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dc.contributor.authorKIM, SEOKWOO-
dc.contributor.authorCHOI, DONG GU-
dc.date.accessioned2024-05-07T05:20:30Z-
dc.date.available2024-05-07T05:20:30Z-
dc.date.created2024-04-16-
dc.date.issued2024-08-
dc.identifier.issn0377-2217-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/123148-
dc.description.abstractIn many energy markets, the trade amount of electricity must be committed to before the actual supply. This study explores one consecutive operational challenge for a virtual power plant—the optimal bidding for highly uncertain distributed energy resources in a day-ahead electricity market. The optimal bidding problem is formulated as a scenario-based multi-stage stochastic optimization model. However, the scenario-tree approach raises two consequent issues—scenario overfitting and massive computation cost. This study addresses the issues by deploying a sample robust optimization approach with linear decision rules. A tractable robust counterpart is derived from the model where the uncertainty appears in a nonlinear objective and constraints. By applying the decision rules to the balancing policy, the original model can be reduced to a two-stage stochastic mixed-integer programming model and then efficiently solved by adopting a dual decomposition method combined with heuristics. Based on real-world business data, a numerical experiment is conducted with several benchmark models. The results verify the superior performance of our proposed approach based on increased out-of-sample profits and decreased overestimation of in-sample profits.-
dc.languageEnglish-
dc.publisherElsevier BV-
dc.relation.isPartOfEuropean Journal of Operational Research-
dc.titleA sample robust optimal bidding model for a virtual power plant-
dc.typeArticle-
dc.identifier.doi10.1016/j.ejor.2024.03.001-
dc.type.rimsART-
dc.identifier.bibliographicCitationEuropean Journal of Operational Research, v.316, no.3, pp.1101 - 1113-
dc.identifier.wosid001229699200001-
dc.citation.endPage1113-
dc.citation.number3-
dc.citation.startPage1101-
dc.citation.titleEuropean Journal of Operational Research-
dc.citation.volume316-
dc.contributor.affiliatedAuthorKIM, SEOKWOO-
dc.contributor.affiliatedAuthorCHOI, DONG GU-
dc.identifier.scopusid2-s2.0-85186650449-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.type.docTypeArticle-
dc.subject.keywordPlusDUAL DECOMPOSITION-
dc.subject.keywordPlusLINEAR-PROGRAMS-
dc.subject.keywordPlusOPTIMIZATION-
dc.subject.keywordPlusSTRATEGY-
dc.subject.keywordPlusENERGY-
dc.subject.keywordPlusMARKET-
dc.subject.keywordPlusSYSTEMS-
dc.subject.keywordPlusREFORMULATIONS-
dc.subject.keywordPlusFLEXIBILITY-
dc.subject.keywordPlusRESOURCES-
dc.subject.keywordAuthorOR in energy-
dc.subject.keywordAuthorStochastic programming-
dc.subject.keywordAuthorAuctions/bidding-
dc.subject.keywordAuthorSample robust optimization-
dc.subject.keywordAuthorLinear decision rules-
dc.relation.journalWebOfScienceCategoryManagement-
dc.relation.journalWebOfScienceCategoryOperations Research & Management Science-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBusiness & Economics-
dc.relation.journalResearchAreaOperations Research & Management Science-

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최동구CHOI, DONG GU
Dept. of Industrial & Management Eng.
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