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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.more » « less
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Lagos, Tomás; Choi, Junyeong; Segundo, Brittany; Gan, Jianbang; Ntaimo, Lewis; Prokopyev, Oleg A (, European Journal of Operational Research)
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Yang, Hanbin; Rhodes, Noah; Yang, Haoxiang; Roald, Line; Ntaimo, Lewis (, IEEE Transactions on Power Systems)
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