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Natural wildfire becomes increasingly frequent as climate change evolves,posing a growing threat to power systems, while grid failures simultaneouslyfuel the most destructive wildfires. Preemptive de-energization of gridequipment is effective in mitigating grid-induced wildfires but may causesignificant power outages during natural wildfires. This paper proposes a noveltwo-stage stochastic program for planning preemptive de-energization and solvesit via an enhanced Lagrangian cut decomposition algorithm. We model wildfireevents as stochastic disruptions with random magnitude and timing. Thestochastic program maximizes the electricity delivered while proactivelyde-energizing components over multiple time periods to reduce wildfire risks.We use a cellular automaton process to sample grid failure and wildfirescenarios driven by realistic risk and environmental factors. We test ourmethod on an augmented version of the RTS-GLMC test case in Southern Californiaand compare it with four benchmark cases, including deterministic,wait-and-see, and robust optimization formulations as well as a comparison withprior wildfire risk optimization. Our method reduces wildfire damage costs andload-shedding losses, and our nominal plan is robust against uncertaintyperturbation.

Multi-period Power System Risk Minimization under Wildfire Disruptions