DC Field | Value | Language |
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dc.contributor.author | KIM, SEOKWOO | - |
dc.contributor.author | CHOI, DONG GU | - |
dc.date.accessioned | 2024-05-07T05:20:30Z | - |
dc.date.available | 2024-05-07T05:20:30Z | - |
dc.date.created | 2024-04-16 | - |
dc.date.issued | 2024-08 | - |
dc.identifier.issn | 0377-2217 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/123148 | - |
dc.description.abstract | In 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.language | English | - |
dc.publisher | Elsevier BV | - |
dc.relation.isPartOf | European Journal of Operational Research | - |
dc.title | A sample robust optimal bidding model for a virtual power plant | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.ejor.2024.03.001 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | European Journal of Operational Research, v.316, no.3, pp.1101 - 1113 | - |
dc.identifier.wosid | 001229699200001 | - |
dc.citation.endPage | 1113 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1101 | - |
dc.citation.title | European Journal of Operational Research | - |
dc.citation.volume | 316 | - |
dc.contributor.affiliatedAuthor | KIM, SEOKWOO | - |
dc.contributor.affiliatedAuthor | CHOI, DONG GU | - |
dc.identifier.scopusid | 2-s2.0-85186650449 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | DUAL DECOMPOSITION | - |
dc.subject.keywordPlus | LINEAR-PROGRAMS | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | STRATEGY | - |
dc.subject.keywordPlus | ENERGY | - |
dc.subject.keywordPlus | MARKET | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordPlus | REFORMULATIONS | - |
dc.subject.keywordPlus | FLEXIBILITY | - |
dc.subject.keywordPlus | RESOURCES | - |
dc.subject.keywordAuthor | OR in energy | - |
dc.subject.keywordAuthor | Stochastic programming | - |
dc.subject.keywordAuthor | Auctions/bidding | - |
dc.subject.keywordAuthor | Sample robust optimization | - |
dc.subject.keywordAuthor | Linear decision rules | - |
dc.relation.journalWebOfScienceCategory | Management | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Business & Economics | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
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