  Natural wildfire becomes increasingly frequent as climate change evolves,
posing a growing threat to power systems, while grid failures simultaneously
fuel the most destructive wildfires. Preemptive de-energization of grid
equipment is effective in mitigating grid-induced wildfires but may cause
significant power outages during natural wildfires. This paper proposes a novel
two-stage stochastic program for planning preemptive de-energization and solves
it via an enhanced Lagrangian cut decomposition algorithm. We model wildfire
events as stochastic disruptions with random magnitude and timing. The
stochastic program maximizes the electricity delivered while proactively
de-energizing components over multiple time periods to reduce wildfire risks.
We use a cellular automaton process to sample grid failure and wildfire
scenarios driven by realistic risk and environmental factors. We test our
method on an augmented version of the RTS-GLMC test case in Southern California
and compare it with four benchmark cases, including deterministic,
wait-and-see, and robust optimization formulations as well as a comparison with
prior wildfire risk optimization. Our method reduces wildfire damage costs and
load-shedding losses, and our nominal plan is robust against uncertainty
perturbation.
