Active Power Correction Strategies Based on Deep Reinforcement Learning—Part Ⅱ:A Distributed Solution for AdaptabilityOACSTPCD
Active Power Correction Strategies Based on Deep Reinforcement Learning—Part Ⅱ:A Distributed Solution for Adaptability
Siyuan Chen;Jiajun Duan;Yuyang Bai;Jun Zhang;Di Shi;Zhiwei Wang;Xuzhu Dong;Yuanzhang Sun
School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,ChinaGEIRI North America,San Jose,CA 95134,USASchool of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,ChinaSchool of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,ChinaGEIRI North America,San Jose,CA 95134,USAGEIRI North America,San Jose,CA 95134,USASchool of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,ChinaSchool of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,China
Active power correction strategiesdistributed deep reinforcement learningNash equilibriumrenewable energiesstochastic game
Active power correction strategiesdistributed deep reinforcement learningNash equilibriumrenewable energiesstochastic game
《中国电机工程学会电力与能源系统学报（英文版）》 2022 (4)
1134-1144,11
This work was supported by the National Key R&D Program of China under Grant 2018AAA0101502.
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