A Deep Reinforcement Learning Algorithm for the Power Order Optimization Allocation of AGC in Interconnected Power Grids

查看全文 作　　者：Lei [1]Xi;Lipeng [1]Zhou;Lang [2]Liu;Dongliang [3]Duan;Yanchun [1]Xu;Liuqing [4]Yang;Shouxiang [5]Wang

机构地区：[1]College of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,China;[2]State Grid Xianning Electric Power Supply Company,Xianning 437100,China;[3]Department of Electrical and Computer Engineering,University of Wyoming,Laramie,WY,USA;[4]Department of Electrical and Computer Engineering,Colorado State University,Fort Collins,CO,USA;[5]Key Laboratory of Smart Grid of Ministry of Education,Tianjin University,Tianjin 300072,China

出　　处：《CSEE Journal of Power and Energy Systems》索引2020年第6卷第3期,共12页

基　　金：This work was supported in part by the National Natural Science Foundation of China under Grant No.51707102. 摘　　要：The integration of distributed generations(solar power,wind power),energy storage devices,and electric vehicles,causes unpredictable disturbances in power grids.It has become a top priority to coordinate the distributed generations,loads,and energy storages in order to better facilitate the utilization of new energy.Therefore,a novel algorithm based on deep reinforcement learning,namely the deep PDWoLF-PHC(policy dynamics based win or learn fast-policy hill climbing)network(DPDPN),is proposed to allocate power order among the various generators.The proposed algorithm combines the decision mechanism of reinforcement learning with the prediction mechanism of a deep neural network to obtain the optimal coordinated control for the source-grid-load.Consequently it solves the problem brought by stochastic disturbances and improves the utilization rate of new energy.Simulations are conducted with the case of the improved IEEE two-area and a case in the Guangdong power grid.Results show that the adaptability and control performance of the power system are improved using the proposed algorithm as compared with using other existing strategies. 关键词：Automatic generation control deep reinforcement learning DPDPN power order allocation

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A Deep Reinforcement Learning Algorithm for the Power Order Optimization Allocation of AGC in Interconnected Power Grids

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