  The frequency of wildfire disasters has surged five-fold in the past 50 years
due to climate change. Preemptive de-energization is a potent strategy to
mitigate wildfire risks but substantially impacts customers. We propose a
multistage stochastic programming model for proactive de-energization planning,
aiming to minimize economic loss while accomplishing a fair load delivery. We
model wildfire disruptions as stochastic disruptions with varying timing and
intensity, introduce a cutting-plane decomposition algorithm, and test our
approach on the RTS-GLMC test case. Our model consistently offers a robust and
fair de-energization plan that mitigates wildfire damage costs and minimizes
load-shedding losses, particularly when pre-disruption restoration is
considered.
