Dynamic Transmission Line Switching Amidst Wildfire-Prone Weather Under Decision-Dependent Uncertainty

TL;DR

This paper develops a multi-stage optimization model for dynamically adjusting transmission line configurations during wildfires, incorporating decision-dependent uncertainties to improve grid resilience and response efficiency.

Contribution

It introduces a novel multi-stage robust optimization framework that accounts for decision-dependent uncertainties in wildfire scenarios, with enhanced computational methods for real-time application.

Findings

Dynamic policies outperform static ones in wildfire scenarios

Proposed algorithms significantly reduce computational time

Realistic case study demonstrates improved grid resilience

Abstract

During dry and windy seasons, environmental conditions significantly increase the risk of wildfires, exposing power grids to disruptions caused by transmission line failures. Wildfire propagation exacerbates grid vulnerability, potentially leading to prolonged power outages. To address this challenge, we propose a multi-stage optimization model that dynamically adjusts transmission grid topology in response to wildfire propagation, aiming to develop an optimal response policy. By accounting for decision-dependent uncertainty, where line survival probabilities depend on usage, we employ distributionally robust optimization to model uncertainty in line survival distributions. We adapt the stochastic nested decomposition algorithm and derive a deterministic upper bound for its finite convergence. To enhance computational efficiency, we exploit the Lagrangian dual problem structure for a faster generation of Lagrangian cuts. Using realistic data from the California transmission grid, we demonstrate the superior performance of dynamic response policies against two-stage alternatives through a comprehensive case study. In addition, we construct easy-to-implement policies that significantly reduce computational burden while maintaining good performance in real-time deployment.