Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation

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dc.contributor.authorHu, Zhixiongen
dc.contributor.authorXu, Yijunen
dc.contributor.authorKorkali, Merten
dc.contributor.authorChen, Xiaoen
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorTong, Charles H.en
dc.date.accessioned2024-01-22T14:53:38Zen
dc.date.available2024-01-22T14:53:38Zen
dc.date.issued2020en
dc.description.abstractThe increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To solve this stochastic economic dispatch, the conventional Monte Carlo method is prohibitively time consuming for medium- and large-scale power systems. To overcome this problem, we propose in this paper a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Based on the dimension-reduction results obtained by the Karhunen-Loeve expansion, a Gaussian-process emulator is constructed. This surrogate allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus system reveal that the proposed method has an excellent performance as compared to the traditional Monte Carlo method.en
dc.description.versionPublished versionen
dc.format.extent5 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/isgt45199.2020.9087714en
dc.identifier.isbn9781728131030en
dc.identifier.issn2167-9665en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117516en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsPublic Domain (U.S.)en
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.titleUncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulationen
dc.title.serial2020 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT)en
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherProceedings Paperen
dc.type.otherBook in seriesen
pubs.finish-date2020-02-20en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.start-date2020-02-17en

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