Modeling Adversarial Wildfires for Power Grid Disruption

TL;DR

This paper introduces a novel mixed-integer conic programming approach to model adversarial wildfires that threaten power grid infrastructure, enabling better planning for wildfire-induced outages.

Contribution

It develops a new convex relaxation of wildfire spread dynamics and integrates it into power system resilience analysis, addressing limitations of traditional wildfire models.

Findings

Identifies minimum time-to-outage for power grid elements under wildfire scenarios.

Determines maximum load shed due to wildfire-induced outages.

Provides a framework for operational resilience against wildfires.

Abstract

Electric power infrastructure faces increasing risk of damage and disruption due to wildfire. Operators of power grids in wildfire-prone regions must consider the potential impacts of unpredictable fires. However, traditional wildfire models do not effectively describe worst-case, or even high-impact, fire behavior. To address this issue, we propose a mixed-integer conic program to characterize an adversarial wildfire that targets infrastructure while respecting realistic fire spread dynamics. We design a wind-assisted fire spread set based on the Rothermel fire spread model and propose principled convex relaxations of this set, including a new relaxation of the inner product over Euclidean balls. We present test cases derived from the recent Park, Eaton, and Palisades fires in California and solve models to identify the minimum time-to-outage of multiple-element contingencies and the maximum load shed associated with a sequence of element outages caused by a realistic wildfire. We use the minimum time-to-outage values to screen contingencies and construct security-constrained optimal power flow models that promote operational resilience against wildfire.