Multi-Agent Hierarchical Graph Attention Reinforcement Learning for Grid-Aware Energy Management

作     者：FENG Bingyi FENG Mingxiao WANG Minrui ZHOU Wengang LI Houqiang FENG Bingyi;FENG Mingxiao;WANG Minrui;ZHOU Wengang;LI Houqiang

作者机构：University of Science and Technology of ChinaHefei 230026China 

出 版 物：《中兴通讯技术:英文版》 (ZTE communications)

年 卷 期：2023年第21卷第3期

页      面：11-21页

学科分类：080904[工学-电磁场与微波技术] 12[管理学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Key R&D Program of China under Grant No.2022ZD0119802 National Natural Science Foundation of China under Grant No.61836011 

主　　题：demand-side management graph neural networks multi-agent reinforcement learning voltage regulation 

摘      要：The increasing adoption of renewable energy has posed challenges for voltage regulation in power distribution *** energy management,which includes the control of smart inverters and energy management systems,is a trending way to mitigate this ***,existing multi-agent reinforcement learning methods for grid-aware energy management have not sufficiently considered the importance of agent cooperation and the unique characteristics of the grid,which leads to limited *** this study,we propose a new approach named multi-agent hierarchical graph attention reinforcement learning framework(MAHGA)to stabilize the ***,under the paradigm of centralized training and decentralized execution,we model the power distribution network as a novel hierarchical graph containing the agent-level topology and the bus-level *** a hierarchical graph attention model is devised to capture the complex correlation between ***,we incorporate graph contrastive learning as an auxiliary task in the reinforcement learning process to improve representation learning from *** on several real-world scenarios reveal that our approach achieves the best performance and can reduce the number of voltage violations remarkably.