area is able to balance supply and demand within a specific threshold. Compliance with the North American Electric Reliability Corporation (NERC) reliability standards requires that the CPS2 score must exceed 90% (i.e., the balancing area must maintain adequate balance for 90% of the 10-min periods). The combination of representing DA forecast errors in the DA commitments, using 1-min PV data to simulate RT balancing, and estimates of reliability performance through the CPS2 metric, all factors that are important to operating systems with increasing amounts of PV, makes this study unique in its scope.« less

Optimize Short Term load Forcasting Anomalous Based Feed Forward Backpropagation

NASA Astrophysics Data System (ADS)

Mulyadi, Y.; Abdullah, A. G.; Rohmah, K. A.

2017-03-01

This paper contains the Short-Term Load Forecasting (STLF) using artificial neural network especially feed forward back propagation algorithm which is particularly optimized in order to getting a reduced error value result. Electrical load forecasting target is a holiday that hasn’t identical pattern and different from weekday’s pattern, in other words the pattern of holiday load is an anomalous. Under these conditions, the level of forecasting accuracy will be decrease. Hence we need a method that capable to reducing error value in anomalous load forecasting. Learning process of algorithm is supervised or controlled, then some parameters are arranged before performing computation process. Momentum constant a value is set at 0.8 which serve as a reference because it has the greatest converge tendency. Learning rate selection is made up to 2 decimal digits. In addition, hidden layer and input component are tested in several variation of number also. The test result leads to the conclusion that the number of hidden layer impact on the forecasting accuracy and test duration determined by the number of iterations when performing input data until it reaches the maximum of a parameter value.

Performance of Optimization Heuristics for the Operational Planning of Multi-energy Storage Systems

NASA Astrophysics Data System (ADS)

Haas, J.; Schradi, J.; Nowak, W.

2016-12-01

In the transition to low-carbon energy sources, energy storage systems (ESS) will play an increasingly important role. Particularly in the context of solar power challenges (variability, uncertainty), ESS can provide valuable services: energy shifting, ramping, robustness against forecast errors, frequency support, etc. However, these qualities are rarely modelled in the operational planning of power systems because of the involved computational burden, especially when multiple ESS technologies are involved. This work assesses two optimization heuristics for speeding up the optimal operation problem. It compares their accuracy (in terms of costs) and speed against a reference solution. The first heuristic (H1) is based on a merit order. Here, the ESS are sorted from lower to higher operational costs (including cycling costs). For each time step, the cheapest available ESS is used first, followed by the second one and so on, until matching the net load (demand minus available renewable generation). The second heuristic (H2) uses the Fourier transform to detect the main frequencies that compose the net load. A specific ESS is assigned to each frequency range, aiming to smoothen the net load. Finally, the reference solution is obtained with a mixed integer linear program (MILP). H1, H2 and MILP are subject to technical constraints (energy/power balance, ramping rates, on/off states...). Costs due to operation, replacement (cycling) and unserved energy are considered. Four typical days of a system with a high share of solar energy were used in several test cases, varying the resolution from one second to fifteen minutes. H1 and H2 achieve accuracies of about 90% and 95% in average, and speed-up times of two to three and one to two orders of magnitude, respectively. The use of the heuristics looks promising in the context of planning the expansion of power systems, especially when their loss of accuracy is outweighed by solar or wind forecast errors.

7 CFR 1710.203 - Requirement to prepare a load forecast-distribution borrowers.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Requirement to prepare a load forecast-distribution borrowers. 1710.203 Section 1710.203 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load...

The time series approach to short term load forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hagan, M.T.; Behr, S.M.

The application of time series analysis methods to load forecasting is reviewed. It is shown than Box and Jenkins time series models, in particular, are well suited to this application. The logical and organized procedures for model development using the autocorrelation function make these models particularly attractive. One of the drawbacks of these models is the inability to accurately represent the nonlinear relationship between load and temperature. A simple procedure for overcoming this difficulty is introduced, and several Box and Jenkins models are compared with a forecasting procedure currently used by a utility company.

Improved Weather and Power Forecasts for Energy Operations - the German Research Project EWeLiNE

NASA Astrophysics Data System (ADS)

Lundgren, Kristina; Siefert, Malte; Hagedorn, Renate; Majewski, Detlev

2014-05-01

The German energy system is going through a fundamental change. Based on the energy plans of the German federal government, the share of electrical power production from renewables should increase to 35% by 2020. This means that, in the near future at certain times renewable energies will provide a major part of Germany's power production. Operating a power supply system with a large share of weather-dependent power sources in a secure way requires improved power forecasts. One of the most promising strategies to improve the existing wind power and PV power forecasts is to optimize the underlying weather forecasts and to enhance the collaboration between the meteorology and energy sectors. Deutscher Wetterdienst addresses these challenges in collaboration with Fraunhofer IWES within the research project EWeLiNE. The overarching goal of the project is to improve the wind and PV power forecasts by combining improved power forecast models and optimized weather forecasts. During the project, the numerical weather prediction models COSMO-DE and COSMO-DE-EPS (Ensemble Prediction System) by Deutscher Wetterdienst will be generally optimized towards improved wind power and PV forecasts. For instance, it will be investigated whether the assimilation of new types of data, e.g. power production data, can lead to improved weather forecasts. With regard to the probabilistic forecasts, the focus is on the generation of ensembles and ensemble calibration. One important aspect of the project is to integrate the probabilistic information into decision making processes by developing user-specified products. In this paper we give an overview of the project and present first results.

Toward quantitative forecasts of volcanic ash dispersal: Using satellite retrievals for optimal estimation of source terms

NASA Astrophysics Data System (ADS)

Zidikheri, Meelis J.; Lucas, Christopher; Potts, Rodney J.

2017-08-01

Airborne volcanic ash is a hazard to aviation. There is an increasing demand for quantitative forecasts of ash properties such as ash mass load to allow airline operators to better manage the risks of flying through airspace likely to be contaminated by ash. In this paper we show how satellite-derived mass load information at times prior to the issuance of the latest forecast can be used to estimate various model parameters that are not easily obtained by other means such as the distribution of mass of the ash column at the volcano. This in turn leads to better forecasts of ash mass load. We demonstrate the efficacy of this approach using several case studies.

Analysis of recurrent neural networks for short-term energy load forecasting

NASA Astrophysics Data System (ADS)

Di Persio, Luca; Honchar, Oleksandr

2017-11-01

Short-term forecasts have recently gained an increasing attention because of the rise of competitive electricity markets. In fact, short-terms forecast of possible future loads turn out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. Latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected. In the present work we first review novel approaches to energy load forecasting based on recurrent neural network, focusing our attention on long/short term memory architectures (LSTMs). Such type of artificial neural networks have been widely applied to problems dealing with sequential data such it happens, e.g., in socio-economics settings, for text recognition purposes, concerning video signals, etc., always showing their effectiveness to model complex temporal data. Moreover, we consider different novel variations of basic LSTMs, such as sequence-to-sequence approach and bidirectional LSTMs, aiming at providing effective models for energy load data. Last but not least, we test all the described algorithms on real energy load data showing not only that deep recurrent networks can be successfully applied to energy load forecasting, but also that this approach can be extended to other problems based on time series prediction.

Evaluation and prediction of solar radiation for energy management based on neural networks

NASA Astrophysics Data System (ADS)

Aldoshina, O. V.; Van Tai, Dinh

2017-08-01

Currently, there is a high rate of distribution of renewable energy sources and distributed power generation based on intelligent networks; therefore, meteorological forecasts are particularly useful for planning and managing the energy system in order to increase its overall efficiency and productivity. The application of artificial neural networks (ANN) in the field of photovoltaic energy is presented in this article. Implemented in this study, two periodically repeating dynamic ANS, that are the concentration of the time delay of a neural network (CTDNN) and the non-linear autoregression of a network with exogenous inputs of the NAEI, are used in the development of a model for estimating and daily forecasting of solar radiation. ANN show good productivity, as reliable and accurate models of daily solar radiation are obtained. This allows to successfully predict the photovoltaic output power for this installation. The potential of the proposed method for controlling the energy of the electrical network is shown using the example of the application of the NAEI network for predicting the electric load.

Analysis of temperature changes on three-phase synchronous generator using infrared: comparison between balanced and unbalanced load

NASA Astrophysics Data System (ADS)

Amien, S.; Yoga, W.; Fahmi, F.

2018-02-01

Synchronous generators are a major tool in an electrical energy generating systems, the load supplied by the generator is unbalanced. This paper discusses the effect of synchronous generator temperature on the condition of balanced load and unbalanced load, which will then be compared with the measurement result of both states of the generator. Unbalanced loads can be caused by various asymmetric disturbances in the power system and the failure of load forecasting studies so that the load distribution in each phase is not the same and causing the excessive heat of the generator. The method used in data collection was by using an infrared thermometer and resistance calculation method. The temperature comparison result between the resistive, inductive and capacitive loads in the highest temperature balance occured when the generator is loaded with a resistive load, where T = 31.9 ° C and t = 65 minutes. While in a state of unbalanced load the highest temperature occured when the generator is loaded with a capacitive load, where T = 40.1 ° C and t = 60 minutes. By understanding this behavior, we can maintain the generator for longer operation life.

Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts

DTIC Science & Technology

2013-12-01

ahead scheduling, Weather forecast , Wind power , Photovoltaic Power 15. NUMBER OF PAGES 107 16. PRICE CODE 17. SECURITY CLASSIFICATION OF...cost can be reached by accurately anticipating the future renewable power productions. This thesis suggests the use of weather forecasts to establish...reached by accurately anticipating the future renewable power productions. This thesis suggests the use of weather forecasts to establish day-ahead

Wind power forecasting: IEA Wind Task 36 & future research issues

NASA Astrophysics Data System (ADS)

Giebel, G.; Cline, J.; Frank, H.; Shaw, W.; Pinson, P.; Hodge, B.-M.; Kariniotakis, G.; Madsen, J.; Möhrlen, C.

2016-09-01

This paper presents the new International Energy Agency Wind Task 36 on Forecasting, and invites to collaborate within the group. Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, MetOffice, met.no, DMI,...), operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented.

Evolution of damage during deformation in porous granular materials (Louis Néel Medal Lecture)

NASA Astrophysics Data System (ADS)

Main, Ian

2014-05-01

'Crackling noise' occurs in a wide variety of systems that respond to external forcing in an intermittent way, leading to sudden bursts of energy release similar to those heard when crunching up a piece of paper or listening to a fire. In mineral magnetism ('Barkhausen') crackling noise occurs due to sudden changes in the size and orientation of microscopic ferromagnetic domains when the external magnetic field is changed. In rock physics sudden changes in internal stress associated with microscopically brittle failure events lead to acoustic emissions that can be recorded on the sample boundary, and used to infer the state of internal damage. Crackling noise is inherently stochastic, but the population of events often exhibits remarkably robust scaling properties, in terms of the source area, duration, energy, and in the waiting time between events. Here I describe how these scaling properties emerge and evolve spontaneously in a fully-dynamic discrete element model of sedimentary rocks subject to uniaxial compression at a constant strain rate. The discrete elements have structural disorder similar to that of a real rock, and this is the only source of heterogeneity. Despite the stationary loading and the lack of any time-dependent weakening processes, the results are all characterized by emergent power law distributions over a broad range of scales, in agreement with experimental observation. As deformation evolves, the scaling exponents change systematically in a way that is similar to the evolution of damage in experiments on real sedimentary rocks. The potential for real-time failure forecasting is examined by using synthetic and real data from laboratory tests and prior to volcanic eruptions. The combination of non-linearity and an irreducible stochastic component leads to significant variations in the precision and accuracy of the forecast failure time, leading to a significant proportion of 'false alarms' (forecast too early) and 'missed events' (forecast too late), as well as an over-optimistic assessments of forecasting power and quality when the failure time is known (the 'benefit of hindsight'). The evolution becomes progressively more complex, and the forecasting power diminishes, in going from ideal synthetics to controlled laboratory tests to open natural systems at larger scales in space and time.

Confidence intervals in Flow Forecasting by using artificial neural networks

NASA Astrophysics Data System (ADS)

Panagoulia, Dionysia; Tsekouras, George

2014-05-01

One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input variable of different ANN structures [3]. The performance of each ANN structure is evaluated by the voting analysis based on eleven criteria, which are the root mean square error (RMSE), the correlation index (R), the mean absolute percentage error (MAPE), the mean percentage error (MPE), the mean percentage error (ME), the percentage volume in errors (VE), the percentage error in peak (MF), the normalized mean bias error (NMBE), the normalized root mean bias error (NRMSE), the Nash-Sutcliffe model efficiency coefficient (E) and the modified Nash-Sutcliffe model efficiency coefficient (E1). The next day flow for the test set is calculated using the best ANN structure's model. Consequently, the confidence intervals of various confidence levels for training, evaluation and test sets are compared in order to explore the generalisation dynamics of confidence intervals from training and evaluation sets. [1] H.S. Hippert, C.E. Pedreira, R.C. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Trans. on Power Systems, vol. 16, no. 1, 2001, pp. 44-55. [2] G. J. Tsekouras, N.E. Mastorakis, F.D. Kanellos, V.T. Kontargyri, C.D. Tsirekis, I.S. Karanasiou, Ch.N. Elias, A.D. Salis, P.A. Kontaxis, A.A. Gialketsi: "Short term load forecasting in Greek interconnected power system using ANN: Confidence Interval using a novel re-sampling technique with corrective Factor", WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, (CSECS '10), Vouliagmeni, Athens, Greece, December 29-31, 2010. [3] D. Panagoulia, I. Trichakis, G. J. Tsekouras: "Flow Forecasting via Artificial Neural Networks - A Study for Input Variables conditioned on atmospheric circulation", European Geosciences Union, General Assembly 2012 (NH1.1 / AS1.16 - Extreme meteorological and hydrological events induced by severe weather and climate change), Vienna, Austria, 22-27 April 2012.

Operational Planning of Channel Airlift Missions Using Forecasted Demand

DTIC Science & Technology

2013-03-01

tailored to the specific problem ( Metaheuristics , 2005). As seen in the section Cargo Loading Algorithm , heuristic methods are often iterative...that are equivalent to the forecasted cargo amount. The simulated pallets are then used in a heuristic cargo loading algorithm . The loading... algorithm places cargo onto available aircraft (based on real schedules) given the date and the destination and outputs statistics based on the aircraft ton

The Future of Low-Carbon Electricity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greenblatt, Jeffery B.; Brown, Nicholas R.; Slaybaugh, Rachel

Here, we review future global demand for electricity and major technologies positioned to supply itwith minimal greenhouse gas (GHG) emissions: renewables (wind, solar, water, geothermal and biomass), nuclear fission, and fossil power with CO 2 capture and sequestration. Two breakthrough technologies (space solar power and nuclear fusion) are discussed as exciting but uncertain additional options for low net GHG emissions (“low-carbon”) electricity generation. Grid integration technologies (monitoring and forecasting of transmission and distribution systems, demand-side load management, energy storage, and load balancing with low-carbon fuel substitutes) are also discussed. For each topic, recent historical trends and future prospects are reviewed,more » along with technical challenges, costs and other issues as appropriate. While no technology represents an ideal solution, their strengths can be enhanced by deployment in combination, along with grid integration that forms a critical set of enabling technologies to assure a reliable and robust future low-carbon electricity system.« less

The Future of Low-Carbon Electricity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greenblatt, Jeffery B.; Brown, Nicholas R.; Slaybaugh, Rachel

We review future global demand for electricity and major technologies positioned to supply it with minimal greenhouse gas (GHG) emissions: renewables (wind, solar, water, geothermal, and biomass), nuclear fission, and fossil power with CO2 capture and sequestration. We discuss two breakthrough technologies (space solar power and nuclear fusion) as exciting but uncertain additional options for low-net GHG emissions (i.e., low-carbon) electricity generation. In addition, we discuss grid integration technologies (monitoring and forecasting of transmission and distribution systems, demand-side load management, energy storage, and load balancing with low-carbon fuel substitutes). For each topic, recent historical trends and future prospects are reviewed,more » along with technical challenges, costs, and other issues as appropriate. Although no technology represents an ideal solution, their strengths can be enhanced by deployment in combination, along with grid integration that forms a critical set of enabling technologies to assure a reliable and robust future low-carbon electricity system.« less

The Future of Low-Carbon Electricity

DOE PAGES

Greenblatt, Jeffery B.; Brown, Nicholas R.; Slaybaugh, Rachel; ...

2017-07-10

Here, we review future global demand for electricity and major technologies positioned to supply itwith minimal greenhouse gas (GHG) emissions: renewables (wind, solar, water, geothermal and biomass), nuclear fission, and fossil power with CO 2 capture and sequestration. Two breakthrough technologies (space solar power and nuclear fusion) are discussed as exciting but uncertain additional options for low net GHG emissions (“low-carbon”) electricity generation. Grid integration technologies (monitoring and forecasting of transmission and distribution systems, demand-side load management, energy storage, and load balancing with low-carbon fuel substitutes) are also discussed. For each topic, recent historical trends and future prospects are reviewed,more » along with technical challenges, costs and other issues as appropriate. While no technology represents an ideal solution, their strengths can be enhanced by deployment in combination, along with grid integration that forms a critical set of enabling technologies to assure a reliable and robust future low-carbon electricity system.« less

Wind Power Forecasting Error Distributions: An International Comparison; Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Lew, D.; Milligan, M.

2012-09-01

Wind power forecasting is expected to be an important enabler for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that do occur can be critical to system operation functions, such as the setting of operating reserve levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations.

An Example of Unsupervised Networks Kohonen's Self-Organizing Feature Map

NASA Technical Reports Server (NTRS)

Niebur, Dagmar

1995-01-01

Kohonen's self-organizing feature map belongs to a class of unsupervised artificial neural network commonly referred to as topographic maps. It serves two purposes, the quantization and dimensionality reduction of date. A short description of its history and its biological context is given. We show that the inherent classification properties of the feature map make it a suitable candidate for solving the classification task in power system areas like load forecasting, fault diagnosis and security assessment.

Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Florita, A.; Hodge, B. M.; Milligan, M.

2012-08-01

The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites andmore » for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.« less

Application and verification of ECMWF seasonal forecast for wind energy

NASA Astrophysics Data System (ADS)

Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

2015-04-01

A good understanding of long-term annual energy production (AEP) is crucial when assessing the business case of investing in green energy like wind power. The art of wind-resource assessment has emerged into a scientific discipline on its own, which has advanced at high pace over the last decade. This has resulted in continuous improvement of the AEP accuracy and, therefore, increase in business case certainty. Harvesting the full potential output of a wind farm or a portfolio of wind farms depends heavily on optimizing operation and management strategy. The necessary information for short-term planning (up to 14 days) is provided by standard weather and power forecasting services, and the long-term plans are based on climatology. However, the wind-power industry is lacking quality information on intermediate scales of the expected variability in seasonal and intra-annual variations and their geographical distribution. The seasonal power forecast presented here is designed to bridge this gap. The seasonal power production forecast is based on the ECMWF seasonal weather forecast and the Vestas' high-resolution, mesoscale weather library. The seasonal weather forecast is enriched through a layer of statistical post-processing added to relate large-scale wind speed anomalies to mesoscale climatology. The resulting predicted energy production anomalies, thus, include mesoscale effects not captured by the global forecasting systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power forecast. In theory, the wind power is proportional to the cube of wind speed. However, due to the nature of turbine design, this exponent is close to 3 only at low wind speeds, becomes smaller as the wind speed increases, and above 11-13 m/s the power output remains constant, called the rated power. The non-linear relationship between wind speed and the power output generally increases sensitivity of the forecasted power to the wind speed anomalies. On the other hand, in some cases and areas where turbines operate close to, or above the rated power, the sensitivity of power forecast is reduced. Thus, the seasonal power forecasting system requires good knowledge of the changes in frequency of events with sufficient wind speeds to have acceptable skill. The scientific background for the Vestas seasonal power forecasting system is described and the relationship between predicted monthly wind speed anomalies and observed wind energy production are investigated for a number of operating wind farms in different climate zones. Current challenges will be discussed and some future research and development areas identified.

Wind power forecasting: IEA Wind Task 36 & future research issues

DOE PAGES

Giebel, G.; Cline, J.; Frank, H.; ...

2016-10-03

Here, this paper presents the new International Energy Agency Wind Task 36 on Forecasting, and invites to collaborate within the group. Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, MetOffice, met.no, DMI,...), operational forecaster and forecast users. The Taskmore » is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented.« less

2014 Gulf of Mexico Hypoxia Forecast

USGS Publications Warehouse

Scavia, Donald; Evans, Mary Anne; Obenour, Dan

2014-01-01

The Gulf of Mexico annual summer hypoxia forecasts are based on average May total nitrogen loads from the Mississippi River basin for that year. The load estimate, recently released by USGS, is 4,761 metric tons per day. Based on that estimate, we predict the area of this summer’s hypoxic zone to be 14,000 square kilometers (95% credible interval, 8,000 to 20,000) – an “average year”. Our forecast hypoxic volume is 50 km3 (95% credible interval, 20 to 77).

An application of ensemble/multi model approach for wind power production forecasting

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Pinson, P.; Hagedorn, R.; Decimi, G.; Sperati, S.

2011-02-01

The wind power forecasts of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast applied in this study is based on meteorological models that provide the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. For this purpose a training of a Neural Network (NN) to link directly the forecasted meteorological data and the power data has been performed. One wind farm has been examined located in a mountain area in the south of Italy (Sicily). First we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by the combination of models (RAMS, ECMWF deterministic, LAMI). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error (normalized by nominal power) of at least 1% compared to the singles models approach. Finally we have focused on the possibility of using the ensemble model system (EPS by ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first three days ahead period.

The useful potential of using existing data to uniquely identify predictable wind events and regimes, part 1

NASA Technical Reports Server (NTRS)

Trettel, D. W.; Aquino, J. T.; Piazza, T. R.; Taylor, L. E.; Trask, D. C.

1982-01-01

Correlations between standard meteorological data and wind power generation potential were developed. Combined with appropriate wind forecasts, these correlations can be useful to load dispatchers to supplement conventional energy sources. Hourly wind data were analyzed for four sites, each exhibiting a unique physiography. These sites are Amarillo, Texas; Ludington, Michigan; Montauk Point, New York; and San Gorgonio, California. Synoptic weather maps and tables are presented to illustrate various wind 'regimes' at these sites.

Quantitative impact of aerosols on numerical weather prediction. Part I: Direct radiative forcing

NASA Astrophysics Data System (ADS)

Marquis, J. W.; Zhang, J.; Reid, J. S.; Benedetti, A.; Christensen, M.

2017-12-01

While the effects of aerosols on climate have been extensively studied over the past two decades, the impacts of aerosols on operational weather forecasts have not been carefully quantified. Despite this lack of quantification, aerosol plumes can impact weather forecasts directly by reducing surface reaching solar radiation and indirectly through affecting remotely sensed data that are used for weather forecasts. In part I of this study, the direct impact of smoke aerosol plumes on surface temperature forecasts are quantified using a smoke aerosol event affecting the United States Upper-Midwest in 2015. NCEP, ECMWF and UKMO model forecast surface temperature uncertainties are studied with respect to aerosol loading. Smoke aerosol direct cooling efficiencies are derived and the potential of including aerosol particles in operational forecasts is discussed, with the consideration of aerosol trends, especially over regions with heavy aerosol loading.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mendes, J.; Bessa, R.J.; Keko, H.

Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highlymore » dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.« less

Advancements in Wind Integration Study Data Modeling: The Wind Integration National Dataset (WIND) Toolkit; Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draxl, C.; Hodge, B. M.; Orwig, K.

2013-10-01

Regional wind integration studies in the United States require detailed wind power output data at many locations to perform simulations of how the power system will operate under high-penetration scenarios. The wind data sets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as be time synchronized with available load profiles. The Wind Integration National Dataset (WIND) Toolkit described in this paper fulfills these requirements. A wind resource dataset, wind power production time series, and simulated forecasts from a numerical weather predictionmore » model run on a nationwide 2-km grid at 5-min resolution will be made publicly available for more than 110,000 onshore and offshore wind power production sites.« less

An application of ensemble/multi model approach for wind power production forecast.

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Decimi, G.; Hagedorn, R.; Sperati, S.

2010-09-01

The wind power forecast of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast is based on a mesoscale meteorological models that provides the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. The corrected wind data are then used as input in the wind farm power curve to obtain the power forecast. These computations require historical time series of wind measured data (by an anemometer located in the wind farm or on the nacelle) and power data in order to be able to perform the statistical analysis on the past. For this purpose a Neural Network (NN) is trained on the past data and then applied in the forecast task. Considering that the anemometer measurements are not always available in a wind farm a different approach has also been adopted. A training of the NN to link directly the forecasted meteorological data and the power data has also been performed. The normalized RMSE forecast error seems to be lower in most cases by following the second approach. We have examined two wind farms, one located in Denmark on flat terrain and one located in a mountain area in the south of Italy (Sicily). In both cases we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by using two or more models (RAMS, ECMWF deterministic, LAMI, HIRLAM). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error of at least 1% compared to the singles models approach. Moreover the use of a deterministic global model, (e.g. ECMWF deterministic model) seems to reach similar level of accuracy of those of the mesocale models (LAMI and RAMS). Finally we have focused on the possibility of using the ensemble model (ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first day ahead period. In fact low spreads often correspond to low forecast error. For longer forecast horizon the correlation between RMSE and ensemble spread decrease becoming too low to be used for this purpose.

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, WanYin; Zhang, Jie; Florita, Anthony

2015-12-08

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance,more » cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giebel, G.; Cline, J.; Frank, H.

Here, this paper presents the new International Energy Agency Wind Task 36 on Forecasting, and invites to collaborate within the group. Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, MetOffice, met.no, DMI,...), operational forecaster and forecast users. The Taskmore » is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented.« less

Optimizing Microgrid Architecture on Department of Defense Installations

DTIC Science & Technology

2014-09-01

PPA power purchase agreement PV photovoltaic QDR Quadrennial Defense Review SNL Sandia National Laboratory SPIDERS Smart Power Infrastructure...a MILP that dispatches fuel-based generators with consideration to an ensemble of forecasted inputs from renewable power sources, subject to physical...wind power project costs by region: 2012 projects, from [30]. 6. Weather Forecasts Weather forecasts are often presented as a single prediction

Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Optis, Michael; Scott, George N.; Draxl, Caroline

The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present.more » Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.« less

Online Analysis of Wind and Solar Part II: Transmission Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian

2012-01-31

To facilitate wider penetration of renewable resources without compromising system reliability concerns arising from the lack of predictability of intermittent renewable resources, a tool for use by California Independent System Operator (CAISO) power grid operators was developed by Pacific Northwest National Laboratory (PNNL) in conjunction with CAISO with funding from California Energy Commission. The tool analyzes and displays the impacts of uncertainties in forecasts of loads and renewable generation on: (1) congestion, (2)voltage and transient stability margins, and (3)voltage reductions and reactive power margins. The impacts are analyzed in the base case and under user-specified contingencies.A prototype of the toolmore » has been developed and implemented in software.« less

Near real time wind energy forecasting incorporating wind tunnel modeling

NASA Astrophysics Data System (ADS)

Lubitz, William David

A series of experiments and investigations were carried out to inform the development of a day-ahead wind power forecasting system. An experimental near-real time wind power forecasting system was designed and constructed that operates on a desktop PC and forecasts 12--48 hours in advance. The system uses model output of the Eta regional scale forecast (RSF) to forecast the power production of a wind farm in the Altamont Pass, California, USA from 12 to 48 hours in advance. It is of modular construction and designed to also allow diagnostic forecasting using archived RSF data, thereby allowing different methods of completing each forecasting step to be tested and compared using the same input data. Wind-tunnel investigations of the effect of wind direction and hill geometry on wind speed-up above a hill were conducted. Field data from an Altamont Pass, California site was used to evaluate several speed-up prediction algorithms, both with and without wind direction adjustment. These algorithms were found to be of limited usefulness for the complex terrain case evaluated. Wind-tunnel and numerical simulation-based methods were developed for determining a wind farm power curve (the relation between meteorological conditions at a point in the wind farm and the power production of the wind farm). Both methods, as well as two methods based on fits to historical data, ultimately showed similar levels of accuracy: mean absolute errors predicting power production of 5 to 7 percent of the wind farm power capacity. The downscaling of RSF forecast data to the wind farm was found to be complicated by the presence of complex terrain. Poor results using the geostrophic drag law and regression methods motivated the development of a database search method that is capable of forecasting not only wind speeds but also power production with accuracy better than persistence.

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, J.; Hodge, B. M.; Florita, A.

2013-10-01

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The resultsmore » show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.« less

Commercial equipment loads: End-Use Load and Consumer Assessment Program (ELCAP)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pratt, R.G.; Williamson, M.A.; Richman, E.E.

1990-07-01

The Office of Energy Resources of the Bonneville Power Administration is generally responsible for the agency's power and conservation resource planning. As associated responsibility which supports a variety of office functions is the analysis of historical trends in and determinants of energy consumption. The Office of Energy Resources' End-Use Research Section operates a comprehensive data collection program to provide pertinent information to support demand-side planning, load forecasting, and demand-side program development and delivery. Part of this on-going program is known as the End-Use Load and Consumer Assessment Program (ELCAP), an effort designed to collect electricity usage data through direct monitoringmore » of end-use loads in buildings. This program is conducted for Bonneville by the Pacific Northwest Laboratory. This report provides detailed information on electricity consumption of miscellaneous equipment from the commercial portion of ELCAP. Miscellaneous equipment includes all commercial end-uses except heating, ventilating, air conditioning, and central lighting systems. Some examples of end-uses covered in this report are office equipment, computers, task lighting, refrigeration, and food preparation. Electricity consumption estimates, in kilowatt-hours per square food per year, are provided for each end-use by building type. The following types of buildings are covered: office, retail, restaurant, grocery, warehouse, school, university, and hotel/motel. 6 refs., 35 figs., 12 tabs.« less

Solar and Wind Forecasting | Grid Modernization | NREL

Science.gov Websites

and Wind Forecasting Solar and Wind Forecasting As solar and wind power become more common system operators. An aerial photo of the National Wind Technology Center's PV arrays. Capabilities value of accurate forecasting Wind power visualization to direct questions and feedback during industry

Evaluating the Impacts of Real-Time Pricing on the Cost and Value of Wind Generation

DOE PAGES

Siohansi, Ramteen

2010-05-01

One of the costs associated with integrating wind generation into a power system is the cost of redispatching the system in real-time due to day-ahead wind resource forecast errors. One possible way of reducing these redispatch costs is to introduce demand response in the form of real-time pricing (RTP), which could allow electricity demand to respond to actual real-time wind resource availability using price signals. A day-ahead unit commitment model with day-ahead wind forecasts and a real-time dispatch model with actual wind resource availability is used to estimate system operations in a high wind penetration scenario. System operations are comparedmore » to a perfect foresight benchmark, in which actual wind resource availability is known day-ahead. The results show that wind integration costs with fixed demands can be high, both due to real-time redispatch costs and lost load. It is demonstrated that introducing RTP can reduce redispatch costs and eliminate loss of load events. Finally, social surplus with wind generation and RTP is compared to a system with neither and the results demonstrate that introducing wind and RTP into a market can result in superadditive surplus gains.« less

How important is getting the land surface energy exchange correct in WRF for wind energy forecasting?

NASA Astrophysics Data System (ADS)

Wharton, S.; Simpson, M.; Osuna, J. L.; Newman, J. F.; Biraud, S.

2013-12-01

Wind power forecasting is plagued with difficulties in accurately predicting the occurrence and intensity of atmospheric conditions at the heights spanned by industrial-scale turbines (~ 40 to 200 m above ground level). Better simulation of the relevant physics would enable operational practices such as integration of large fractions of wind power into power grids, scheduling maintenance on wind energy facilities, and deciding design criteria based on complex loads for next-generation turbines and siting. Accurately simulating the surface energy processes in numerical models may be critically important for wind energy forecasting as energy exchange at the surface strongly drives atmospheric mixing (i.e., stability) in the lower layers of the planetary boundary layer (PBL), which in turn largely determines wind shear and turbulence at heights found in the turbine rotor-disk. We hypothesize that simulating accurate a surface-atmosphere energy coupling should lead to more accurate predictions of wind speed and turbulence at heights within the turbine rotor-disk. Here, we tested 10 different land surface model configurations in the Weather Research and Forecasting (WRF) model including Noah, Noah-MP, SSiB, Pleim-Xiu, RUC, and others to evaluate (1) the accuracy of simulated surface energy fluxes to flux tower measurements, (2) the accuracy of forecasted wind speeds to observations at rotor-disk heights, and (3) the sensitivity of forecasting hub-height rotor disk wind speed to the choice of land surface model. WRF was run for four, two-week periods covering both summer and winter periods over the Southern Great Plains ARM site in Oklahoma. Continuous measurements of surface energy fluxes and lidar-based wind speed, direction and turbulence were also available. The SGP ARM site provided an ideal location for this evaluation as it centrally located in the wind-rich Great Plains and multi-MW wind farms are rapidly expanding in the area. We found significant differences in simulated wind speeds at rotor-disk heights from WRF which indicated, in part, the sensitivity of lower PBL winds to surface energy exchange. We also found significant differences in energy partitioning between sensible heat and latent energy depending on choice of land surface model. Overall, the most consistent, accurate model results were produced using Noah-MP. Noah-MP was most accurate at simulating energy fluxes and wind shear. Hub-height wind speed, however, was predicted with most accuracy with Pleim-Xiu. This suggests that simulating wind shear in the surface layer is consistent with accurately simulating surface energy exchange while the exact magnitudes of wind speed may be more strongly influenced by the PBL dynamics. As the nation is working towards a 20% wind energy goal by 2030, increasing the accuracy of wind forecasting at rotor-disk heights becomes more important considering that utilities require wind farms to estimate their power generation 24 to 36 hours ahead and face penalties for inaccuracies in those forecasts.

Influence of Forecast Accuracy of Photovoltaic Power Output on Capacity Optimization of Microgrid Composition under 30 min Power Balancing Control

NASA Astrophysics Data System (ADS)

Sone, Akihito; Kato, Takeyoshi; Shimakage, Toyonari; Suzuoki, Yasuo

A microgrid (MG) is one of the measures for enhancing the high penetration of renewable energy (RE)-based distributed generators (DGs). If a number of MGs are controlled to maintain the predetermined electricity demand including RE-based DGs as negative demand, they would contribute to supply-demand balancing of whole electric power system. For constructing a MG economically, the capacity optimization of controllable DGs against RE-based DGs is essential. By using a numerical simulation model developed based on a demonstrative study on a MG using PAFC and NaS battery as controllable DGs and photovoltaic power generation system (PVS) as a RE-based DG, this study discusses the influence of forecast accuracy of PVS output on the capacity optimization. Three forecast cases with different accuracy are compared. The main results are as follows. Even with no forecast error during every 30 min. as the ideal forecast method, the required capacity of NaS battery reaches about 40% of PVS capacity for mitigating the instantaneous forecast error within 30 min. The required capacity to compensate for the forecast error is doubled with the actual forecast method. The influence of forecast error can be reduced by adjusting the scheduled power output of controllable DGs according to the weather forecast. Besides, the required capacity can be reduced significantly if the error of balancing control in a MG is acceptable for a few percentages of periods, because the total periods of large forecast error is not so often.

Mathematic simulation of mining company’s power demand forecast (by example of “Neryungri” coal strip mine)

NASA Astrophysics Data System (ADS)

Antonenkov, D. V.; Solovev, D. B.

2017-10-01

The article covers the aspects of forecasting and consideration of the wholesale market environment in generating the power demand forecast. Major mining companies that operate in conditions of the present day power market have to provide a reliable energy demand request for a certain time period ahead, thus ensuring sufficient reduction of financial losses associated with deviations of the actual power demand from the expected figures. Normally, under the power supply agreement, the consumer is bound to provide a per-month and per-hour request annually. It means that the consumer has to generate one-month-ahead short-term and medium-term hourly forecasts. The authors discovered that empiric distributions of “Yakutugol”, Holding Joint Stock Company, power demand belong to the sustainable rank parameter H-distribution type used for generating forecasts based on extrapolation of such distribution parameters. For this reason they justify the need to apply the mathematic rank analysis in short-term forecasting of the contracted power demand of “Neryungri” coil strip mine being a component of the technocenosis-type system of the mining company “Yakutugol”, Holding JSC.

A forecasting model for power consumption of high energy-consuming industries based on system dynamics

NASA Astrophysics Data System (ADS)

Zhou, Zongchuan; Dang, Dongsheng; Qi, Caijuan; Tian, Hongliang

2018-02-01

It is of great significance to make accurate forecasting for the power consumption of high energy-consuming industries. A forecasting model for power consumption of high energy-consuming industries based on system dynamics is proposed in this paper. First, several factors that have influence on the development of high energy-consuming industries in recent years are carefully dissected. Next, by analysing the relationship between each factor and power consumption, the system dynamics flow diagram and equations are set up to reflect the relevant relationships among variables. In the end, the validity of the model is verified by forecasting the power consumption of electrolytic aluminium industry in Ningxia according to the proposed model.

2013 Gulf of Mexico Hypoxia Forecast

USGS Publications Warehouse

Scavia, Donald; Evans, Mary Anne; Obenour, Dan

2013-01-01

The Gulf of Mexico annual summer hypoxia forecasts are based on average May total nitrogen loads from the Mississippi River basin for that year. The load estimate, recently released by USGS, is 7,316 metric tons per day. Based on that estimate, we predict the area of this summer’s hypoxic zone to be 18,900 square kilometers (95% credible interval, 13,400 to 24,200), the 7th largest reported and about the size of New Jersey. Our forecast hypoxic volume is 74.5 km3 (95% credible interval, 51.5 to 97.0), also the 7th largest on record.

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE PAGES

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.; ...

2017-07-11

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Online Analysis of Wind and Solar Part I: Ramping Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

Etingov, Pavel V.; Ma, Jian; Makarov, Yuri V.

2012-01-31

To facilitate wider penetration of renewable resources without compromising system reliability concerns arising from the lack of predictability of intermittent renewable resources, a tool for use by California Independent System Operator (CAISO) power grid operators was developed by Pacific Northwest National Laboratory (PNNL) in conjunction with CAISO with funding from California Energy Commission. This tool predicts and displays additional capacity and ramping requirements caused by uncertainties in forecasts of loads and renewable generation. The tool is currently operational in the CAISO operations center. This is one of two final reports on the project.

Interactive Management and Updating of Spatial Data Bases

NASA Technical Reports Server (NTRS)

French, P.; Taylor, M.

1982-01-01

The decision making process, whether for power plant siting, load forecasting or energy resource planning, invariably involves a blend of analytical methods and judgement. Management decisions can be improved by the implementation of techniques which permit an increased comprehension of results from analytical models. Even where analytical procedures are not required, decisions can be aided by improving the methods used to examine spatially and temporally variant data. How the use of computer aided planning (CAP) programs and the selection of a predominant data structure, can improve the decision making process is discussed.

Study on Battery Capacity for Grid-connection Power Planning with Forecasts in Clustered Photovoltaic Systems

NASA Astrophysics Data System (ADS)

Shimada, Takae; Kawasaki, Norihiro; Ueda, Yuzuru; Sugihara, Hiroyuki; Kurokawa, Kosuke

This paper aims to clarify the battery capacity required by a residential area with densely grid-connected photovoltaic (PV) systems. This paper proposes a planning method of tomorrow's grid-connection power from/to the external electric power system by using demand power forecasting and insolation forecasting for PV power predictions, and defines a operation method of the electricity storage device to control the grid-connection power as planned. A residential area consisting of 389 houses consuming 2390 MWh/year of electricity with 2390kW PV systems is simulated based on measured data and actual forecasts. The simulation results show that 8.3MWh of battery capacity is required in the conditions of half-hour planning and 1% or less of planning error ratio and PV output limiting loss ratio. The results also show that existing technologies of forecasting reduce required battery capacity to 49%, and increase the allowable installing PV amount to 210%.

7 CFR 1710.204 - Filing requirements for borrowers that must maintain an approved load forecast on an ongoing basis.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Filing requirements for borrowers that must maintain an approved load forecast on an ongoing basis. 1710.204 Section 1710.204 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO...

Comparison of the economic impact of different wind power forecast systems for producers

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.

2014-05-01

Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a probabilistic energy forecast system.

Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

DOE PAGES

Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; ...

2015-11-10

Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

Quasi-static time-series simulation using OpenDSS in IEEE distribution feeder model with high PV penetration and its impact on solar forecasting

NASA Astrophysics Data System (ADS)

Mohammed, Touseef Ahmed Faisal

Since 2000, renewable electricity installations in the United States (excluding hydropower) have more than tripled. Renewable electricity has grown at a compounded annual average of nearly 14% per year from 2000-2010. Wind, Concentrated Solar Power (CSP) and solar Photo Voltaic (PV) are the fastest growing renewable energy sectors. In 2010 in the U.S., solar PV grew over 71% and CSP grew by 18% from the previous year. Globally renewable electricity installations have more than quadrupled from 2000-2010. Solar PV generation grew by a factor of more than 28 between 2000 and 2010. The amount of CSP and solar PV installations are increasing on the distribution grid. These PV installations transmit electrical current from the load centers to the generating stations. But the transmission and distribution grid have been designed for uni-directional flow of electrical energy from generating stations to load centers. This causes imbalances in voltage and switchgear of the electrical circuitry. With the continuous rise in PV installations, analysis of voltage profile and penetration levels remain an active area of research. Standard distributed photovoltaic (PV) generators represented in simulation studies do not reflect the exact location and variability properties such as distance between interconnection points to substations, voltage regulators, solar irradiance and other environmental factors. Quasi-Static simulations assist in peak load planning hour and day ahead as it gives a time sequence analysis to help in generation allocation. Simulation models can be daily, hourly or yearly depending on duty cycle and dynamics of the system. High penetration of PV into the power grid changes the voltage profile and power flow dynamically in the distribution circuits due to the inherent variability of PV. There are a number of modeling and simulations tools available for the study of such high penetration PV scenarios. This thesis will specifically utilize OpenDSS, a open source Distribution System Simulator developed by Electric Power Research Institute, to simulate grid voltage profile with a large scale PV system under quasi-static time series considering variations of PV output in seconds, minutes, and the average daily load variations. A 13 bus IEEE distribution feeder model is utilized with distributed residential and commercial scale PV at different buses for simulation studies. Time series simulations are discussed for various modes of operation considering dynamic PV penetration at different time periods in a day. In addition, this thesis demonstrates simulations taking into account the presence of moving cloud for solar forecasting studies.

Ensemble forecasting for renewable energy applications - status and current challenges for their generation and verification

NASA Astrophysics Data System (ADS)

Pinson, Pierre

2016-04-01

The operational management of renewable energy generation in power systems and electricity markets requires forecasts in various forms, e.g., deterministic or probabilistic, continuous or categorical, depending upon the decision process at hand. Besides, such forecasts may also be necessary at various spatial and temporal scales, from high temporal resolutions (in the order of minutes) and very localized for an offshore wind farm, to coarser temporal resolutions (hours) and covering a whole country for day-ahead power scheduling problems. As of today, weather predictions are a common input to forecasting methodologies for renewable energy generation. Since for most decision processes, optimal decisions can only be made if accounting for forecast uncertainties, ensemble predictions and density forecasts are increasingly seen as the product of choice. After discussing some of the basic approaches to obtaining ensemble forecasts of renewable power generation, it will be argued that space-time trajectories of renewable power production may or may not be necessitate post-processing ensemble forecasts for relevant weather variables. Example approaches and test case applications will be covered, e.g., looking at the Horns Rev offshore wind farm in Denmark, or gridded forecasts for the whole continental Europe. Eventually, we will illustrate some of the limitations of current frameworks to forecast verification, which actually make it difficult to fully assess the quality of post-processing approaches to obtain renewable energy predictions.

Impacts of Short-Term Solar Power Forecasts in System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibanez, Eduardo; Krad, Ibrahim; Hodge, Bri-Mathias

2016-05-05

Solar generation is experiencing an exponential growth in power systems worldwide and, along with wind power, is posing new challenges to power system operations. Those challenges are characterized by an increase of system variability and uncertainty across many time scales: from days, down to hours, minutes, and seconds. Much of the research in the area has focused on the effect of solar forecasting across hours or days. This paper presents a methodology to capture the effect of short-term forecasting strategies and analyzes the economic and reliability implications of utilizing a simple, yet effective forecasting method for solar PV in intra-daymore » operations.« less

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan

2015-10-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reductionmore » in the amount of reserves that must be held to accommodate the uncertainty of solar power output.« less

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan

2015-08-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reductionmore » in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.« less

Automatic load forecasting. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, D.J.; Vemuri, S.

A method which lends itself to on-line forecasting of hourly electric loads is presented and the results of its use are compared to models developed using the Box-Jenkins method. The method consists of processing the historical hourly loads with a sequential least-squares estimator to identify a finite order autoregressive model which in turn is used to obtain a parsimonious autoregressive-moving average model. A procedure is also defined for incorporating temperature as a variable to improve forecasts where loads are temperature dependent. The method presented has several advantages in comparison to the Box-Jenkins method including much less human intervention and improvedmore » model identification. The method has been tested using three-hourly data from the Lincoln Electric System, Lincoln, Nebraska. In the exhaustive analyses performed on this data base this method produced significantly better results than the Box-Jenkins method. The method also proved to be more robust in that greater confidence could be placed in the accuracy of models based upon the various measures available at the identification stage.« less

Forecast Inaccuracies in Power Plant Projects From Project Managers' Perspectives

NASA Astrophysics Data System (ADS)

Sanabria, Orlando

Guided by organizational theory, this phenomenological study explored the factors affecting forecast preparation and inaccuracies during the construction of fossil fuel-fired power plants in the United States. Forecast inaccuracies can create financial stress and uncertain profits during the project construction phase. A combination of purposeful and snowball sampling supported the selection of participants. Twenty project managers with over 15 years of experience in power generation and project experience across the United States were interviewed within a 2-month period. From the inductive codification and descriptive analysis, 5 themes emerged: (a) project monitoring, (b) cost control, (c) management review frequency, (d) factors to achieve a precise forecast, and (e) factors causing forecast inaccuracies. The findings of the study showed the factors necessary to achieve a precise forecast includes a detailed project schedule, accurate labor cost estimates, monthly project reviews and risk assessment, and proper utilization of accounting systems to monitor costs. The primary factors reported as causing forecast inaccuracies were cost overruns by subcontractors, scope gaps, labor cost and availability of labor, and equipment and material cost. Results of this study could improve planning accuracy and the effective use of resources during construction of power plants. The study results could contribute to social change by providing a framework to project managers to lessen forecast inaccuracies, and promote construction of power plants that will generate employment opportunities and economic development.

A temperature match based optimization method for daily load prediction considering DLC effect

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Z.

This paper presents a unique optimization method for short term load forecasting. The new method is based on the optimal template temperature match between the future and past temperatures. The optimal error reduction technique is a new concept introduced in this paper. Two case studies show that for hourly load forecasting, this method can yield results as good as the rather complicated Box-Jenkins Transfer Function method, and better than the Box-Jenkins method; for peak load prediction, this method is comparable in accuracy to the neural network method with back propagation, and can produce more accurate results than the multi-linear regressionmore » method. The DLC effect on system load is also considered in this method.« less

Forecasting Electric Power Generation of Photovoltaic Power System for Energy Network

NASA Astrophysics Data System (ADS)

Kudo, Mitsuru; Takeuchi, Akira; Nozaki, Yousuke; Endo, Hisahito; Sumita, Jiro

Recently, there has been an increase in concern about the global environment. Interest is growing in developing an energy network by which new energy systems such as photovoltaic and fuel cells generate power locally and electric power and heat are controlled with a communications network. We developed the power generation forecast method for photovoltaic power systems in an energy network. The method makes use of weather information and regression analysis. We carried out forecasting power output of the photovoltaic power system installed in Expo 2005, Aichi Japan. As a result of comparing measurements with a prediction values, the average prediction error per day was about 26% of the measured power.

Building the Sun4Cast System: Improvements in Solar Power Forecasting

DOE PAGES

Haupt, Sue Ellen; Kosovic, Branko; Jensen, Tara; ...

2017-06-16

The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities’ customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.more » The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.« less

Building the Sun4Cast System: Improvements in Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen; Kosovic, Branko; Jensen, Tara

The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities’ customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.more » The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.« less

Chesapeake Bay hypoxic volume forecasts and results

USGS Publications Warehouse

Scavia, Donald; Evans, Mary Anne

2013-01-01

The 2013 Forecast - Given the average Jan-May 2013 total nitrogen load of 162,028 kg/day, this summer’s hypoxia volume forecast is 6.1 km3, slightly smaller than average size for the period of record and almost the same as 2012. The late July 2013 measured volume was 6.92 km3.

Influence of Forecast Accuracy of Photovoltaic Power Output on Facility Planning and Operation of Microgrid under 30 min Power Balancing Control

NASA Astrophysics Data System (ADS)

Kato, Takeyoshi; Sone, Akihito; Shimakage, Toyonari; Suzuoki, Yasuo

A microgrid (MG) is one of the measures for enhancing the high penetration of renewable energy (RE)-based distributed generators (DGs). For constructing a MG economically, the capacity optimization of controllable DGs against RE-based DGs is essential. By using a numerical simulation model developed based on the demonstrative studies on a MG using PAFC and NaS battery as controllable DGs and photovoltaic power generation system (PVS) as a RE-based DG, this study discusses the influence of forecast accuracy of PVS output on the capacity optimization and daily operation evaluated with the cost. The main results are as follows. The required capacity of NaS battery must be increased by 10-40% against the ideal situation without the forecast error of PVS power output. The influence of forecast error on the received grid electricity would not be so significant on annual basis because the positive and negative forecast error varies with days. The annual total cost of facility and operation increases by 2-7% due to the forecast error applied in this study. The impact of forecast error on the facility optimization and operation optimization is almost the same each other at a few percentages, implying that the forecast accuracy should be improved in terms of both the number of times with large forecast error and the average error.

Integrating Solar PV in Utility System Operations: Analytical Framework and Arizona Case Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Jing; Botterud, Audun; Mills, Andrew

2015-06-01

A systematic framework is proposed to estimate the impact on operating costs due to uncertainty and variability in renewable resources. The framework quantifies the integration costs associated with subhourly variability and uncertainty as well as day-ahead forecasting errors in solar PV (photovoltaics) power. A case study illustrates how changes in system operations may affect these costs for a utility in the southwestern United States (Arizona Public Service Company). We conduct an extensive sensitivity analysis under different assumptions about balancing reserves, system flexibility, fuel prices, and forecasting errors. We find that high solar PV penetrations may lead to operational challenges, particularlymore » during low-load and high solar periods. Increased system flexibility is essential for minimizing integration costs and maintaining reliability. In a set of sensitivity cases where such flexibility is provided, in part, by flexible operations of nuclear power plants, the estimated integration costs vary between $1.0 and $4.4/MWh-PV for a PV penetration level of 17%. The integration costs are primarily due to higher needs for hour-ahead balancing reserves to address the increased sub-hourly variability and uncertainty in the PV resource. (C) 2015 Elsevier Ltd. All rights reserved.« less

Solar Photovoltaic and Liquid Natural Gas Opportunities for Command Naval Region Hawaii

DTIC Science & Technology

2014-12-01

Utilities Commission xii PV Photovoltaic Pwr Power RE Renewable Energy Re-gas Regasification RFP Request For Proposal RMI Rocky... forecasted LS diesel price and the forecasted LNG delivered-to-the- power -plant cost. The forecast for LS diesel by FGE from year 2020–2030 is seen...annual/html/epa_08_01.html Electric Power Research Institute. (July, 2010). Addressing solar photovoltaic operations and maintenance challenges: A

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

NASA Astrophysics Data System (ADS)

Efremenko, Vladimir; Belyaevsky, Roman; Skrebneva, Evgeniya

2017-11-01

In article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground coal mining depends on electric power expense of the main technological processes and size of conditionally constant costs. The important direction of increase of power efficiency of coal mining is forecasting of a power consumption and monitoring of electric power expense. One of the main approaches to reducing of electric power costs is increase in accuracy of the enterprise demand in the wholesale electric power market. It is offered to use artificial neural networks to forecasting of day-ahead power consumption with hourly breakdown. At the same time use of neural and indistinct (hybrid) systems on the principles of fuzzy logic, neural networks and genetic algorithms is more preferable. This model allows to do exact short-term forecasts at a small array of input data. A set of the input parameters characterizing mining-and-geological and technological features of the enterprise is offered.

Using Bayes Model Averaging for Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Preede Revheim, Pål; Beyer, Hans Georg

2014-05-01

For operational purposes predictions of the forecasts of the lumped output of groups of wind farms spread over larger geographic areas will often be of interest. A naive approach is to make forecasts for each individual site and sum them up to get the group forecast. It is however well documented that a better choice is to use a model that also takes advantage of spatial smoothing effects. It might however be the case that some sites tends to more accurately reflect the total output of the region, either in general or for certain wind directions. It will then be of interest giving these a greater influence over the group forecast. Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles. Raftery et al. [1] show how BMA can be used for statistical post processing of forecast ensembles, producing PDFs of future weather quantities. The BMA predictive PDF of a future weather quantity is a weighted average of the ensemble members' PDFs, where the weights can be interpreted as posterior probabilities and reflect the ensemble members' contribution to overall forecasting skill over a training period. In Revheim and Beyer [2] the BMA procedure used in Sloughter, Gneiting and Raftery [3] were found to produce fairly accurate PDFs for the future mean wind speed of a group of sites from the single sites wind speeds. However, when the procedure was attempted applied to wind power it resulted in either problems with the estimation of the parameters (mainly caused by longer consecutive periods of no power production) or severe underestimation (mainly caused by problems with reflecting the power curve). In this paper the problems that arose when applying BMA to wind power forecasting is met through two strategies. First, the BMA procedure is run with a combination of single site wind speeds and single site wind power production as input. This solves the problem with longer consecutive periods where the input data does not contain information, but it has the disadvantage of nearly doubling the number of model parameters to be estimated. Second, the BMA procedure is run with group mean wind power as the response variable instead of group mean wind speed. This also solves the problem with longer consecutive periods without information in the input data, but it leaves the power curve to also be estimated from the data. [1] Raftery, A. E., et al. (2005). Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Weather Review, 133, 1155-1174. [2]Revheim, P. P. and H. G. Beyer (2013). Using Bayesian Model Averaging for wind farm group forecasts. EWEA Wind Power Forecasting Technology Workshop,Rotterdam, 4-5 December 2013. [3]Sloughter, J. M., T. Gneiting and A. E. Raftery (2010). Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging. Journal of the American Statistical Association, Vol. 105, No. 489, 25-35

An Operational Short-Term Forecasting System for Regional Hydropower Management

NASA Astrophysics Data System (ADS)

Gronewold, A.; Labuhn, K. A.; Calappi, T. J.; MacNeil, A.

2017-12-01

The Niagara River is the natural outlet of Lake Erie and drains four of the five Great lakes. The river is used to move commerce and is home to both sport fishing and tourism industries. It also provides nearly 5 million kilowatts of hydropower for approximately 3.9 million homes. Due to a complex international treaty and the necessity of balancing water needs for an extensive tourism industry, the power entities operating on the river require detailed and accurate short-term river flow forecasts to maximize power output. A new forecast system is being evaluated that takes advantage of several previously independent components including the NOAA Lake Erie operational Forecast System (LEOFS), a previously developed HEC-RAS model, input from the New York Power Authority(NYPA) and Ontario Power Generation (OPG) and lateral flow forecasts for some of the tributaries provided by the NOAA Northeast River Forecast Center (NERFC). The Corps of Engineers updated the HEC-RAS model of the upper Niagara River to use the output forcing from LEOFS and a planned Grass Island Pool elevation provided by the power entities. The entire system has been integrated at the NERFC; it will be run multiple times per day with results provided to the Niagara River Control Center operators. The new model helps improve discharge forecasts by better accounting for dynamic conditions on Lake Erie. LEOFS captures seiche events on the lake that are often several meters of displacement from still water level. These seiche events translate into flow spikes that HEC-RAS routes downstream. Knowledge of the peak arrival time helps improve operational decisions at the Grass Island Pool. This poster will compare and contrast results from the existing operational flow forecast and the new integrated LEOFS/HEC-RAS forecast. This additional model will supply the Niagara River Control Center operators with multiple forecasts of flow to help improve forecasting under a wider variety of conditions.

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2015-09-01

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

IEA Wind Task 36 Forecasting

NASA Astrophysics Data System (ADS)

Giebel, Gregor; Cline, Joel; Frank, Helmut; Shaw, Will; Pinson, Pierre; Hodge, Bri-Mathias; Kariniotakis, Georges; Sempreviva, Anna Maria; Draxl, Caroline

2017-04-01

Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Wind Power Forecasting tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, …) and operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets for verification. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts aiming at industry and forecasters alike. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions, especially probabilistic ones. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Joel Cline of the US Department of Energy. Collaboration in the task is solicited from everyone interested in the forecasting business. We will collaborate with IEA Task 31 Wakebench, which developed the Windbench benchmarking platform, which this task will use for forecasting benchmarks. The task runs for three years, 2016-2018. Main deliverables are an up-to-date list of current projects and main project results, including datasets which can be used by researchers around the world to improve their own models, an IEA Recommended Practice on performance evaluation of probabilistic forecasts, a position paper regarding the use of probabilistic forecasts, and one or more benchmark studies implemented on the Windbench platform hosted at CENER. Additionally, spreading of relevant information in both the forecasters and the users community is paramount. The poster also shows the work done in the first half of the Task, e.g. the collection of available datasets and the learnings from a public workshop on 9 June in Barcelona on Experiences with the Use of Forecasts and Gaps in Research. Participation is open for all interested parties in member states of the IEA Annex on Wind Power, see ieawind.org for the up-to-date list. For collaboration, please contact the author grgi@dtu.dk).

Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power

NASA Astrophysics Data System (ADS)

Bracale, Antonio; Carpinelli, Guido; Di Fazio, Annarita; Khormali, Shahab

2014-01-01

Distribution systems are undergoing significant changes as they evolve toward the grids of the future, which are known as smart grids (SGs). The perspective of SGs is to facilitate large-scale penetration of distributed generation using renewable energy sources (RESs), encourage the efficient use of energy, reduce systems' losses, and improve the quality of power. Photovoltaic (PV) systems have become one of the most promising RESs due to the expected cost reduction and the increased efficiency of PV panels and interfacing converters. The ability to forecast power-production information accurately and reliably is of primary importance for the appropriate management of an SG and for making decisions relative to the energy market. Several forecasting methods have been proposed, and many indices have been used to quantify the accuracy of the forecasts of PV power production. Unfortunately, the indices that have been used have deficiencies and usually do not directly account for the economic consequences of forecasting errors in the framework of liberalized electricity markets. In this paper, advanced, more accurate indices are proposed that account directly for the economic consequences of forecasting errors. The proposed indices also were compared to the most frequently used indices in order to demonstrate their different, improved capability. The comparisons were based on the results obtained using a forecasting method based on an artificial neural network. This method was chosen because it was deemed to be one of the most promising methods available due to its capability for forecasting PV power. Numerical applications also are presented that considered an actual PV plant to provide evidence of the forecasting performances of all of the indices that were considered.

Wind power application research on the fusion of the determination and ensemble prediction

NASA Astrophysics Data System (ADS)

Lan, Shi; Lina, Xu; Yuzhu, Hao

2017-07-01

The fused product of wind speed for the wind farm is designed through the use of wind speed products of ensemble prediction from the European Centre for Medium-Range Weather Forecasts (ECMWF) and professional numerical model products on wind power based on Mesoscale Model5 (MM5) and Beijing Rapid Update Cycle (BJ-RUC), which are suitable for short-term wind power forecasting and electric dispatch. The single-valued forecast is formed by calculating the different ensemble statistics of the Bayesian probabilistic forecasting representing the uncertainty of ECMWF ensemble prediction. Using autoregressive integrated moving average (ARIMA) model to improve the time resolution of the single-valued forecast, and based on the Bayesian model averaging (BMA) and the deterministic numerical model prediction, the optimal wind speed forecasting curve and the confidence interval are provided. The result shows that the fusion forecast has made obvious improvement to the accuracy relative to the existing numerical forecasting products. Compared with the 0-24 h existing deterministic forecast in the validation period, the mean absolute error (MAE) is decreased by 24.3 % and the correlation coefficient (R) is increased by 12.5 %. In comparison with the ECMWF ensemble forecast, the MAE is reduced by 11.7 %, and R is increased 14.5 %. Additionally, MAE did not increase with the prolongation of the forecast ahead.

Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables

DOE Office of Scientific and Technical Information (OSTI.GOV)

DallAnese, Emiliano; Baker, Kyri; Summers, Tyler

This paper focuses on distribution systems featuring renewable energy sources (RESs) and energy storage systems, and presents an AC optimal power flow (OPF) approach to optimize system-level performance objectives while coping with uncertainty in both RES generation and loads. The proposed method hinges on a chance-constrained AC OPF formulation where probabilistic constraints are utilized to enforce voltage regulation with prescribed probability. A computationally more affordable convex reformulation is developed by resorting to suitable linear approximations of the AC power-flow equations as well as convex approximations of the chance constraints. The approximate chance constraints provide conservative bounds that hold for arbitrarymore » distributions of the forecasting errors. An adaptive strategy is then obtained by embedding the proposed AC OPF task into a model predictive control framework. Finally, a distributed solver is developed to strategically distribute the solution of the optimization problems across utility and customers.« less

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Wu, Hongyu; Florita, Anthony R.

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was comparedmore » through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.« less

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

DOE PAGES

Wang, Qin; Wu, Hongyu; Florita, Anthony R.; ...

2016-11-11

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was comparedmore » through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.« less

BLOND, a building-level office environment dataset of typical electrical appliances.

PubMed

Kriechbaumer, Thomas; Jacobsen, Hans-Arno

2018-03-27

Energy metering has gained popularity as conventional meters are replaced by electronic smart meters that promise energy savings and higher comfort levels for occupants. Achieving these goals requires a deeper understanding of consumption patterns to reduce the energy footprint: load profile forecasting, power disaggregation, appliance identification, startup event detection, etc. Publicly available datasets are used to test, verify, and benchmark possible solutions to these problems. For this purpose, we present the BLOND dataset: continuous energy measurements of a typical office environment at high sampling rates with common appliances and load profiles. We provide voltage and current readings for aggregated circuits and matching fully-labeled ground truth data (individual appliance measurements). The dataset contains 53 appliances (16 classes) in a 3-phase power grid. BLOND-50 contains 213 days of measurements sampled at 50kSps (aggregate) and 6.4kSps (individual appliances). BLOND-250 consists of the same setup: 50 days, 250kSps (aggregate), 50kSps (individual appliances). These are the longest continuous measurements at such high sampling rates and fully-labeled ground truth we are aware of.

BLOND, a building-level office environment dataset of typical electrical appliances

NASA Astrophysics Data System (ADS)

Kriechbaumer, Thomas; Jacobsen, Hans-Arno

2018-03-01

Energy metering has gained popularity as conventional meters are replaced by electronic smart meters that promise energy savings and higher comfort levels for occupants. Achieving these goals requires a deeper understanding of consumption patterns to reduce the energy footprint: load profile forecasting, power disaggregation, appliance identification, startup event detection, etc. Publicly available datasets are used to test, verify, and benchmark possible solutions to these problems. For this purpose, we present the BLOND dataset: continuous energy measurements of a typical office environment at high sampling rates with common appliances and load profiles. We provide voltage and current readings for aggregated circuits and matching fully-labeled ground truth data (individual appliance measurements). The dataset contains 53 appliances (16 classes) in a 3-phase power grid. BLOND-50 contains 213 days of measurements sampled at 50kSps (aggregate) and 6.4kSps (individual appliances). BLOND-250 consists of the same setup: 50 days, 250kSps (aggregate), 50kSps (individual appliances). These are the longest continuous measurements at such high sampling rates and fully-labeled ground truth we are aware of.

BLOND, a building-level office environment dataset of typical electrical appliances

PubMed Central

Kriechbaumer, Thomas; Jacobsen, Hans-Arno

2018-01-01

Energy metering has gained popularity as conventional meters are replaced by electronic smart meters that promise energy savings and higher comfort levels for occupants. Achieving these goals requires a deeper understanding of consumption patterns to reduce the energy footprint: load profile forecasting, power disaggregation, appliance identification, startup event detection, etc. Publicly available datasets are used to test, verify, and benchmark possible solutions to these problems. For this purpose, we present the BLOND dataset: continuous energy measurements of a typical office environment at high sampling rates with common appliances and load profiles. We provide voltage and current readings for aggregated circuits and matching fully-labeled ground truth data (individual appliance measurements). The dataset contains 53 appliances (16 classes) in a 3-phase power grid. BLOND-50 contains 213 days of measurements sampled at 50kSps (aggregate) and 6.4kSps (individual appliances). BLOND-250 consists of the same setup: 50 days, 250kSps (aggregate), 50kSps (individual appliances). These are the longest continuous measurements at such high sampling rates and fully-labeled ground truth we are aware of. PMID:29583141

Water and Power Systems Co-optimization under a High Performance Computing Framework

NASA Astrophysics Data System (ADS)

Xuan, Y.; Arumugam, S.; DeCarolis, J.; Mahinthakumar, K.

2016-12-01

Water and energy systems optimizations are traditionally being treated as two separate processes, despite their intrinsic interconnections (e.g., water is used for hydropower generation, and thermoelectric cooling requires a large amount of water withdrawal). Given the challenges of urbanization, technology uncertainty and resource constraints, and the imminent threat of climate change, a cyberinfrastructure is needed to facilitate and expedite research into the complex management of these two systems. To address these issues, we developed a High Performance Computing (HPC) framework for stochastic co-optimization of water and energy resources to inform water allocation and electricity demand. The project aims to improve conjunctive management of water and power systems under climate change by incorporating improved ensemble forecast models of streamflow and power demand. First, by downscaling and spatio-temporally disaggregating multimodel climate forecasts from General Circulation Models (GCMs), temperature and precipitation forecasts are obtained and input into multi-reservoir and power systems models. Extended from Optimus (Optimization Methods for Universal Simulators), the framework drives the multi-reservoir model and power system model, Temoa (Tools for Energy Model Optimization and Analysis), and uses Particle Swarm Optimization (PSO) algorithm to solve high dimensional stochastic problems. The utility of climate forecasts on the cost of water and power systems operations is assessed and quantified based on different forecast scenarios (i.e., no-forecast, multimodel forecast and perfect forecast). Analysis of risk management actions and renewable energy deployments will be investigated for the Catawba River basin, an area with adequate hydroclimate predicting skill and a critical basin with 11 reservoirs that supplies water and generates power for both North and South Carolina. Further research using this scalable decision supporting framework will provide understanding and elucidate the intricate and interdependent relationship between water and energy systems and enhance the security of these two critical public infrastructures.

Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Guodong; Xu, Yan; Tomsovic, Kevin

In this paper, we propose an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG and price responsive loads. The microgrid coordinates the energy consumption or production of its components and trades electricity in both the day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total costmore » of operation minus total benefit of demand. This formulation can be solved by mixed integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator, a battery and a responsive load show the advantage of stochastic optimization in addition to robust optimization.« less

Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization

DOE PAGES

Liu, Guodong; Xu, Yan; Tomsovic, Kevin

2016-01-01

In this paper, we propose an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG and price responsive loads. The microgrid coordinates the energy consumption or production of its components and trades electricity in both the day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total costmore » of operation minus total benefit of demand. This formulation can be solved by mixed integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator, a battery and a responsive load show the advantage of stochastic optimization in addition to robust optimization.« less

Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Martinez-Anido, Carlo Brancucci; Wu, Hongyu

Wind power forecasting is an important tool in power system operations to address variability and uncertainty. Accurately doing so is important to reducing the occurrence and length of curtailment, enhancing market efficiency, and improving the operational reliability of the bulk power system. This research quantifies the value of wind power forecasting improvements in the IEEE 118-bus test system as modified to emulate the generation mixes of Midcontinent, California, and New England independent system operator balancing authority areas. To measure the economic value, a commercially available production cost modeling tool was used to simulate the multi-timescale unit commitment (UC) and economicmore » dispatch process for calculating the cost savings and curtailment reductions. To measure the reliability improvements, an in-house tool, FESTIV, was used to calculate the system's area control error and the North American Electric Reliability Corporation Control Performance Standard 2. The approach allowed scientific reproducibility of results and cross-validation of the tools. A total of 270 scenarios were evaluated to accommodate the variation of three factors: generation mix, wind penetration level, and wind fore-casting improvements. The modified IEEE 118-bus systems utilized 1 year of data at multiple timescales, including the day-ahead UC, 4-hour-ahead UC, and 5-min real-time dispatch. The value of improved wind power forecasting was found to be strongly tied to the conventional generation mix, existence of energy storage devices, and the penetration level of wind energy. The simulation results demonstrate that wind power forecasting brings clear benefits to power system operations.« less

Data-driven forecasting algorithms for building energy consumption

NASA Astrophysics Data System (ADS)

Noh, Hae Young; Rajagopal, Ram

2013-04-01

This paper introduces two forecasting methods for building energy consumption data that are recorded from smart meters in high resolution. For utility companies, it is important to reliably forecast the aggregate consumption profile to determine energy supply for the next day and prevent any crisis. The proposed methods involve forecasting individual load on the basis of their measurement history and weather data without using complicated models of building system. The first method is most efficient for a very short-term prediction, such as the prediction period of one hour, and uses a simple adaptive time-series model. For a longer-term prediction, a nonparametric Gaussian process has been applied to forecast the load profiles and their uncertainty bounds to predict a day-ahead. These methods are computationally simple and adaptive and thus suitable for analyzing a large set of data whose pattern changes over the time. These forecasting methods are applied to several sets of building energy consumption data for lighting and heating-ventilation-air-conditioning (HVAC) systems collected from a campus building at Stanford University. The measurements are collected every minute, and corresponding weather data are provided hourly. The results show that the proposed algorithms can predict those energy consumption data with high accuracy.

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

NASA Astrophysics Data System (ADS)

Delle Monache, L.; Shahriari, M.; Cervone, G.

2017-12-01

We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

Adequacy assessment of composite generation and transmission systems incorporating wind energy conversion systems

NASA Astrophysics Data System (ADS)

Gao, Yi

The development and utilization of wind energy for satisfying electrical demand has received considerable attention in recent years due to its tremendous environmental, social and economic benefits, together with public support and government incentives. Electric power generation from wind energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of wind energy facilities therefore affect power system reliability in a different manner than those of conventional systems. The reliability impact of such a highly variable energy source is an important aspect that must be assessed when the wind power penetration is significant. The focus of the research described in this thesis is on the utilization of state sampling Monte Carlo simulation in wind integrated bulk electric system reliability analysis and the application of these concepts in system planning and decision making. Load forecast uncertainty is an important factor in long range planning and system development. This thesis describes two approximate approaches developed to reduce the number of steps in a load duration curve which includes load forecast uncertainty, and to provide reasonably accurate generating and bulk system reliability index predictions. The developed approaches are illustrated by application to two composite test systems. A method of generating correlated random numbers with uniform distributions and a specified correlation coefficient in the state sampling method is proposed and used to conduct adequacy assessment in generating systems and in bulk electric systems containing correlated wind farms in this thesis. The studies described show that it is possible to use the state sampling Monte Carlo simulation technique to quantitatively assess the reliability implications associated with adding wind power to a composite generation and transmission system including the effects of multiple correlated wind sites. This is an important development as it permits correlated wind farms to be incorporated in large practical system studies without requiring excessive increases in computer solution time. The procedures described in this thesis for creating monthly and seasonal wind farm models should prove useful in situations where time period models are required to incorporate scheduled maintenance of generation and transmission facilities. There is growing interest in combining deterministic considerations with probabilistic assessment in order to evaluate the quantitative system risk and conduct bulk power system planning. A relatively new approach that incorporates deterministic and probabilistic considerations in a single risk assessment framework has been designated as the joint deterministic-probabilistic approach. The research work described in this thesis illustrates that the joint deterministic-probabilistic approach can be effectively used to integrate wind power in bulk electric system planning. The studies described in this thesis show that the application of the joint deterministic-probabilistic method provides more stringent results for a system with wind power than the traditional deterministic N-1 method because the joint deterministic-probabilistic technique is driven by the deterministic N-1 criterion with an added probabilistic perspective which recognizes the power output characteristics of a wind turbine generator.

Power Grid Maintenance Scheduling Intelligence Arrangement Supporting System Based on Power Flow Forecasting

NASA Astrophysics Data System (ADS)

Xie, Chang; Wen, Jing; Liu, Wenying; Wang, Jiaming

With the development of intelligent dispatching, the intelligence level of network control center full-service urgent need to raise. As an important daily work of network control center, the application of maintenance scheduling intelligent arrangement to achieve high-quality and safety operation of power grid is very important. By analyzing the shortages of the traditional maintenance scheduling software, this paper designs a power grid maintenance scheduling intelligence arrangement supporting system based on power flow forecasting, which uses the advanced technologies in maintenance scheduling, such as artificial intelligence, online security checking, intelligent visualization techniques. It implements the online security checking of maintenance scheduling based on power flow forecasting and power flow adjusting based on visualization, in order to make the maintenance scheduling arrangement moreintelligent and visual.

Stochastic Multi-Timescale Power System Operations With Variable Wind Generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Hongyu; Krad, Ibrahim; Florita, Anthony

This paper describes a novel set of stochastic unit commitment and economic dispatch models that consider stochastic loads and variable generation at multiple operational timescales. The stochastic model includes four distinct stages: stochastic day-ahead security-constrained unit commitment (SCUC), stochastic real-time SCUC, stochastic real-time security-constrained economic dispatch (SCED), and deterministic automatic generation control (AGC). These sub-models are integrated together such that they are continually updated with decisions passed from one to another. The progressive hedging algorithm (PHA) is applied to solve the stochastic models to maintain the computational tractability of the proposed models. Comparative case studies with deterministic approaches are conductedmore » in low wind and high wind penetration scenarios to highlight the advantages of the proposed methodology, one with perfect forecasts and the other with current state-of-the-art but imperfect deterministic forecasts. The effectiveness of the proposed method is evaluated with sensitivity tests using both economic and reliability metrics to provide a broader view of its impact.« less

The Delicate Analysis of Short-Term Load Forecasting

NASA Astrophysics Data System (ADS)

Song, Changwei; Zheng, Yuan

2017-05-01

This paper proposes a new method for short-term load forecasting based on the similar day method, correlation coefficient and Fast Fourier Transform (FFT) to achieve the precision analysis of load variation from three aspects (typical day, correlation coefficient, spectral analysis) and three dimensions (time dimension, industry dimensions, the main factors influencing the load characteristic such as national policies, regional economic, holidays, electricity and so on). First, the branch algorithm one-class-SVM is adopted to selection the typical day. Second, correlation coefficient method is used to obtain the direction and strength of the linear relationship between two random variables, which can reflect the influence caused by the customer macro policy and the scale of production to the electricity price. Third, Fourier transform residual error correction model is proposed to reflect the nature of load extracting from the residual error. Finally, simulation result indicates the validity and engineering practicability of the proposed method.

Advancing solar energy forecasting through the underlying physics

NASA Astrophysics Data System (ADS)

Yang, H.; Ghonima, M. S.; Zhong, X.; Ozge, B.; Kurtz, B.; Wu, E.; Mejia, F. A.; Zamora, M.; Wang, G.; Clemesha, R.; Norris, J. R.; Heus, T.; Kleissl, J. P.

2017-12-01

As solar power comprises an increasingly large portion of the energy generation mix, the ability to accurately forecast solar photovoltaic generation becomes increasingly important. Due to the variability of solar power caused by cloud cover, knowledge of both the magnitude and timing of expected solar power production ahead of time facilitates the integration of solar power onto the electric grid by reducing electricity generation from traditional ancillary generators such as gas and oil power plants, as well as decreasing the ramping of all generators, reducing start and shutdown costs, and minimizing solar power curtailment, thereby providing annual economic value. The time scales involved in both the energy markets and solar variability range from intra-hour to several days ahead. This wide range of time horizons led to the development of a multitude of techniques, with each offering unique advantages in specific applications. For example, sky imagery provides site-specific forecasts on the minute-scale. Statistical techniques including machine learning algorithms are commonly used in the intra-day forecast horizon for regional applications, while numerical weather prediction models can provide mesoscale forecasts on both the intra-day and days-ahead time scale. This talk will provide an overview of the challenges unique to each technique and highlight the advances in their ongoing development which come alongside advances in the fundamental physics underneath.

The impact of wind power on electricity prices

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brancucci Martinez-Anido, Carlo; Brinkman, Greg; Hodge, Bri-Mathias

This paper investigates the impact of wind power on electricity prices using a production cost model of the Independent System Operator - New England power system. Different scenarios in terms of wind penetration, wind forecasts, and wind curtailment are modeled in order to analyze the impact of wind power on electricity prices for different wind penetration levels and for different levels of wind power visibility and controllability. The analysis concludes that electricity price volatility increases even as electricity prices decrease with increasing wind penetration levels. The impact of wind power on price volatility is larger in the shorter term (5-minmore » compared to hour-to-hour). The results presented show that over-forecasting wind power increases electricity prices while under-forecasting wind power reduces them. The modeling results also show that controlling wind power by allowing curtailment increases electricity prices, and for higher wind penetrations it also reduces their volatility.« less

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

PubMed

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

PubMed Central

Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627

Ecological Forecasting: Microbial Contamination and Atmospheric Loadings of Nutrients to Land and Water

EPA Science Inventory

The development of ecological forecasts, namely, methodologies to predict the chemical, biological, and physical changes in terrestrial and aquatic ecosystems is desirable so that effective strategies for reducing the adverse impacts of human activities and extreme natural events...

Medium- and long-term electric power demand forecasting based on the big data of smart city

NASA Astrophysics Data System (ADS)

Wei, Zhanmeng; Li, Xiyuan; Li, Xizhong; Hu, Qinghe; Zhang, Haiyang; Cui, Pengjie

2017-08-01

Based on the smart city, this paper proposed a new electric power demand forecasting model, which integrates external data such as meteorological information, geographic information, population information, enterprise information and economic information into the big database, and uses an improved algorithm to analyse the electric power demand and provide decision support for decision makers. The data mining technology is used to synthesize kinds of information, and the information of electric power customers is analysed optimally. The scientific forecasting is made based on the trend of electricity demand, and a smart city in north-eastern China is taken as a sample.

Scaling forecast models for wind turbulence and wind turbine power intermittency

NASA Astrophysics Data System (ADS)

Duran Medina, Olmo; Schmitt, Francois G.; Calif, Rudy

2017-04-01

The intermittency of the wind turbine power remains an important issue for the massive development of this renewable energy. The energy peaks injected in the electric grid produce difficulties in the energy distribution management. Hence, a correct forecast of the wind power in the short and middle term is needed due to the high unpredictability of the intermittency phenomenon. We consider a statistical approach through the analysis and characterization of stochastic fluctuations. The theoretical framework is the multifractal modelisation of wind velocity fluctuations. Here, we consider three wind turbine data where two possess a direct drive technology. Those turbines are producing energy in real exploitation conditions and allow to test our forecast models of power production at a different time horizons. Two forecast models were developed based on two physical principles observed in the wind and the power time series: the scaling properties on the one hand and the intermittency in the wind power increments on the other. The first tool is related to the intermittency through a multifractal lognormal fit of the power fluctuations. The second tool is based on an analogy of the power scaling properties with a fractional brownian motion. Indeed, an inner long-term memory is found in both time series. Both models show encouraging results since a correct tendency of the signal is respected over different time scales. Those tools are first steps to a search of efficient forecasting approaches for grid adaptation facing the wind energy fluctuations.

Cooperative Strategy for Optimal Management of Smart Grids by Wavelet RNNs and Cloud Computing.

PubMed

Napoli, Christian; Pappalardo, Giuseppe; Tina, Giuseppe Marco; Tramontana, Emiliano

2016-08-01

Advanced smart grids have several power sources that contribute with their own irregular dynamic to the power production, while load nodes have another dynamic. Several factors have to be considered when using the owned power sources for satisfying the demand, i.e., production rate, battery charge and status, variable cost of externally bought energy, and so on. The objective of this paper is to develop appropriate neural network architectures that automatically and continuously govern power production and dispatch, in order to maximize the overall benefit over a long time. Such a control will improve the fundamental work of a smart grid. For this, status data of several components have to be gathered, and then an estimate of future power production and demand is needed. Hence, the neural network-driven forecasts are apt in this paper for renewable nonprogrammable energy sources. Then, the produced energy as well as the stored one can be supplied to consumers inside a smart grid, by means of digital technology. Among the sought benefits, reduced costs and increasing reliability and transparency are paramount.

Short time ahead wind power production forecast

NASA Astrophysics Data System (ADS)

Sapronova, Alla; Meissner, Catherine; Mana, Matteo

2016-09-01

An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast.

Towards a More Accurate Solar Power Forecast By Improving NWP Model Physics

NASA Astrophysics Data System (ADS)

Köhler, C.; Lee, D.; Steiner, A.; Ritter, B.

2014-12-01

The growing importance and successive expansion of renewable energies raise new challenges for decision makers, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the uncertainties associated with the large share of weather-dependent power sources. Precise power forecast, well-timed energy trading on the stock market, and electrical grid stability can be maintained. The research project EWeLiNE is a collaboration of the German Weather Service (DWD), the Fraunhofer Institute (IWES) and three German transmission system operators (TSOs). Together, wind and photovoltaic (PV) power forecasts shall be improved by combining optimized NWP and enhanced power forecast models. The conducted work focuses on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. Not only the representation of the model cloud characteristics, but also special events like Sahara dust over Germany and the solar eclipse in 2015 are treated and their effect on solar power accounted for. An overview of the EWeLiNE project and results of the ongoing research will be presented.

Does NASA SMAP Improve the Accuracy of Power Outage Models?

NASA Astrophysics Data System (ADS)

Quiring, S. M.; McRoberts, D. B.; Toy, B.; Alvarado, B.

2016-12-01

Electric power utilities make critical decisions in the days prior to hurricane landfall that are primarily based on the estimated impact to their service area. For example, utilities must determine how many repair crews to request from other utilities, the amount of material and equipment they will need to make repairs, and where in their geographically expansive service area to station crews and materials. Accurate forecasts of the impact of an approaching hurricane within their service area are critical for utilities in balancing the costs and benefits of different levels of resources. The Hurricane Outage Prediction Model (HOPM) are a family of statistical models that utilize predictions of tropical cyclone windspeed and duration of strong winds, along with power system and environmental variables (e.g., soil moisture, long-term precipitation), to forecast the number and location of power outages. This project assesses whether using NASA SMAP soil moisture improves the accuracy of power outage forecasts as compared to using model-derived soil moisture from NLDAS-2. A sensitivity analysis is employed since there have been very few tropical cyclones making landfall in the United States since SMAP was launched. The HOPM is used to predict power outages for 13 historical tropical cyclones and the model is run using twice, once with NLDAS soil moisture and once with SMAP soil moisture. Our results demonstrate that using SMAP soil moisture can have a significant impact on power outage predictions. SMAP has the potential to enhance the accuracy of power outage forecasts. Improved outage forecasts reduce the duration of power outages which reduces economic losses and accelerates recovery.

The Research of Regression Method for Forecasting Monthly Electricity Sales Considering Coupled Multi-factor

NASA Astrophysics Data System (ADS)

Wang, Jiangbo; Liu, Junhui; Li, Tiantian; Yin, Shuo; He, Xinhui

2018-01-01

The monthly electricity sales forecasting is a basic work to ensure the safety of the power system. This paper presented a monthly electricity sales forecasting method which comprehensively considers the coupled multi-factors of temperature, economic growth, electric power replacement and business expansion. The mathematical model is constructed by using regression method. The simulation results show that the proposed method is accurate and effective.

Managing the space-time-load continuum in TMDL planning: A case study for understanding groundwater loads through advanced mapping techniques

EPA Science Inventory

The lag time between groundwater recharge and discharge in a watershed and the potential groundwater load to streams is an important factor in forecasting responses to future land use practices. We call this concept managing the “space-time-load continuum”. It’s understood that i...

An evaluation of the impact of biomass burning smoke aerosol particles on near surface temperature forecasts

NASA Astrophysics Data System (ADS)

Zhang, J.; Reid, J. S.; Benedetti, A.; Christensen, M.; Marquis, J. W.

2016-12-01

Currently, with the improvements in aerosol forecast accuracies through aerosol data assimilation, the community is unavoidably facing a scientific question: is it worth the computational time to insert real-time aerosol analyses into numerical models for weather forecasts? In this study, by analyzing a significant biomass burning aerosol event that occurred in 2015 over the Northern part of the Central US, the impact of aerosol particles on near-surface temperature forecasts is evaluated. The aerosol direct surface cooling efficiency, which links surface temperature changes to aerosol loading, is derived from observational-based data for the first time. The potential of including real-time aerosol analyses into weather forecasting models for near surface temperature forecasts is also investigated.

The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freedman, Jeffrey M.; Manobianco, John; Schroeder, John

This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10more » - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.« less

A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

DOE PAGES

Bessa, Ricardo; Möhrlen, Corinna; Fundel, Vanessa; ...

2017-09-14

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding ofmore » its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. Our paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. Furthermore, a set of recommendations for standardization and improved training of operators are provided along with examples of best practices.« less

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bessa, Ricardo; Möhrlen, Corinna; Fundel, Vanessa

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding ofmore » its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. Our paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. Furthermore, a set of recommendations for standardization and improved training of operators are provided along with examples of best practices.« less

On-line algorithms for forecasting hourly loads of an electric utility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vemuri, S.; Huang, W.L.; Nelson, D.J.

A method that lends itself to on-line forecasting of hourly electric loads is presented, and the results of its use are compared to models developed using the Box-Jenkins method. The method consits of processing the historical hourly loads with a sequential least-squares estimator to identify a finite-order autoregressive model which, in turn, is used to obtain a parsimonious autoregressive-moving average model. The method presented has several advantages in comparison with the Box-Jenkins method including much-less human intervention, improved model identification, and better results. The method is also more robust in that greater confidence can be placed in the accuracy ofmore » models based upon the various measures available at the identification stage.« less

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

NASA Astrophysics Data System (ADS)

Lee, Joseph C. Y.; Lundquist, Julie K.

2017-11-01

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

DOE PAGES

Lee, Joseph C. Y.; Lundquist, Julie K.

2017-11-23

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. Our paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustratemore » with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.« less

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Joseph C. Y.; Lundquist, Julie K.

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. Our paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustratemore » with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.« less

2007 Wholesale Power Rate Case Initial Proposal : Wholesale Power Rate Development Study.

DOE Office of Scientific and Technical Information (OSTI.GOV)

United States. Bonneville Power Administration.

The Wholesale Power Rate Development Study (WPRDS) calculates BPA proposed rates based on information either developed in the WPRDS or supplied by the other studies that comprise the BPA rate proposal. All of these studies, and accompanying documentation, provide the details of computations and assumptions. In general, information about loads and resources is provided by the Load Resource Study (LRS), WP-07-E-BPA-01, and the LRS Documentation, WP-07-E-BPA-01A. Revenue requirements information, as well as the Planned Net Revenues for Risk (PNNR), is provided in the Revenue Requirement Study, WP-07-E-BPA-02, and its accompanying Revenue Requirement Study Documentation, WP-07-E-BPA-02A and WP-07-E-BPA-02B. The Market Pricemore » Forecast Study (MPFS), WP-07-E-BPA-03, and the MPFS Documentation, WP-07-E-BPA-03A, provide the WPRDS with information regarding seasonal and diurnal differentiation of energy rates, as well information regarding monthly market prices for Demand Rates. In addition, this study provides information for the pricing of unbundled power products. The Risk Analysis Study, WP-07-E-BPA-04, and the Risk Analysis Study Documentation, WP-07-E-BPA-04A, provide short-term balancing purchases as well as secondary energy sales and revenue. The Section 7(b)(2) Rate Test Study, WP-07-E-BPA-06, and the Section 7(b)(2) Rate Test Study Documentation, WP-07-E-BPA-06A, implement Section 7(b)(2) of the Northwest Power Act to ensure that BPA preference customers firm power rates applied to their general requirements are no higher than rates calculated using specific assumptions in the Northwest Power Act.« less

Chesapeake Bay Hypoxic Volume Forecasts and Results

USGS Publications Warehouse

Evans, Mary Anne; Scavia, Donald

2013-01-01

Given the average Jan-May 2013 total nitrogen load of 162,028 kg/day, this summer's hypoxia volume forecast is 6.1 km3, slightly smaller than average size for the period of record and almost the same as 2012. The late July 2013 measured volume was 6.92 km3.

Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations.

NASA Astrophysics Data System (ADS)

Ángel Prósper Fernández, Miguel; Casal, Carlos Otero; Canoura Fernández, Felipe; Miguez-Macho, Gonzalo

2017-04-01

Regional meteorological models are becoming a generalized tool for forecasting wind resource, due to their capacity to simulate local flow dynamics impacting wind farm production. This study focuses on the production forecast and validation of a real onshore wind farm using high horizontal and vertical resolution WRF (Weather Research and Forecasting) model simulations. The wind farm is located in Galicia, in the northwest of Spain, in a complex terrain region with high wind resource. Utilizing the Fitch scheme, specific for wind farms, a period of one year is simulated with a daily operational forecasting set-up. Power and wind predictions are obtained and compared with real data provided by the management company. Results show that WRF is able to yield good wind power operational predictions for this kind of wind farms, due to a good representation of the planetary boundary layer behaviour of the region and the good performance of the Fitch scheme under these conditions.

Towards Optimal Operation of the Reservoir System in Upper Yellow River: Incorporating Long- and Short-term Operations and Using Rolling Updated Hydrologic Forecast Information

NASA Astrophysics Data System (ADS)

Si, Y.; Li, X.; Li, T.; Huang, Y.; Yin, D.

2016-12-01

The cascade reservoirs in Upper Yellow River (UYR), one of the largest hydropower bases in China, play a vital role in peak load and frequency regulation for Northwest China Power Grid. The joint operation of this system has been put forward for years whereas has not come into effect due to management difficulties and inflow uncertainties, and thus there is still considerable improvement room for hydropower production. This study presents a decision support framework incorporating long- and short-term operation of the reservoir system. For long-term operation, we maximize hydropower production of the reservoir system using historical hydrological data of multiple years, and derive operating rule curves for storage reservoirs. For short-term operation, we develop a program consisting of three modules, namely hydrologic forecast module, reservoir operation module and coordination module. The coordination module is responsible for calling the hydrologic forecast module to acquire predicted inflow within a short-term horizon, and transferring the information to the reservoir operation module to generate optimal release decision. With the hydrologic forecast information updated, the rolling short-term optimization is iterated until the end of operation period, where the long-term operating curves serve as the ending storage target. As an application, the Digital Yellow River Integrated Model (referred to as "DYRIM", which is specially designed for runoff-sediment simulation in the Yellow River basin by Tsinghua University) is used in the hydrologic forecast module, and the successive linear programming (SLP) in the reservoir operation module. The application in the reservoir system of UYR demonstrates that the framework can effectively support real-time decision making, and ensure both computational accuracy and speed. Furthermore, it is worth noting that the general framework can be extended to any other reservoir system with any or combination of hydrological model(s) to forecast and any solver to optimize the operation of reservoir system.

Payette River Basin Project: Improving Operational Forecasting in Complex Terrain through Chemistry

NASA Astrophysics Data System (ADS)

Blestrud, D.; Kunkel, M. L.; Parkinson, S.; Holbrook, V. P.; Benner, S. G.; Fisher, J.

2015-12-01

Idaho Power Company (IPC) is an investor owned hydroelectric based utility, serving customers throughout southern Idaho and eastern Oregon. The University of Arizona (UA) runs an operational 1.8-km resolution Weather and Research Forecast (WRF) model for IPC, which is incorporated into IPC near and real-time forecasts for hydro, solar and wind generation, load servicing and a large-scale wintertime cloud seeding operation to increase winter snowpack. Winter snowpack is critical to IPC, as hydropower provides ~50% of the company's generation needs. In efforts to improve IPC's near-term forecasts and operational guidance to its cloud seeding program, IPC is working extensively with UA and the National Center for Atmospheric Research (NCAR) to improve WRF performance in the complex terrain of central Idaho. As part of this project, NCAR has developed a WRF based cloud seeding module (WRF CS) to deliver high-resolution, tailored forecasts to provide accurate guidance for IPC's operations. Working with Boise State University (BSU), IPC is conducting a multiyear campaign to validate the WRF CS's ability to account for and disperse the cloud seeding agent (AgI) within the boundary layer. This improved understanding of how WRF handles the AgI dispersion and fate will improve the understanding and ultimately the performance of WRF to forecast other parameters. As part of this campaign, IPC has developed an extensive ground based monitoring network including a Remote Area Snow Sampling Device (RASSD) that provides spatially and temporally discrete snow samples during active cloud seeding periods. To quantify AgI dispersion in the complex terrain, BSU conducts trace element analysis using LA-ICP-MS on the RASSD sampled snow to provide measurements (at the 10-12 level) of incorporated AgI, measurements are compare directly with WRF CS's estimates of distributed AgI. Modeling and analysis results from previous year's research and plans for coming seasons will be presented.

Impact of the 4 April 2014 Saharan dust outbreak on the photovoltaic power generation in Germany

NASA Astrophysics Data System (ADS)

Rieger, Daniel; Steiner, Andrea; Bachmann, Vanessa; Gasch, Philipp; Förstner, Jochen; Deetz, Konrad; Vogel, Bernhard; Vogel, Heike

2017-11-01

The importance for reliable forecasts of incoming solar radiation is growing rapidly, especially for those countries with an increasing share in photovoltaic (PV) power production. The reliability of solar radiation forecasts depends mainly on the representation of clouds and aerosol particles absorbing and scattering radiation. Especially under extreme aerosol conditions, numerical weather prediction has a systematic bias in the solar radiation forecast. This is caused by the design of numerical weather prediction models, which typically account for the direct impact of aerosol particles on radiation using climatological mean values and the impact on cloud formation assuming spatially and temporally homogeneous aerosol concentrations. These model deficiencies in turn can lead to significant economic losses under extreme aerosol conditions. For Germany, Saharan dust outbreaks occurring 5 to 15 times per year for several days each are prominent examples for conditions, under which numerical weather prediction struggles to forecast solar radiation adequately. We investigate the impact of mineral dust on the PV-power generation during a Saharan dust outbreak over Germany on 4 April 2014 using ICON-ART, which is the current German numerical weather prediction model extended by modules accounting for trace substances and related feedback processes. We find an overall improvement of the PV-power forecast for 65 % of the pyranometer stations in Germany. Of the nine stations with very high differences between forecast and measurement, eight stations show an improvement. Furthermore, we quantify the direct radiative effects and indirect radiative effects of mineral dust. For our study, direct effects account for 64 %, indirect effects for 20 % and synergistic interaction effects for 16 % of the differences between the forecast including mineral dust radiative effects and the forecast neglecting mineral dust.

Youth temperament, harsh parenting, and variation in the oxytocin receptor gene forecast allostatic load during emerging adulthood.

PubMed

Brody, Gene H; Yu, Tianyi; Barton, Allen W; Miller, Gregory E; Chen, Edith

2017-08-01

An association has been found between receipt of harsh parenting in childhood and adult health problems. However, this research has been principally retrospective, has treated children as passive recipients of parental behavior, and has overlooked individual differences in youth responsivity to harsh parenting. In a 10-year multiple-wave prospective study of African American families, we addressed these issues by focusing on the influence of polymorphisms in the oxytocin receptor gene (OXTR), variants of which appear to buffer or amplify responses to environmental stress. The participants were 303 youths, with a mean age of 11.2 at the first assessment, and their parents, all of whom were genotyped for variations in the rs53576 (A/G) polymorphism. Teachers rated preadolescent (ages 11 to 13) emotionally intense and distractible temperaments, and adolescents (ages 15 and 16) reported receipt of harsh parenting. Allostatic load was assessed during young adulthood (ages 20 and 21). Difficult preadolescent temperament forecast elevated receipt of harsh parenting in adolescence, and adolescents who experienced harsh parenting evinced high allostatic load during young adulthood. However, these associations emerged only among children and parents who carried A alleles of the OXTR genotype. The results suggest the oxytocin system operates along with temperament and parenting to forecast young adults' allostatic load.

Analog-Based Postprocessing of Navigation-Related Hydrological Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Hemri, S.; Klein, B.

2017-11-01

Inland waterway transport benefits from probabilistic forecasts of water levels as they allow to optimize the ship load and, hence, to minimize the transport costs. Probabilistic state-of-the-art hydrologic ensemble forecasts inherit biases and dispersion errors from the atmospheric ensemble forecasts they are driven with. The use of statistical postprocessing techniques like ensemble model output statistics (EMOS) allows for a reduction of these systematic errors by fitting a statistical model based on training data. In this study, training periods for EMOS are selected based on forecast analogs, i.e., historical forecasts that are similar to the forecast to be verified. Due to the strong autocorrelation of water levels, forecast analogs have to be selected based on entire forecast hydrographs in order to guarantee similar hydrograph shapes. Custom-tailored measures of similarity for forecast hydrographs comprise hydrological series distance (SD), the hydrological matching algorithm (HMA), and dynamic time warping (DTW). Verification against observations reveals that EMOS forecasts for water level at three gauges along the river Rhine with training periods selected based on SD, HMA, and DTW compare favorably with reference EMOS forecasts, which are based on either seasonal training periods or on training periods obtained by dividing the hydrological forecast trajectories into runoff regimes.

Forecasting volcanic eruptions and other material failure phenomena: An evaluation of the failure forecast method

NASA Astrophysics Data System (ADS)

Bell, Andrew F.; Naylor, Mark; Heap, Michael J.; Main, Ian G.

2011-08-01

Power-law accelerations in the mean rate of strain, earthquakes and other precursors have been widely reported prior to material failure phenomena, including volcanic eruptions, landslides and laboratory deformation experiments, as predicted by several theoretical models. The Failure Forecast Method (FFM), which linearizes the power-law trend, has been routinely used to forecast the failure time in retrospective analyses; however, its performance has never been formally evaluated. Here we use synthetic and real data, recorded in laboratory brittle creep experiments and at volcanoes, to show that the assumptions of the FFM are inconsistent with the error structure of the data, leading to biased and imprecise forecasts. We show that a Generalized Linear Model method provides higher-quality forecasts that converge more accurately to the eventual failure time, accounting for the appropriate error distributions. This approach should be employed in place of the FFM to provide reliable quantitative forecasts and estimate their associated uncertainties.

Characterizing Time Series Data Diversity for Wind Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Chartan, Erol Kevin; Feng, Cong

Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the importantmore » information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.« less

Data on Support Vector Machines (SVM) model to forecast photovoltaic power.

PubMed

Malvoni, M; De Giorgi, M G; Congedo, P M

2016-12-01

The data concern the photovoltaic (PV) power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled "Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data" (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015) [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA) are applied to the Least Squares Support Vector Machines (LS-SVM) to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

Projected electric power demands for the Potomac Electric Power Company. Volume 1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Estomin, S.; Kahal, M.

1984-03-01

This three-volume report presents the results of an econometric forecast of peak and electric power demands for the Potomac Electric Power Company (PEPCO) through the year 2002. Volume I describes the methodology, the results of the econometric estimations, the forecast assumptions and the calculated forecasts of peak demand and energy usage. Separate sets of models were developed for the Maryland Suburbs (Montgomery and Prince George's counties), the District of Columbia and Southern Maryland (served by a wholesale customer of PEPCO). For each of the three jurisdictions, energy equations were estimated for residential and commercial/industrial customers for both summer and wintermore » seasons. For the District of Columbia, summer and winter equations for energy sales to the federal government were also estimated. Equations were also estimated for street lighting and energy losses. Noneconometric techniques were employed to forecast energy sales to the Northern Virginia suburbs, Metrorail and federal government facilities located in Maryland.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Kyri; Toomey, Bridget

Evolving power systems with increasing levels of stochasticity call for a need to solve optimal power flow problems with large quantities of random variables. Weather forecasts, electricity prices, and shifting load patterns introduce higher levels of uncertainty and can yield optimization problems that are difficult to solve in an efficient manner. Solution methods for single chance constraints in optimal power flow problems have been considered in the literature, ensuring single constraints are satisfied with a prescribed probability; however, joint chance constraints, ensuring multiple constraints are simultaneously satisfied, have predominantly been solved via scenario-based approaches or by utilizing Boole's inequality asmore » an upper bound. In this paper, joint chance constraints are used to solve an AC optimal power flow problem while preventing overvoltages in distribution grids under high penetrations of photovoltaic systems. A tighter version of Boole's inequality is derived and used to provide a new upper bound on the joint chance constraint, and simulation results are shown demonstrating the benefit of the proposed upper bound. The new framework allows for a less conservative and more computationally efficient solution to considering joint chance constraints, specifically regarding preventing overvoltages.« less

A Multiagent Energy Management System for a Small Microgrid Equipped with Power Sources and Energy Storage Units

NASA Astrophysics Data System (ADS)

Radziszewska, Weronika; Nahorski, Zbigniew

An Energy Management System (EMS) for a small microgrid is presented, with both demand and production side management. The microgrid is equipped with renewable and controllable power sources (like a micro gas turbine), energy storage units (batteries and flywheels). Energy load is partially scheduled to avoid extreme peaks of power demand and to possibly match forecasted energy supply from the renewable power sources. To balance the energy in the network on line, a multiagent system is used. Intelligent agents of each device are proactively acting towards balancing the energy in the network, and at the same time optimizing the cost of operation of the whole system. A semi-market mechanism is used to match a demand and a production of the energy. Simulations show that the time of reaching a balanced state does not exceed 1 s, which is fast enough to let execute proper balancing actions, e.g. change an operating point of a controllable energy source. Simulators of sources and consumption devices were implemented in order to carry out exhaustive tests.

Feasibility study for the swaziland/mozambique interconector. Executive summary of the final report. Export trade information

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1997-11-01

This study, conducted by Black & Veatch, was funded by the U.S. Trade and Development Agency. The report, produced for the Ministry of National Resources, Energy and Environment (MNRE) of Swaziland, determines the least cost capacity expansion option to meet the future power demand and system reliability criteria of Swaziland, with particular emphasis on the propsoed Interconnector between Swaziland and Mozambique. Volume 1 contains the Executive Summary and is divided into the following sections: (1.0) Study Objectives; (2.0) Swaziland and its Economy; (3.0) The Power Sector Structure in Swaziland; (4.0) Electric Power Resources; (5.0) Past Demand Growth; (6.0) Load andmore » Energy Forecasts; (7.0) Need for Power; (8.0) Generation and Transmission Capacity Addition Option; (9.0) SEB Expansion Plan Scenario Development; (10.0) EDM Expansion Plan Development; (11.0) Cost Sharing of the Interconnector; (12.0) Interconnector Options and Environmental Evaluation; (13.0) Generation/Transmission Trade Offs; (14.0) EPC RFP and Draft Interconnection Agreement; (15.0) Transmission System Study; (16.0) Conclusions and Recommendations.« less

The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilczak, James M.; Finley, Cathy; Freedman, Jeff

The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collectionmore » of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.« less

Forecasting Global Horizontal Irradiance Using the LETKF and a Combination of Advected Satellite Images and Sparse Ground Sensors

NASA Astrophysics Data System (ADS)

Harty, T. M.; Lorenzo, A.; Holmgren, W.; Morzfeld, M.

2017-12-01

The irradiance incident on a solar panel is the main factor in determining the power output of that panel. For this reason, accurate global horizontal irradiance (GHI) estimates and forecasts are critical when determining the optimal location for a solar power plant, forecasting utility scale solar power production, or forecasting distributed, behind the meter rooftop solar power production. Satellite images provide a basis for producing the GHI estimates needed to undertake these objectives. The focus of this work is to combine satellite derived GHI estimates with ground sensor measurements and an advection model. The idea is to use accurate but sparsely distributed ground sensors to improve satellite derived GHI estimates which can cover large areas (the size of a city or a region of the United States). We use a Bayesian framework to perform the data assimilation, which enables us to produce irradiance forecasts and associated uncertainties which incorporate both satellite and ground sensor data. Within this framework, we utilize satellite images taken from the GOES-15 geostationary satellite (available every 15-30 minutes) as well as ground data taken from irradiance sensors and rooftop solar arrays (available every 5 minutes). The advection model, driven by wind forecasts from a numerical weather model, simulates cloud motion between measurements. We use the Local Ensemble Transform Kalman Filter (LETKF) to perform the data assimilation. We present preliminary results towards making such a system useful in an operational context. We explain how localization and inflation in the LETKF, perturbations of wind-fields, and random perturbations of the advection model, affect the accuracy of our estimates and forecasts. We present experiments showing the accuracy of our forecasted GHI over forecast-horizons of 15 mins to 1 hr. The limitations of our approach and future improvements are also discussed.

Benefits of an ultra large and multiresolution ensemble for estimating available wind power

NASA Astrophysics Data System (ADS)

Berndt, Jonas; Hoppe, Charlotte; Elbern, Hendrik

2016-04-01

In this study we investigate the benefits of an ultra large ensemble with up to 1000 members including multiple nesting with a target horizontal resolution of 1 km. The ensemble shall be used as a basis to detect events of extreme errors in wind power forecasting. Forecast value is the wind vector at wind turbine hub height (~ 100 m) in the short range (1 to 24 hour). Current wind power forecast systems rest already on NWP ensemble models. However, only calibrated ensembles from meteorological institutions serve as input so far, with limited spatial resolution (˜10 - 80 km) and member number (˜ 50). Perturbations related to the specific merits of wind power production are yet missing. Thus, single extreme error events which are not detected by such ensemble power forecasts occur infrequently. The numerical forecast model used in this study is the Weather Research and Forecasting Model (WRF). Model uncertainties are represented by stochastic parametrization of sub-grid processes via stochastically perturbed parametrization tendencies and in conjunction via the complementary stochastic kinetic-energy backscatter scheme already provided by WRF. We perform continuous ensemble updates by comparing each ensemble member with available observations using a sequential importance resampling filter to improve the model accuracy while maintaining ensemble spread. Additionally, we use different ensemble systems from global models (ECMWF and GFS) as input and boundary conditions to capture different synoptic conditions. Critical weather situations which are connected to extreme error events are located and corresponding perturbation techniques are applied. The demanding computational effort is overcome by utilising the supercomputer JUQUEEN at the Forschungszentrum Juelich.

Using NCAR Yellowstone for PhotoVoltaic Power Forecasts with Artificial Neural Networks and an Analog Ensemble

NASA Astrophysics Data System (ADS)

Cervone, G.; Clemente-Harding, L.; Alessandrini, S.; Delle Monache, L.

2016-12-01

A methodology based on Artificial Neural Networks (ANN) and an Analog Ensemble (AnEn) is presented to generate 72-hour deterministic and probabilistic forecasts of power generated by photovoltaic (PV) power plants using input from a numerical weather prediction model and computed astronomical variables. ANN and AnEn are used individually and in combination to generate forecasts for three solar power plant located in Italy. The computational scalability of the proposed solution is tested using synthetic data simulating 4,450 PV power stations. The NCAR Yellowstone supercomputer is employed to test the parallel implementation of the proposed solution, ranging from 1 node (32 cores) to 4,450 nodes (141,140 cores). Results show that a combined AnEn + ANN solution yields best results, and that the proposed solution is well suited for massive scale computation.

Traffic-load forecasting using weigh-in-motion data

DOT National Transportation Integrated Search

1997-03-01

Vehicular traffic loading is a crucial consideration for the design and maintenance of pavements. With the help of weigh-in-motion (WIM) systems, the information about date, time, speed, lane of travel, lateral lane position, axle spacing, and wheel ...

2006 Pacific Northwest Loads and Resources Study.

DOE Office of Scientific and Technical Information (OSTI.GOV)

United States. Bonneville Power Administration.

2006-03-01

The Pacific Northwest Loads and Resources Study (White Book), which is published annually by the Bonneville Power Administration (BPA), establishes one of the planning bases for supplying electricity to customers. The White Book contains projections of regional and Federal system load and resource capabilities, along with relevant definitions and explanations. The White Book also contains information obtained from formalized resource planning reports and data submittals including those from individual utilities, the Northwest Power and Conservation Council (Council), and the Pacific Northwest Utilities Conference Committee (PNUCC). The White Book is not an operational planning guide, nor is it used for determiningmore » BPA revenues, although the database that generates the data for the White Book analysis contributes to the development of BPA's inventory and ratemaking processes. Operation of the Federal Columbia River Power System (FCRPS) is based on a set of criteria different from that used for resource planning decisions. Operational planning is dependent upon real-time or near-term knowledge of system conditions that include expectations of river flows and runoff, market opportunities, availability of reservoir storage, energy exchanges, and other factors affecting the dynamics of operating a power system. The load resource balance of both the Federal system and the region is determined by comparing resource availability to an expected level of total retail electricity consumption. Resources include projected energy capability plus contract purchases. Loads include a forecast of retail obligations plus contract obligations. Surplus energy is available when resources are greater than loads. This surplus energy could be marketed to increase revenues. Energy deficits occur when resources are less than loads. These energy deficits will be met by any combination of the following: better-than-critical water conditions, demand-side management and conservation programs, permanent loss of loads due to economic conditions or closures, additional contract purchases, and/or the addition of new generating resources. This study incorporates information on Pacific Northwest (PNW) regional retail loads, contract obligations, and contract resources. This loads and resources analysis simulates the operation of the power system in the PNW. The simulated hydro operation incorporates plant characteristics, streamflows, and non-power requirements from the current Pacific Northwest Coordination Agreement (PNCA). Additional resource capability estimates were provided by BPA, PNW Federal agency, public agency, cooperative, U.S. Bureau of Reclamation (USBR), and investor-owned utility (IOU) customers furnished through annual PNUCC data submittals for 2005 and/or direct submittals to BPA. The 2006 White Book is presented in two documents: (1) this summary document of Federal system and PNW region loads and resources, and (2) a technical appendix which presents regional loads, grouped by major PNW utility categories, and detailed contract and resource information. The technical appendix is available only in electronic form. Individual customer information for marketer contracts is not detailed due to confidentiality agreements. The 2006 White Book analysis updates the 2004 White Book. This analysis shows projections of the Federal system and region's yearly average annual energy consumption and resource availability for the study period, OY 2007-2016. The study also presents projections of Federal system and region expected 1-hour monthly peak demand, monthly energy demand, monthly 1-hour peak generating capability, and monthly energy generation for OY 2007, 2011, and 2016. BPA is investigating a new approach in capacity planning depicting the monthly Federal system 120-hour peak generating capability and 120-hour peak surplus/deficit for OY 2007, 2011, and 2016. This document analyzes the PNW's projected loads and available generating resources in two parts: (1) the loads and resources of the Federal system, for which BPA is the marketing agency; and (2) the larger PNW regional power system loads and resources that include the Federal system as well other PNW entities.« less

Microgrid optimal scheduling considering impact of high penetration wind generation

NASA Astrophysics Data System (ADS)

Alanazi, Abdulaziz

The objective of this thesis is to study the impact of high penetration wind energy in economic and reliable operation of microgrids. Wind power is variable, i.e., constantly changing, and nondispatchable, i.e., cannot be controlled by the microgrid controller. Thus an accurate forecasting of wind power is an essential task in order to study its impacts in microgrid operation. Two commonly used forecasting methods including Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) have been used in this thesis to improve the wind power forecasting. The forecasting error is calculated using a Mean Absolute Percentage Error (MAPE) and is improved using the ANN. The wind forecast is further used in the microgrid optimal scheduling problem. The microgrid optimal scheduling is performed by developing a viable model for security-constrained unit commitment (SCUC) based on mixed-integer linear programing (MILP) method. The proposed SCUC is solved for various wind penetration levels and the relationship between the total cost and the wind power penetration is found. In order to reduce microgrid power transfer fluctuations, an additional constraint is proposed and added to the SCUC formulation. The new constraint would control the time-based fluctuations. The impact of the constraint on microgrid SCUC results is tested and validated with numerical analysis. Finally, the applicability of proposed models is demonstrated through numerical simulations.

Valuing hydrological forecasts for a pumped storage assisted hydro facility

NASA Astrophysics Data System (ADS)

Zhao, Guangzhi; Davison, Matt

2009-07-01

SummaryThis paper estimates the value of a perfectly accurate short-term hydrological forecast to the operator of a hydro electricity generating facility which can sell its power at time varying but predictable prices. The expected value of a less accurate forecast will be smaller. We assume a simple random model for water inflows and that the costs of operating the facility, including water charges, will be the same whether or not its operator has inflow forecasts. Thus, the improvement in value from better hydrological prediction results from the increased ability of the forecast using facility to sell its power at high prices. The value of the forecast is therefore the difference between the sales of a facility operated over some time horizon with a perfect forecast, and the sales of a similar facility operated over the same time horizon with similar water inflows which, though governed by the same random model, cannot be forecast. This paper shows that the value of the forecast is an increasing function of the inflow process variance and quantifies how much the value of this perfect forecast increases with the variance of the water inflow process. Because the lifetime of hydroelectric facilities is long, the small increase observed here can lead to an increase in the profitability of hydropower investments.

Short-Term Energy Outlook Model Documentation: Electricity Generation and Fuel Consumption Models

EIA Publications

2014-01-01

The electricity generation and fuel consumption models of the Short-Term Energy Outlook (STEO) model provide forecasts of electricity generation from various types of energy sources and forecasts of the quantities of fossil fuels consumed for power generation. The structure of the electricity industry and the behavior of power generators varies between different areas of the United States. In order to capture these differences, the STEO electricity supply and fuel consumption models are designed to provide forecasts for the four primary Census regions.

Solar power satellite system definition study. Volume 1, phase 1: Executive summary

NASA Technical Reports Server (NTRS)

1979-01-01

A systems definition study of the solar satellite system (SPS) is presented. The technical feasibility of solar power satellites based on forecasts of technical capability in the various applicable technologies is assessed. The performance, cost, operational characteristics, reliability, and the suitability of SPS's as power generators for typical commercial electricity grids are discussed. The uncertainties inherent in the system characteristics forecasts are assessed.

Development of personal pollen information—the next generation of pollen information and a step forward for hay fever sufferers

NASA Astrophysics Data System (ADS)

Kmenta, Maximilian; Bastl, Katharina; Jäger, Siegfried; Berger, Uwe

2014-10-01

Pollen allergies affect a large part of the European population and are considered likely to increase. User feedback indicates that there are difficulties in providing proper information and valid forecasts using traditional methods of aerobiology due to a variety of factors. Allergen content, pollen loads, and pollen allergy symptoms vary per region and year. The first steps in challenging such issues have already been undertaken. A personalized pollen-related symptom forecast is thought to be a possible answer. However, attempts made thus far have not led to an improvement in daily forecasting procedures. This study describes a model that was launched in 2013 in Austria to provide the first available personal pollen information. This system includes innovative forecast models using bi-hourly pollen data, traditional pollen forecasts based on historical data, meteorological data, and recent symptom data from the patient's hayfever diary. Furthermore, it calculates the personal symptom load in real time, in particular, the entries of the previous 5 days, to classify users. The personal pollen information was made available in Austria on the Austrian pollen information website and via a mobile pollen application, described herein for the first time. It is supposed that the inclusion of personal symptoms will lead to major improvements in pollen information concerning hay fever sufferers.

Evaluation of Pollen Apps Forecasts: The Need for Quality Control in an eHealth Service.

PubMed

Bastl, Katharina; Berger, Uwe; Kmenta, Maximilian

2017-05-08

Pollen forecasts are highly valuable for allergen avoidance and thus raising the quality of life of persons concerned by pollen allergies. They are considered as valuable free services for the public. Careful scientific evaluation of pollen forecasts in terms of accurateness and reliability has not been available till date. The aim of this study was to analyze 9 mobile apps, which deliver pollen information and pollen forecasts, with a focus on their accurateness regarding the prediction of the pollen load in the grass pollen season 2016 to assess their usefulness for pollen allergy sufferers. The following number of apps was evaluated for each location: 3 apps for Vienna (Austria), 4 apps for Berlin (Germany), and 1 app each for Basel (Switzerland) and London (United Kingdom). All mobile apps were freely available. Today's grass pollen forecast was compared throughout the defined grass pollen season at each respective location with measured grass pollen concentrations. Hit rates were calculated for the exact performance and for a tolerance in a range of ±2 and ±4 pollen per cubic meter. In general, for most apps, hit rates score around 50% (6 apps). It was found that 1 app showed better results, whereas 3 apps performed less well. Hit rates increased when calculated with tolerances for most apps. In contrast, the forecast for the "readiness to flower" for grasses was performed at a sufficiently accurate level, although only two apps provided such a forecast. The last of those forecasts coincided with the first moderate grass pollen load on the predicted day or 3 days after and performed even from about a month before well within the range of 3 days. Advertisement was present in 3 of the 9 analyzed apps, whereas an imprint mentioning institutions with experience in pollen forecasting was present in only three other apps. The quality of pollen forecasts is in need of improvement, and quality control for pollen forecasts is recommended to avoid potential harm to pollen allergy sufferers due to inadequate forecasts. The inclusion of information on reliability of provided forecasts and a similar handling regarding probabilistic weather forecasts should be considered. ©Katharina Bastl, Uwe Berger, Maximilian Kmenta. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.05.2017.

Evaluation of Pollen Apps Forecasts: The Need for Quality Control in an eHealth Service

PubMed Central

Berger, Uwe; Kmenta, Maximilian

2017-01-01

Background Pollen forecasts are highly valuable for allergen avoidance and thus raising the quality of life of persons concerned by pollen allergies. They are considered as valuable free services for the public. Careful scientific evaluation of pollen forecasts in terms of accurateness and reliability has not been available till date. Objective The aim of this study was to analyze 9 mobile apps, which deliver pollen information and pollen forecasts, with a focus on their accurateness regarding the prediction of the pollen load in the grass pollen season 2016 to assess their usefulness for pollen allergy sufferers. Methods The following number of apps was evaluated for each location: 3 apps for Vienna (Austria), 4 apps for Berlin (Germany), and 1 app each for Basel (Switzerland) and London (United Kingdom). All mobile apps were freely available. Today’s grass pollen forecast was compared throughout the defined grass pollen season at each respective location with measured grass pollen concentrations. Hit rates were calculated for the exact performance and for a tolerance in a range of ±2 and ±4 pollen per cubic meter. Results In general, for most apps, hit rates score around 50% (6 apps). It was found that 1 app showed better results, whereas 3 apps performed less well. Hit rates increased when calculated with tolerances for most apps. In contrast, the forecast for the “readiness to flower” for grasses was performed at a sufficiently accurate level, although only two apps provided such a forecast. The last of those forecasts coincided with the first moderate grass pollen load on the predicted day or 3 days after and performed even from about a month before well within the range of 3 days. Advertisement was present in 3 of the 9 analyzed apps, whereas an imprint mentioning institutions with experience in pollen forecasting was present in only three other apps. Conclusions The quality of pollen forecasts is in need of improvement, and quality control for pollen forecasts is recommended to avoid potential harm to pollen allergy sufferers due to inadequate forecasts. The inclusion of information on reliability of provided forecasts and a similar handling regarding probabilistic weather forecasts should be considered. PMID:28483740

7 CFR 1710.302 - Financial forecasts-power supply borrowers.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Financial forecasts-power supply borrowers. 1710.302 Section 1710.302 Agriculture Regulations of the Department of Agriculture (Continued) RURAL UTILITIES SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Long-Range Financial...

Improving Agent Bidding in Power Stock Markets through a Data Mining Enhanced Agent Platform

NASA Astrophysics Data System (ADS)

Chrysopoulos, Anthony C.; Symeonidis, Andreas L.; Mitkas, Pericles A.

Like in any other auctioning environment, entities participating in Power Stock Markets have to compete against other in order to maximize own revenue. Towards the satisfaction of their goal, these entities (agents - human or software ones) may adopt different types of strategies - from na?ve to extremely complex ones - in order to identify the most profitable goods compilation, the appropriate price to buy or sell etc, always under time pressure and auction environment constraints. Decisions become even more difficult to make in case one takes the vast volumes of historical data available into account: goods’ prices, market fluctuations, bidding habits and buying opportunities. Within the context of this paper we present Cassandra, a multi-agent platform that exploits data mining, in order to extract efficient models for predicting Power Settlement prices and Power Load values in typical Day-ahead Power markets. The functionality of Cassandra is discussed, while focus is given on the bidding mechanism of Cassandra’s agents, and the way data mining analysis is performed in order to generate the optimal forecasting models. Cassandra has been tested in a real-world scenario, with data derived from the Greek Energy Stock market.

Using JPSS VIIRS Fire Radiative Power Data to Forecast Biomass Burning Emissions and Smoke Transport by the High Resolution Rapid Refresh Model

NASA Astrophysics Data System (ADS)

Ahmadov, R.; Grell, G. A.; James, E.; Alexander, C.; Stewart, J.; Benjamin, S.; McKeen, S. A.; Csiszar, I. A.; Tsidulko, M.; Pierce, R. B.; Pereira, G.; Freitas, S. R.; Goldberg, M.

2017-12-01

We present a new real-time smoke modeling system, the High Resolution Rapid Refresh coupled with smoke (HRRR-Smoke), to simulate biomass burning (BB) emissions, plume rise and smoke transport in real time. The HRRR is the NOAA Earth System Research Laboratory's 3km grid spacing version of the Weather Research and Forecasting (WRF) model used for weather forecasting. Here we make use of WRF-Chem (the WRF model coupled with chemistry) and simulate fine particulate matter (smoke) emissions emitted by BB. The HRRR-Smoke modeling system ingests fire radiative power (FRP) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (S-NPP) satellite to calculate BB emissions. The FRP product is based on processing 750m resolution "M" bands. The algorithms for fire detection and FRP retrieval are consistent with those used to generate the MODIS fire detection data. For the purpose of ingesting VIIRS fire data into the HRRR-Smoke model, text files are generated to provide the location and detection confidence of fire pixels, as well as FRP. The VIIRS FRP data from the text files are processed and remapped over the HRRR-Smoke model domains. We process the FRP data to calculate BB emissions (smoldering part) and fire size for the model input. In addition, HRRR-Smoke uses the FRP data to simulate the injection height for the flaming emissions using concurrently simulated meteorological fields by the model. Currently, there are two 3km resolution domains covering the contiguous US and Alaska which are used to simulate smoke in real time. In our presentation, we focus on the CONUS domain. HRRR-Smoke is initialized 4 times per day to forecast smoke concentrations for the next 36 hours. The VIIRS FRP data, as well as near-surface and vertically integrated smoke mass concentrations are visualized for every forecast hour. These plots are provided to the public via the HRRR-Smoke web-page: https://rapidrefresh.noaa.gov/HRRRsmoke/. Model evaluations for a case study are presented, where simulated smoke concentrations are compared with hourly PM2.5 measurements from EPA's Air Quality System network. These comparisons demonstrate the model's ability in simulating high aerosol loadings during major wildfire events in the western US.

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual modelmore » has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.« less

Method for assigning sites to projected generic nuclear power plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holter, G.M.; Purcell, W.L.; Shutz, M.E.

1986-07-01

Pacific Northwest Laboratory developed a method for forecasting potential locations and startup sequences of nuclear power plants that will be required in the future but have not yet been specifically identified by electric utilities. Use of the method results in numerical ratings for potential nuclear power plant sites located in each of the 10 federal energy regions. The rating for each potential site is obtained from numerical factors assigned to each of 5 primary siting characteristics: (1) cooling water availability, (2) site land area, (3) power transmission land area, (4) proximity to metropolitan areas, and (5) utility plans for themore » site. The sequence of plant startups in each federal energy region is obtained by use of the numerical ratings and the forecasts of generic nuclear power plant startups obtained from the EIA Middle Case electricity forecast. Sites are assigned to generic plants in chronological order according to startup date.« less

SCADA-based Operator Support System for Power Plant Equipment Fault Forecasting

NASA Astrophysics Data System (ADS)

Mayadevi, N.; Ushakumari, S. S.; Vinodchandra, S. S.

2014-12-01

Power plant equipment must be monitored closely to prevent failures from disrupting plant availability. Online monitoring technology integrated with hybrid forecasting techniques can be used to prevent plant equipment faults. A self learning rule-based expert system is proposed in this paper for fault forecasting in power plants controlled by supervisory control and data acquisition (SCADA) system. Self-learning utilizes associative data mining algorithms on the SCADA history database to form new rules that can dynamically update the knowledge base of the rule-based expert system. In this study, a number of popular associative learning algorithms are considered for rule formation. Data mining results show that the Tertius algorithm is best suited for developing a learning engine for power plants. For real-time monitoring of the plant condition, graphical models are constructed by K-means clustering. To build a time-series forecasting model, a multi layer preceptron (MLP) is used. Once created, the models are updated in the model library to provide an adaptive environment for the proposed system. Graphical user interface (GUI) illustrates the variation of all sensor values affecting a particular alarm/fault, as well as the step-by-step procedure for avoiding critical situations and consequent plant shutdown. The forecasting performance is evaluated by computing the mean absolute error and root mean square error of the predictions.

Next Day Price Forecasting in Deregulated Market by Combination of Artificial Neural Network and ARIMA Time Series Models

NASA Astrophysics Data System (ADS)

Areekul, Phatchakorn; Senjyu, Tomonobu; Urasaki, Naomitsu; Yona, Atsushi

Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

Tracking Cloud Motion and Deformation for Short-Term Photovoltaic Power Forecasting

NASA Astrophysics Data System (ADS)

Good, Garrett; Siefert, Malte; Fritz, Rafael; Saint-Drenan, Yves-Marie; Dobschinski, Jan

2016-04-01

With the increasing role of photovoltaic power production, the need to accurately forecast and anticipate weather-driven elements like cloud cover has become ever more important. Of particular concern is forecasting on the short-term (up to several hours), for which the most recent full weather simulation may no longer provide the most accurate information in light of real-time satellite measurements. We discuss the application of the image correlation velocimetry technique described by Tokumaru & Dimotakis (1995) (for calculating flow fields from images) to measure deformations of various orders based on recent satellite imagery, with the goal of not only more accurately forecasting the advection of cloud structures, but their continued deformation as well.

Travel demand forecasting models: a comparison of EMME/2 and QUR II using a real-world network.

DOT National Transportation Integrated Search

2000-10-01

In order to automate the travel demand forecasting process in urban transportation planning, a number of : commercial computer based travel demand forecasting models have been developed, which have provided : transportation planners with powerful and...

Minimization of Impact from Electric Vehicle Supply Equipment to the Electric Grid Using a Dynamically Controlled Battery Bank for Peak Load Shaving

DOE Office of Scientific and Technical Information (OSTI.GOV)

Castello, Charles C

This research presents a comparison of two control systems for peak load shaving using local solar power generation (i.e., photovoltaic array) and local energy storage (i.e., battery bank). The purpose is to minimize load demand of electric vehicle supply equipment (EVSE) on the electric grid. A static and dynamic control system is compared to decrease demand from EVSE. Static control of the battery bank is based on charging and discharging to the electric grid at fixed times. Dynamic control, with 15-minute resolution, forecasts EVSE load based on data analysis of collected data. In the proposed dynamic control system, the sigmoidmore » function is used to shave peak loads while limiting scenarios that can quickly drain the battery bank. These control systems are applied to Oak Ridge National Laboratory s (ORNL) solar-assisted electric vehicle (EV) charging stations. This installation is composed of three independently grid-tied sub-systems: (1) 25 EVSE; (2) 47 kW photovoltaic (PV) array; and (3) 60 kWh battery bank. The dynamic control system achieved the greatest peak load shaving, up to 34% on a cloudy day and 38% on a sunny day. The static control system was not ideal; peak load shaving was 14.6% on a cloudy day and 12.7% on a sunny day. Simulations based on ORNL data shows solar-assisted EV charging stations combined with the proposed dynamic battery control system can negate up to 89% of EVSE load demand on sunny days.« less

Clarus multi-state regional demonstrations, evaluation of use case #2 : seasonal load restriction tool.

DOT National Transportation Integrated Search

2011-07-01

This report presents the results of an evaluation of the demonstration of an experimental seasonal load restriction decision support tool. This system offers state DOTs subsurface condition forecasts (such as moisture, temperature, and freeze-thaw tr...

A multiscale forecasting method for power plant fleet management

NASA Astrophysics Data System (ADS)

Chen, Hongmei

In recent years the electric power industry has been challenged by a high level of uncertainty and volatility brought on by deregulation and globalization. A power producer must minimize the life cycle cost while meeting stringent safety and regulatory requirements and fulfilling customer demand for high reliability. Therefore, to achieve true system excellence, a more sophisticated system-level decision-making process with a more accurate forecasting support system to manage diverse and often widely dispersed generation units as a single, easily scaled and deployed fleet system in order to fully utilize the critical assets of a power producer has been created as a response. The process takes into account the time horizon for each of the major decision actions taken in a power plant and develops methods for information sharing between them. These decisions are highly interrelated and no optimal operation can be achieved without sharing information in the overall process. The process includes a forecasting system to provide information for planning for uncertainty. A new forecasting method is proposed, which utilizes a synergy of several modeling techniques properly combined at different time-scales of the forecasting objects. It can not only take advantages of the abundant historical data but also take into account the impact of pertinent driving forces from the external business environment to achieve more accurate forecasting results. Then block bootstrap is utilized to measure the bias in the estimate of the expected life cycle cost which will actually be needed to drive the business for a power plant in the long run. Finally, scenario analysis is used to provide a composite picture of future developments for decision making or strategic planning. The decision-making process is applied to a typical power producer chosen to represent challenging customer demand during high-demand periods. The process enhances system excellence by providing more accurate market information, evaluating the impact of external business environment, and considering cross-scale interactions between decision actions. Along with this process, system operation strategies, maintenance schedules, and capacity expansion plans that guide the operation of the power plant are optimally identified, and the total life cycle costs are estimated.

A Novel Wind Speed Forecasting Model for Wind Farms of Northwest China

NASA Astrophysics Data System (ADS)

Wang, Jian-Zhou; Wang, Yun

2017-01-01

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon's Signed-Rank test, and Morgan-Granger-Newbold test tell us that the proposed model is different from the compared models.

Linked Hydrologic-Hydrodynamic Model Framework to Forecast Impacts of Rivers on Beach Water Quality

NASA Astrophysics Data System (ADS)

Anderson, E. J.; Fry, L. M.; Kramer, E.; Ritzenthaler, A.

2014-12-01

The goal of NOAA's beach quality forecasting program is to use a multi-faceted approach to aid in detection and prediction of bacteria in recreational waters. In particular, our focus has been on the connection between tributary loads and bacteria concentrations at nearby beaches. While there is a clear link between stormwater runoff and beach water quality, quantifying the contribution of river loadings to nearshore bacterial concentrations is complicated due to multiple processes that drive bacterial concentrations in rivers as well as those processes affecting the fate and transport of bacteria upon exiting the rivers. In order to forecast potential impacts of rivers on beach water quality, we developed a linked hydrologic-hydrodynamic water quality framework that simulates accumulation and washoff of bacteria from the landscape, and then predicts the fate and transport of washed off bacteria from the watershed to the coastal zone. The framework includes a watershed model (IHACRES) to predict fecal indicator bacteria (FIB) loadings to the coastal environment (accumulation, wash-off, die-off) as a function of effective rainfall. These loadings are input into a coastal hydrodynamic model (FVCOM), including a bacteria transport model (Lagrangian particle), to simulate 3D bacteria transport within the coastal environment. This modeling system provides predictive tools to assist local managers in decision-making to reduce human health threats.

Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parks, K.; Wan, Y. H.; Wiener, G.

2011-10-01

The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'),more » or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are higher. In organized electricity markets, units that are committed for reliability reasons are paid their offer price even when prevailing market prices are lower. Often, these uplift charges are allocated to market participants that caused the inefficient dispatch in the first place. Thus, wind energy facilities are burdened with their share of costs proportional to their forecast errors. For Xcel Energy, wind energy uncertainty costs manifest depending on specific market structures. In the Public Service of Colorado (PSCo), inefficient commitment and dispatch caused by wind uncertainty increases fuel costs. Wind resources participating in the Midwest Independent System Operator (MISO) footprint make substantial payments in the real-time markets to true-up their day-ahead positions and are additionally burdened with deviation charges called a Revenue Sufficiency Guarantee (RSG) to cover out of market costs associated with operations. Southwest Public Service (SPS) wind plants cause both commitment inefficiencies and are charged Southwest Power Pool (SPP) imbalance payments due to wind uncertainty and variability. Wind energy forecasting helps mitigate these costs. Wind integration studies for the PSCo and Northern States Power (NSP) operating companies have projected increasing costs as more wind is installed on the system due to forecast error. It follows that reducing forecast error would reduce these costs. This is echoed by large scale studies in neighboring regions and states that have recommended adoption of state-of-the-art wind forecasting tools in day-ahead and real-time planning and operations. Further, Xcel Energy concluded reduction of the normalized mean absolute error by one percent would have reduced costs in 2008 by over $1 million annually in PSCo alone. The value of reducing forecast error prompted Xcel Energy to make substantial investments in wind energy forecasting research and development.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mathias, Paul M.; Afshar, Kash; Zheng, Feng

This paper describes an unusual solvent regeneration method unique to CO₂BOLs and other switchable ionic liquids; utilizing changes in polarity to shift the free energy of the system. The degree of CO₂ loading in CO₂BOLs is known to control the polarity of the solvent; conversely, polarity could be exploited as a means to control CO₂ loading. In this process, a chemically inert non-polar “antisolvent” is added to aid in de-complexing CO₂ from a CO₂-rich CO₂BOL. The addition of this polarity assist reduces temperatures required for regeneration of CO₂BOLs by as much as 76 °C. The lower regeneration temperatures realized withmore » this polarity change allow for reduced solvent attrition and thermal degradation. Furthermore, the polarity assist shows considerable promise for reducing regeneration energy of CO₂BOL solvents, and separation of the CO₂BOL from the antisolvent is as simple as cooling the mixture below the upper critical solution temperature. Vapour-liquid equilibrium and liquid-liquid equilibrium measurements of a candidate CO₂BOL with CO₂ with and without an antisolvent were completed. From this data, we present the evidence and impacts of a polarity change on a CO₂BOL. Thermodynamic models and analysis of the system were constructed using ASPEN Plus, and forecasts preliminary process configurations and feasibility are also presented. Lastly, projections of solvent performance for removing CO₂ from a sub-critical coal fired power plant (total net power and parasitic load) are presented with and without this polarity assist and compared to DOE’s Case 10 MEA baseline.« less

Self-heating forecasting for thick laminate specimens in fatigue

NASA Astrophysics Data System (ADS)

Lahuerta, F.; Westphal, T.; Nijssen, R. P. L.

2014-12-01

Thick laminate sections can be found from the tip to the root in most common wind turbine blade designs. Obtaining accurate and reliable design data for thick laminates is subject of investigations, which include experiments on thick laminate coupons. Due to the poor thermal conductivity properties of composites and the material self-heating that occurs during the fatigue loading, high temperature gradients may appear through the laminate thickness. In the case of thick laminates in high load regimes, the core temperature might influence the mechanical properties, leading to premature failures. In the present work a method to forecast the self-heating of thick laminates in fatigue loading is presented. The mechanical loading is related with the laminate self-heating, via the cyclic strain energy and the energy loss ratio. Based on this internal volumetric heat load a thermal model is built and solved to obtain the temperature distribution in the transient state. Based on experimental measurements of the energy loss factor for 10mm thick coupons, the method is described and the resulting predictions are compared with experimental surface temperature measurements on 10 and 30mm UD thick laminate specimens.

Forecasting staffing needs for productivity management in hospital laboratories.

PubMed

Pang, C Y; Swint, J M

1985-12-01

Daily and weekly prediction models are developed to help forecast hospital laboratory work load for the entire laboratory and individual sections of the laboratory. The models are tested using historical data obtained from hospital census and laboratory log books of a 90-bed southwestern hospital. The results indicate that the predictor variables account for 50%, 81%, 56%, and 82% of the daily work load variation for chemistry, hematology, and microbiology sections, and for the entire laboratory, respectively. Equivalent results for the weekly model are 53%, 72%, 12%, and 78% for the same respective sections. On the basis of the predicted work load, staffing assessment is made and a productivity monitoring system constructed. The purpose of such a system is to assist laboratory management in efforts to utilize laboratory manpower in a more efficient and cost-effective manner.

An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting.

PubMed

Hippert, Henrique S; Taylor, James W

2010-04-01

Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for automatic neural network modelling within a Bayesian framework, as applied to six samples containing daily load and weather data for four different countries. We analyse input selection as carried out by the Bayesian 'automatic relevance determination', and the usefulness of the Bayesian 'evidence' for the selection of the best structure (in terms of number of neurones), as compared to methods based on cross-validation. Copyright 2009 Elsevier Ltd. All rights reserved.

Efficient Resources Provisioning Based on Load Forecasting in Cloud

PubMed Central

Hu, Rongdong; Jiang, Jingfei; Liu, Guangming; Wang, Lixin

2014-01-01

Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements. PMID:24701160

Forecasting electricity usage using univariate time series models

NASA Astrophysics Data System (ADS)

Hock-Eam, Lim; Chee-Yin, Yip

2014-12-01

Electricity is one of the important energy sources. A sufficient supply of electricity is vital to support a country's development and growth. Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before. It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method. In literature, there are mixed evidences on the best forecasting methods of electricity demand. This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.

Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model ensembles downscaled by analog ensemble using self-organizing maps

NASA Astrophysics Data System (ADS)

Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji

2016-04-01

Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.

Spring thaw predictor & development of real time spring load restrictions.

DOT National Transportation Integrated Search

2011-02-01

This report summarizes the results of a study to develop a correlation between weather forecasts and the : spring thaw in order to reduce the duration of load limits on New Hampshire roadways. The study used a falling : weight deflectometer at 10 sit...

Managing the space-time-load continuum in TMDL planning: a case study for understanding groundwaer loads through advanced mapping techniques

Treesearch

Phillip Harte; Marcel Belaval; Andrea Traviglia

2016-01-01

The lag time between groundwater recharge and discharge in a watershed and the potential groundwater load to streams is an important factor in forecasting responses to future land use practices. We call this concept managing the “space-time-load continuum.” It’s understood that in any given watershed, the response function (the load at any given time) will differ for...

Why do electricity policy and competitive markets fail to use advanced PV systems to improve distribution power quality?

DOE Office of Scientific and Technical Information (OSTI.GOV)

McHenry, Mark P.; Johnson, Jay; Hightower, Mike

The increasing pressure for network operators to meet distribution network power quality standards with increasing peak loads, renewable energy targets, and advances in automated distributed power electronics and communications is forcing policy-makers to understand new means to distribute costs and benefits within electricity markets. Discussions surrounding how distributed generation (DG) exhibits active voltage regulation and power factor/reactive power control and other power quality capabilities are complicated by uncertainties of baseline local distribution network power quality and to whom and how costs and benefits of improved electricity infrastructure will be allocated. DG providing ancillary services that dynamically respond to the networkmore » characteristics could lead to major network improvements. With proper market structures renewable energy systems could greatly improve power quality on distribution systems with nearly no additional cost to the grid operators. Renewable DG does have variability challenges, though this issue can be overcome with energy storage, forecasting, and advanced inverter functionality. This paper presents real data from a large-scale grid-connected PV array with large-scale storage and explores effective mitigation measures for PV system variability. As a result, we discuss useful inverter technical knowledge for policy-makers to mitigate ongoing inflation of electricity network tariff components by new DG interconnection requirements or electricity markets which value power quality and control.« less

Why do electricity policy and competitive markets fail to use advanced PV systems to improve distribution power quality?

DOE PAGES

McHenry, Mark P.; Johnson, Jay; Hightower, Mike

2016-01-01

The increasing pressure for network operators to meet distribution network power quality standards with increasing peak loads, renewable energy targets, and advances in automated distributed power electronics and communications is forcing policy-makers to understand new means to distribute costs and benefits within electricity markets. Discussions surrounding how distributed generation (DG) exhibits active voltage regulation and power factor/reactive power control and other power quality capabilities are complicated by uncertainties of baseline local distribution network power quality and to whom and how costs and benefits of improved electricity infrastructure will be allocated. DG providing ancillary services that dynamically respond to the networkmore » characteristics could lead to major network improvements. With proper market structures renewable energy systems could greatly improve power quality on distribution systems with nearly no additional cost to the grid operators. Renewable DG does have variability challenges, though this issue can be overcome with energy storage, forecasting, and advanced inverter functionality. This paper presents real data from a large-scale grid-connected PV array with large-scale storage and explores effective mitigation measures for PV system variability. As a result, we discuss useful inverter technical knowledge for policy-makers to mitigate ongoing inflation of electricity network tariff components by new DG interconnection requirements or electricity markets which value power quality and control.« less

Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events

NASA Astrophysics Data System (ADS)

DeChant, C. M.; Moradkhani, H.

2014-12-01

Hydrometeorological events (i.e. floods, droughts, precipitation) are increasingly being forecasted probabilistically, owing to the uncertainties in the underlying causes of the phenomenon. In these forecasts, the probability of the event, over some lead time, is estimated based on some model simulations or predictive indicators. By issuing probabilistic forecasts, agencies may communicate the uncertainty in the event occurring. Assuming that the assigned probability of the event is correct, which is referred to as a reliable forecast, the end user may perform some risk management based on the potential damages resulting from the event. Alternatively, an unreliable forecast may give false impressions of the actual risk, leading to improper decision making when protecting resources from extreme events. Due to this requisite for reliable forecasts to perform effective risk management, this study takes a renewed look at reliability assessment in event forecasts. Illustrative experiments will be presented, showing deficiencies in the commonly available approaches (Brier Score, Reliability Diagram). Overall, it is shown that the conventional reliability assessment techniques do not maximize the ability to distinguish between a reliable and unreliable forecast. In this regard, a theoretical formulation of the probabilistic event forecast verification framework will be presented. From this analysis, hypothesis testing with the Poisson-Binomial distribution is the most exact model available for the verification framework, and therefore maximizes one's ability to distinguish between a reliable and unreliable forecast. Application of this verification system was also examined within a real forecasting case study, highlighting the additional statistical power provided with the use of the Poisson-Binomial distribution.

Generation co-ordination and energy trading systems in an open market

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eichler, R.

1998-07-01

The power industry in many parts of the world is currently undergoing dramatic changes: deregulation, privatization, competition and 3rd party access are the keywords. The major trends are summarized at the beginning of the paper to provide the basis for the evolving consequences for the power generation industry. In the restructured environment of the Open Market power generation companies frequently are organizationally separated from transmission, distribution, and supply and now have to sell their product directly to customers. This necessitates the introduction of energy trading support functions for both bilateral trading and power exchange trading. On the other hand, theremore » is a close relationship between energy trading and the technical process of energy production. The paper discusses design principles for software systems supporting maximum economic benefits. First practical application experience is also presented. The energy trading process requires the break up of proprietary databases and proprietary data structures as this process has a major need to communicate with external partners who normally use different systems. This directly leads to 3rd party products for the database, standardized data structures and standardized communication protocols. The Open Market environment calls for new and modified planning functions: in some cases measured value information necessary for updating load forecasts cannot be directly achieved. This leads to the need for an estimator of the actual load situation, a completely new function. Power scheduling has to take care of the generation company's balance but it need not always be forced to 0. Regulating services from the grid companies can be used instead. This gives the scheduling functions additional freedom for determining more economic overall solutions considering both purchase and services and sales of energy.« less

Intra-Minute Cloud Passing Forecasting Based on a Low Cost IoT Sensor-A Solution for Smoothing the Output Power of PV Power Plants.

PubMed

Sukič, Primož; Štumberger, Gorazd

2017-05-13

Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly.

Intra-Minute Cloud Passing Forecasting Based on a Low Cost IoT Sensor—A Solution for Smoothing the Output Power of PV Power Plants

PubMed Central

Sukič, Primož; Štumberger, Gorazd

2017-01-01

Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly. PMID:28505078

Reassessing hypoxia forecasts for the Gulf of Mexico.

PubMed

Scavia, Donald; Donnelly, Kristina A

2007-12-01

Gulf of Mexico hypoxia has received considerable scientific and policy attention because of its potential ecological and economic impacts and implications for agriculture within its massive watershed. A 2000 assessment concluded that increased nitrate load to the Gulf since the 1950s was the primary cause of large-scale hypoxia areas. More recently, models have suggested that large-scale hypoxia did not start untilthe mid-1970s, and that a 40-45% nitrogen load reduction may be needed to reach the hypoxia area goal of the Hypoxia Action Plan. Recently, USGS revised nutrient load estimates to the Gulf, and the Action Plan reassessment has questioned the role of phosphorus versus nitrogen in controlling hypoxia. In this paper, we re-evaluate model simulations, hindcasts, and forecasts using revised nitrogen loads, and testthe ability of a phosphorus-driven version of the model to reproduce hypoxia trends. Our analysis suggests that, if phosphorus is limiting now, it became so because of relative increases in nitrogen loads during the 1970s and 1980s. While our model suggests nitrogen load reductions of 37-45% or phosphorus load reductions of 40-50% below the 1980-1996 average are needed, we caution that a phosphorus-only strategy is potentially dangerous, and suggest it would be prudent to reduce both.

Electric energy demand and supply prospects for California

NASA Technical Reports Server (NTRS)

Jones, H. G. M.

1978-01-01

A recent history of electricity forecasting in California is given. Dealing with forecasts and regulatory uncertainty is discussed. Graphs are presented for: (1) Los Angeles Department of Water and Power and Pacific Gas and Electric present and projected reserve margins; (2) California electricity peak demand forecast; and (3) California electricity production.

Flood Forecasting in Wales: Challenges and Solutions

NASA Astrophysics Data System (ADS)

How, Andrew; Williams, Christopher

2015-04-01

With steep, fast-responding river catchments, exposed coastal reaches with large tidal ranges and large population densities in some of the most at-risk areas; flood forecasting in Wales presents many varied challenges. Utilising advances in computing power and learning from best practice within the United Kingdom and abroad have seen significant improvements in recent years - however, many challenges still remain. Developments in computing and increased processing power comes with a significant price tag; greater numbers of data sources and ensemble feeds brings a better understanding of uncertainty but the wealth of data needs careful management to ensure a clear message of risk is disseminated; new modelling techniques utilise better and faster computation, but lack the history of record and experience gained from the continued use of more established forecasting models. As a flood forecasting team we work to develop coastal and fluvial forecasting models, set them up for operational use and manage the duty role that runs the models in real time. An overview of our current operational flood forecasting system will be presented, along with a discussion on some of the solutions we have in place to address the challenges we face. These include: • real-time updating of fluvial models • rainfall forecasting verification • ensemble forecast data • longer range forecast data • contingency models • offshore to nearshore wave transformation • calculation of wave overtopping

Controller for thermostatically controlled loads

DOEpatents

Lu, Ning; Zhang, Yu; Du, Pengwei; Makarov, Yuri V.

2016-06-07

A system and method of controlling aggregated thermostatically controlled appliances (TCAs) for demand response is disclosed. A targeted load profile is formulated and a forecasted load profile is generated. The TCAs within an "on" or "off" control group are prioritized based on their operating temperatures. The "on" or "off" status of the TCAs is determined. Command signals are sent to turn on or turn off the TCAs.

Diagnostics of glass fiber reinforced polymers and comparative analysis of their fabrication techniques with the use of acoustic emission

NASA Astrophysics Data System (ADS)

Bashkov, O. V.; Bryansky, A. A.; Panin, S. V.; Zaikov, V. I.

2016-11-01

Strength properties of the glass fiber reinforced polymers (GFRP) fabricated by vacuum and vacuum autoclave molding techniques were analyzed. Measurements of porosity of the GFRP parts manufactured by various molding techniques were conducted with the help of optical microscopy. On the basis of experimental data obtained by means of acoustic emission hardware/software setup, the technique for running diagnostics and forecasting the bearing capacity of polymeric composite materials based on the result of three-point bending tests has been developed. The operation principle of the technique is underlined by the evaluation of the power function index change which takes place on the dependence of the total acoustic emission counts versus the loading stress.

Economic analysis for transmission operation and planning

NASA Astrophysics Data System (ADS)

Zhou, Qun

2011-12-01

Restructuring of the electric power industry has caused dramatic changes in the use of transmission system. The increasing congestion conditions as well as the necessity of integrating renewable energy introduce new challenges and uncertainties to transmission operation and planning. Accurate short-term congestion forecasting facilitates market traders in bidding and trading activities. Cost sharing and recovery issue is a major impediment for long-term transmission investment to integrate renewable energy. In this research, a new short-term forecasting algorithm is proposed for predicting congestion, LMPs, and other power system variables based on the concept of system patterns. The advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce the forecasting error. The advantage relative to previously proposed structural forecasting methods is that data requirements are substantially reduced. Forecasting results based on a NYISO case study demonstrate the feasibility and accuracy of the proposed algorithm. Moreover, a negotiation methodology is developed to guide transmission investment for integrating renewable energy. Built on Nash Bargaining theory, the negotiation of investment plans and payment rate can proceed between renewable generation and transmission companies for cost sharing and recovery. The proposed approach is applied to Garver's six bus system. The numerical results demonstrate fairness and efficiency of the approach, and hence can be used as guidelines for renewable energy investors. The results also shed light on policy-making of renewable energy subsidies.

Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry (Invited)

NASA Astrophysics Data System (ADS)

Mahoney, W. P.; Wiener, G.; Liu, Y.; Myers, W.; Johnson, D.

2010-12-01

Wind energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions. As wind energy portfolios expand, this forecast problem is taking on new urgency because wind forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy. Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for renewable energy management. In early 2009, the National Center for Atmospheric Research (NCAR) began a collaborative project with Xcel Energy Services, Inc. to perform research and develop technologies to improve Xcel Energy's ability to increase the amount of wind energy in their generation portfolio. The agreement and scope of work was designed to provide highly detailed, localized wind energy forecasts to enable Xcel Energy to more efficiently integrate electricity generated from wind into the power grid. The wind prediction technologies are designed to help Xcel Energy operators make critical decisions about powering down traditional coal and natural gas-powered plants when sufficient wind energy is predicted. The wind prediction technologies have been designed to cover Xcel Energy wind resources spanning a region from Wisconsin to New Mexico. The goal of the project is not only to improve Xcel Energy’s wind energy prediction capabilities, but also to make technological advancements in wind and wind energy prediction, expand our knowledge of boundary layer meteorology, and share the results across the renewable energy industry. To generate wind energy forecasts, NCAR is incorporating observations of current atmospheric conditions from a variety of sources including satellites, aircraft, weather radars, ground-based weather stations, wind profilers, and even wind sensors on individual wind turbines. The information is utilized by several technologies including: a) the Weather Research and Forecasting (WRF) model, which generates finely detailed simulations of future atmospheric conditions, b) the Real-Time Four-Dimensional Data Assimilation System (RTFDDA), which performs continuous data assimilation providing the WRF model with continuous updates of the initial atmospheric state, 3) the Dynamic Integrated Forecast System (DICast®), which statistically optimizes the forecasts using all predictors, and 4) a suite of wind-to-power algorithms that convert wind speed to power for a wide range of wind farms with varying real-time data availability capabilities. In addition to these core wind energy prediction capabilities, NCAR implemented a high-resolution (10 km grid increment) 30-member ensemble RTFDDA prediction system that provides information on the expected range of wind power over a 72-hour forecast period covering Xcel Energy’s service areas. This talk will include descriptions of these capabilities and report on several topics including initial results of next-day forecasts and nowcasts of wind energy ramp events, influence of local observations on forecast skill, and overall lessons learned to date.

A SIMPLE MODEL FOR FORECASTING THE EFFECTS OF NITROGEN LOADS ON CHESAPEAKE BAY HYPOXIA

EPA Science Inventory

The causes and consequences of oxygen depletion in Chesapeake Bay have been the focus of research, assessment, and policy action over the past several decades. An ongoing scientific re-evaluation of what nutrients load reductions are necessary to meet the water quality goals is ...

A three-stage birandom program for unit commitment with wind power uncertainty.

PubMed

Zhang, Na; Li, Weidong; Liu, Rao; Lv, Quan; Sun, Liang

2014-01-01

The integration of large-scale wind power adds a significant uncertainty to power system planning and operating. The wind forecast error is decreased with the forecast horizon, particularly when it is from one day to several hours ahead. Integrating intraday unit commitment (UC) adjustment process based on updated ultra-short term wind forecast information is one way to improve the dispatching results. A novel three-stage UC decision method, in which the day-ahead UC decisions are determined in the first stage, the intraday UC adjustment decisions of subfast start units are determined in the second stage, and the UC decisions of fast-start units and dispatching decisions are determined in the third stage is presented. Accordingly, a three-stage birandom UC model is presented, in which the intraday hours-ahead forecasted wind power is formulated as a birandom variable, and the intraday UC adjustment event is formulated as a birandom event. The equilibrium chance constraint is employed to ensure the reliability requirement. A birandom simulation based hybrid genetic algorithm is designed to solve the proposed model. Some computational results indicate that the proposed model provides UC decisions with lower expected total costs.

Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Florita, Anthony R; Elgindy, Tarek; Hodge, Brian S

A framework for assessing the performance of short-term solar forecasting is presented in conjunction with a range of numerical results using global horizontal irradiation (GHI) from the open-source Surface Radiation Budget (SURFRAD) data network. A suite of popular machine learning algorithms is compared according to a set of statistically distinct metrics and benchmarked against the persistence-of-cloudiness forecast and a cloud motion forecast. Results show significant improvement compared to the benchmarks with trade-offs among the machine learning algorithms depending on the desired error metric. Training inputs include time series observations of GHI for a history of years, historical weather and atmosphericmore » measurements, and corresponding date and time stamps such that training sensitivities might be inferred. Prediction outputs are GHI forecasts for 1, 2, 3, and 4 hours ahead of the issue time, and they are made for every month of the year for 7 locations. Photovoltaic power and energy outputs can then be made using the solar forecasts to better understand power system impacts.« less

Ensemble Data Assimilation of Photovoltaic Power Information in the Convection-permitting High-Resolution Model COSMO-DE

NASA Astrophysics Data System (ADS)

Declair, Stefan; Saint-Drenan, Yves-Marie; Potthast, Roland

2017-04-01

Determining the amount of weather dependent renewable energy is a demanding task for transmission system operators (TSOs) and wind and photovoltaic (PV) prediction errors require the use of reserve power, which generate costs and can - in extreme cases - endanger the security of supply. In the project EWeLiNE funded by the German government, the German Weather Service and the Fraunhofer Institute on Wind Energy and Energy System Technology develop innovative weather- and power forecasting models and tools for grid integration of weather dependent renewable energy. The key part in energy prediction process chains is the numerical weather prediction (NWP) system. Irradiation forecasts from NWP systems are however subject to several sources of error. For PV power prediction, weaknesses of the NWP model to correctly forecast i.e. low stratus, absorption of condensed water or aerosol optical depths are the main sources of errors. Inaccurate radiation schemes (i.e. the two-stream parametrization) are also known as a deficit of NWP systems with regard to irradiation forecast. To mitigate errors like these, latest observations can be used in a pre-processing technique called data assimilation (DA). In DA, not only the initial fields are provided, but the model is also synchronized with reality - the observations - and hence forecast errors are reduced. Besides conventional observation networks like radiosondes, synoptic observations or air reports of wind, pressure and humidity, the number of observations measuring meteorological information indirectly by means of remote sensing such as satellite radiances, radar reflectivities or GPS slant delays strongly increases. Numerous PV plants installed in Germany potentially represent a dense meteorological network assessing irradiation through their power measurements. Forecast accuracy may thus be enhanced by extending the observations in the assimilation by this new source of information. PV power plants can provide information on clouds, aerosol optical depth or low stratus in terms of remote sensing: the power output is strongly dependent on perturbations along the slant between sun position and PV panel. Since these data are not limited to the vertical column above or below the detector, it may thus complement satellite data and compensate weaknesses in the radiation scheme. In this contribution, the used DA technique (Local Ensemble Transform Kalman Filter, LETKF) is shortly sketched. Furthermore, the computation of the model power equivalents is described and first results are presented and discussed.

A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solarmore » power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs out to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts up to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-So far physics to develop and dissipate clouds. MAE improvements for MAD-WRF for forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components by themselves provide improvement over Smart Persistence, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast. The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We find that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%. The SunCast™ system requires substantial software engineering to blend all of the new model components as well as existing publically available NWP model runs. To do this we use an expert system for the Nowcasting blender and the Dynamic Integrated foreCast (DICast®) system for the NWP models. These two systems are then blended, we use an empirical power conversion method to convert the irradiance predictions to power, then apply an analog ensemble (AnEn) approach to further tune the forecast as well as to estimate its uncertainty. The AnEn module decreased RMSE (root mean squared error) by 17% over the blended SunCast™ power forecasts and provided skill in the probabilistic forecast with a Brier Skill Score of 0.55. In addition, we have also developed a Gridded Atmospheric Forecast System (GRAFS) in parallel, leveraging cost share funds. An economic evaluation based on Production Cost Modeling in the Public Service Company of Colorado showed that the observed 50% improvement in forecast accuracy will save their customers $819,200 with the projected MW deployment for 2024. Using econometrics, NCAR has scaled this savings to a national level and shown that an annual expected savings for this 50% forecast error reduction ranges from $11M in 2015 to $43M expected in 2040 with increased solar deployment. This amounts to a $455M discounted savings over the 26 year period of analysis.« less

Projected electric power demands for the Potomac Electric Power Company

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilson, J.W.

1975-07-01

Included are chapters on the background of the Potomac Electric Power Company, forecasting future power demand, demand modeling, accuracy of market predictions, and total power system requirements. (DG)

Evaluating space weather forecasts of geomagnetic activity from a user perspective

NASA Astrophysics Data System (ADS)

Thomson, A. W. P.

2000-12-01

Decision Theory can be used as a tool for discussing the relative costs of complacency and false alarms with users of space weather forecasts. We describe a new metric for the value of space weather forecasts, derived from Decision Theory. In particular we give equations for the level of accuracy that a forecast must exceed in order to be useful to a specific customer. The technique is illustrated by simplified example forecasts for global geomagnetic activity and for geophysical exploration and power grid management in the British Isles.

Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

NASA Astrophysics Data System (ADS)

Arumugam, S.; Libera, D.

2017-12-01

Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Zhang, Yingchen

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vectormore » regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.« less

A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Zack, J. W.

2015-12-01

Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble, which is a case-matching scheme. The presentation will provide (1) an overview of each method and the experimental design, (2) performance comparisons based on standard metrics such as bias, MAE and RMSE, (3) a summary of the performance characteristics of each approach and (4) a preview of further experiments to be conducted.

Assessment of a new seasonal to inter-annual operational Great Lakes water supply, water levels, and connecting channel flow forecasting system

NASA Astrophysics Data System (ADS)

Gronewold, A.; Fry, L. M.; Hunter, T.; Pei, L.; Smith, J.; Lucier, H.; Mueller, R.

2017-12-01

The U.S. Army Corps of Engineers (USACE) has recently operationalized a suite of ensemble forecasts of Net Basin Supply (NBS), water levels, and connecting channel flows that was developed through a collaboration among USACE, NOAA's Great Lakes Environmental Research Laboratory, Ontario Power Generation (OPG), New York Power Authority (NYPA), and the Niagara River Control Center (NRCC). These forecasts are meant to provide reliable projections of potential extremes in daily discharge in the Niagara and St. Lawrence Rivers over a long time horizon (5 years). The suite of forecasts includes eight configurations that vary by (a) NBS model configuration, (b) meteorological forcings, and (c) incorporation of seasonal climate projections through the use of weighting. Forecasts are updated on a weekly basis, and represent the first operational forecasts of Great Lakes water levels and flows that span daily to inter-annual horizons and employ realistic regulation logic and lake-to-lake routing. We will present results from a hindcast assessment conducted during the transition from research to operation, as well as early indications of success rates determined through operational verification of forecasts. Assessment will include an exploration of the relative skill of various forecast configurations at different time horizons and the potential for application to hydropower decision making and Great Lakes water management.

Improved predictions of atmospheric icing in Norway

NASA Astrophysics Data System (ADS)

Engdahl, Bjørg Jenny; Nygaard, Bjørn Egil; Thompson, Gregory; Bengtsson, Lisa; Berntsen, Terje

2017-04-01

Atmospheric icing of ground structures is a problem in cold climate locations such as Norway. During the 2013/2014 winter season two major power lines in southern Norway suffered severe damage due to ice loads exceeding their design values by two to three times. Better methods are needed to estimate the ice loads that affect various infrastructure, and better models are needed to improve the prediction of severe icing events. The Wind, Ice and Snow loads Impact on Infrastructure and the Natural Environment (WISLINE) project, was initiated to address this problem and to explore how a changing climate may affect the ice loads in Norway. Creating better forecasts of icing requires a proper simulation of supercooled liquid water (SLW). Preliminary results show that the operational numerical weather prediction model (HARMONIE-AROME) at MET-Norway generates considerably lower values of SLW as compared with the WRF model when run with the Thompson microphysics scheme. Therefore, we are piecewise implementing specific processes found in the Thompson scheme into the AROME model and testing the resulting impacts to prediction of SLW and structural icing. Both idealized and real icing cases are carried out to test the newly modified AROME microphysics scheme. Besides conventional observations, a unique set of specialized instrumentation for icing measurements are used for validation. Initial results of this investigation will be presented at the conference.

The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin Wilde, Principal Investigator

2012-12-31

ABSTRACT Application of Real-Time Offsite Measurements in Improved Short-Term Wind Ramp Prediction Skill Improved forecasting performance immediately preceding wind ramp events is of preeminent concern to most wind energy companies, system operators, and balancing authorities. The value of near real-time hub height-level wind data and more general meteorological measurements to short-term wind power forecasting is well understood. For some sites, access to onsite measured wind data - even historical - can reduce forecast error in the short-range to medium-range horizons by as much as 50%. Unfortunately, valuable free-stream wind measurements at tall tower are not typically available at most windmore » plants, thereby forcing wind forecasters to rely upon wind measurements below hub height and/or turbine nacelle anemometry. Free-stream measurements can be appropriately scaled to hub-height levels, using existing empirically-derived relationships that account for surface roughness and turbulence. But there is large uncertainty in these relationships for a given time of day and state of the boundary layer. Alternatively, forecasts can rely entirely on turbine anemometry measurements, though such measurements are themselves subject to wake effects that are not stationary. The void in free-stream hub-height level measurements of wind can be filled by remote sensing (e.g., sodar, lidar, and radar). However, the expense of such equipment may not be sustainable. There is a growing market for traditional anemometry on tall tower networks, maintained by third parties to the forecasting process (i.e., independent of forecasters and the forecast users). This study examines the value of offsite tall-tower data from the WINDataNOW Technology network for short-horizon wind power predictions at a wind farm in northern Montana. The presentation shall describe successful physical and statistical techniques for its application and the practicality of its application in an operational setting. It shall be demonstrated that when used properly, the real-time offsite measurements materially improve wind ramp capture and prediction statistics, when compared to traditional wind forecasting techniques and to a simple persistence model.« less

Performance assessment of deterministic and probabilistic weather predictions for the short-term optimization of a tropical hydropower reservoir

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Schwanenberg, Dirk; Alvarado, Rodolfo; Assis dos Reis, Alberto; Naumann, Steffi; Collischonn, Walter

2016-04-01

Hydropower is the most important electricity source in Brazil. During recent years, it accounted for 60% to 70% of the total electric power supply. Marginal costs of hydropower are lower than for thermal power plants, therefore, there is a strong economic motivation to maximize its share. On the other hand, hydropower depends on the availability of water, which has a natural variability. Its extremes lead to the risks of power production deficits during droughts and safety issues in the reservoir and downstream river reaches during flood events. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for the short-term reservoir management, the use of probabilistic ensemble forecasts and stochastic optimization techniques receives growing attention and a number of researches have shown its benefit. The present work shows one of the first hindcasting and closed-loop control experiments for a multi-purpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project (HPP) Três Marias, located in southeast Brazil. The HPP reservoir is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control at Pirapora City located 120 km downstream of the dam. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts with 50 ensemble members of the ECMWF are used as forcing of the MGB-IPH hydrological model to generate streamflow forecasts over a period of 2 years. The online optimization depends on a deterministic and multi-stage stochastic version of a model predictive control scheme. Results for the perfect forecasts show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts with shorter lead times of up to 15 days shows the practical benefit of actual operational data. It appears that the use of stochastic optimization combined with ensemble forecasts leads to a significant higher level of flood protection without compromising the HPP's energy production.

Overview and Meteorological Validation of the Wind Integration National Dataset toolkit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draxl, C.; Hodge, B. M.; Clifton, A.

2015-04-13

The Wind Integration National Dataset (WIND) Toolkit described in this report fulfills these requirements, and constitutes a state-of-the-art national wind resource data set covering the contiguous United States from 2007 to 2013 for use in a variety of next-generation wind integration analyses and wind power planning. The toolkit is a wind resource data set, wind forecast data set, and wind power production and forecast data set derived from the Weather Research and Forecasting (WRF) numerical weather prediction model. WIND Toolkit data are available online for over 116,000 land-based and 10,000 offshore sites representing existing and potential wind facilities.

Does the OVX matter for volatility forecasting? Evidence from the crude oil market

NASA Astrophysics Data System (ADS)

Lv, Wendai

2018-02-01

In this paper, I investigate that whether the OVX and its truncated parts with a certain threshold can significantly help in forecasting the oil futures price volatility basing on the Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). In-sample estimation results show that the OVX has a significantly positive impact on futures volatility. The impact of large OVX on future volatility has slightly powerful compared to the small ones. Moreover, the HARQ-RV model outperforms the HAR-RV in predicting the oil futures volatility. More importantly, the decomposed OVX have more powerful in forecasting the oil futures price volatility compared to the OVX itself.

[Forecast of costs of ecodependent cancer treatment for the development of management decisions].

PubMed

Krasovskiy, V O

2014-01-01

The methodical approach for probabilistic forecasting and differentiation of treatment of costs of ecodependent cancer cases has been elaborated. The modality is useful in the organization of medical aid to cancer patients, in developing management decisions for the reduction the occupational load on the population, as well as in solutions problems in compensation to the population economic and social loss from industrial plants.

HEPS4Power - Extended-range Hydrometeorological Ensemble Predictions for Improved Hydropower Operations and Revenues

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Monhart, Samuel; Liniger, Mark; Spririg, Christoph; Jordan, Fred; Zappa, Massimiliano

2015-04-01

In recent years large progresses have been achieved in the operational prediction of floods and hydrological drought with up to ten days lead time. Both the public and the private sectors are currently using probabilistic runoff forecast in order to monitoring water resources and take actions when critical conditions are to be expected. The use of extended-range predictions with lead times exceeding 10 days is not yet established. The hydropower sector in particular might have large benefits from using hydro meteorological forecasts for the next 15 to 60 days in order to optimize the operations and the revenues from their watersheds, dams, captions, turbines and pumps. The new Swiss Competence Centers in Energy Research (SCCER) targets at boosting research related to energy issues in Switzerland. The objective of HEPS4POWER is to demonstrate that operational extended-range hydro meteorological forecasts have the potential to become very valuable tools for fine tuning the production of energy from hydropower systems. The project team covers a specific system-oriented value chain starting from the collection and forecast of meteorological data (MeteoSwiss), leading to the operational application of state-of-the-art hydrological models (WSL) and terminating with the experience in data presentation and power production forecasts for end-users (e-dric.ch). The first task of the HEPS4POWER will be the downscaling and post-processing of ensemble extended-range meteorological forecasts (EPS). The goal is to provide well-tailored forecasts of probabilistic nature that should be reliable in statistical and localized at catchment or even station level. The hydrology related task will consist in feeding the post-processed meteorological forecasts into a HEPS using a multi-model approach by implementing models with different complexity. Also in the case of the hydrological ensemble predictions, post-processing techniques need to be tested in order to improve the quality of the forecasts against observed discharge. Analysis should be specifically oriented to the maximisation of hydroelectricity production. Thus, verification metrics should include economic measures like cost loss approaches. The final step will include the transfer of the HEPS system to several hydropower systems, the connection with the energy market prices and the development of probabilistic multi-reservoir production and management optimizations guidelines. The baseline model chain yielding three-days forecasts established for a hydropower system in southern-Switzerland will be presented alongside with the work-plan to achieve seasonal ensemble predictions.

Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall'Anese, Emiliano; Baker, Kyri; Summers, Tyler

2016-12-01

The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative boundsmore » that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.« less

Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Yingchen; Yang, Rui; Jiang, Huaiguang

For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system operation is increasing. Further, the variability of renewable energy resources has been considered a liability to the grid. However, electricity consumption has shown the same level of variability and uncertainty, and this is sometimes overlooked. This article investigates data analytics and forecasting methodsmore » to identify correlations between electricity consumption behavior and distributed photovoltaic (PV) output. The forecasting results feed into a predictive energy management system that optimizes energy consumption in the near future to balance customer demand and power system needs.« less

A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Krishnan, Venkat K; Zhang, Jie

Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic wind power ramp forecast (cp-WPRF) model based on Copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model (GMM) is adopted to characterize the WPR uncertaintymore » and features. The Canonical Maximum Likelihood (CML) method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion (BIC) from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval (PI) based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.« less

Development of a Neural Network-Based Renewable Energy Forecasting Framework for Process Industries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Soobin; Ryu, Jun-Hyung; Hodge, Bri-Mathias

2016-06-25

This paper presents a neural network-based forecasting framework for photovoltaic power (PV) generation as a decision-supporting tool to employ renewable energies in the process industry. The applicability of the proposed framework is illustrated by comparing its performance against other methodologies such as linear and nonlinear time series modelling approaches. A case study of an actual PV power plant in South Korea is presented.

Spatial Pattern Classification for More Accurate Forecasting of Variable Energy Resources

NASA Astrophysics Data System (ADS)

Novakovskaia, E.; Hayes, C.; Collier, C.

2014-12-01

The accuracy of solar and wind forecasts is becoming increasingly essential as grid operators continue to integrate additional renewable generation onto the electric grid. Forecast errors affect rate payers, grid operators, wind and solar plant maintenance crews and energy traders through increases in prices, project down time or lost revenue. While extensive and beneficial efforts were undertaken in recent years to improve physical weather models for a broad spectrum of applications these improvements have generally not been sufficient to meet the accuracy demands of system planners. For renewables, these models are often used in conjunction with additional statistical models utilizing both meteorological observations and the power generation data. Forecast accuracy can be dependent on specific weather regimes for a given location. To account for these dependencies it is important that parameterizations used in statistical models change as the regime changes. An automated tool, based on an artificial neural network model, has been developed to identify different weather regimes as they impact power output forecast accuracy at wind or solar farms. In this study, improvements in forecast accuracy were analyzed for varying time horizons for wind farms and utility-scale PV plants located in different geographical regions.

Towards more accurate wind and solar power prediction by improving NWP model physics

NASA Astrophysics Data System (ADS)

Steiner, Andrea; Köhler, Carmen; von Schumann, Jonas; Ritter, Bodo

2014-05-01

The growing importance and successive expansion of renewable energies raise new challenges for decision makers, economists, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the errors and provide an a priori estimate of remaining uncertainties associated with the large share of weather-dependent power sources. For this purpose it is essential to optimize NWP model forecasts with respect to those prognostic variables which are relevant for wind and solar power plants. An improved weather forecast serves as the basis for a sophisticated power forecasts. Consequently, a well-timed energy trading on the stock market, and electrical grid stability can be maintained. The German Weather Service (DWD) currently is involved with two projects concerning research in the field of renewable energy, namely ORKA*) and EWeLiNE**). Whereas the latter is in collaboration with the Fraunhofer Institute (IWES), the project ORKA is led by energy & meteo systems (emsys). Both cooperate with German transmission system operators. The goal of the projects is to improve wind and photovoltaic (PV) power forecasts by combining optimized NWP and enhanced power forecast models. In this context, the German Weather Service aims to improve its model system, including the ensemble forecasting system, by working on data assimilation, model physics and statistical post processing. This presentation is focused on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. First steps leading to improved physical parameterization schemes within the NWP-model are presented. Wind mast measurements reaching up to 200 m height above ground are used for the estimation of the (NWP) wind forecast error at heights relevant for wind energy plants. One particular problem is the daily cycle in wind speed. The transition from stable stratification during nighttime to well mixed conditions during the day presents a big challenge to NWP models. Fast decrease and successive increase in hub-height wind speed after sunrise, and the formation of nocturnal low level jets will be discussed. For PV, the life cycle of low stratus clouds and fog is crucial. Capturing these processes correctly depends on the accurate simulation of diffusion or vertical momentum transport and the interaction with other atmospheric and soil processes within the numerical weather model. Results from Single Column Model simulations and 3d case studies will be presented. Emphasis is placed on wind forecasts; however, some references to highlights concerning the PV-developments will also be given. *) ORKA: Optimierung von Ensembleprognosen regenerativer Einspeisung für den Kürzestfristbereich am Anwendungsbeispiel der Netzsicherheitsrechnungen **) EWeLiNE: Erstellung innovativer Wetter- und Leistungsprognosemodelle für die Netzintegration wetterabhängiger Energieträger, www.projekt-eweline.de

An experimental study of summertime coastal fog and its inland penetration in Northern California

NASA Astrophysics Data System (ADS)

Lucena Kreppel Paes, P.; Torres, P.; Faloona, I. C.; Torregrosa, A.; Gultepe, I.

2012-12-01

The occurrence and continental inundation of marine stratocumulus and fog along the California Coast during summer has been linked to many environmental concerns including redwood ecosystem vitality, air traffic control, power grid load balancing, and radiatve climate forcing. An exploratory study was instigated this past summer at the Bodega Marine Laboratory and Pepperwood Preserve, a large nature reserve located 40 km inland in Sonoma County, in order to investigate fog formation, persistence, and penetration through the orographic gap in the Pacific coastal mountain range. Analysis of the synoptic patterns and in-situ meteorological observations, including visibility and boundary layer depth, are presented with the aim of improving fog forecasts and elucidating the principal physical parameters that control summertime fog formation and dissipation along the Northern California Coast.

Analysis of the electricity demand of Greece for optimal planning of a large-scale hybrid renewable energy system

NASA Astrophysics Data System (ADS)

Tyralis, Hristos; Karakatsanis, Georgios; Tzouka, Katerina; Mamassis, Nikos

2015-04-01

The Greek electricity system is examined for the period 2002-2014. The demand load data are analysed at various time scales (hourly, daily, seasonal and annual) and they are related to the mean daily temperature and the gross domestic product (GDP) of Greece for the same time period. The prediction of energy demand, a product of the Greek Independent Power Transmission Operator, is also compared with the demand load. Interesting results about the change of the electricity demand scheme after the year 2010 are derived. This change is related to the decrease of the GDP, during the period 2010-2014. The results of the analysis will be used in the development of an energy forecasting system which will be a part of a framework for optimal planning of a large-scale hybrid renewable energy system in which hydropower plays the dominant role. Acknowledgement: This research was funded by the Greek General Secretariat for Research and Technology through the research project Combined REnewable Systems for Sustainable ENergy DevelOpment (CRESSENDO; grant number 5145)

Visualizing and Integrating AFSCN Utilization into a Common Operational Picture

NASA Astrophysics Data System (ADS)

Hays, B.; Carlile, A.; Mitchell, T.

The Department of Defense (DoD) and the 50th Space Network Operations Group Studies and Analysis branch (50th SCS/SCXI), located at Schriever AFB Colorado, face the unique challenge of forecasting the expected near term and future utilization of the Air Force Satellite Control Network (AFSCN). The forecasting timeframe covers the planned load from the current date to ten years out. The various satellite missions, satellite requirements, orbital regions, and ground architecture dynamics provide the model inputs and constraints that are used in generating the forecasted load. The AFSCN is the largest network the Air Force uses to control satellites worldwide. Each day, network personnel perform over 500 scheduled events-from satellite maneuvers to critical data downloads. The Forecasting Objective is to provide leadership with the insights necessary to manage the network today and tomorrow. For both today's needs and future needs, SCXI develops AFSCN utilization forecasts to optimize the ground system's coverage and capacity to meet user satellite requirements. SCXI also performs satellite program specific studies to determine network support feasibility. STK and STK Scheduler form the core of the tools used by SCXI. To establish this tool suite, we had to evaluate, evolve, and validate both the COTS products and our own developed code and processes. This began with calibrating the network model to emulate the real life scheduling environment of the AFSCN. Multiple STK Scheduler optimizing (de-confliction) algorithms, including Multi-Pass, Sequential, Random, and Neural, were evaluated and adjusted to determine applicability to the model and the accuracy of the prediction. Additionally, the scheduling Figure of Merit (FOM), which permits custom weighting of various parameters, was analyzed and tested to achieve the most accurate real life result. With the inherent capabilities of STK and the ability to wrap and automate output, SCXI is now able to visually communicate satellite loads in a manner never seen before in AFSCN management meetings. Scenarios such as regional antenna load stress, satellite missed opportunities, and the overall network "big picture" can be visually displayed in 3D versus the textual and line graph methods used for many years. This is the first step towards an integrated space awareness picture with an operational focus. SCXI is working on taking the visual forecast concept farther and begin fusing multiple sources of data to build a 50 SW Common Operating Picture (COP). The vision is to integrate more effective orbital determination processes, resource outages, current and forecasted satellite mission requirements, and future architectural changes into a real-time visual status to enable quick and responsive decisions. This COP would be utilized in a Wing Operations Center to provide up to the minute network status on where satellites are, which ground resources are in contact with them, and what resources are down. The ability to quickly absorb and process this data will enhance decision analysis and save valuable time in both day to day operations and wartime scenarios.

Fishing for Novel Approaches to Ecosystem Service Forecasts

EPA Science Inventory

The ecosystem service concept provides a powerful framework for conserving species and the environments they depend upon. Describing current distributions of ecosystem services and forecasting their future distributions have therefore become central objectives in many conservati...

Design of online monitoring and forecasting system for electrical equipment temperature of prefabricated substation based on WSN

NASA Astrophysics Data System (ADS)

Qi, Weiran; Miao, Hongxia; Miao, Xuejiao; Xiao, Xuanxuan; Yan, Kuo

2016-10-01

In order to ensure the safe and stable operation of the prefabricated substations, temperature sensing subsystem, temperature remote monitoring and management subsystem, forecast subsystem are designed in the paper. Wireless temperature sensing subsystem which consists of temperature sensor and MCU sends the electrical equipment temperature to the remote monitoring center by wireless sensor network. Remote monitoring center can realize the remote monitoring and prediction by monitoring and management subsystem and forecast subsystem. Real-time monitoring of power equipment temperature, history inquiry database, user management, password settings, etc., were achieved by monitoring and management subsystem. In temperature forecast subsystem, firstly, the chaos of the temperature data was verified and phase space is reconstructed. Then Support Vector Machine - Particle Swarm Optimization (SVM-PSO) was used to predict the temperature of the power equipment in prefabricated substations. The simulation results found that compared with the traditional methods SVM-PSO has higher prediction accuracy.

Research on regional numerical weather prediction

NASA Technical Reports Server (NTRS)

Kreitzberg, C. W.

1976-01-01

Extension of the predictive power of dynamic weather forecasting to scales below the conventional synoptic or cyclonic scales in the near future is assessed. Lower costs per computation, more powerful computers, and a 100 km mesh over the North American area (with coarser mesh extending beyond it) are noted at present. Doubling the resolution even locally (to 50 km mesh) would entail a 16-fold increase in costs (including vertical resolution and halving the time interval), and constraints on domain size and length of forecast. Boundary conditions would be provided by the surrounding 100 km mesh, and time-varying lateral boundary conditions can be considered to handle moving phenomena. More physical processes to treat, more efficient numerical techniques, and faster computers (improved software and hardware) backing up satellite and radar data could produce further improvements in forecasting in the 1980s. Boundary layer modeling, initialization techniques, and quantitative precipitation forecasting are singled out among key tasks.

Multi-load Groups Coordinated Load Control Strategy Considering Power Network Constraints

NASA Astrophysics Data System (ADS)

Liu, Meng; Zhao, Binchao; Wang, Jun; Zhang, Guohui; Wang, Xin

2017-05-01

Loads with energy storage property can actively participate in power balance for power systems, this paper takes air conditioner as a controllable load example, proposing a multi-load groups coordinated load control strategy considering power network constraints. Firstly, two load control modes considering recovery of load diversity are designed, blocking power oscillation of aggregated air conditioners. As the same time, air conditioner temperature setpoint recovery control strategy is presented to avoid power recovery peak. Considering inherent characteristics of two load control modes, an coordinated load control mode is designed by combining the both. Basing on this, a multi-load groups coordinated load control strategy is proposed. During the implementing of load control, power network constraints should be satisfied. An indice which can reflect the security of power system operating is defined. By minimizing its value through optimization, the change of air conditioning loads’ aggregated power on each load bus can be calculated. Simulations are conducted on an air conditioners group and New England 10-generator 39-bus system, verifying the effectiveness of the proposed multi-load groups coordinated load control strategy considering power network constraints.

Hourly Wind Speed Interval Prediction in Arid Regions

NASA Astrophysics Data System (ADS)

Chaouch, M.; Ouarda, T.

2013-12-01

The long and extended warm and dry summers, the low rate of rain and humidity are the main factors that explain the increase of electricity consumption in hot arid regions. In such regions, the ventilating and air-conditioning installations, that are typically the most energy-intensive among energy consumption activities, are essential for securing healthy, safe and suitable indoor thermal conditions for building occupants and stored materials. The use of renewable energy resources such as solar and wind represents one of the most relevant solutions to overcome the increase of the electricity demand challenge. In the recent years, wind energy is gaining more importance among the researchers worldwide. Wind energy is intermittent in nature and hence the power system scheduling and dynamic control of wind turbine requires an estimate of wind energy. Accurate forecast of wind speed is a challenging task for the wind energy research field. In fact, due to the large variability of wind speed caused by the unpredictable and dynamic nature of the earth's atmosphere, there are many fluctuations in wind power production. This inherent variability of wind speed is the main cause of the uncertainty observed in wind power generation. Furthermore, producing wind power forecasts might be obtained indirectly by modeling the wind speed series and then transforming the forecasts through a power curve. Wind speed forecasting techniques have received substantial attention recently and several models have been developed. Basically two main approaches have been proposed in the literature: (1) physical models such as Numerical Weather Forecast and (2) statistical models such as Autoregressive integrated moving average (ARIMA) models, Neural Networks. While the initial focus in the literature has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. In short term context, probabilistic forecasts might be more relevant than point forecasts for the planner to build scenarios In this paper, we are interested in estimating predictive intervals of the hourly wind speed measures in few cities in United Arab emirates (UAE). More precisely, given a wind speed time series, our target is to forecast the wind speed at any specific hour during the day and provide in addition an interval with the coverage probability 0

Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

Short-term electric power demand forecasting based on economic-electricity transmission model

NASA Astrophysics Data System (ADS)

Li, Wenfeng; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Wang, Yubin Mao; Wang, Jiangbo; He, Dandan

2018-04-01

Short-term electricity demand forecasting is the basic work to ensure safe operation of the power system. In this paper, a practical economic electricity transmission model (EETM) is built. With the intelligent adaptive modeling capabilities of Prognoz Platform 7.2, the econometric model consists of three industrial added value and income levels is firstly built, the electricity demand transmission model is also built. By multiple regression, moving averages and seasonal decomposition, the problem of multiple correlations between variables is effectively overcome in EETM. The validity of EETM is proved by comparison with the actual value of Henan Province. Finally, EETM model is used to forecast the electricity consumption of the 1-4 quarter of 2018.

Operational wave now- and forecast in the German Bight as a basis for the assessment of wave-induced hydrodynamic loads on coastal dikes

NASA Astrophysics Data System (ADS)

Dreier, Norman; Fröhle, Peter

2017-12-01

The knowledge of the wave-induced hydrodynamic loads on coastal dikes including their temporal and spatial resolution on the dike in combination with actual water levels is of crucial importance of any risk-based early warning system. As a basis for the assessment of the wave-induced hydrodynamic loads, an operational wave now- and forecast system is set up that consists of i) available field measurements from the federal and local authorities and ii) data from numerical simulation of waves in the German Bight using the SWAN wave model. In this study, results of the hindcast of deep water wave conditions during the winter storm on 5-6 December, 2013 (German name `Xaver') are shown and compared with available measurements. Moreover field measurements of wave run-up from the local authorities at a sea dike on the German North Sea Island of Pellworm are presented and compared against calculated wave run-up using the EurOtop (2016) approach.

Electrical distribution studies for the 200 Area tank farms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fisler, J.B.

1994-08-26

This is an engineering study providing reliability numbers for various design configurations as well as computer analyses (Captor/Dapper) of the existing distribution system to the 480V side of the unit substations. The objective of the study was to assure the adequacy of the existing electrical system components from the connection at the high voltage supply point through the transformation and distribution equipment to the point where it is reduced to its useful voltage level. It also was to evaluate the reasonableness of proposed solutions of identified deficiencies and recommendations of possible alternate solutions. The electrical utilities are normally considered themore » most vital of the utility systems on a site because all other utility systems depend on electrical power. The system accepts electric power from the external sources, reduces it to a lower voltage, and distributes it to end-use points throughout the site. By classic definition, all utility systems extend to a point 5 feet from the facility perimeter. An exception is made to this definition for the electric utilities at this site. The electrical Utility System ends at the low voltage section of the unit substation, which reduces the voltage from 13.8 kV to 2,400, 480, 277/480 or 120/208 volts. These transformers are located at various distances from existing facilities. The adequacy of the distribution system which transports the power from the main substation to the individual area substations and other load centers is evaluated and factored into the impact of the future load forecast.« less

Acceleration to failure in geophysical signals prior to laboratory rock failure and volcanic eruptions (Invited)

NASA Astrophysics Data System (ADS)

Main, I. G.; Bell, A. F.; Greenhough, J.; Heap, M. J.; Meredith, P. G.

2010-12-01

The nucleation processes that ultimately lead to earthquakes, volcanic eruptions, rock bursts in mines, and landslides from cliff slopes are likely to be controlled at some scale by brittle failure of the Earth’s crust. In laboratory brittle deformation experiments geophysical signals commonly exhibit an accelerating trend prior to dynamic failure. Similar signals have been observed prior to volcanic eruptions, including volcano-tectonic earthquake event and moment release rates. Despite a large amount of effort in the search, no such statistically robust systematic trend is found prior to natural earthquakes. Here we describe the results of a suite of laboratory tests on Mount Etna Basalt and other rocks to examine the nature of the non-linear scaling from laboratory to field conditions, notably using laboratory ‘creep’ tests to reduce the boundary strain rate to conditions more similar to those in the field. Seismic event rate, seismic moment release rate and rate of porosity change show a classic ‘bathtub’ graph that can be derived from a simple damage model based on separate transient and accelerating sub-critical crack growth mechanisms, resulting from separate processes of negative and positive feedback in the population dynamics. The signals exhibit clear precursors based on formal statistical model tests using maximum likelihood techniques with Poisson errors. After correcting for the finite loading time of the signal, the results show a transient creep rate that decays as a classic Omori law for earthquake aftershocks, and remarkably with an exponent near unity, as commonly observed for natural earthquake sequences. The accelerating trend follows an inverse power law when fitted in retrospect, i.e. with prior knowledge of the failure time. In contrast the strain measured on the sample boundary shows a less obvious but still accelerating signal that is often absent altogether in natural strain data prior to volcanic eruptions. To test the forecasting power of such constitutive rules in prospective mode, we examine the forecast quality of several synthetic trials, by adding representative statistical fluctuations, due to finite real-time sampling effects, to an underlying accelerating trend. Metrics of forecast quality change systematically and dramatically with time. In particular the model accuracy increases, and the forecast bias decreases, as the failure time approaches.

David Palchak | NREL

Science.gov Websites

Electrical load forecasting with artificial neural networks Demand-side management optimization with Matlab -58491. D. Palchak, S. Suryanarayanan, and D. Zimmerle. "An Artificial Neural Network in Short-Term

How can monthly to seasonal forecasts help to better manage power systems? (Invited)

NASA Astrophysics Data System (ADS)

Dubus, L.; Troccoli, A.

2013-12-01

The energy industry increasingly depends on weather and climate, at all space and time scales. This is especially true in countries with volunteer renewable energies development policies. There is no doubt that Energy and Meteorology is a burgeoning inter-sectoral discipline. It is also clear that the catalyst for the stronger interaction between these two sectors is the renewed and fervent interest in renewable energies, especially wind and solar power. Recent progress in meteorology has led to a marked increase in the knowledge of the climate system and in the ability to forecast climate on monthly to seasonal time scales. Several studies have already demonstrated the effectiveness of using these forecasts for energy operations, for instance for hydro-power applications. However, it is also obvious that scientific progress on its own is not sufficient to increase the value of weather forecasts. The process of integration of new meteorological products into operational tools and decision making processes is not straightforward but it is at least as important as the scientific discovery. In turn, such integration requires effective communication between users and providers of these products. We will present some important aspects of energy systems in which monthly to seasonal forecasts can bring useful, if not vital, information, and we will give some examples of encouraging energy/meteorology collaborations. We will also provide some suggestions for a strengthened collaboration into the future.

Applicability of internet search index for asthma admission forecast using machine learning.

PubMed

Luo, Li; Liao, Chengcheng; Zhang, Fengyi; Zhang, Wei; Li, Chunyang; Qiu, Zhixin; Huang, Debin

2018-04-15

This study aimed to determine whether a search index could provide insight into trends in asthma admission in China. An Internet search index is a powerful tool to monitor and predict epidemic outbreaks. However, whether using an internet search index can significantly improve asthma admissions forecasts remains unknown. The long-term goal is to develop a surveillance system to help early detection and interventions for asthma and to avoid asthma health care resource shortages in advance. In this study, we used a search index combined with air pollution data, weather data, and historical admissions data to forecast asthma admissions using machine learning. Results demonstrated that the best area under the curve in the test set that can be achieved is 0.832, using all predictors mentioned earlier. A search index is a powerful predictor in asthma admissions forecast, and a recent search index can reflect current asthma admissions with a lag-effect to a certain extent. The addition of a real-time, easily accessible search index improves forecasting capabilities and demonstrates the predictive potential of search index. Copyright © 2018 John Wiley & Sons, Ltd.

Predictability of Solar Radiation for Photovoltaics systems over Europe: from short-term to seasonal time-scales

NASA Astrophysics Data System (ADS)

De Felice, Matteo; Petitta, Marcello; Ruti, Paolo

2014-05-01

Photovoltaic diffusion is steadily growing on Europe, passing from a capacity of almost 14 GWp in 2011 to 21.5 GWp in 2012 [1]. Having accurate forecast is needed for planning and operational purposes, with the possibility to model and predict solar variability at different time-scales. This study examines the predictability of daily surface solar radiation comparing ECMWF operational forecasts with CM-SAF satellite measurements on the Meteosat (MSG) full disk domain. Operational forecasts used are the IFS system up to 10 days and the System4 seasonal forecast up to three months. Forecast are analysed considering average and variance of errors, showing error maps and average on specific domains with respect to prediction lead times. In all the cases, forecasts are compared with predictions obtained using persistence and state-of-art time-series models. We can observe a wide range of errors, with the performance of forecasts dramatically affected by orography and season. Lower errors are on southern Italy and Spain, with errors on some areas consistently under 10% up to ten days during summer (JJA). Finally, we conclude the study with some insight on how to "translate" the error on solar radiation to error on solar power production using available production data from solar power plants. [1] EurObserver, "Baromètre Photovoltaïque, Le journal des énergies renouvables, April 2012."

Short-term forecasts gain in accuracy. [Regression technique using ''Box-Jenkins'' analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

Box-Jenkins time-series models offer accuracy for short-term forecasts that compare with large-scale macroeconomic forecasts. Utilities need to be able to forecast peak demand in order to plan their generating, transmitting, and distribution systems. This new method differs from conventional models by not assuming specific data patterns, but by fitting available data into a tentative pattern on the basis of auto-correlations. Three types of models (autoregressive, moving average, or mixed autoregressive/moving average) can be used according to which provides the most appropriate combination of autocorrelations and related derivatives. Major steps in choosing a model are identifying potential models, estimating the parametersmore » of the problem, and running a diagnostic check to see if the model fits the parameters. The Box-Jenkins technique is well suited for seasonal patterns, which makes it possible to have as short as hourly forecasts of load demand. With accuracy up to two years, the method will allow electricity price-elasticity forecasting that can be applied to facility planning and rate design. (DCK)« less

A Comparison of Synoptic Classification Methods for Application to Wind Power Prediction

NASA Astrophysics Data System (ADS)

Fowler, P.; Basu, S.

2008-12-01

Wind energy is a highly variable resource. To make it competitive with other sources of energy for integration on the power grid, at the very least, a day-ahead forecast of power output must be available. In many grid operations worldwide, next-day power output is scheduled in 30 minute intervals and grid management routinely occurs at real time. Maintenance and repairs require costly time to complete and must be scheduled along with normal operations. Revenue is dependent on the reliability of the entire system. In other words, there is financial and managerial benefit to short-term prediction of wind power. One approach to short-term forecasting is to combine a data centric method such as an artificial neural network with a physically based approach like numerical weather prediction (NWP). The key is in associating high-dimensional NWP model output with the most appropriately trained neural network. Because neural networks perform the best in the situations they are designed for, one can hypothesize that if one can identify similar recurring states in historical weather data, this data can be used to train multiple custom designed neural networks to be used when called upon by numerical prediction. Identifying similar recurring states may offer insight to how a neural network forecast can be improved, but amassing the knowledge and utilizing it efficiently in the time required for power prediction would be difficult for a human to master, thus showing the advantage of classification. Classification methods are important tools for short-term forecasting because they can be unsupervised, objective, and computationally quick. They primarily involve categorizing data sets in to dominant weather classes, but there are numerous ways to define a class and a great variety in interpretation of the results. In the present study a collection of classification methods are used on a sampling of atmospheric variables from the North American Regional Reanalysis data set. The results will be discussed in relation to their use for short-term wind power forecasting by neural networks.

Coordinated Scheduling for Interdependent Electric Power and Natural Gas Infrastructures

DOE PAGES

Zlotnik, Anatoly; Roald, Line; Backhaus, Scott; ...

2016-03-24

The extensive installation of gas-fired power plants in many parts of the world has led electric systems to depend heavily on reliable gas supplies. The use of gas-fired generators for peak load and reserve provision causes high intraday variability in withdrawals from high-pressure gas transmission systems. Such variability can lead to gas price fluctuations and supply disruptions that affect electric generator dispatch, electricity prices, and threaten the security of power systems and gas pipelines. These infrastructures function on vastly different spatio-temporal scales, which prevents current practices for separate operations and market clearing from being coordinated. Here in this article, wemore » apply new techniques for control of dynamic gas flows on pipeline networks to examine day-ahead scheduling of electric generator dispatch and gas compressor operation for different levels of integration, spanning from separate forecasting, and simulation to combined optimal control. We formulate multiple coordination scenarios and develop tractable physically accurate computational implementations. These scenarios are compared using an integrated model of test networks for power and gas systems with 24 nodes and 24 pipes, respectively, which are coupled through gas-fired generators. The analysis quantifies the economic efficiency and security benefits of gas-electric coordination and dynamic gas system operation.« less

Improving volcanic sulfur dioxide cloud dispersal forecasts by progressive assimilation of satellite observations

NASA Astrophysics Data System (ADS)

Boichu, Marie; Clarisse, Lieven; Khvorostyanov, Dmitry; Clerbaux, Cathy

2014-04-01

Forecasting the dispersal of volcanic clouds during an eruption is of primary importance, especially for ensuring aviation safety. As volcanic emissions are characterized by rapid variations of emission rate and height, the (generally) high level of uncertainty in the emission parameters represents a critical issue that limits the robustness of volcanic cloud dispersal forecasts. An inverse modeling scheme, combining satellite observations of the volcanic cloud with a regional chemistry-transport model, allows reconstructing this source term at high temporal resolution. We demonstrate here how a progressive assimilation of freshly acquired satellite observations, via such an inverse modeling procedure, allows for delivering robust sulfur dioxide (SO2) cloud dispersal forecasts during the eruption. This approach provides a computationally cheap estimate of the expected location and mass loading of volcanic clouds, including the identification of SO2-rich parts.

FAA Aviation Forecasts Fiscal Years 1988-1999.

DTIC Science & Technology

1988-02-01

in the 48 contiguous States, Hawaii, Puerto Rico, and the U.S. Virgin Islands. Excluded fromn the data base is activity in Alaska, other U.S...passengerl miles increased b%-’"’. 17.2 percent. Traffic in Hawaii, Puerto Rico, and the U.S. Virgin Islands, ., Ihio wevr, haid slower growth with passenger...trip length for Hawaii/Puerto Rico/ Virgin Islands is expected to remain constant at 98.0 miles over the forecast period. The average industry load

1992 five year battery forecast

DOE Office of Scientific and Technical Information (OSTI.GOV)

Amistadi, D.

1992-12-01

Five-year trends for automotive and industrial batteries are projected. Topic covered include: SLI shipments; lead consumption; automotive batteries (5-year annual growth rates); industrial batteries (standby power and motive power); estimated average battery life by area/country for 1989; US motor vehicle registrations; replacement battery shipments; potential lead consumption in electric vehicles; BCI recycling rates for lead-acid batteries; US average car/light truck battery life; channels of distribution; replacement battery inventory end July; 2nd US battery shipment forecast.

Exploring the calibration of a wind forecast ensemble for energy applications

NASA Astrophysics Data System (ADS)

Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

2015-04-01

In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw ensemble to the calibrated ensemble. The calibrated wind forecasts are evaluated first with univariate probabilistic scores and additionally with diagnostics of wind ramps in order to assess the time-consistency of the calibrated ensemble members.

A Wind Forecasting System for Energy Application

NASA Astrophysics Data System (ADS)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2010-05-01

Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.

2003 Pacific Northwest Loads and Resources Study.

DOE Office of Scientific and Technical Information (OSTI.GOV)

United States. Bonneville Power Administration.

2003-12-01

The Pacific Northwest Loads and Resources Study (White Book), which is published annually by the Bonneville Power Administration (BPA), establishes one of the planning bases for supplying electricity to customers. The White Book contains projections of regional and Federal system load and resource capabilities, along with relevant definitions and explanations. The White Book also contains information obtained from formalized resource planning reports and data submittals including those from individual utilities, the Northwest Power and Conservation Council (Council), and the Pacific Northwest Utilities Conference Committee (PNUCC). The White Book is not an operational planning guide, nor is it used for determiningmore » BPA revenues, although the database that generates the data for the White Book analysis contributes to the development of BPA's inventory and ratemaking processes. Operation of the Federal Columbia River Power System (FCRPS) is based on a set of criteria different from that used for resource planning decisions. Operational planning is dependent upon real-time or near-term knowledge of system conditions that include expectations of river flows and runoff, market opportunities, availability of reservoir storage, energy exchanges, and other factors affecting the dynamics of operating a power system. In this loads and resources study, resource availability is compared to an expected level of total retail electricity consumption. The forecasted annual energy electricity retail load plus contract obligations are subtracted from the sum of the projected annual energy capability of existing resources and contract purchases to determine whether BPA and/or the region will be surplus or deficit. Surplus energy is available when resources are greater than loads. This energy could be marketed to increase revenues. Deficits occur when resources are less than loads. Energy deficits could be met by any combination of the following: better-than-critical water conditions, demand-side management and conservation programs, permanent loss of a load (i.e., due to economic conditions or closures), additional contract purchases, and/or new generating resources. The loads and resources analysis in this study simulates the operation of the power system under the Pacific Northwest Coordination Agreement (PNCA). The PNCA defines the planning and operation of seventeen U.S. Pacific Northwest utilities and other parties with generating facilities within the region's hydroelectric (hydro) system. The hydroregulation study used for the 2003 White Book incorporates measures from the National Oceanographic and Atmospheric Administration Fisheries (NOAA Fisheries) Biological Opinion dated December 2000, and the U.S. Fish and Wildlife Service's 2000 Biological Opinion (2000 FCRPS BiOps) for the Snake River and Columbia River projects. These measures include: (1) Increased flow augmentation for juvenile fish migrations in the Snake and Columbia rivers in the spring and summer; (2) Mandatory spill requirements at the Lower Snake and Columbia dams to provide for non-turbine passage routes for juvenile fish migrants; and (3) Additional flows for Kootenai River white sturgeon in the spring. The hydroregulation criteria for this analysis includes: an updated Detailed Operation Plan for Treaty reservoirs for Operating Year (OY) 2004, updated PNCA planning criteria for OY 2003, and revised juvenile fish bypass spill levels for 2000 FCRPS BiOps implementation. The 2003 White Book is presented in two documents: (1) this summary document of Federal system and PNW region loads and resources, and (2) a technical appendix which presents regional loads, grouped by major PNW utility categories, and detailed contract and resource information. The technical appendix is available only in electronic form. Individual customer information regarding marketer contracts is not detailed due to confidentiality agreements. The 2003 White Book analysis updates the December 2002 White Book. This analysis projects the yearly average energy consumption and resource availability for the study period, OY 2005 through 2014. The study shows the Federal system's and the region's expected monthly peak demand, monthly energy demand, monthly peak generating capability, and monthly energy generation for OY 2005, 2009, and 2014. The Federal system and regional monthly capacity surplus/deficit projections are summarized for the 10 operating years of the study period. This document analyzes the PNW's projected loads and available generating resources in two parts: (1) the loads and resources of the Federal system, for which BPA is the marketing agency; and (2) the larger PNW regional power system loads and resources that include the Federal system as well other PNW entities.« less

2004 Pacific Northwest Loads and Resources Study.

DOE Office of Scientific and Technical Information (OSTI.GOV)

United States. Bonneville Power Administration.

2004-12-01

The Pacific Northwest Loads and Resources Study (White Book), which is published annually by the Bonneville Power Administration (BPA), establishes one of the planning bases for supplying electricity to customers. The White Book contains projections of regional and Federal system load and resource capabilities, along with relevant definitions and explanations. The White Book also contains information obtained from formalized resource planning reports and data submittals including those from individual utilities, the Northwest Power and Conservation Council (Council), and the Pacific Northwest Utilities Conference Committee (PNUCC). The White Book is not an operational planning guide, nor is it used for determiningmore » BPA revenues, although the database that generates the data for the White Book analysis contributes to the development of BPA's inventory and ratemaking processes. Operation of the Federal Columbia River Power System (FCRPS) is based on a set of criteria different from that used for resource planning decisions. Operational planning is dependent upon real-time or near-term knowledge of system conditions that include expectations of river flows and runoff, market opportunities, availability of reservoir storage, energy exchanges, and other factors affecting the dynamics of operating a power system. The load resource balance of BPA and/or the region is determined by comparing resource availability to an expected level of total retail electricity consumption. Resources include projected energy capability plus contract purchases. Loads include a forecast of retail obligations plus contract obligations. Surplus energy is available when resources are greater than loads. This energy could be marketed to increase revenues. Energy deficits occur when resources are less than loads. These deficits could be met by any combination of the following: better-than-critical water conditions, demand-side management and conservation programs, permanent loss of loads due to economic conditions or closures, additional contract purchases, and/or the addition of new generating resources. The loads and resources analysis in this study simulates the operation of the power system under the current Pacific Northwest Coordination Agreement (PNCA). The PNCA defines the planning and operation of seventeen U.S. Pacific Northwest utilities and other parties with generating facilities within the region's hydroelectric (hydro) system. The hydroregulation study used for the 2004 White Book incorporates measures from the National Oceanographic and Atmospheric Administration Fisheries (NOAA Fisheries) Biological Opinion dated December 2000, and the U.S. Fish and Wildlife Service's 2000 Biological Opinion (2000 FCRPS BiOps) for the Snake River and Columbia River projects. These measures include: (1) Increased flow augmentation for juvenile fish migrations in the Snake and Columbia rivers in the spring and summer; (2) Mandatory spill requirements at the Lower Snake and Columbia dams to provide for non-turbine passage routes for juvenile fish migrants; and (3) Additional flows for Kootenai River white sturgeon in the spring; The hydroregulation criteria for this analysis includes the following: (1) Detailed Operation Plan operation for Treaty reservoirs for Operating Year (OY) 2004; (2) PNCA planning criteria for OY 2004; and (3) Juvenile fish bypass spill levels for 2000 FCRPS BiOps implementation. The 2004 White Book is presented in two documents: (1) this summary document of Federal system and PNW region loads and resources, and (2) a technical appendix which presents regional loads, grouped by major PNW utility categories, and detailed contract and resource information. The technical appendix is available only in electronic form. Individual customer information for marketer contracts is not detailed due to confidentiality agreements. The 2004 White Book analysis updates the 2003 White Book. This analysis projects the yearly average energy consumption and resource availability for the study period, OY 2006 through 2015. The study shows the Federal system's and the region's expected monthly peak demand, monthly energy demand, monthly peak generating capability, and monthly energy generation for OY 2006, 2010, and 2015. The Federal system and regional monthly capacity surplus/deficit projections are summarized for the 10 operating years of the study period. This document analyzes the PNW's projected loads and available generating resources in two parts: (1) the loads and resources of the Federal system, for which BPA is the marketing agency; and (2) the larger PNW regional power system loads and resources that include the Federal system as well other PNW entities.« less

Load power device, system and method of load control and management employing load identification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Yi; Luebke, Charles John; Schoepf, Thomas J.

A load power device includes a power input, at least one power output for at least one load, a plurality of sensors structured to sense voltage and current at the at least one power output, and a processor. The processor provides: (a) load identification based upon the sensed voltage and current, and (b) load control and management based upon the load identification.

Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications.

PubMed

Sabry, A H; W Hasan, W Z; Ab Kadir, M Z A; Radzi, M A M; Shafie, S

2018-01-01

The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.

Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications

PubMed Central

W. Hasan, W. Z.

2018-01-01

The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system’s modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model. PMID:29351554

Reduced-Order Models for Load Management in the Power Grid

NASA Astrophysics Data System (ADS)

Alizadeh, Mahnoosh

In recent years, considerable research efforts have been directed towards designing control schemes that can leverage the inherent flexibility of electricity demand that is not tapped into in today's electricity markets. It is expected that these control schemes will be carried out by for-profit entities referred to as aggregators that operate at the edge of the power grid network. While the aggregator control problem is receiving much attention, more high-level questions of how these aggregators should plan their market participation, interact with the main grid and with each other, remain rather understudied. Answering these questions requires a large-scale model for the aggregate flexibility that can be harnessed from the a population of customers, particularly for residences and small businesses. The contribution of this thesis towards this goal is divided into three parts: In Chapter 3, a reduced-order model for a large population of heterogeneous appliances is provided by clustering load profiles that share similar degrees of freedom together. The use of such reduced-order model for system planning and optimal market decision making requires a foresighted approximation of the number of appliances that will join each cluster. Thus, Chapter 4 provides a systematic framework to generate such forecasts for the case of Electric Vehicles, based on real-world battery charging data. While these two chapters set aside the economic side that is naturally involved with participation in demand response programs and mainly focus on the control problem, Chapter 5 is dedicated to the study of optimal pricing mechanisms in order to recruit heterogeneous customers in a demand response program in which an aggregator can directly manage their appliances' load under their specified preferences. Prices are proportional to the wholesale market savings that can result from each recruitment event.

Quantitative impact of aerosols on numerical weather prediction. Part II: Impacts to IR radiance assimilation

NASA Astrophysics Data System (ADS)

Marquis, J. W.; Campbell, J. R.; Oyola, M. I.; Ruston, B. C.; Zhang, J.

2017-12-01

This is part II of a two-part series examining the impacts of aerosol particles on weather forecasts. In this study, the aerosol indirect effects on weather forecasts are explored by examining the temperature and moisture analysis associated with assimilating dust contaminated hyperspectral infrared radiances. The dust induced temperature and moisture biases are quantified for different aerosol vertical distribution and loading scenarios. The overall impacts of dust contamination on temperature and moisture forecasts are quantified over the west coast of Africa, with the assistance of aerosol retrievals from AERONET, MPL, and CALIOP. At last, methods for improving hyperspectral infrared data assimilation in dust contaminated regions are proposed.

7 CFR 1710.152 - Primary support documents.

Code of Federal Regulations, 2010 CFR

2010-01-01

... capital investments required to serve a borrower's planned new loads, improve service reliability and... manager to guide the system toward its financial goals. The forecast submitted in support of a loan...

Probabilistic Weather Information Tailored to the Needs of Transmission System Operators

NASA Astrophysics Data System (ADS)

Alberts, I.; Stauch, V.; Lee, D.; Hagedorn, R.

2014-12-01

Reliable and accurate forecasts for wind and photovoltaic (PV) power production are essential for stable transmission systems. A high potential for improving the wind and PV power forecasts lies in optimizing the weather forecasts, since these energy sources are highly weather dependent. For this reason the main objective of the German research project EWeLiNE is to improve the quality the underlying numerical weather predictions towards energy operations. In this project, the German Meteorological Service (DWD), the Fraunhofer Institute for Wind Energy and Energy System Technology, and three of the German transmission system operators (TSOs) are working together to improve the weather and power forecasts. Probabilistic predictions are of particular interest, as the quantification of uncertainties provides an important tool for risk management. Theoretical considerations suggest that it can be advantageous to use probabilistic information to represent and respond to the remaining uncertainties in the forecasts. However, it remains a challenge to integrate this information into the decision making processes related to market participation and power systems operations. The project is planned and carried out in close cooperation with the involved TSOs in order to ensure the usability of the products developed. It will conclude with a demonstration phase, in which the improved models and newly developed products are combined into a process chain and used to provide information to TSOs in a real-time decision support tool. The use of a web-based development platform enables short development cycles and agile adaptation to evolving user needs. This contribution will present the EWeLiNE project and discuss ideas on how to incorporate probabilistic information into the users' current decision making processes.

Uncertainties in Forecasting Streamflow using Entropy Theory

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2017-12-01

Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.

Using Flow Charts to Visualize the Decision-Making Process in Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Aung, M. T. Y.; Myat, T.; Zheng, Y.; Mays, M. L.; Ngwira, C.; Damas, M. C.

2016-12-01

Our society today relies heavily on technological systems such as satellites, navigation systems, power grids and aviation. These systems are very sensitive to space weather disturbances. When Earth-directed space weather driven by the Sun arrives at the Earth, it causes changes to the Earth's radiation environment and the magnetosphere. Strong disturbances in the magnetosphere of the Earth are responsible for geomagnetic storms that can last from hours to days depending on strength of storms. Geomagnetic storms can severely impact critical infrastructure on Earth, such as the electric power grid, and Solar Energetic Particles that can endanger life in outer space. How can we lessen these adverse effects? They can be lessened through the early warning signals sent by space weather forecasters before CME or high-speed stream arrives. A space weather forecaster's duty is to send predicted notifications to high-tech industries and NASA missions so that they could take extra measures for protection. NASA space weather forecasters make prediction decisions by following certain steps and processes from the time an event occurs at the sun all the way to the impact locations. However, there has never been a tool that helps these forecasters visualize the decision process until now. A flow chart is created to help forecasters visualize the decision process. This flow chart provides basic knowledge of space weather and can be used to train future space weather forecasters. It also helps to cut down the training period and increase consistency in forecasting. The flow chart is also a great reference for people who are already familiar with space weather.

Operating health analysis of electric power systems

NASA Astrophysics Data System (ADS)

Fotuhi-Firuzabad, Mahmud

The required level of operating reserve to be maintained by an electric power system can be determined using both deterministic and probabilistic techniques. Despite the obvious disadvantages of deterministic approaches there is still considerable reluctance to apply probabilistic techniques due to the difficulty of interpreting a single numerical risk index and the lack of sufficient information provided by a single index. A practical way to overcome difficulties is to embed deterministic considerations in the probabilistic indices in order to monitor the system well-being. The system well-being can be designated as healthy, marginal and at risk. The concept of system well-being is examined and extended in this thesis to cover the overall area of operating reserve assessment. Operating reserve evaluation involves the two distinctly different aspects of unit commitment and the dispatch of the committed units. Unit commitment health analysis involves the determination of which unit should be committed to satisfy the operating criteria. The concepts developed for unit commitment health, margin and risk are extended in this thesis to evaluate the response well-being of a generating system. A procedure is presented to determine the optimum dispatch of the committed units to satisfy the response criteria. The impact on the response wellbeing being of variations in the margin time, required regulating margin and load forecast uncertainty are illustrated. The effects on the response well-being of rapid start units, interruptible loads and postponable outages are also illustrated. System well-being is, in general, greatly improved by interconnection with other power systems. The well-being concepts are extended to evaluate the spinning reserve requirements in interconnected systems. The interconnected system unit commitment problem is decomposed into two subproblems in which unit scheduling is performed in each isolated system followed by interconnected system evaluation. A procedure is illustrated to determine the well-being indices of the overall interconnected system. Under normal operating conditions, the system may also be able to carry a limited amount of interruptible load on top of its firm load without violating the operating criterion. An energy based approach is presented to determine the optimum interruptible load carrying capability in both the isolated and interconnected systems. Composite system spinning reserve assessment and composite system well-being are also examined in this research work. The impacts on the composite well-being of operating reserve considerations such as stand-by units, interruptible loads and the physical locations of these resources are illustrated. It is expected that the well-being framework and the concepts developed in this research work will prove extremely useful in the new competitive utility environment.

Load controller and method to enhance effective capacity of a photovoltaic power supply

DOEpatents

Perez, Richard

2000-01-01

A load controller and method are provided for maximizing effective capacity of a non-controllable, renewable power supply coupled to a variable electrical load also coupled to a conventional power grid. Effective capacity is enhanced by monitoring power output of the renewable supply and loading, and comparing the loading against the power output and a load adjustment threshold determined from an expected peak loading. A value for a load adjustment parameter is calculated by subtracting the renewable supply output and the load adjustment parameter from the current load. This value is then employed to control the variable load in an amount proportional to the value of the load control parameter when the parameter is within a predefined range. By so controlling the load, the effective capacity of the non-controllable, renewable power supply is increased without any attempt at operational feedback control of the renewable supply. The renewable supply may comprise, for example, a photovoltaic power supply or a wind-based power supply.

The Use of Artificial Neural Networks for Forecasting the Electric Demand of Stand-Alone Consumers

NASA Astrophysics Data System (ADS)

Ivanin, O. A.; Direktor, L. B.

2018-05-01

The problem of short-term forecasting of electric power demand of stand-alone consumers (small inhabited localities) situated outside centralized power supply areas is considered. The basic approaches to modeling the electric power demand depending on the forecasting time frame and the problems set, as well as the specific features of such modeling, are described. The advantages and disadvantages of the methods used for the short-term forecast of the electric demand are indicated, and difficulties involved in the solution of the problem are outlined. The basic principles of arranging artificial neural networks are set forth; it is also shown that the proposed method is preferable when the input information necessary for prediction is lacking or incomplete. The selection of the parameters that should be included into the list of the input data for modeling the electric power demand of residential areas using artificial neural networks is validated. The structure of a neural network is proposed for solving the problem of modeling the electric power demand of residential areas. The specific features of generation of the training dataset are outlined. The results of test modeling of daily electric demand curves for some settlements of Kamchatka and Yakutia based on known actual electric demand curves are provided. The reliability of the test modeling has been validated. A high value of the deviation of the modeled curve from the reference curve obtained in one of the four reference calculations is explained. The input data and the predicted power demand curves for the rural settlement of Kuokuiskii Nasleg are provided. The power demand curves were modeled for four characteristic days of the year, and they can be used in the future for designing a power supply system for the settlement. To enhance the accuracy of the method, a series of measures based on specific features of a neural network's functioning are proposed.

Interactive Forecasting with the National Weather Service River Forecast System

NASA Technical Reports Server (NTRS)

Smith, George F.; Page, Donna

1993-01-01

The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

Towards the intrahour forecasting of direct normal irradiance using sky-imaging data.

PubMed

Nou, Julien; Chauvin, Rémi; Eynard, Julien; Thil, Stéphane; Grieu, Stéphane

2018-04-01

Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon [Formula: see text] is up to [Formula: see text] ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model (Ineichen and Perez, 2002) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models - either a persistence of DNI or a persistence of the clear-sky index - are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about [Formula: see text], with [Formula: see text], and [Formula: see text], with [Formula: see text].

7 CFR 1710.303 - Power cost studies-power supply borrowers.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 11 2011-01-01 2011-01-01 false Power cost studies-power supply borrowers. 1710.303... AND GUARANTEES Long-Range Financial Forecasts § 1710.303 Power cost studies—power supply borrowers. (a... facilities shall be supported by a power cost study to demonstrate that the proposed generation and...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Youssef, Tarek A.; El Hariri, Mohamad; Elsayed, Ahmed T.

The smart grid is seen as a power system with realtime communication and control capabilities between the consumer and the utility. This modern platform facilitates the optimization in energy usage based on several factors including environmental, price preferences, and system technical issues. In this paper a real-time energy management system (EMS) for microgrids or nanogrids was developed. The developed system involves an online optimization scheme to adapt its parameters based on previous, current, and forecasted future system states. The communication requirements for all EMS modules were analyzed and are all integrated over a data distribution service (DDS) Ethernet network withmore » appropriate quality of service (QoS) profiles. In conclusion, the developed EMS was emulated with actual residential energy consumption and irradiance data from Miami, Florida and proved its effectiveness in reducing consumers’ bills and achieving flat peak load profiles.« less

Advanced decision modeling for real time variable tolling : development and testing of a data collection platform.

DOT National Transportation Integrated Search

2012-06-01

Our current ability to forecast demand on tolled facilities has not kept pace with advances in decision sciences and : technological innovation. The current forecasting methods suffer from lack of descriptive power of actual behavior because : of the...

7 CFR 1710.210 - Waiver of requirements or approval criteria.

Code of Federal Regulations, 2010 CFR

2010-01-01

... SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load Forecasts § 1710.210 Waiver of requirements or approval criteria. For good cause...

7 CFR 1710.210 - Waiver of requirements or approval criteria.

Code of Federal Regulations, 2011 CFR

2011-01-01

... SERVICE, DEPARTMENT OF AGRICULTURE GENERAL AND PRE-LOAN POLICIES AND PROCEDURES COMMON TO ELECTRIC LOANS AND GUARANTEES Load Forecasts § 1710.210 Waiver of requirements or approval criteria. For good cause...

Evaluation of the performance of a meso-scale NWP model to forecast solar irradiance on Reunion Island for photovoltaic power applications

NASA Astrophysics Data System (ADS)

Kalecinski, Natacha; Haeffelin, Martial; Badosa, Jordi; Periard, Christophe

2013-04-01

Solar photovoltaic power is a predominant source of electrical power on Reunion Island, regularly providing near 30% of electrical power demand for a few hours per day. However solar power on Reunion Island is strongly modulated by clouds in small temporal and spatial scales. Today regional regulations require that new solar photovoltaic plants be combined with storage systems to reduce electrical power fluctuations on the grid. Hence cloud and solar irradiance forecasting becomes an important tool to help optimize the operation of new solar photovoltaic plants on Reunion Island. Reunion Island, located in the South West of the Indian Ocean, is exposed to persistent trade winds, most of all in winter. In summer, the southward motion of the ITCZ brings atmospheric instabilities on the island and weakens trade winds. This context together with the complex topography of Reunion Island, which is about 60 km wide, with two high summits (3070 and 2512 m) connected by a 1500 m plateau, makes cloudiness very heterogeneous. High cloudiness variability is found between mountain and coastal areas and between the windward, leeward and lateral regions defined with respect to the synoptic wind direction. A detailed study of local dynamics variability is necessary to better understand cloud life cycles around the island. In the presented work, our approach to explore the short-term solar irradiance forecast at local scales is to use the deterministic output from a meso-scale numerical weather prediction (NWP) model, AROME, developed by Meteo France. To start we evaluate the performance of the deterministic forecast from AROME by using meteorological measurements from 21 meteorological ground stations widely spread around the island (and with altitudes from 8 to 2245 m). Ground measurements include solar irradiation, wind speed and direction, relative humidity, air temperature, precipitation and pressure. Secondly we study in the model the local dynamics and thermodynamics that control cloud development and solar irradiance in order to define new predictors to improve probabilistic forecast of solar irradiance.

Forecasting stock market volatility: Do realized skewness and kurtosis help?

NASA Astrophysics Data System (ADS)

Mei, Dexiang; Liu, Jing; Ma, Feng; Chen, Wang

2017-09-01

In this study, we investigate the predictability of the realized skewness (RSK) and realized kurtosis (RKU) to stock market volatility, that has not been addressed in the existing studies. Out-of-sample results show that RSK, which can significantly improve forecast accuracy in mid- and long-term, is more powerful than RKU in forecasting volatility. Whereas these variables are useless in short-term forecasting. Furthermore, we employ the realized kernel (RK) for the robustness analysis and the conclusions are consistent with the RV measures. Our results are of great importance for portfolio allocation and financial risk management.

Documentation of volume 3 of the 1978 Energy Information Administration annual report to congress

NASA Astrophysics Data System (ADS)

1980-02-01

In a preliminary overview of the projection process, the relationship between energy prices, supply, and demand is addressed. Topics treated in detail include a description of energy economic interactions, assumptions regarding world oil prices, and energy modeling in the long term beyond 1995. Subsequent sections present the general approach and methodology underlying the forecasts, and define and describe the alternative projection series and their associated assumptions. Short term forecasting, midterm forecasting, long term forecasting of petroleum, coal, and gas supplies are included. The role of nuclear power as an energy source is also discussed.

The ecological forecast horizon, and examples of its uses and determinants

PubMed Central

Petchey, Owen L; Pontarp, Mikael; Massie, Thomas M; Kéfi, Sonia; Ozgul, Arpat; Weilenmann, Maja; Palamara, Gian Marco; Altermatt, Florian; Matthews, Blake; Levine, Jonathan M; Childs, Dylan Z; McGill, Brian J; Schaepman, Michael E; Schmid, Bernhard; Spaak, Piet; Beckerman, Andrew P; Pennekamp, Frank; Pearse, Ian S; Vasseur, David

2015-01-01

Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental variation, demographic stochasticity and evolution), level of ecological organisation (e.g. population or community), and organismal properties (e.g. body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development. PMID:25960188

Short-Term Global Horizontal Irradiance Forecasting Based on Sky Imaging and Pattern Recognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Feng, Cong; Cui, Mingjian

Accurate short-term forecasting is crucial for solar integration in the power grid. In this paper, a classification forecasting framework based on pattern recognition is developed for 1-hour-ahead global horizontal irradiance (GHI) forecasting. Three sets of models in the forecasting framework are trained by the data partitioned from the preprocessing analysis. The first two sets of models forecast GHI for the first four daylight hours of each day. Then the GHI values in the remaining hours are forecasted by an optimal machine learning model determined based on a weather pattern classification model in the third model set. The weather pattern ismore » determined by a support vector machine (SVM) classifier. The developed framework is validated by the GHI and sky imaging data from the National Renewable Energy Laboratory (NREL). Results show that the developed short-term forecasting framework outperforms the persistence benchmark by 16% in terms of the normalized mean absolute error and 25% in terms of the normalized root mean square error.« less

Restrictive loads powered by separate or by common electrical sources

NASA Technical Reports Server (NTRS)

Appelbaum, J.

1989-01-01

In designing a multiple load electrical system, the designer may wish to compare the performance of two setups: a common electrical source powering all loads, or separate electrical sources powering individual loads. Three types of electrical sources: an ideal voltage source, an ideal current source, and solar cell source powering resistive loads were analyzed for their performances in separate and common source systems. A mathematical proof is given, for each case, indicating the merit of the separate or common source system. The main conclusions are: (1) identical resistive loads powered by ideal voltage sources perform the same in both system setups, (2) nonidentical resistive loads powered by ideal voltage sources perform the same in both system setups, (3) nonidentical resistive loads powered by ideal current sources have higher performance in separate source systems, and (4) nonidentical resistive loads powered by solar cells have higher performance in a common source system for a wide range of load resistances.

Improved Modeling Tools Development for High Penetration Solar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Washom, Byron; Meagher, Kevin

2014-12-11

One of the significant objectives of the High Penetration solar research is to help the DOE understand, anticipate, and minimize grid operation impacts as more solar resources are added to the electric power system. For Task 2.2, an effective, reliable approach to predicting solar energy availability for energy generation forecasts using the University of California, San Diego (UCSD) Sky Imager technology has been demonstrated. Granular cloud and ramp forecasts for the next 5 to 20 minutes over an area of 10 square miles were developed. Sky images taken every 30 seconds are processed to determine cloud locations and cloud motionmore » vectors yielding future cloud shadow locations respective to distributed generation or utility solar power plants in the area. The performance of the method depends on cloud characteristics. On days with more advective cloud conditions, the developed method outperforms persistence forecasts by up to 30% (based on mean absolute error). On days with dynamic conditions, the method performs worse than persistence. Sky Imagers hold promise for ramp forecasting and ramp mitigation in conjunction with inverter controls and energy storage. The pre-commercial Sky Imager solar forecasting algorithm was documented with licensing information and was a Sunshot website highlight.« less

Dynamic linear models to explore time-varying suspended sediment-discharge rating curves

NASA Astrophysics Data System (ADS)

Ahn, Kuk-Hyun; Yellen, Brian; Steinschneider, Scott

2017-06-01

This study presents a new method to examine long-term dynamics in sediment yield using time-varying sediment-discharge rating curves. Dynamic linear models (DLMs) are introduced as a time series filter that can assess how the relationship between streamflow and sediment concentration or load changes over time in response to a wide variety of natural and anthropogenic watershed disturbances or long-term changes. The filter operates by updating parameter values using a recursive Bayesian design that responds to 1 day-ahead forecast errors while also accounting for observational noise. The estimated time series of rating curve parameters can then be used to diagnose multiscale (daily-decadal) variability in sediment yield after accounting for fluctuations in streamflow. The technique is applied in a case study examining changes in turbidity load, a proxy for sediment load, in the Esopus Creek watershed, part of the New York City drinking water supply system. The results show that turbidity load exhibits a complex array of variability across time scales. The DLM highlights flood event-driven positive hysteresis, where turbidity load remained elevated for months after large flood events, as a major component of dynamic behavior in the rating curve relationship. The DLM also produces more accurate 1 day-ahead loading forecasts compared to other static and time-varying rating curve methods. The results suggest that DLMs provide a useful tool for diagnosing changes in sediment-discharge relationships over time and may help identify variability in sediment concentrations and loads that can be used to inform dynamic water quality management.

Load controller and method to enhance effective capacity of a photovoltaic power supply using a dynamically determined expected peak loading

DOEpatents

Perez, Richard

2005-05-03

A load controller and method are provided for maximizing effective capacity of a non-controllable, renewable power supply coupled to a variable electrical load also coupled to a conventional power grid. Effective capacity is enhanced by monitoring power output of the renewable supply and loading, and comparing the loading against the power output and a load adjustment threshold determined from an expected peak loading. A value for a load adjustment parameter is calculated by subtracting the renewable supply output and the load adjustment parameter from the current load. This value is then employed to control the variable load in an amount proportional to the value of the load control parameter when the parameter is within a predefined range. By so controlling the load, the effective capacity of the non-controllable, renewable power supply is increased without any attempt at operational feedback control of the renewable supply.

A Numerical Simulation (Study) of a Strong West Coast December 2014 Winter Storm

NASA Astrophysics Data System (ADS)

Smelser, I.; Xu, L.; Amerault, C. M.; Baker, N. L.; Satterfield, E.; Chua, B.

2016-12-01

From December 10 through December 13, 2014, a powerful winter storm swept across the western US coastal states bringing widespread power outages, numerous downed trees and power lines, heavy rains, flooding and even a tornado in the Los Angeles basin. This windstorm was the strongest since October 2009, and was similar to classic wind storms such as the 1962 Columbus Day Storm (Read, 2015).The storm started developing over the Pacific Ocean north of Hawaii on Nov. 30, and formed an atmospheric river that eventually stretched from Hawaii to the west coast. The storm initially hit the Pacific Northwest on Dec. 9th and then split. The highest precipitation amounts started in British Colombia and moved south along the coast. By the Dec. 11th, the highest precipitation amounts were near San Francisco (CA). The peak wind gust (14.4 ms-1) for Monterey (CA) occurred at 1116Z on Dec. 11th while the heaviest 6-hr precipitation (42.9 mm) occurred between 18Z on Dec. 11th to 00Z on Dec. 12th. By Dec. 12th, the storm was centered over Southern California.This storm was poorly forecast by many operational NWP models even 2-3 days in advance (Mass, 2014). The NCEP Global Forecast System (GFS) showed considerably variability between successive model runs, and significant differences existed between Environment Canada, UK Met Office and ECMWF model forecasts. To study this extreme weather event, we used the Navy global (NAVGEM) and mesoscale (COAMPS®) NWP models, and compared the resulting forecasts to observations, satellite imagery and ECMWF (TIGGE) forecasts. NAVGEM, with Hybrid 4DVar, was run with a resolution of 31 km, and generated the boundary conditions for COAMPS® 4DVar and forecasts, that were run with triple-nested grids of 27, 9, and 3 km. The MesoWest data from the University of Utah were used for forecast verification, and to locate the times of highest precipitation and wind speed for different points along the coast. Both the online API and the python module were used to access and pull information from the data base. Overall, both NAVGEM and COAMPS® predicted the storm well. NAVGEM predicted the storm to be slower and more powerful than the analyses. The NAVGEM analysis and corresponding 5-day forecast accumulated 6-hr precipitation (Fig. 1) for Dec. 12th at 00Z agree well with the observed precipitation (4.29 cm) for Monterey (KMRY).

Fitting and forecasting coupled dark energy in the non-linear regime

DOE Office of Scientific and Technical Information (OSTI.GOV)

Casas, Santiago; Amendola, Luca; Pettorino, Valeria

2016-01-01

We consider cosmological models in which dark matter feels a fifth force mediated by the dark energy scalar field, also known as coupled dark energy. Our interest resides in estimating forecasts for future surveys like Euclid when we take into account non-linear effects, relying on new fitting functions that reproduce the non-linear matter power spectrum obtained from N-body simulations. We obtain fitting functions for models in which the dark matter-dark energy coupling is constant. Their validity is demonstrated for all available simulations in the redshift range 0z=–1.6 and wave modes below 0k=1 h/Mpc. These fitting formulas can be used tomore » test the predictions of the model in the non-linear regime without the need for additional computing-intensive N-body simulations. We then use these fitting functions to perform forecasts on the constraining power that future galaxy-redshift surveys like Euclid will have on the coupling parameter, using the Fisher matrix method for galaxy clustering (GC) and weak lensing (WL). We find that by using information in the non-linear power spectrum, and combining the GC and WL probes, we can constrain the dark matter-dark energy coupling constant squared, β{sup 2}, with precision smaller than 4% and all other cosmological parameters better than 1%, which is a considerable improvement of more than an order of magnitude compared to corresponding linear power spectrum forecasts with the same survey specifications.« less

Systems modeling and analysis for Saudi Arabian electric power requirements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Al-Mohawes, N.A.

This thesis addresses the long-range generation planning problem in Saudi Arabia up to the year 2000. The first part presents various models for electric energy consumption in the residential and industrial sectors. These models can be used by the decision makers for the purposes of policy analysis, evaluation, and forecasting. Forecasts of energy in each sector are obtained from two different models for each sector. These models are based on two forecasting techniques: (1) Hybrid econometric/time series model. The idea of adaptive smoothing was utilized to produce forecasts under several scenarios. (2) Box-Jenkins time series technique. Box-Jenkins models and forecastsmore » are developed for the monthly number of electric consumers and the monthly energy consumption per consumer. The results obtained indicate that high energy consumption is expected during the coming two decades which necessitate serious energy assessment and optimization. Optimization of a mix of energy sources was considered using the group multiattribute utility (MAU) function. The results of MAU for three classes of decision makers (managerial, technical, and consumers) are developed through personal interactions. The computer package WASP was also used to develop a tentative optimum plan. According to this plan, four heavy-water nuclear power plants (800 MW) and four light-water nuclear power plants (1200 MW) have to be introduced by the year 2000 in addition to sixteen oil-fired power plants (400 MW) and nine gas turbines (100 MW).« less

Medium-term electric power demand forecasting based on economic-electricity transmission model

NASA Astrophysics Data System (ADS)

Li, Wenfeng; Bao, Fangmin; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Mao, Yubin; Wang, Jiangbo; Liu, Junhui

2018-06-01

Electric demand forecasting is a basic work to ensure the safe operation of power system. Based on the theories of experimental economics and econometrics, this paper introduces Prognoz Platform 7.2 intelligent adaptive modeling platform, and constructs the economic electricity transmission model that considers the economic development scenarios and the dynamic adjustment of industrial structure to predict the region's annual electricity demand, and the accurate prediction of the whole society's electricity consumption is realized. Firstly, based on the theories of experimental economics and econometrics, this dissertation attempts to find the economic indicator variables that drive the most economical growth of electricity consumption and availability, and build an annual regional macroeconomic forecast model that takes into account the dynamic adjustment of industrial structure. Secondly, it innovatively put forward the economic electricity directed conduction theory and constructed the economic power transfer function to realize the group forecast of the primary industry + rural residents living electricity consumption, urban residents living electricity, the second industry electricity consumption, the tertiary industry electricity consumption; By comparing with the actual value of economy and electricity in Henan province in 2016, the validity of EETM model is proved, and the electricity consumption of the whole province from 2017 to 2018 is predicted finally.

Increasing the temporal resolution of direct normal solar irradiance forecasted series

NASA Astrophysics Data System (ADS)

Fernández-Peruchena, Carlos M.; Gastón, Martin; Schroedter-Homscheidt, Marion; Marco, Isabel Martínez; Casado-Rubio, José L.; García-Moya, José Antonio

2017-06-01

A detailed knowledge of the solar resource is a critical point in the design and control of Concentrating Solar Power (CSP) plants. In particular, accurate forecasting of solar irradiance is essential for the efficient operation of solar thermal power plants, the management of energy markets, and the widespread implementation of this technology. Numerical weather prediction (NWP) models are commonly used for solar radiation forecasting. In the ECMWF deterministic forecasting system, all forecast parameters are commercially available worldwide at 3-hourly intervals. Unfortunately, as Direct Normal solar Irradiance (DNI) exhibits a great variability due to the dynamic effects of passing clouds, 3-h time resolution is insufficient for accurate simulations of CSP plants due to their nonlinear response to DNI, governed by various thermal inertias due to their complex response characteristics. DNI series of hourly or sub-hourly frequency resolution are normally used for an accurate modeling and analysis of transient processes in CSP technologies. In this context, the objective of this study is to propose a methodology for generating synthetic DNI time series at 1-h (or higher) temporal resolution from 3-h DNI series. The methodology is based upon patterns as being defined with help of the clear-sky envelope approach together with a forecast of maximum DNI value, and it has been validated with high quality measured DNI data.

Optimization of GM(1,1) power model

NASA Astrophysics Data System (ADS)

Luo, Dang; Sun, Yu-ling; Song, Bo

2013-10-01

GM (1,1) power model is the expansion of traditional GM (1,1) model and Grey Verhulst model. Compared with the traditional models, GM (1,1) power model has the following advantage: The power exponent in the model which best matches the actual data values can be found by certain technology. So, GM (1,1) power model can reflect nonlinear features of the data, simulate and forecast with high accuracy. It's very important to determine the best power exponent during the modeling process. In this paper, according to the GM(1,1) power model of albino equation is Bernoulli equation, through variable substitution, turning it into the GM(1,1) model of the linear albino equation form, and then through the grey differential equation properly built, established GM(1,1) power model, and parameters with pattern search method solution. Finally, we illustrate the effectiveness of the new methods with the example of simulating and forecasting the promotion rates from senior secondary schools to higher education in China.

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

NASA Astrophysics Data System (ADS)

Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

2017-11-01

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

Making the Grid "Smart" Through "Smart" Microgrids: Real-Time Power Management of Microgrids with Multiple Distributed Generation Sources Using Intelligent Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nehrir, M. Hashem

In this Project we collaborated with two DOE National Laboratories, Pacific Northwest National Lab (PNNL) and Lawrence Berkeley National Lab (LBL). Dr. Hammerstrom of PNNL initially supported our project and was on the graduate committee of one of the Ph.D. students (graduated in 2014) who was supported by this project. He is also a committee member of a current graduate student of the PI who was supported by this project in the last two years (August 2014-July 2016). The graduate student is now supported be the Electrical and Computer Engineering (ECE) Department at Montana State University (MSU). Dr. Chris Marneymore » of LBL provided actual load data, and the software WEBOPT developed at LBL for microgrid (MG) design for our project. NEC-Labs America, a private industry, also supported our project, providing expert support and modest financial support. We also used the software “HOMER,” originally developed at the National Renewable Energy Laboratory (NREL) and the most recent version made available to us by HOMER Energy, Inc., for MG (hybrid energy system) unit sizing. We compared the findings from WebOpt and HOMER and designed appropriately sized hybrid systems for our case studies. The objective of the project was to investigate real-time power management strategies for MGs using intelligent control, considering maximum feasible energy sustainability, reliability and efficiency while, minimizing cost and undesired environmental impact (emissions). Through analytic and simulation studies, we evaluated the suitability of several heuristic and artificial-intelligence (AI)-based optimization techniques that had potential for real-time MG power management, including genetic algorithms (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and multi-agent systems (MAS), which is based on the negotiation of smart software-based agents. We found that PSO and MAS, in particular, distributed MAS, were more efficient and better suited for our work. We investigated the following: • Intelligent load control - demand response (DR) - for frequency stabilization in islanded MGs (partially supported by PNNL). • The impact of high penetration of solar photovoltaic (PV)-generated power at the distribution level (partially supported by PNNL). • The application of AI approaches to renewable (wind, PV) power forecasting (proposed by the reviewers of our proposal). • Application of AI approaches and DR for real-time MG power management (partially supported by NEC Labs-America) • Application of DR in dealing with the variability of wind power • Real-time MG power management using DR and storage (partially supported by NEC Labs-America) • Application of DR in enhancing the performance of load-frequency controller • MAS-based whole-sale and retail power market design for smart grid A« less

Research on power source structure optimization for East China Power Grid

NASA Astrophysics Data System (ADS)

Xu, Lingjun; Sang, Da; Zhang, Jianping; Tang, Chunyi; Xu, Da

2017-05-01

The structure of east china power grid is not reasonable for the coal power takes a much higher proportion than hydropower, at present the coal power takes charge of most peak load regulation, and the pressure of peak load regulation cannot be ignored. The nuclear power, wind power, photovoltaic, other clean energy and hydropower, coal power and wind power from outside will be actively developed in future, which increases the pressure of peak load regulation. According to development of economic and social, Load status and load prediction, status quo and planning of power source and the characteristics of power source, the peak load regulation balance is carried out and put forward a reasonable plan of power source allocation. The ultimate aim is to optimize the power source structure and to provide reference for power source allocation in east china.

A High Resolution Tropical Cyclone Power Outage Forecasting Model for the Continental United States

NASA Astrophysics Data System (ADS)

Pino, J. V.; Quiring, S. M.; Guikema, S.; Shashaani, S.; Linger, S.; Backhaus, S.

2017-12-01

Tropical cyclones cause extensive damage to the power infrastructure system throughout the United States. This damage can leave millions without power for extended periods of time, as most recently seen with Hurricane Matthew (2016). Accurate and timely prediction of power outages are essential for utility companies, emergency management agencies, and governmental organizations. Here we present a high-resolution (250 m x 250 m) hurricane power outage model for the United States. The model uses only publicly-available data to make predictions. It uses forecasts of storm variables such as maximum 3-second wind gust, duration of strong winds > 20 m s-2, soil moisture, and precipitation. It also incorporates static environmental variables such as elevation characteristics, land cover type, population density, tree species data, and root zone depth. A web tool was established for use by the Department of Energy (DOE) so that the model can be used for real-time outage forecasting or for synthetic tropical cyclones as an exercise in emergency management. This web tool provides DOE decision-makers with high impact analytic results and products that can be disseminated to federal, local, and state agencies. The results then aid utility companies in their pre- and post-storm activities, thus decreasing restoration times and lowering costs.

46 CFR 112.15-10 - Loads on systems without a temporary emergency power source.

Code of Federal Regulations, 2013 CFR

2013-10-01

...) ELECTRICAL ENGINEERING EMERGENCY LIGHTING AND POWER SYSTEMS Emergency Loads § 112.15-10 Loads on systems without a temporary emergency power source. If there is no temporary emergency power source, the loads... 46 Shipping 4 2013-10-01 2013-10-01 false Loads on systems without a temporary emergency power...

46 CFR 112.15-10 - Loads on systems without a temporary emergency power source.

Code of Federal Regulations, 2014 CFR

2014-10-01

...) ELECTRICAL ENGINEERING EMERGENCY LIGHTING AND POWER SYSTEMS Emergency Loads § 112.15-10 Loads on systems without a temporary emergency power source. If there is no temporary emergency power source, the loads... 46 Shipping 4 2014-10-01 2014-10-01 false Loads on systems without a temporary emergency power...

46 CFR 112.15-10 - Loads on systems without a temporary emergency power source.

Code of Federal Regulations, 2012 CFR

2012-10-01

...) ELECTRICAL ENGINEERING EMERGENCY LIGHTING AND POWER SYSTEMS Emergency Loads § 112.15-10 Loads on systems without a temporary emergency power source. If there is no temporary emergency power source, the loads... 46 Shipping 4 2012-10-01 2012-10-01 false Loads on systems without a temporary emergency power...

46 CFR 112.15-10 - Loads on systems without a temporary emergency power source.

Code of Federal Regulations, 2011 CFR

2011-10-01

...) ELECTRICAL ENGINEERING EMERGENCY LIGHTING AND POWER SYSTEMS Emergency Loads § 112.15-10 Loads on systems without a temporary emergency power source. If there is no temporary emergency power source, the loads... 46 Shipping 4 2011-10-01 2011-10-01 false Loads on systems without a temporary emergency power...

46 CFR 112.15-10 - Loads on systems without a temporary emergency power source.

Code of Federal Regulations, 2010 CFR

2010-10-01

...) ELECTRICAL ENGINEERING EMERGENCY LIGHTING AND POWER SYSTEMS Emergency Loads § 112.15-10 Loads on systems without a temporary emergency power source. If there is no temporary emergency power source, the loads... 46 Shipping 4 2010-10-01 2010-10-01 false Loads on systems without a temporary emergency power...

Load controller and method to enhance effective capacity of a photovotaic power supply using a dynamically determined expected peak loading

DOEpatents

Perez, Richard

2003-04-01

A load controller and method are provided for maximizing effective capacity of a non-controllable, renewable power supply coupled to a variable electrical load also coupled to a conventional power grid. Effective capacity is enhanced by monitoring power output of the renewable supply and loading, and comparing the loading against the power output and a load adjustment threshold determined from an expected peak loading. A value for a load adjustment parameter is calculated by subtracting the renewable supply output and the load adjustment parameter from the current load. This value is then employed to control the variable load in an amount proportional to the value of the load control parameter when the parameter is within a predefined range. By so controlling the load, the effective capacity of the non-controllable, renewable power supply is increased without any attempt at operational feedback control of the renewable supply. The expected peak loading of the variable load can be dynamically determined within a defined time interval with reference to variations in the variable load.

HexSim: A flexible simulation model for forecasting wildlife responses to multiple interacting stressors

EPA Science Inventory

With SERDP funding, we have improved upon a popular life history simulator (PATCH), and in doing so produced a powerful new forecasting tool (HexSim). PATCH, our starting point, was spatially explicit and individual-based, and was useful for evaluating a range of terrestrial lif...

Defining conservation priorities using fragmentation forecasts

Treesearch

David Wear; John Pye; Kurt H. Riitters

2004-01-01

Methods are developed for forecasting the effects of population and economic growth on the distribution of interior forest habitat. An application to the southeastern United States shows that models provide significant explanatory power with regard to the observed distribution of interior forest. Estimates for economic and biophysical variables are significant and...

Load power device and system for real-time execution of hierarchical load identification algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Yi; Madane, Mayura Arun; Zambare, Prachi Suresh

A load power device includes a power input; at least one power output for at least one load; and a plurality of sensors structured to sense voltage and current at the at least one power output. A processor is structured to provide real-time execution of: (a) a plurality of load identification algorithms, and (b) event detection and operating mode detection for the at least one load.

An actual load forecasting methodology by interval grey modeling based on the fractional calculus.

PubMed

Yang, Yang; Xue, Dingyü

2017-07-17

The operation processes for thermal power plant are measured by the real-time data, and a large number of historical interval data can be obtained from the dataset. Within defined periods of time, the interval information could provide important information for decision making and equipment maintenance. Actual load is one of the most important parameters, and the trends hidden in the historical data will show the overall operation status of the equipments. However, based on the interval grey parameter numbers, the modeling and prediction process is more complicated than the one with real numbers. In order not lose any information, the geometric coordinate features are used by the coordinates of area and middle point lines in this paper, which are proved with the same information as the original interval data. The grey prediction model for interval grey number by the fractional-order accumulation calculus is proposed. Compared with integer-order model, the proposed method could have more freedom with better performance for modeling and prediction, which can be widely used in the modeling process and prediction for the small amount interval historical industry sequence samples. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A probabilistic neural network based approach for predicting the output power of wind turbines

NASA Astrophysics Data System (ADS)

Tabatabaei, Sajad

2017-03-01

Finding the authentic predicting tools of eliminating the uncertainty of wind speed forecasts is highly required while wind power sources are strongly penetrating. Recently, traditional predicting models of generating point forecasts have no longer been trustee. Thus, the present paper aims at utilising the concept of prediction intervals (PIs) to assess the uncertainty of wind power generation in power systems. Besides, this paper uses a newly introduced non-parametric approach called lower upper bound estimation (LUBE) to build the PIs since the forecasting errors are unable to be modelled properly by applying distribution probability functions. In the present proposed LUBE method, a PI combination-based fuzzy framework is used to overcome the performance instability of neutral networks (NNs) used in LUBE. In comparison to other methods, this formulation more suitably has satisfied the PI coverage and PI normalised average width (PINAW). Since this non-linear problem has a high complexity, a new heuristic-based optimisation algorithm comprising a novel modification is introduced to solve the aforesaid problems. Based on data sets taken from a wind farm in Australia, the feasibility and satisfying performance of the suggested method have been investigated.

14 CFR 25.363 - Side load on engine and auxiliary power unit mounts.

Code of Federal Regulations, 2011 CFR

2011-01-01

... § 25.363 Side load on engine and auxiliary power unit mounts. (a) Each engine and auxiliary power unit... the side load on the engine and auxiliary power unit mount, at least equal to the maximum load factor... 14 Aeronautics and Space 1 2011-01-01 2011-01-01 false Side load on engine and auxiliary power...

14 CFR 25.363 - Side load on engine and auxiliary power unit mounts.

Code of Federal Regulations, 2010 CFR

2010-01-01

... § 25.363 Side load on engine and auxiliary power unit mounts. (a) Each engine and auxiliary power unit... the side load on the engine and auxiliary power unit mount, at least equal to the maximum load factor... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Side load on engine and auxiliary power...

14 CFR 25.363 - Side load on engine and auxiliary power unit mounts.

Code of Federal Regulations, 2013 CFR

2013-01-01

... § 25.363 Side load on engine and auxiliary power unit mounts. (a) Each engine and auxiliary power unit... the side load on the engine and auxiliary power unit mount, at least equal to the maximum load factor... 14 Aeronautics and Space 1 2013-01-01 2013-01-01 false Side load on engine and auxiliary power...

14 CFR 25.363 - Side load on engine and auxiliary power unit mounts.

Code of Federal Regulations, 2014 CFR

2014-01-01

... § 25.363 Side load on engine and auxiliary power unit mounts. (a) Each engine and auxiliary power unit... the side load on the engine and auxiliary power unit mount, at least equal to the maximum load factor... 14 Aeronautics and Space 1 2014-01-01 2014-01-01 false Side load on engine and auxiliary power...

14 CFR 25.363 - Side load on engine and auxiliary power unit mounts.

Code of Federal Regulations, 2012 CFR

2012-01-01

... § 25.363 Side load on engine and auxiliary power unit mounts. (a) Each engine and auxiliary power unit... the side load on the engine and auxiliary power unit mount, at least equal to the maximum load factor... 14 Aeronautics and Space 1 2012-01-01 2012-01-01 false Side load on engine and auxiliary power...

Ensemble Data Assimilation of Wind and Photovoltaic Power Information in the Convection-permitting High-Resolution Model COSMO-DE

NASA Astrophysics Data System (ADS)

Declair, Stefan; Saint-Drenan, Yves-Marie; Potthast, Roland

2016-04-01

Determining the amount of weather dependent renewable energy is a demanding task for transmission system operators (TSOs) and wind and photovoltaic (PV) prediction errors require the use of reserve power, which generate costs and can - in extreme cases - endanger the security of supply. In the project EWeLiNE funded by the German government, the German Weather Service and the Fraunhofer Institute on Wind Energy and Energy System Technology develop innovative weather- and power forecasting models and tools for grid integration of weather dependent renewable energy. The key part in energy prediction process chains is the numerical weather prediction (NWP) system. Wind speed and irradiation forecast from NWP system are however subject to several sources of error. The quality of the wind power prediction is mainly penalized by forecast error of the NWP model in the planetary boundary layer (PBL), which is characterized by high spatial and temporal fluctuations of the wind speed. For PV power prediction, weaknesses of the NWP model to correctly forecast i.e. low stratus, the absorption of condensed water or aerosol optical depth are the main sources of errors. Inaccurate radiation schemes (i.e. the two-stream parametrization) are also known as a deficit of NWP systems with regard to irradiation forecast. To mitigate errors like these, NWP model data can be corrected by post-processing techniques such as model output statistics and calibration using historical observational data. Additionally, latest observations can be used in a pre-processing technique called data assimilation (DA). In DA, not only the initial fields are provided, but the model is also synchronized with reality - the observations - and hence the model error is reduced in the forecast. Besides conventional observation networks like radiosondes, synoptic observations or air reports of wind, pressure and humidity, the number of observations measuring meteorological information indirectly such as satellite radiances, radar reflectivities or GPS slant delays strongly increases. The numerous wind farm and PV plants installed in Germany potentially represent a dense meteorological network assessing irradiation and wind speed through their power measurements. The accuracy of the NWP data may thus be enhanced by extending the observations in the assimilation by this new source of information. Wind power data can serve as indirect measurements of wind speed at hub height. The impact on the NWP model is potentially interesting since conventional observation network lacks measurements in this part of the PBL. Photovoltaic power plants can provide information on clouds, aerosol optical depth or low stratus in terms of remote sensing: the power output is strongly dependent on perturbations along the slant between sun position and PV panel. Additionally, since the latter kind of data is not limited to the vertical column above or below the detector. It may thus complement satellite data and compensate weaknesses in the radiation scheme. In this contribution, the DA method (Local Ensemble Transform Kalman Filter, LETKF) is shortly sketched. Furthermore, the computation of the model power equivalents is described and first assimilation results are presented and discussed.

Earthquake forecasting during the complex Amatrice-Norcia seismic sequence

PubMed Central

Marzocchi, Warner; Taroni, Matteo; Falcone, Giuseppe

2017-01-01

Earthquake forecasting is the ultimate challenge for seismologists, because it condenses the scientific knowledge about the earthquake occurrence process, and it is an essential component of any sound risk mitigation planning. It is commonly assumed that, in the short term, trustworthy earthquake forecasts are possible only for typical aftershock sequences, where the largest shock is followed by many smaller earthquakes that decay with time according to the Omori power law. We show that the current Italian operational earthquake forecasting system issued statistically reliable and skillful space-time-magnitude forecasts of the largest earthquakes during the complex 2016–2017 Amatrice-Norcia sequence, which is characterized by several bursts of seismicity and a significant deviation from the Omori law. This capability to deliver statistically reliable forecasts is an essential component of any program to assist public decision-makers and citizens in the challenging risk management of complex seismic sequences. PMID:28924610

Earthquake forecasting during the complex Amatrice-Norcia seismic sequence.

PubMed

Marzocchi, Warner; Taroni, Matteo; Falcone, Giuseppe

2017-09-01

Earthquake forecasting is the ultimate challenge for seismologists, because it condenses the scientific knowledge about the earthquake occurrence process, and it is an essential component of any sound risk mitigation planning. It is commonly assumed that, in the short term, trustworthy earthquake forecasts are possible only for typical aftershock sequences, where the largest shock is followed by many smaller earthquakes that decay with time according to the Omori power law. We show that the current Italian operational earthquake forecasting system issued statistically reliable and skillful space-time-magnitude forecasts of the largest earthquakes during the complex 2016-2017 Amatrice-Norcia sequence, which is characterized by several bursts of seismicity and a significant deviation from the Omori law. This capability to deliver statistically reliable forecasts is an essential component of any program to assist public decision-makers and citizens in the challenging risk management of complex seismic sequences.

NCAR's Experimental Real-time Convection-allowing Ensemble Prediction System

NASA Astrophysics Data System (ADS)

Schwartz, C. S.; Romine, G. S.; Sobash, R.; Fossell, K.

2016-12-01

Since April 2015, the National Center for Atmospheric Research's (NCAR's) Mesoscale and Microscale Meteorology (MMM) Laboratory, in collaboration with NCAR's Computational Information Systems Laboratory (CISL), has been producing daily, real-time, 10-member, 48-hr ensemble forecasts with 3-km horizontal grid spacing over the conterminous United States (http://ensemble.ucar.edu). These computationally-intensive, next-generation forecasts are produced on the Yellowstone supercomputer, have been embraced by both amateur and professional weather forecasters, are widely used by NCAR and university researchers, and receive considerable attention on social media. Initial conditions are supplied by NCAR's Data Assimilation Research Testbed (DART) software and the forecast model is NCAR's Weather Research and Forecasting (WRF) model; both WRF and DART are community tools. This presentation will focus on cutting-edge research results leveraging the ensemble dataset, including winter weather predictability, severe weather forecasting, and power outage modeling. Additionally, the unique design of the real-time analysis and forecast system and computational challenges and solutions will be described.

Forecast Method of Solar Irradiance with Just-In-Time Modeling

NASA Astrophysics Data System (ADS)

Suzuki, Takanobu; Goto, Yusuke; Terazono, Takahiro; Wakao, Shinji; Oozeki, Takashi

PV power output mainly depends on the solar irradiance which is affected by various meteorological factors. So, it is required to predict solar irradiance in the future for the efficient operation of PV systems. In this paper, we develop a novel approach for solar irradiance forecast, in which we introduce to combine the black-box model (JIT Modeling) with the physical model (GPV data). We investigate the predictive accuracy of solar irradiance over wide controlled-area of each electric power company by utilizing the measured data on the 44 observation points throughout Japan offered by JMA and the 64 points around Kanto by NEDO. Finally, we propose the application forecast method of solar irradiance to the point which is difficulty in compiling the database. And we consider the influence of different GPV default time on solar irradiance prediction.

Power variables and bilateral force differences during unloaded and loaded squat jumps in high performance alpine ski racers.

PubMed

Patterson, Carson; Raschner, Christian; Platzer, Hans-Peter

2009-05-01

The purpose of this paper was to investigate the power-load relationship and to compare power variables and bilateral force imbalances between sexes with squat jumps. Twenty men and 17 women, all members of the Austrian alpine ski team (junior and European Cup), performed unloaded and loaded (barbell loads equal to 25, 50, 75, and 100% body weight [BW]) squat jumps with free weights using a specially designed spotting system. Ground reaction force records from 2 force platforms were used to calculate relative average power (P), relative average power in the first 100 ms of the jump (P01), relative average power in the first 200 ms of the jump (P02), jump height, percentage of best jump height (%Jump), and maximal force difference between dominant and nondominant leg (Fmaxdiff). The men displayed significantly higher values at all loads for P and jump height (p < 0.05). No significant differences were found in P01. The men had significantly higher P02 at all loads except 75% BW). Maximum P was reached at light loads (men at 25% BW and women at 0% BW), and P decreased uniformly thereafter. Individual power-load curves show a deflection point. It is proposed that the load where the power-load deflection point occurs be used as the power training load and not the load at which maximum P is reached. It is also proposed that loads not be described in %1-repetition maximum (RM), but as %BW. This system can be used to safely assess and train power with loaded jumps and free weights.

Econometrics 101: forecasting demystified

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crow, R.T.

1980-05-01

Forecasting by econometric modeling is described in a commonsense way which omits much of the technical jargon. A trend of continuous growth is no longer an adequate forecasting tool. Today's forecasters must consider rapid changes in price, policies, regulations, capital availability, and the cost of being wrong. A forecasting model is designed by identifying future influences on electricity purchases and quantifying their relationships to each other. A record is produced which can be evaluated and used to make corrections in the models. Residential consumption is used to illustrate how this works and to demonstrate how power consumption is also relatedmore » to the purchase and use of equipment. While models can quantify behavioral relationships, they cannot account for the impacts of non-price factors because of limited data. (DCK)« less

Forecasting seeing and parameters of long-exposure images by means of ARIMA

NASA Astrophysics Data System (ADS)

Kornilov, Matwey V.

2016-02-01

Atmospheric turbulence is the one of the major limiting factors for ground-based astronomical observations. In this paper, the problem of short-term forecasting seeing is discussed. The real data that were obtained by atmospheric optical turbulence (OT) measurements above Mount Shatdzhatmaz in 2007-2013 have been analysed. Linear auto-regressive integrated moving average (ARIMA) models are used for the forecasting. A new procedure for forecasting the image characteristics of direct astronomical observations (central image intensity, full width at half maximum, radius encircling 80 % of the energy) has been proposed. Probability density functions of the forecast of these quantities are 1.5-2 times thinner than the respective unconditional probability density functions. Overall, this study found that the described technique could adequately describe temporal stochastic variations of the OT power.

Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game

NASA Astrophysics Data System (ADS)

Arnal, Louise; Ramos, Maria-Helena; Coughlan de Perez, Erin; Cloke, Hannah Louise; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk Jan; Pappenberger, Florian

2016-08-01

Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecast uncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called "How much are you prepared to pay for a forecast?". The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydro-meteorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.

HexSim: A flexible simulation model for forecasting wildlife responses to multiple interacting stressors - ESRP Meeting

EPA Science Inventory

With SERDP funding, we have improved upon a popular life history simulator (PATCH), and indoing so produced a powerful new forecasting tool (HexSim). PATCH, our starting point, was spatially explicit and individual-based, and was useful for evaluating a range of terrestrial life...

A DDS-Based Energy Management Framework for Small Microgrid Operation and Control

DOE PAGES

Youssef, Tarek A.; El Hariri, Mohamad; Elsayed, Ahmed T.; ...

2017-09-26

The smart grid is seen as a power system with realtime communication and control capabilities between the consumer and the utility. This modern platform facilitates the optimization in energy usage based on several factors including environmental, price preferences, and system technical issues. In this paper a real-time energy management system (EMS) for microgrids or nanogrids was developed. The developed system involves an online optimization scheme to adapt its parameters based on previous, current, and forecasted future system states. The communication requirements for all EMS modules were analyzed and are all integrated over a data distribution service (DDS) Ethernet network withmore » appropriate quality of service (QoS) profiles. In conclusion, the developed EMS was emulated with actual residential energy consumption and irradiance data from Miami, Florida and proved its effectiveness in reducing consumers’ bills and achieving flat peak load profiles.« less

Practice of Meteorological Services in Turpan Solar Eco-City in China (Invited)

NASA Astrophysics Data System (ADS)

Shen, Y.; Chang, R.; He, X.; Jiang, Y.; Zhao, D.; Ma, J.

2013-12-01

Turpan Solar Eco-City is located in Gobi in Northwest China, which is one of the National New Energy Demonstration Urban. The city was planed and designed from October of 2008 and constructed from May of 2010, and the first phase of the project has been completed by October of 2013. Energy supply in Turpan Solar Eco-City is mainly from PV power, which is installed in all of the roof and the total capacity is 13.4MW. During the planning and designing of the city, and the running of the smart grid, meteorological services have played an important role. 1) Solar Energy Resource Assessment during Planning Phase. According to the observed data from meteorological stations in recent 30 years, solar energy resource was assessed and available PV power generation capacity was calculated. The results showed that PV power generation capacity is 1.3 times the power consumption, that is, solar energy resource in Turpan is rich. 2) Key Meteorological Parameters Determination for Architectural Design. A professional solar energy resource station was constructed and the observational items included Global Horizontal Irradiance, Inclined Total Solar Irradiance at 30 degree, Inclined Total Solar Irradiance at local latitude, and so on. According these measured data, the optical inclined angle for PV array was determined, that is, 30 degree. The results indicated that the annual irradiation on inclined plane with optimal angle is 1.4% higher than the inclined surface with latitude angle, and 23.16% higher than the horizontal plane. The diffuse ratio and annual variation of the solar elevation angle are two major factors that influence the irradiation on inclined plane. 3) Solar Energy Resource Forecast for Smart Grid. Weather Research Forecast (WRF) model was used to forecast the hourly solar radiation of future 72 hours and the measured irradiance data was used to forecast the minutely solar radiation of future 4 hours. The forecast results were submitted to smart grid and used to regulate the local grid and the city gird.

Stationarity test with a direct test for heteroskedasticity in exchange rate forecasting models

NASA Astrophysics Data System (ADS)

Khin, Aye Aye; Chau, Wong Hong; Seong, Lim Chee; Bin, Raymond Ling Leh; Teng, Kevin Low Lock

2017-05-01

Global economic has been decreasing in the recent years, manifested by the greater exchange rates volatility on international commodity market. This study attempts to analyze some prominent exchange rate forecasting models on Malaysian commodity trading: univariate ARIMA, ARCH and GARCH models in conjunction with stationarity test on residual diagnosis direct testing of heteroskedasticity. All forecasting models utilized the monthly data from 1990 to 2015. Given a total of 312 observations, the data used to forecast both short-term and long-term exchange rate. The forecasting power statistics suggested that the forecasting performance of ARIMA (1, 1, 1) model is more efficient than the ARCH (1) and GARCH (1, 1) models. For ex-post forecast, exchange rate was increased from RM 3.50 per USD in January 2015 to RM 4.47 per USD in December 2015 based on the baseline data. For short-term ex-ante forecast, the analysis results indicate a decrease in exchange rate on 2016 June (RM 4.27 per USD) as compared with 2015 December. A more appropriate forecasting method of exchange rate is vital to aid the decision-making process and planning on the sustainable commodities' production in the world economy.

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.

PubMed

Anwar, Mohammad Y; Lewnard, Joseph A; Parikh, Sunil; Pitzer, Virginia E

2016-11-22

Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

Dynamics of analyst forecasts and emergence of complexity: Role of information disparity

PubMed Central

Ahn, Kwangwon

2017-01-01

We report complex phenomena arising among financial analysts, who gather information and generate investment advice, and elucidate them with the help of a theoretical model. Understanding how analysts form their forecasts is important in better understanding the financial market. Carrying out big-data analysis of the analyst forecast data from I/B/E/S for nearly thirty years, we find skew distributions as evidence for emergence of complexity, and show how information asymmetry or disparity affects financial analysts’ forming their forecasts. Here regulations, information dissemination throughout a fiscal year, and interactions among financial analysts are regarded as the proxy for a lower level of information disparity. It is found that financial analysts with better access to information display contrasting behaviors: a few analysts become bolder and issue forecasts independent of other forecasts while the majority of analysts issue more accurate forecasts and flock to each other. Main body of our sample of optimistic forecasts fits a log-normal distribution, with the tail displaying a power law. Based on the Yule process, we propose a model for the dynamics of issuing forecasts, incorporating interactions between analysts. Explaining nicely empirical data on analyst forecasts, this provides an appealing instance of understanding social phenomena in the perspective of complex systems. PMID:28498831

A temporal-spatial postprocessing model for probabilistic run-off forecast. With a case study from Ulla-Førre with five catchments and ten lead times

NASA Astrophysics Data System (ADS)

Engeland, K.; Steinsland, I.

2012-04-01

This work is driven by the needs of next generation short term optimization methodology for hydro power production. Stochastic optimization are about to be introduced; i.e. optimizing when available resources (water) and utility (prices) are uncertain. In this paper we focus on the available resources, i.e. water, where uncertainty mainly comes from uncertainty in future runoff. When optimizing a water system all catchments and several lead times have to be considered simultaneously. Depending on the system of hydropower reservoirs, it might be a set of headwater catchments, a system of upstream /downstream reservoirs where water used from one catchment /dam arrives in a lower catchment maybe days later, or a combination of both. The aim of this paper is therefore to construct a simultaneous probabilistic forecast for several catchments and lead times, i.e. to provide a predictive distribution for the forecasts. Stochastic optimization methods need samples/ensembles of run-off forecasts as input. Hence, it should also be possible to sample from our probabilistic forecast. A post-processing approach is taken, and an error model based on Box- Cox transformation, power transform and a temporal-spatial copula model is used. It accounts for both between catchment and between lead time dependencies. In operational use it is strait forward to sample run-off ensembles from this models that inherits the catchment and lead time dependencies. The methodology is tested and demonstrated in the Ulla-Førre river system, and simultaneous probabilistic forecasts for five catchments and ten lead times are constructed. The methodology has enough flexibility to model operationally important features in this case study such as hetroscadasety, lead-time varying temporal dependency and lead-time varying inter-catchment dependency. Our model is evaluated using CRPS for marginal predictive distributions and energy score for joint predictive distribution. It is tested against deterministic run-off forecast, climatology forecast and a persistent forecast, and is found to be the better probabilistic forecast for lead time grater then two. From an operational point of view the results are interesting as the between catchment dependency gets stronger with longer lead-times.

Performance of the 'material Failure Forecast Method' in real-time situations: A Bayesian approach applied on effusive and explosive eruptions

NASA Astrophysics Data System (ADS)

Boué, A.; Lesage, P.; Cortés, G.; Valette, B.; Reyes-Dávila, G.; Arámbula-Mendoza, R.; Budi-Santoso, A.

2016-11-01

Most attempts of deterministic eruption forecasting are based on the material Failure Forecast Method (FFM). This method assumes that a precursory observable, such as the rate of seismic activity, can be described by a simple power law which presents a singularity at a time close to the eruption onset. Until now, this method has been applied only in a small number of cases, generally for forecasts in hindsight. In this paper, a rigorous Bayesian approach of the FFM designed for real-time applications is applied. Using an automatic recognition system, seismo-volcanic events are detected and classified according to their physical mechanism and time series of probability distributions of the rates of events are calculated. At each time of observation, a Bayesian inversion provides estimations of the exponent of the power law and of the time of eruption, together with their probability density functions. Two criteria are defined in order to evaluate the quality and reliability of the forecasts. Our automated procedure has allowed the analysis of long, continuous seismic time series: 13 years from Volcán de Colima, Mexico, 10 years from Piton de la Fournaise, Reunion Island, France, and several months from Merapi volcano, Java, Indonesia. The new forecasting approach has been applied to 64 pre-eruptive sequences which present various types of dominant seismic activity (volcano-tectonic or long-period events) and patterns of seismicity with different level of complexity. This has allowed us to test the FFM assumptions, to determine in which conditions the method can be applied, and to quantify the success rate of the forecasts. 62% of the precursory sequences analysed are suitable for the application of FFM and half of the total number of eruptions are successfully forecast in hindsight. In real-time, the method allows for the successful forecast of 36% of all the eruptions considered. Nevertheless, real-time forecasts are successful for 83% of the cases that fulfil the reliability criteria. Therefore, good confidence on the method is obtained when the reliability criteria are met.

Research and application of thermal power unit’s load dynamic adjustment based on extraction steam

NASA Astrophysics Data System (ADS)

Li, Jun; Li, Huicong; Li, Weiwei

2018-02-01

The rapid development of heat and power generation in large power plant has caused tremendous constraints on the load adjustment of power grids and power plants. By introducing the thermodynamic system of thermal power unit, the relationship between thermal power extraction steam and unit’s load has analyzed and calculated. The practical application results show that power capability of the unit affected by extraction and it is not conducive to adjust the grid frequency. By monitoring the load adjustment capacity of thermal power units, especially the combined heat and power generating units, the upper and lower limits of the unit load can be dynamically adjusted by the operator on the grid side. The grid regulation and control departments can effectively control the load adjustable intervals of the operating units and provide reliable for the cooperative action of the power grid and power plants, to ensure the safety and stability of the power grid.

Forecasting selenium discharges to the San Francisco Bay-Delta Estuary: ecological effects of a proposed San Luis drain extension

USGS Publications Warehouse

Luoma, Samuel N.; Presser, Theresa S.

2000-01-01

During the next few years, federal and state agencies may be required to evaluate proposals and discharge permits that could significantly change selenium (Se) inputs to the San Francisco Bay-Delta Estuary (Bay-Delta), particularly in the North Bay (i.e., Suisun Bay and San Pablo Bay). These decisions may include discharge requirements for an extension of the San Luis Drain (SLD) to the estuary to convey subsurface agricultural drainage from the western San Joaquin Valley (SJV), a renewal of an agreement to allow the existing portion of the SLD to convey subsurface agricultural drainage to a tributary of the San Joaquin River (SJR) (coincident with changes in flow patterns of the lower SJR), and refinements to promulgated Se criteria for the protection of aquatic life for the estuary. Understanding the biotransfer of Se is essential to evaluating the fate and impact of proposed changes in Se discharges to the Bay-Delta. However, past monitoring programs have not addressed the specific protocols necessary for an element that bioaccumulates. Confusion about Se threats in the past have stemmed from failure to consider the full complexity of the processes that result in Se toxicity. Past studies show that predators are more at risk from Se contamination than their prey, making it difficult to use traditional methods to predict risk from environmental concentrations alone. In this report, we employ a novel procedure to model the fate of Se under different, potentially realistic load scenarios from the SJV. For each potential load, we progressively forecast the resulting environmental concentrations, speciation, transformation to particulate form, bioaccumulation by invertebrates, trophic transfer to predators, and effects in those predators. Enough is known to establish a first order understanding of effects should Se be discharged directly into the North Bay via a conveyance such as the SLD. Our approach uses 1) existing knowledge concerning the biogeochemical reactions of Se (e.g., speciation, partitioning between dissolved and particulate forms, and bivalve assimilation efficiency) and 2) site-specific data mainly from 1986 to 1996 on clams and bottom-feeding fish and birds. Forecasts of Se loading from oil refineries and agricultural drainage from the SJV enable the calculation of a composite freshwater endmember Se concentration at the head of the estuary and at Carquinez Strait as a foundation for modeling. Our analysis of effects also takes into account the mode of conveyance for agricultural drainage (i.e., the SLD or SJR). The effects of variable flows on a seasonal or monthly basis from the Sacramento River and SJR are also considered. The results of our forecasts for external SJV watershed sources of Se mirror predictions made since 1955 of a worsening salt (and by inference, Se) buildup exacerbated by the arid climate and irrigation for agricultural use. We show that the reservoir of Se in the SJV is sufficient to provide loading at an annual rate of approximately 42,500 pounds (lbs) of Se to a Bay-Delta disposal point for 63 to 304 years at the lower range of our projections, even if influx of Se from the California Coast Ranges could be curtailed. Disposal of wastewaters on an annual basis outside of the SJV may slow the degradation of valley resources, but drainage alone cannot alleviate the salt and Se buildup in the SJV, at least within a century. Our forecasts show the different proportions of Se loading to the Bay-Delta. Oil refinery loads from 1986 to 1992 ranged from 11 to 15 lbs Se per day; with treatment and cleanup, loads decreased to 3 lbs Se per day in 1999. In contrast, SJV agricultural drainage loads could range from of 45 to 117 lbs Se per day across a set of reasonable conditions. Components of this valley-wide load include five source subareas (i.e., Grassland, Westlands, Tulare, Kern, and Northern) based on water and drainage management. Loads vary per subarea mainly because of proximity of the s

Sensitivity analysis of numerical weather prediction radiative schemes to forecast direct solar radiation over Australia

NASA Astrophysics Data System (ADS)

Mukkavilli, S. K.; Kay, M. J.; Taylor, R.; Prasad, A. A.; Troccoli, A.

2014-12-01

The Australian Solar Energy Forecasting System (ASEFS) project requires forecasting timeframes which range from nowcasting to long-term forecasts (minutes to two years). As concentrating solar power (CSP) plant operators are one of the key stakeholders in the national energy market, research and development enhancements for direct normal irradiance (DNI) forecasts is a major subtask. This project involves comparing different radiative scheme codes to improve day ahead DNI forecasts on the national supercomputing infrastructure running mesoscale simulations on NOAA's Weather Research & Forecast (WRF) model. ASEFS also requires aerosol data fusion for improving accurate representation of spatio-temporally variable atmospheric aerosols to reduce DNI bias error in clear sky conditions over southern Queensland & New South Wales where solar power is vulnerable to uncertainities from frequent aerosol radiative events such as bush fires and desert dust. Initial results from thirteen years of Bureau of Meteorology's (BOM) deseasonalised DNI and MODIS NASA-Terra aerosol optical depth (AOD) anomalies demonstrated strong negative correlations in north and southeast Australia along with strong variability in AOD (~0.03-0.05). Radiative transfer schemes, DNI and AOD anomaly correlations will be discussed for the population and transmission grid centric regions where current and planned CSP plants dispatch electricity to capture peak prices in the market. Aerosol and solar irradiance datasets include satellite and ground based assimilations from the national BOM, regional aerosol researchers and agencies. The presentation will provide an overview of this ASEFS project task on WRF and results to date. The overall goal of this ASEFS subtask is to develop a hybrid numerical weather prediction (NWP) and statistical/machine learning multi-model ensemble strategy that meets future operational requirements of CSP plant operators.

Forecasting seizures in dogs with naturally occurring epilepsy.

PubMed

Howbert, J Jeffry; Patterson, Edward E; Stead, S Matt; Brinkmann, Ben; Vasoli, Vincent; Crepeau, Daniel; Vite, Charles H; Sturges, Beverly; Ruedebusch, Vanessa; Mavoori, Jaideep; Leyde, Kent; Sheffield, W Douglas; Litt, Brian; Worrell, Gregory A

2014-01-01

Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-70 Hz), and high-gamma (70-180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring.

Forecasting Seizures in Dogs with Naturally Occurring Epilepsy

PubMed Central

Stead, S. Matt; Brinkmann, Ben; Vasoli, Vincent; Crepeau, Daniel; Vite, Charles H.; Sturges, Beverly; Ruedebusch, Vanessa; Mavoori, Jaideep; Leyde, Kent; Sheffield, W. Douglas; Litt, Brian; Worrell, Gregory A.

2014-01-01

Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), low-gamma (30–70 Hz), and high-gamma (70–180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring. PMID:24416133

Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

NASA Astrophysics Data System (ADS)

O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

2015-04-01

Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

Forecasting malaria in a highly endemic country using environmental and clinical predictors.

PubMed

Zinszer, Kate; Kigozi, Ruth; Charland, Katia; Dorsey, Grant; Brewer, Timothy F; Brownstein, John S; Kamya, Moses R; Buckeridge, David L

2015-06-18

Malaria thrives in poor tropical and subtropical countries where local resources are limited. Accurate disease forecasts can provide public and clinical health services with the information needed to implement targeted approaches for malaria control that make effective use of limited resources. The objective of this study was to determine the relevance of environmental and clinical predictors of malaria across different settings in Uganda. Forecasting models were based on health facility data collected by the Uganda Malaria Surveillance Project and satellite-derived rainfall, temperature, and vegetation estimates from 2006 to 2013. Facility-specific forecasting models of confirmed malaria were developed using multivariate autoregressive integrated moving average models and produced weekly forecast horizons over a 52-week forecasting period. The model with the most accurate forecasts varied by site and by forecast horizon. Clinical predictors were retained in the models with the highest predictive power for all facility sites. The average error over the 52 forecasting horizons ranged from 26 to 128% whereas the cumulative burden forecast error ranged from 2 to 22%. Clinical data, such as drug treatment, could be used to improve the accuracy of malaria predictions in endemic settings when coupled with environmental predictors. Further exploration of malaria forecasting is necessary to improve its accuracy and value in practice, including examining other environmental and intervention predictors, including insecticide-treated nets.

Evaluation of TxDOT'S traffic data collection and load forecasting process

DOT National Transportation Integrated Search

2001-01-01

This study had two primary objectives: (1) compare current Texas Department of Transportation (TxDOT) procedures and protocols with the state-of-the-practice and the needs of its data customers; and (2) develop enhanced traffic collection, archival, ...

Using Maximal Isometric Force to Determine the Optimal Load for Measuring Dynamic Muscle Power

NASA Technical Reports Server (NTRS)

Spiering, Barry A.; Lee, Stuart M. C.; Mulavara, Ajitkumar P.; Bentley, Jason R.; Nash, Roxanne E.; Sinka, Joseph; Bloomberg, Jacob J.

2009-01-01

Maximal power output occurs when subjects perform ballistic exercises using loads of 30-50% of one-repetition maximum (1-RM). However, performing 1-RM testing prior to power measurement requires considerable time, especially when testing involves multiple exercises. Maximal isometric force (MIF), which requires substantially less time to measure than 1-RM, might be an acceptable alternative for determining the optimal load for power testing. PURPOSE: To determine the optimal load based on MIF for maximizing dynamic power output during leg press and bench press exercises. METHODS: Twenty healthy volunteers (12 men and 8 women; mean +/- SD age: 31+/-6 y; body mass: 72 +/- 15 kg) performed isometric leg press and bench press movements, during which MIF was measured using force plates. Subsequently, subjects performed ballistic leg press and bench press exercises using loads corresponding to 20%, 30%, 40%, 50%, and 60% of MIF presented in randomized order. Maximal instantaneous power was calculated during the ballistic exercise tests using force plates and position transducers. Repeated-measures ANOVA and Fisher LSD post hoc tests were used to determine the load(s) that elicited maximal power output. RESULTS: For the leg press power test, six subjects were unable to be tested at 20% and 30% MIF because these loads were less than the lightest possible load (i.e., the weight of the unloaded leg press sled assembly [31.4 kg]). For the bench press power test, five subjects were unable to be tested at 20% MIF because these loads were less than the weight of the unloaded aluminum bar (i.e., 11.4 kg). Therefore, these loads were excluded from analysis. A trend (p = 0.07) for a main effect of load existed for the leg press exercise, indicating that the 40% MIF load tended to elicit greater power output than the 60% MIF load (effect size = 0.38). A significant (p . 0.05) main effect of load existed for the bench press exercise; post hoc analysis indicated that the effect of load on power output was: 30% > 40% > 50% = 60%. CONCLUSION: Loads of 40% and 30% of MIF elicit maximal power output during dynamic leg presses and bench presses, respectively. These findings are similar to those obtained when loading is based on 1-RM.

Assessment of wind energy potential in Poland

NASA Astrophysics Data System (ADS)

Starosta, Katarzyna; Linkowska, Joanna; Mazur, Andrzej

2014-05-01

The aim of the presentation is to show the suitability of using numerical model wind speed forecasts for the wind power industry applications in Poland. In accordance with the guidelines of the European Union, the consumption of wind energy in Poland is rapidly increasing. According to the report of Energy Regulatory Office from 30 March 2013, the installed capacity of wind power in Poland was 2807MW from 765 wind power stations. Wind energy is strongly dependent on the meteorological conditions. Based on the climatological wind speed data, potential energy zones within the area of Poland have been developed (H. Lorenc). They are the first criterion for assessing the location of the wind farm. However, for exact monitoring of a given wind farm location the prognostic data from numerical model forecasts are necessary. For the practical interpretation and further post-processing, the verification of the model data is very important. Polish Institute Meteorology and Water Management - National Research Institute (IMWM-NRI) runs an operational model COSMO (Consortium for Small-scale Modelling, version 4.8) using two nested domains at horizontal resolutions of 7 km and 2.8 km. The model produces 36 hour and 78 hour forecasts from 00 UTC, for 2.8 km and 7 km domain resolutions respectively. Numerical forecasts were compared with the observation of 60 SYNOP and 3 TEMP stations in Poland, using VERSUS2 (Unified System Verification Survey 2) and R package. For every zone the set of statistical indices (ME, MAE, RMSE) was calculated. Forecast errors for aerological profiles are shown for Polish TEMP stations at Wrocław, Legionowo and Łeba. The current studies are connected with a topic of the COST ES1002 WIRE-Weather Intelligence for Renewable Energies.

Multimode power processor

DOEpatents

O'Sullivan, G.A.; O'Sullivan, J.A.

1999-07-27

In one embodiment, a power processor which operates in three modes: an inverter mode wherein power is delivered from a battery to an AC power grid or load; a battery charger mode wherein the battery is charged by a generator; and a parallel mode wherein the generator supplies power to the AC power grid or load in parallel with the battery. In the parallel mode, the system adapts to arbitrary non-linear loads. The power processor may operate on a per-phase basis wherein the load may be synthetically transferred from one phase to another by way of a bumpless transfer which causes no interruption of power to the load when transferring energy sources. Voltage transients and frequency transients delivered to the load when switching between the generator and battery sources are minimized, thereby providing an uninterruptible power supply. The power processor may be used as part of a hybrid electrical power source system which may contain, in one embodiment, a photovoltaic array, diesel engine, and battery power sources. 31 figs.

Multimode power processor

DOEpatents

O'Sullivan, George A.; O'Sullivan, Joseph A.

1999-01-01

In one embodiment, a power processor which operates in three modes: an inverter mode wherein power is delivered from a battery to an AC power grid or load; a battery charger mode wherein the battery is charged by a generator; and a parallel mode wherein the generator supplies power to the AC power grid or load in parallel with the battery. In the parallel mode, the system adapts to arbitrary non-linear loads. The power processor may operate on a per-phase basis wherein the load may be synthetically transferred from one phase to another by way of a bumpless transfer which causes no interruption of power to the load when transferring energy sources. Voltage transients and frequency transients delivered to the load when switching between the generator and battery sources are minimized, thereby providing an uninterruptible power supply. The power processor may be used as part of a hybrid electrical power source system which may contain, in one embodiment, a photovoltaic array, diesel engine, and battery power sources.

Applications of a shadow camera system for energy meteorology

NASA Astrophysics Data System (ADS)

Kuhn, Pascal; Wilbert, Stefan; Prahl, Christoph; Garsche, Dominik; Schüler, David; Haase, Thomas; Ramirez, Lourdes; Zarzalejo, Luis; Meyer, Angela; Blanc, Philippe; Pitz-Paal, Robert

2018-02-01

Downward-facing shadow cameras might play a major role in future energy meteorology. Shadow cameras directly image shadows on the ground from an elevated position. They are used to validate other systems (e.g. all-sky imager based nowcasting systems, cloud speed sensors or satellite forecasts) and can potentially provide short term forecasts for solar power plants. Such forecasts are needed for electricity grids with high penetrations of renewable energy and can help to optimize plant operations. In this publication, two key applications of shadow cameras are briefly presented.

Tracking signal test to monitor an intelligent time series forecasting model

NASA Astrophysics Data System (ADS)

Deng, Yan; Jaraiedi, Majid; Iskander, Wafik H.

2004-03-01

Extensive research has been conducted on the subject of Intelligent Time Series forecasting, including many variations on the use of neural networks. However, investigation of model adequacy over time, after the training processes is completed, remains to be fully explored. In this paper we demonstrate a how a smoothed error tracking signals test can be incorporated into a neuro-fuzzy model to monitor the forecasting process and as a statistical measure for keeping the forecasting model up-to-date. The proposed monitoring procedure is effective in the detection of nonrandom changes, due to model inadequacy or lack of unbiasedness in the estimation of model parameters and deviations from the existing patterns. This powerful detection device will result in improved forecast accuracy in the long run. An example data set has been used to demonstrate the application of the proposed method.

System learning approach to assess sustainability and forecast trends in regional dynamics: The San Luis Basin study, Colorado, U.S.A.

EPA Science Inventory

This paper presents a methodology that combines the power of an Artificial Neural Network and Information Theory to forecast variables describing the condition of a regional system. The novelty and strength of this approach is in the application of Fisher information, a key metho...

Geochemical monitoring of drilling fluids; A powerful tool to forecast and detect formation waters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vuataz, F.D.; Brach, M.; Criaud, A.

1990-06-01

This paper describes a method based on the difference between the chemical compositions of formation and drilling fluids for analyzing drilling mud to forecast fluid-producing zones. The method was successfully applied in three boreholes in crystalline rocks in France. Subsequent geophysical logs and hydraulic tests confirmed the occurrence of flowing fractures.

46 CFR 112.15-1 - Temporary emergency loads.

Code of Federal Regulations, 2014 CFR

2014-10-01

... AND POWER SYSTEMS Emergency Loads § 112.15-1 Temporary emergency loads. On vessels required by § 112.05-5(a) to have a temporary emergency power source, the following emergency lighting and power loads must be arranged so that they can be energized from the temporary emergency power source: (a...

46 CFR 112.15-1 - Temporary emergency loads.

Code of Federal Regulations, 2012 CFR

2012-10-01

... AND POWER SYSTEMS Emergency Loads § 112.15-1 Temporary emergency loads. On vessels required by § 112.05-5(a) to have a temporary emergency power source, the following emergency lighting and power loads must be arranged so that they can be energized from the temporary emergency power source: (a...

46 CFR 112.15-1 - Temporary emergency loads.

Code of Federal Regulations, 2013 CFR

2013-10-01

... AND POWER SYSTEMS Emergency Loads § 112.15-1 Temporary emergency loads. On vessels required by § 112.05-5(a) to have a temporary emergency power source, the following emergency lighting and power loads must be arranged so that they can be energized from the temporary emergency power source: (a...

Microgrid Enabled Distributed Energy Solutions (MEDES) Fort Bliss Military Reservation

DTIC Science & Technology

2014-02-01

Logic Controller PF Power Factor PO Performance Objectives PPA Power Purchase Agreements PV Photovoltaic R&D Research and Development RDSI...controller, algorithms perform power flow analysis, short term optimization, and long-term forecasted planning. The power flow analysis ensures...renewable photovoltaic power and energy storage in this microgrid configuration, the available mission operational time of the backup generator can be

A distributed big data storage and data mining framework for solar-generated electricity quantity forecasting

NASA Astrophysics Data System (ADS)

Wang, Jianzong; Chen, Yanjun; Hua, Rui; Wang, Peng; Fu, Jia

2012-02-01

Photovoltaic is a method of generating electrical power by converting solar radiation into direct current electricity using semiconductors that exhibit the photovoltaic effect. Photovoltaic power generation employs solar panels composed of a number of solar cells containing a photovoltaic material. Due to the growing demand for renewable energy sources, the manufacturing of solar cells and photovoltaic arrays has advanced considerably in recent years. Solar photovoltaics are growing rapidly, albeit from a small base, to a total global capacity of 40,000 MW at the end of 2010. More than 100 countries use solar photovoltaics. Driven by advances in technology and increases in manufacturing scale and sophistication, the cost of photovoltaic has declined steadily since the first solar cells were manufactured. Net metering and financial incentives, such as preferential feed-in tariffs for solar-generated electricity; have supported solar photovoltaics installations in many countries. However, the power that generated by solar photovoltaics is affected by the weather and other natural factors dramatically. To predict the photovoltaic energy accurately is of importance for the entire power intelligent dispatch in order to reduce the energy dissipation and maintain the security of power grid. In this paper, we have proposed a big data system--the Solar Photovoltaic Power Forecasting System, called SPPFS to calculate and predict the power according the real-time conditions. In this system, we utilized the distributed mixed database to speed up the rate of collecting, storing and analysis the meteorological data. In order to improve the accuracy of power prediction, the given neural network algorithm has been imported into SPPFS.By adopting abundant experiments, we shows that the framework can provide higher forecast accuracy-error rate less than 15% and obtain low latency of computing by deploying the mixed distributed database architecture for solar-generated electricity.

Forecasts of health care utilization related to pandemic A(H1N1)2009 influenza in the Nord-Pas-de-Calais region, France.

PubMed

Giovannelli, J; Loury, P; Lainé, M; Spaccaferri, G; Hubert, B; Chaud, P

2015-05-01

To describe and evaluate the forecasts of the load that pandemic A(H1N1)2009 influenza would have on the general practitioners (GP) and hospital care systems, especially during its peak, in the Nord-Pas-de-Calais (NPDC) region, France. Modelling study. The epidemic curve was modelled using an assumption of normal distribution of cases. The values for the forecast parameters were estimated from a literature review of observed data from the Southern hemisphere and French Overseas Territories, where the pandemic had already occurred. Two scenarios were considered, one realistic, the other pessimistic, enabling the authors to evaluate the 'reasonable worst case'. Forecasts were then assessed by comparing them with observed data in the NPDC region--of 4 million people. The realistic scenarios forecasts estimated 300,000 cases, 1500 hospitalizations, 225 intensive care units (ICU) admissions for the pandemic wave; 115 hospital beds and 45 ICU beds would be required per day during the peak. The pessimistic scenario's forecasts were 2-3 times higher than the realistic scenario's forecasts. Observed data were: 235,000 cases, 1585 hospitalizations, 58 ICU admissions; and a maximum of 11.6 ICU beds per day. The realistic scenario correctly estimated the temporal distribution of GP and hospitalized cases but overestimated the number of cases admitted to ICU. Obtaining more robust data for parameters estimation--particularly the rate of ICU admission among the population that the authors recommend to use--may provide better forecasts. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

2017-04-01

Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of the forecasting chain (i.e., IHC or MF) could potentially lead to the highest increase in seasonal hydrological forecasting performance, after each forecast update.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico.

PubMed

Johansson, Michael A; Reich, Nicholas G; Hota, Aditi; Brownstein, John S; Santillana, Mauricio

2016-09-26

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

PubMed Central

Johansson, Michael A.; Reich, Nicholas G.; Hota, Aditi; Brownstein, John S.; Santillana, Mauricio

2016-01-01

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model. PMID:27665707