Topology-Driven Multi-Agent Reinforcement Learning for Resilient Load Restoration in Distribution Networks

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Abstract
Distribution system restoration requires coordinated switching decisions to recover de-energized loads subject to network operating constraints. Multi-agent deep reinforcement learning (MADRL) alleviates the scalability challenge of large switching action spaces, yet the decomposition of switching actions among agents remains an important factor. In reconfiguration-based restoration, this decomposition directly affects restoration performance by shaping the feasible restoration paths and supply routes. Existing MADRL-based restoration methods rarely incorporate the network topology into agent organization, leaving potential gains in restoration efficiency unexploited. This paper proposes a topology-driven multi-agent restoration framework that constructs an impedance-weighted graph and applies Leiden community detection to partition the network into agent control regions. Switches crossing community boundaries are assigned to adjacent agents, allowing both agents to operate the corresponding switches. The fault location is used to restrict exploration to fault-relevant agents during training. The resulting multi-agent decision structure is learned within the proposed framework. Experiments on a practical 82-bus 10~kV distribution network demonstrate the effectiveness of the proposed framework.

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