Large-Scale Resilience Planning for Wildfire-Prone Electricity-System via Adaptive Robust Optimization

TL;DR

This paper presents a comprehensive planning framework that optimizes infrastructure configuration and operational responses to mitigate wildfire risk in power systems, balancing safety and service reliability.

Contribution

It introduces a tri-level robust optimization model that jointly considers long-term infrastructure decisions and short-term operational responses under wildfire ignition uncertainty.

Findings

Coordinated planning significantly reduces wildfire risk and service disruptions.

The data-driven uncertainty set effectively captures ignition risk variability.

The scalable algorithm enables practical application to large utility systems.

Abstract

Wildfire risk poses a growing challenge for electric utilities, as powerline failures can ignite wildfires while large fires can disrupt grid operations. Utilities increasingly rely on operational interventions such as Public Safety Power Shutoffs (PSPS) and fast-trip protection to mitigate ignition risk, but these measures can cause widespread service disruptions if deployed indiscriminately. Infrastructure planning decisions--such as feeder sectionalization and protection configuration--play a key role in determining how effectively these interventions can be targeted. We develop a planning framework for wildfire resilience that jointly optimizes long-term infrastructure configuration and short-term operational response under uncertain ignition risk. The problem is formulated as a tri-level optimization model capturing the interaction between infrastructure planning, wildfire risk realization, and adaptive operational decisions. To represent system-wide ignition uncertainty, we construct a data-driven uncertainty set that combines segment-level prediction intervals with group-level uncertainty budgets. Leveraging the model structure, we reformulate the problem as a tractable robust optimization model and develop a scalable column-and-constraint generation algorithm. Synthetic experiments and a large-scale case study on an investor-owned utility distribution system show that coordinated planning of sectionalization and operational mitigation strategies can substantially reduce wildfire risk while maintaining service reliability.