Reinforcement Learning with Enhanced Safety for Optimal Dispatch of Distributed Energy Resources in Active Distribution Networks  

作　　者：Xu Yang Haotian Liu Wenchuan Wu Qi Wang Peng Yu Jiawei Xing Yuejiao Wang 

机构地区：[1]the State Key Laboratory of Power Systems,Department of Electrical Engineering,Tsinghua University,Beijing 100084,China [2]the State Grid Shandong Electric Power Company,Jinan 250000,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第5期1484-1494,共11页现代电力系统与清洁能源学报（英文）

基　　金：supported in part by the National Key Research and Development Plan of China(No.2022YFB2402900);in part by the Science and Technology Project of State Grid Corporation of China“Key Techniques of Adaptive Grid Integration and Active Synchronization for Extremely High Penetration Distributed Photovoltaic Power Generation”(No.52060023001T)。

摘　　要：As numerous distributed energy resources(DERs)are integrated into the distribution networks,the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks(ADNs).Since accurate models are usually unavailable in ADNs,an increasing number of reinforcement learning(RL)based methods have been proposed for the optimal dispatch problem.However,these RL based methods are typically formulated without safety guarantees,which hinders their application in real world.In this paper,we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic(S3AC)for the optimal dispatch of DERs in ADNs,which not only minimizes the operational cost but also satisfies safety constraints during online execution.In the proposed S3AC,the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition(SCADA)system,effectively providing enhanced safety for executed actions.Numerical studies on several IEEE test systems demonstrate the effectiveness and safety of the proposed S3AC.

关 键 词：Reinforcement learning(RL) safety constraint optimal dispatch active distribution network(ADN) distributed energy resource(DER) 

分 类 号：TM727[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]