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.
@article{arxiv.2310.16544,
title = {Multistage Stochastic Program for Mitigating Power System Risks under Wildfire Disruptions},
author = {Hanbin Yang and Noah Rhodes and Haoxiang Yang and Line Roald and Lewis Ntaimo},
journal= {arXiv preprint arXiv:2310.16544},
year = {2024}
}
Comments
8 pages, 6 figures, conference. accepted by PSCC 2024. arXiv admin note: text overlap with arXiv:2305.02933