Graph-Based Heterogeneous Multiagent Reinforcement Learning for Distribution System Service Restoration
提出一种图强化学习方法，利用图注意力网络提取配电网空间特征，并通过多头自注意力增强异构智能体协作，实现高效自主的服务恢复。
Service restoration implemented by multiple distributed energy resources (DERs) is a resilience-enhancing paradigm for modern distribution systems. To address the challenges of complex system modeling and the problem of cooperative control over heterogeneous multiple agents, this article proposes a graph reinforcement learning (G-RL) method based on heterogeneous multiagent systems (MASs). The method leverages graph-structured data to enhance the representation of distribution system states and employs graph attention networks (GATs) to deeply explore the power flow features and spatial characteristics of nodes in the restoration process. Additionally, a multihead self-attention (MHSA) is incorporated to strengthen collaboration among heterogeneous agents, enabling them to focus on relevant information from multiple perspectives during training. Finally, a joint simulation test platform is developed using Python and OpenDSS, and case studies on a 123-bus distribution system are conducted. Experimental results demonstrate that the proposed approach achieves efficient and autonomous service restoration by enhancing spatial feature extraction and improving collaborative decision-making among agents.