Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning  

作　　者：Jie Xu Hongjun Gao Renjun Wang Junyong Liu 

机构地区：[1]the College of Electrical Engineering,Sichuan University,Chengdu,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第3期886-899,共14页现代电力系统与清洁能源学报（英文）

基　　金：supported by the National Natural Science Foundation of China(No.52077146);Sichuan Science and Technology Program(No.2023NSFSC1945)。

摘　　要：The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

关 键 词：RECONFIGURATION active distribution network distributed energy resource real-time control deep reinforcement learning parameter sharing SCALABILITY 

分 类 号：TM732[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]