Multistage stochastic program for mitigating power system risks under wildfire disruptions

The frequency of wildfire disasters has surged fivefold 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. •Formulate a multistage stochastic mixed-integer programming model to manage wildfire risk in power system operations.•Incorporate de-energization and restoration operations to balance wildfire risk and load-shedding cost.•Implement a decomposition algorithm comprising several cut families to efficiently solve the large-scale mixed-integer program.