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

Multistage Stochastic Program for Mitigating Power System Risks under Wildfire Disruptions