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

Shuyi Chen, Shixiang Zhu, Ramteen Sioshansi

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.

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

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.

Paper Structure

This paper contains 21 sections, 2 theorems, 24 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Wildfire Resilience Planning Framework
  4. Planning and Operational Structure
  5. Tri-Level Optimization Model
  6. Uncertainty Quantification for Fire Ignition
  7. Prediction Model for Fire Ignition
  8. Uncertainty Set Construction
  9. Solution Strategy
  10. Reformulation of the Lower-Level Recourse Problem
  11. Outer Master Problem
  12. Mixed Integer Bi-level Subproblem
  13. Convergence Guarantee
  14. Synthetic Data Example
  15. Case Study: California Distribution System
  16. ...and 6 more sections

Key Result

Theorem 1

The uncertainty set $\mathcal{U}$ constructed in eq:uncertainty_set_compact satisfies where In particular, if $\Gamma$ is known, one can simply set $\tilde{\alpha} \coloneqq (\alpha-\epsilon)_+$ and replace $\hat{Q}(\alpha)$ with $\hat{Q}(\tilde{\alpha})$ in eq:bounds. $\blacktriangleleft$$\blacktriangleleft$

Figures (4)

  • Figure 1: The decision pipeline of wildfire resilience planning.
  • Figure 2: Synthetic results across hyperparameters.
  • Figure 3: Overview of the optimal sectionalization and fast-trip configuration in the California case study. The map highlights circuits where sectionalization and fast-trip protection are implemented across the study region, and they are concentrated in high-risk areas, broadly aligning with the CPUC-designated High Fire-Threat Districts (HFTD) cpuc_hftd_map.
  • Figure 4: California case study results under different planning parameters. The maps provide a closer view of sectionalization and fast-trip decisions in Santa Barbara, a high-risk, high-population-density area of California, illustrating how the spatial configuration changes as the sectionalization budget $C$ and reliability constraint $W$ vary. Results for the fast-trip budget $B$ are omitted due to similarity to varying $C$.

Theorems & Definitions (3)

  • Theorem 1: Coverage under $\alpha$-mixing
  • proof
  • Corollary 1: Finite Convergence of Nested CCG ZENG2013457