Event-Driven Deep RL Dispatcher for Post-Storm Distribution System Restoration

Abstract

Natural hazards such as hurricanes and floods damage power grid equipment, forcing operators to replan restoration repeatedly as new information becomes available. This paper develops a deep reinforcement learning (DRL) dispatcher that serves as a real-time decision engine for crew-to-repair assignments. We model restoration as a sequential, information-revealing process and learn an actor-critic policy over compact features such as component status, travel/repair times, crew availability, and marginal restoration value. A feasibility mask blocks unsafe or inoperable actions, such as power flow limits, switching rules, and crew-time constraints, before they are applied. To provide realistic runtime inputs without relying on heavy solvers, we use lightweight surrogates for wind and flood intensities, fragility-based failure, spatial clustering of damage, access impairments, and progressive ticket arrivals. In simulated hurricane and flood events, the learned policy updates crew decisions in real time as new field reports arrive. Because the runtime logic is lightweight, it improves online performance (energy-not-supplied, critical-load restoration time, and travel distance) compared with mixed-integer programs and standard heuristics. The proposed approach is tested on the IEEE 13- and 123-bus feeders with mixed hurricane/flood scenarios.