A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch Considering Wind Power Penetration

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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.authorValinejad, Jaberen
dc.date.accessioned2024-01-23T18:13:53Zen
dc.date.available2024-01-23T18:13:53Zen
dc.date.issued2020-08-10en
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 estimate the uncertainty in the stochastic economic dispatch (SED) problem for the purpose of forecasting, the conventional Monte-Carlo (MC) method is prohibitively time-consuming for practical applications. To overcome this problem, we propose a novel Gaussian-process-emulator (GPE)-based approach to quantify the uncertainty in SED considering wind power penetration. Facing high-dimensional real-world data representing the correlated uncertainties from wind generation, a manifold-learning-based Isomap algorithm is proposed to efficiently represent the low-dimensional hidden probabilistic structure of the data. In this low-dimensional latent space, with Latin hypercube sampling (LHS) as the computer experimental design, a GPE is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated SED model. This reduced-order representative 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 test system reveal the impressive performance of the proposed method.en
dc.description.versionPublished versionen
dc.format.extentPages 671-681en
dc.format.extent11 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TSTE.2020.3015353en
dc.identifier.eissn1949-3037en
dc.identifier.issn1949-3029en
dc.identifier.issue1en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117616en
dc.identifier.volume12en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsPublic Domain (U.S.)en
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.subjectUncertaintyen
dc.subjectComputational modelingen
dc.subjectEconomicsen
dc.subjectBayes methodsen
dc.subjectStochastic processesen
dc.subjectRenewable energy sourcesen
dc.subjectPower systemsen
dc.subjectStochastic economic dispatchen
dc.subjectreduced-order modelingen
dc.subjectmanifold learningen
dc.subjectuncertainty estimationen
dc.subjectrenewable energyen
dc.titleA Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch Considering Wind Power Penetrationen
dc.title.serialIEEE Transactions on Sustainable Energyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
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

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