AlphaZero Based Post-Storm Repair Crew Dispatch for Distribution Grid Restoration

TL;DR

This paper introduces an AlphaZero-based method for real-time dispatching of utility repair crews after storms, optimizing outage restoration in distribution grids through advanced AI techniques.

Contribution

It develops a novel AlphaZero-UVR approach combining neural networks and MCTS for efficient, autonomous crew routing in post-storm grid restoration.

Findings

Efficient outage repair navigation demonstrated in simulations.

AlphaZero-UVR outperforms traditional routing methods.

Real-time dispatching achieved without human intervention.

Abstract

Natural disasters such as storms usually bring significant damages to distribution grids. This paper investigates the optimal routing of utility vehicles to restore outages in the distribution grid as fast as possible after a storm. First, the poststorm repair crew dispatch task with multiple utility vehicles is formulated as a sequential stochastic optimization problem. In the formulated optimization model, the belief state of the power grid is updated according to the phone calls from customers and the information collected by utility vehicles. Second, an AlphaZero[1] based utility vehicle routing (AlphaZero-UVR) approach is developed to achieve the real-time dispatching of the repair crews. The proposed AlphaZero-UVR approach combines deep neural networks with stochastic Monte-Carlo tree search (MCTS) to give a lookahead search decisions, which can learn to navigate repair crews without human guidance. Simulation results show that the proposed approach can efficiently navigate crews to repair all outages.