A Markov Decision Process Framework for Enhancing Power System Resilience during Wildfires under Decision-Dependent Uncertainty

TL;DR

This paper presents a Markov decision process framework to optimize power shutoffs during wildfires, aiming to reduce wildfire risk while managing operational costs in distribution networks.

Contribution

It introduces a state-based decision-making model with an approximate dynamic programming algorithm to improve wildfire resilience in power systems.

Findings

The approach effectively balances wildfire risk mitigation and operational costs.

Case studies demonstrate scalability to large distribution systems.

The framework enhances decision-making for wildfire resilience.

Abstract

Wildfires pose an increasing threat to the safety and reliability of power systems, particularly in distribution networks located in fire-prone regions. To mitigate ignition risk from electrical infrastructure, utilities often employ safety power shutoffs, which proactively de-energize high-risk lines during hazardous weather and restore them once conditions improve. While this strategy can result in temporary load loss, it helps prevent equipment damage and wildfire ignition development in the system. In this paper, we develop a state-based decision-making framework to optimize such switching actions over time, with the goal of minimizing total operational costs throughout a wildfire event. The model represents network topologies as Markov states, with transitions influenced by both exogenous weather conditions and endogenous power flow dynamics. To address the computational challenges posed by the large state and action spaces, we propose an approximate dynamic programming algorithm based on post-decision states. The effectiveness and scalability of the proposed approach are demonstrated through case studies on 54-bus and 138-bus distribution systems, showcasing its potential for enhancing wildfire resilience across different grid configurations.