The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flo...The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flows through the connection points between the distribution systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds along the contracted values, avoiding monetary penalties to the utility or unnecessary amounts of contracted flows that overrates the costumers. This question highlights the necessity of forecast the flows in these connection points in sufficient time to permit the operator to take decisions to avoid flows beyond the contracted ones. In this context, this work presents the development of a neural network based load flow forecaster, being tested two time-series neural models： support vector machines and Bayesian inference applied to multilayered perceptron. The models are applied to real data from a Brazilian distribution utility.展开更多
Probabilistic load forecasting(PLF)is able to present the uncertainty information of the future loads.It is the basis of stochastic power system planning and operation.Recent works on PLF mainly focus on how to develo...Probabilistic load forecasting(PLF)is able to present the uncertainty information of the future loads.It is the basis of stochastic power system planning and operation.Recent works on PLF mainly focus on how to develop and combine forecasting models,while the feature selection issue has not been thoroughly investigated for PLF.This paper fills the gap by proposing a feature selection method for PLF via sparse L1-norm penalized quantile regression.It can be viewed as an extension from point forecasting-based feature selection to probabilistic forecasting-based feature selection.Since both the number of training samples and the number of features to be selected are very large,the feature selection process is casted as a large-scale convex optimization problem.The alternating direction method of multipliers is applied to solve the problem in an efficient manner.We conduct case studies on the open datasets of ten areas.Numerical results show that the proposed feature selection method can improve the performance of the probabilistic forecasting and outperforms traditional least absolute shrinkage and selection operator method.展开更多
This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the iss...This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.展开更多
文摘The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flows through the connection points between the distribution systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds along the contracted values, avoiding monetary penalties to the utility or unnecessary amounts of contracted flows that overrates the costumers. This question highlights the necessity of forecast the flows in these connection points in sufficient time to permit the operator to take decisions to avoid flows beyond the contracted ones. In this context, this work presents the development of a neural network based load flow forecaster, being tested two time-series neural models： support vector machines and Bayesian inference applied to multilayered perceptron. The models are applied to real data from a Brazilian distribution utility.
基金supported by National Key R&D Program of China(No.2016YFB0900100).
文摘Probabilistic load forecasting(PLF)is able to present the uncertainty information of the future loads.It is the basis of stochastic power system planning and operation.Recent works on PLF mainly focus on how to develop and combine forecasting models,while the feature selection issue has not been thoroughly investigated for PLF.This paper fills the gap by proposing a feature selection method for PLF via sparse L1-norm penalized quantile regression.It can be viewed as an extension from point forecasting-based feature selection to probabilistic forecasting-based feature selection.Since both the number of training samples and the number of features to be selected are very large,the feature selection process is casted as a large-scale convex optimization problem.The alternating direction method of multipliers is applied to solve the problem in an efficient manner.We conduct case studies on the open datasets of ten areas.Numerical results show that the proposed feature selection method can improve the performance of the probabilistic forecasting and outperforms traditional least absolute shrinkage and selection operator method.
文摘This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.
