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

arXiv: https://arxiv.org/abs/2601.10044 · HTML: https://mlsanity.com/paper/2601.10044/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 6 of 6)

- [A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management](https://mlsanity.com/paper/2509.09053/) (2025)
- [Learning Optimal Crew Dispatch for Grid Restoration Following an Earthquake](https://mlsanity.com/paper/2509.04308/) (2025)
- [Optimizing Service Restoration in Distribution Systems with Uncertain Repair Time and Demand](https://mlsanity.com/paper/1806.11076/) (2018)
- [Heterogeneous Multi-Agent Proximal Policy Optimization for Power Distribution System Restoration](https://mlsanity.com/paper/2511.14730/) (2025)
- [Data-Driven Outage Restoration Time Prediction via Transfer Learning with Cluster Ensembles](https://mlsanity.com/paper/2112.11374/) (2021)
- [Quantum Optimization for Optimal Power Flow: CVQLS-Augmented Interior Point Method](https://mlsanity.com/paper/2412.14095/) (2024)

## Built on by (most cited first, 4 of 4)

- [An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling](https://mlsanity.com/paper/2606.29117/) (2026)
- [A Deep Reinforcement Learning (DRL)-Based Transformer Method for Solving the Open Shop Scheduling Problem](https://mlsanity.com/paper/2606.13682/) (2026)
- [A Graph Neural Network--Guided Genetic Algorithm for Physical Internet Supply Chain Optimization under Cost Uncertainty](https://mlsanity.com/paper/2608.10245/) (2026)
- [Dynamic Multi-Depot Vehicle Routing with Online Requests: Event-Driven Transformer--DRL and Rolling-Horizon Benchmarking](https://mlsanity.com/paper/2608.13799/) (2026)

## Abstract (arXiv)

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.