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Automatic Generation Control in a distributed power grid Based on Multi-step Reinforcement Learning

Protection and Control of Modern power Systems 2024年第4期9卷 39-50页

作者： Wenmeng Zhao Tuo Zeng Zhihong Liu Lihui Xie Lei Xi Hui Ma the Electric power Research Institute Southern Power GridGuangzhou 510663China the Heyuan power Supply Bureau Guang-dong Power Grid Co.Ltd.Heyuan 517000China the College of Electrical Engineering&New Energy China Three Gorges UniversityYichang 443002China the College of Electrical Engineering&New Energy and Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station China Three Gorges UniversityYichang 443002China IEEE

The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power *** this paper,a novel multi-agent collaborative reinforcement learning algorithm is proposed with automatic optimization,namely,Dyna-DQL,to quickly achieve an optimal coordination solution for the multi-area distributed power *** proposed Dyna framework is combined with double Q-learning to collect and store the environmental *** can iteratively update the agents through buffer replay and real-time *** the environmental data can be fully used to enhance the learning speed of the *** mitigates the negative impact of heavy stochastic disturbances caused by the integration of renewable energy on the control *** are conducted on two different models to validate the effectiveness of the proposed *** results demonstrate that the proposed Dyna-DQL algorithm exhibits superior stability and robustness compared to other reinforcement learning algorithms.

关键词： Automatic generation control Dyna framework distributed power grid multi-agent mod-el-based reinforcement learning

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A Wind power Prediction Framework for distributed power grids

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Energy Engineering 2024年第5期121卷 1291-1307页

作者： Bin Chen Ziyang Li Shipeng Li Qingzhou Zhao Xingdou Liu power grid Design Institute Shandong Electric Power Engineering Consulting Institute Co.Ltd.Jinan250013China School of Electrical Engineering Shandong UniversityJinan250012China

To reduce carbon emissions,clean energy is being integrated into the power *** power is connected to the grid in a distributed form,but its high variability poses a challenge to grid *** article combines wind turbine monitoring data with numerical weather prediction(NWP)data to create a suitable wind power prediction framework for distributed ***,high-precision NWP of the turbine range is achieved using weather research and forecasting models(WRF),and Kriging interpolation locates predicted meteorological data at the turbine ***,a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve,and historical power is reconstructed using variational mode decomposition(VMD)filtering to form input variables in chronological ***,input variables of a single turbine enter the temporal convolutional network(TCN)to complete initial feature extraction,and then integrate the outputs of all TCN layers using Long Short Term Memory Networks(LSTM)to obtain power prediction sequences for all turbine *** proposed method was tested on a wind farm connected to a distributed power grid,and the results showed it to be superior to existing typical methods.

关键词： Wind power prediction distributed power grid WRF mode deep learning variational mode decomposition

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