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

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

机构地区：[1]University of Science and Technology of China,Hefei 230026,China

出　　处：《ZTE Communications》2023年第3期11-21,共11页中兴通讯技术（英文版）

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.

摘　　要：The increasing adoption of renewable energy has posed challenges for voltage regulation in power distribution networks.Gridaware energy management,which includes the control of smart inverters and energy management systems,is a trending way to mitigate this problem.However,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 performance.In this study,we propose a new approach named multi-agent hierarchical graph attention reinforcement learning framework(MAHGA)to stabilize the voltage.Specifically,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 topology.Then a hierarchical graph attention model is devised to capture the complex correlation between agents.Moreover,we incorporate graph contrastive learning as an auxiliary task in the reinforcement learning process to improve representation learning from graphs.Experiments on several real-world scenarios reveal that our approach achieves the best performance and can reduce the number of voltage violations remarkably.

关 键 词：demand-side management graph neural networks multi-agent reinforcement learning voltage regulation 

分 类 号：TN929.5[电子电信—通信与信息系统] TP181[电子电信—信息与通信工程]