Mitigating the Impact of Uncertain Wildfire Risk on Power Grids through Topology Control

TL;DR

This work addresses mitigating wildfire risk in power grids under uncertainty by formulating a two-stage stochastic MILP that jointly optimizes generation, load shedding, and topology switching under PSPS scenarios. It distinguishes pre-event topology control, chosen before uncertainty realization, from post-event control, which adapts topology per scenario, both solved via Progressive Hedging applied to a linear DC-OPF with recourse variables. Experiments on the RTS-GMLC benchmark with literature-based wildfire risk data show that post-event control typically yields lower total cost and load shedding, while high confidence in risk forecasts (larger $\bm R$) makes pre-event control almost as effective. The results demonstrate the scalability of PH for these problems and provide actionable guidance on how forecast accuracy influences the choice between pre- and post-event strategies for wildfire resilience in power systems.

Abstract

Wildfires pose a significant threat to the safe and reliable operations of the electric grid. To mitigate wildfire risk, system operators resort to public safety power shutoffs, or PSPS, that shed load for a subset of customers. As wildfire risk forecasts are stochastic, such decision-making may often be sub-optimal. This paper proposes a two-stage topology control problem that jointly minimizes generation and load-shedding costs in the face of uncertain fire risk. Compared to existing work, we include preand post-event topology control actions and consider scenarios where the wildfire risk is known with low and high confidence. The effectiveness of the proposed approach is demonstrated using a benchmark test system, artificially geo-located in Southern California, and using stochastic wildfire risk data that exists in the literature. Our work provides a crucial study of the comparative benefits of pre-event versus post-event control and the effects of wildfire risk accuracy on each control strategy.

Figures (6)

• Figure 1: Wildfire forecast timeline with control variables pre-event and post-event control policies.
• Figure 2: RTS-GMLC test system with geographic information
• Figure 3: Heat map of line (branch) risk values for the RTS-GMLC system
• Figure 4: Objective value of the pre-event and post-event control problems with varying scenarios for a load scaling of $1.0$ and $1.05$.
• Figure 5: Frequency of switching for different lines under post-event control.