Data-Driven Power System Optimal Decision Making Strategy under Wildfire Events

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dc.contributor.authorHong, Wanshi
dc.contributor.authorWang, Bin
dc.contributor.authorYao, Mengqi
dc.contributor.authorCallaway, Duncan
dc.contributor.authorDale, Larry
dc.contributor.authorHuang, Can
dc.date.accessioned2021-12-24T17:50:44Z
dc.date.available2021-12-24T17:50:44Z
dc.date.issued2022-01-04
dc.description.abstractWildfire activities are increasing in the western United States in recent years, causing escalating threats to power systems. This paper developed an optimal and data-driven decision-making framework that improves power system resilience under wildfire risks. An optimal load shedding plan is formulated based on optimal power flow analysis. To avoid power system cascading failure caused by wildfire, we added additional transmission line flow constraints based on the identification of power lines with high ignition risk. Finally, a data-driven method is developed, leveraging multiple machine learning techniques, to model the complex correlations between input wildfire scenarios and the output power management strategy with significantly reduced computational complexities. The proposed data-driven decision-making framework can reduce the safety impacts on the electricity consumers, improve power system resilience under wildfire events.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.436
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79771
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectResilient Networks
dc.subjectmachine learning
dc.subjectoptimal power flow
dc.subjectwildfire
dc.titleData-Driven Power System Optimal Decision Making Strategy under Wildfire Events
dc.type.dcmitext

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