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Creators/Authors contains: "Yang, Haoxiang"

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  1. ; ; ; ; (, Electric Power Systems Research)
    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. 
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    Full Text Available 
  2. ; ; ; ; (, IEEE Transactions on Power Systems)
    Full Text Available 
  3. (, Advances in Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available