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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2310.16544 |
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| _version_ | 1866929307015708672 |
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| author | Yang, Hanbin Rhodes, Noah Yang, Haoxiang Roald, Line Ntaimo, Lewis |
| author_facet | Yang, Hanbin Rhodes, Noah Yang, Haoxiang Roald, Line Ntaimo, Lewis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_16544 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multistage Stochastic Program for Mitigating Power System Risks under Wildfire Disruptions Yang, Hanbin Rhodes, Noah Yang, Haoxiang Roald, Line Ntaimo, Lewis Optimization and Control 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. |
| title | Multistage Stochastic Program for Mitigating Power System Risks under Wildfire Disruptions |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2310.16544 |