Optimal Power Flow Calculation Considering Large-Scale Photovoltaic Generation Correlation
In order to analyze the impact of large-scale photovoltaic system on the power system, a photovoltaic output prediction method considering the correlation is proposed and the optimal power flow is calculated. Firstly, establish a photovoltaic output model to obtain the attenuation coefficient and fl...
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2020-11-01
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doaj-640207ad6bbe4b26a3735965cba590c12020-12-08T08:41:29ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2020-11-01810.3389/fenrg.2020.590418590418Optimal Power Flow Calculation Considering Large-Scale Photovoltaic Generation CorrelationHe Li0Huijun Li1Weihua Lu2Zhenhao Wang3Jing Bian4Tongliao Power Supply Company, State Grid East Inner Mongolia Electric Power Company, Tongliao, ChinaNARI Technology Development Co., Ltd., Nanjing, ChinaKey Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, ChinaKey Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, ChinaKey Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, ChinaIn order to analyze the impact of large-scale photovoltaic system on the power system, a photovoltaic output prediction method considering the correlation is proposed and the optimal power flow is calculated. Firstly, establish a photovoltaic output model to obtain the attenuation coefficient and fluctuation amount, and analyze the correlation among the multiple photovoltaic power plants through the k-means method. Secondly, the long short-term memory (LSTM) neural network is used as the photovoltaic output prediction model, and the clustered photovoltaic output data is brought into the LSTM model to generate large-scale photovoltaic prediction results with the consideration of the spatial correlation. And an optimal power flow model that takes grid loss and voltage offset as targets is established. Finally, MATLAB is used to verify that the proposed large-scale photovoltaic forecasting method has higher accuracy. The multi-objective optimal power flow calculation is performed based on the NSGA-II algorithm and the modified IEEE systems, and the optimal power flow with photovoltaic output at different times is compared and analyzed.https://www.frontiersin.org/articles/10.3389/fenrg.2020.590418/fulllarge-scale photovoltaiccorrelationneural networkpredictionoptimal power flow |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
He Li Huijun Li Weihua Lu Zhenhao Wang Jing Bian |
spellingShingle |
He Li Huijun Li Weihua Lu Zhenhao Wang Jing Bian Optimal Power Flow Calculation Considering Large-Scale Photovoltaic Generation Correlation Frontiers in Energy Research large-scale photovoltaic correlation neural network prediction optimal power flow |
author_facet |
He Li Huijun Li Weihua Lu Zhenhao Wang Jing Bian |
author_sort |
He Li |
title |
Optimal Power Flow Calculation Considering Large-Scale Photovoltaic Generation Correlation |
title_short |
Optimal Power Flow Calculation Considering Large-Scale Photovoltaic Generation Correlation |
title_full |
Optimal Power Flow Calculation Considering Large-Scale Photovoltaic Generation Correlation |
title_fullStr |
Optimal Power Flow Calculation Considering Large-Scale Photovoltaic Generation Correlation |
title_full_unstemmed |
Optimal Power Flow Calculation Considering Large-Scale Photovoltaic Generation Correlation |
title_sort |
optimal power flow calculation considering large-scale photovoltaic generation correlation |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Energy Research |
issn |
2296-598X |
publishDate |
2020-11-01 |
description |
In order to analyze the impact of large-scale photovoltaic system on the power system, a photovoltaic output prediction method considering the correlation is proposed and the optimal power flow is calculated. Firstly, establish a photovoltaic output model to obtain the attenuation coefficient and fluctuation amount, and analyze the correlation among the multiple photovoltaic power plants through the k-means method. Secondly, the long short-term memory (LSTM) neural network is used as the photovoltaic output prediction model, and the clustered photovoltaic output data is brought into the LSTM model to generate large-scale photovoltaic prediction results with the consideration of the spatial correlation. And an optimal power flow model that takes grid loss and voltage offset as targets is established. Finally, MATLAB is used to verify that the proposed large-scale photovoltaic forecasting method has higher accuracy. The multi-objective optimal power flow calculation is performed based on the NSGA-II algorithm and the modified IEEE systems, and the optimal power flow with photovoltaic output at different times is compared and analyzed. |
topic |
large-scale photovoltaic correlation neural network prediction optimal power flow |
url |
https://www.frontiersin.org/articles/10.3389/fenrg.2020.590418/full |
work_keys_str_mv |
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