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Uniform Price-based Peer-to-peer Energy Trading with Data-driven Market Clearing Price Estimation
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dc.contributor.authorNoorfatima, Nadya-
dc.contributor.authorYoon, Donghyun-
dc.contributor.authorJung, Jaesung-
dc.date.issued2024-01-01-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/37140-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85207457596&origin=inward-
dc.description.abstractThe actual market clearing price (MCP) of the uniform price-based peer-to-peer (UPP2P) energy trading may differ from its optimal value owing to imperfect competition. Meanwhile, in the UPP2P, MCP is essential to determining optimal social welfare and thus allocating trading capacity among participants. Therefore, this study proposes UPP2P energy trading with novel data-driven MCP estimation using the finite horizon Markov decision process (MDP). Using historical data, the non-technical aspects of the UPP2P operation, such as market power, incomplete information, and bidding strategy, can be integrated into the process of predicting optimal MCP. Furthermore, a modified Kirschen network cost allocation (KNCA) method, which can estimate the effect of UPP2P participants on the network reliability and is incorporated to elucidate the optimization of MCP. The proposed UPP2P with data-driven MCP estimation is applied on a noncooperative matrix game-based UPP2P algorithm, and simulation results are given to show the effectiveness of the method.-
dc.description.sponsorshipThis study was supported by the International Energy Joint R&D Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20228530050030).-
dc.language.isoeng-
dc.publisherIEEE Computer Society-
dc.subject.meshEnergy trading-
dc.subject.meshKirschen network cost allocation-
dc.subject.meshMarket clearing-
dc.subject.meshMarkov Decision Processes-
dc.subject.meshMatrix game-
dc.subject.meshNetwork cost allocation-
dc.subject.meshNoncooperative matrix game-
dc.subject.meshPeer to peer-
dc.subject.meshPeer-to-peer energy trading-
dc.subject.meshPrice-based-
dc.subject.meshUniform price-
dc.subject.meshUniform price-based market clearing-
dc.titleUniform Price-based Peer-to-peer Energy Trading with Data-driven Market Clearing Price Estimation-
dc.typeConference-
dc.citation.conferenceDate2024.7.21. ~ 2024.7.25.-
dc.citation.conferenceName2024 IEEE Power and Energy Society General Meeting, PESGM 2024-
dc.citation.edition2024 IEEE Power and Energy Society General Meeting, PESGM 2024-
dc.citation.titleIEEE Power and Energy Society General Meeting-
dc.identifier.bibliographicCitationIEEE Power and Energy Society General Meeting-
dc.identifier.doi10.1109/pesgm51994.2024.10688432-
dc.identifier.scopusid2-s2.0-85207457596-
dc.identifier.urlhttp://ieeexplore.ieee.org/xpl/conferences.jsp-
dc.subject.keywordKirschen network cost allocation-
dc.subject.keywordMarkov decision process-
dc.subject.keywordnoncooperative matrix games-
dc.subject.keywordpeer-to-peer energy trading-
dc.subject.keyworduniform price-based market clearing-
dc.type.otherConference Paper-
dc.description.isoafalse-
dc.subject.subareaEnergy Engineering and Power Technology-
dc.subject.subareaNuclear Energy and Engineering-
dc.subject.subareaRenewable Energy, Sustainability and the Environment-
dc.subject.subareaElectrical and Electronic Engineering-
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