Risk-based hybrid energy management with developing bidding strategy and advanced demand response of grid-connected microgrid based on stochastic/information gap decision theory
DC Field | Value | Language |
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dc.contributor.author | Kim, H.J. | - |
dc.contributor.author | Kim, M.K. | - |
dc.date.accessioned | 2022-01-20T06:40:59Z | - |
dc.date.available | 2022-01-20T06:40:59Z | - |
dc.date.issued | 2021-10 | - |
dc.identifier.issn | 0142-0615 | - |
dc.identifier.issn | 1879-3517 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/53998 | - |
dc.description.abstract | This study evaluates a risk-based hybrid energy management problem by creating a staircase bidding profile for microgrid operators under a confidence-based incentive demand response program. Scenario-based modeling of photovoltaic, wind turbine, and local loads is achieved by implementing a stochastic/information gap decision theory-based optimization technique; the upstream grid price uncertainty is accounted for, based on the errors between the actual and predicted values. By employing a demand response aggregator, the proposed demand response can be applied to reduce the total expected operating cost and enhance the reliability of the microgrid peak-period load, primarily through peak-period load reduction. To demonstrate the applicability and validate the effectiveness of the proposed risk-based hybrid energy management problem, a case study is analyzed and solved by applying an improved particle swarm optimization algorithm. The results demonstrate that the proposed framework can pursue risk-neutral, risk-averse, and risk-seeker strategies to provide microgrid operator with more degrees of freedom for hedging against risks. In addition, to manage price uncertainty in the optimal scheduling of grid-connected microgrid, operators can build staircase bidding curves that can be effectively submitted to the day-ahead market. Further comparative analysis reveals that the proposed method demonstrates superior solution quality and diversity with a reduced computational burden. © 2021 Elsevier Ltd | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Elsevier Ltd | - |
dc.title | Risk-based hybrid energy management with developing bidding strategy and advanced demand response of grid-connected microgrid based on stochastic/information gap decision theory | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.ijepes.2021.107046 | - |
dc.identifier.bibliographicCitation | International Journal of Electrical Power and Energy Systems, v.131 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000663435600005 | - |
dc.identifier.scopusid | 2-s2.0-85103979126 | - |
dc.citation.title | International Journal of Electrical Power and Energy Systems | - |
dc.citation.volume | 131 | - |
dc.type.docType | Article | - |
dc.publisher.location | 영국 | - |
dc.subject.keywordAuthor | Bidding strategy | - |
dc.subject.keywordAuthor | Confidence-based demand response | - |
dc.subject.keywordAuthor | Risk-based hybrid energy management | - |
dc.subject.keywordAuthor | Stochastic/information gap decision theory | - |
dc.subject.keywordAuthor | Uncertainties | - |
dc.subject.keywordPlus | Computation theory | - |
dc.subject.keywordPlus | Decision theory | - |
dc.subject.keywordPlus | Energy management | - |
dc.subject.keywordPlus | Particle swarm optimization (PSO) | - |
dc.subject.keywordPlus | Stairs | - |
dc.subject.keywordPlus | Bidding strategy | - |
dc.subject.keywordPlus | Confidence-based demand response | - |
dc.subject.keywordPlus | Demand response | - |
dc.subject.keywordPlus | Grid-connected | - |
dc.subject.keywordPlus | Hybrid energy | - |
dc.subject.keywordPlus | Micro grid | - |
dc.subject.keywordPlus | Risk-based | - |
dc.subject.keywordPlus | Risk-based hybrid energy management | - |
dc.subject.keywordPlus | Stochastic/information gap decision theory | - |
dc.subject.keywordPlus | Uncertainty | - |
dc.subject.keywordPlus | Stochastic systems | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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