Adaptive composite frequency control of power systems using reinforcement learning

查看全文 作　　者：Chaoxu [1]Mu;Ke [1]Wang;Shiqian [2]Ma;Zhiqiang [2]Chong;Zhen [3]Ni

机构地区：[1]School of Electrical and Information Engineering,Tianjin University,Tianjin,China;[2]State Grid Tianjin Electric Power Company Electric Power Research Institute,Tianjin,China;[3]Department of Electrical Engineering and Computer Science,Florida Atlantic University,Boca Raton,FL,USA

出　　处：《CAAI Transactions on Intelligence Technology》索引2022年第7卷第4期,共14页

基　　金：Science and Technology Project of SGCC(State Grid Corporation of China),Grant/Award Number:5700‐202212197A‐1‐1‐ZN。 摘　　要：With the incorporation of renewable energy,load frequency control(LFC)becomes more challenging due to uncertain power generation and changeable load demands.The electric vehicle(EV)has been a popular transportation and can also provide flexible options to play a role in frequency regulation.In this paper,a novel adaptive composite controller is designed to solve the LFC problem for the interconnected power system with electric vehicles and wind turbine.EVs are used as regulation resources to effectively compensate the power mismatch.First,the sliding mode controller is developed to reduce the random influences caused by the wind turbine generation system.Second,an auxiliary controller with reinforcement learning is proposed to produce adaptive control signals,which will be attached to the primary proportion-integration-differentiation control signal in a realtime manner.Finally,by considering random wind power,load disturbances and output constraints,the proposed scheme is verified on a two-area power system under four different cases.Simulation results demonstrate that the proposed adaptive composite frequency control scheme has a competitive performance with regard to dynamic performance. 关键词：POWER COMPOSITE ATTACHED

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Adaptive composite frequency control of power systems using reinforcement learning

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