Cooperative game theory based peer to peer energy trading algorithm

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dc.contributor.authorMalik, Sweta
dc.contributor.authorDuffy, Maeve
dc.contributor.authorThakur, Subhasis
dc.contributor.authorHayes, Barry
dc.contributor.authorBreslin, John G.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2021-11-12T14:41:00Z
dc.date.available2021-11-12T14:41:00Z
dc.date.issued2020-01
dc.date.updated2021-11-12T14:19:27Z
dc.description.abstractThe energy sector is undergoing a paradigm shift to integrate the increasing volume of embedded renewable energy generation and creating local energy communities or LECs that have been an essential component in increasing the same. Peer to Peer (P2P) energy trading is one of the alternatives to curb the surplus energy flow and would also help in maintaining a dynamic balance between supply and demand in the power grid. In this paper, we propose a P2P energy trading mechanism with distributed solar photovoltaic, community battery storage, and electric vehicle charging points. Game theory is the most widely used approach for P2P energy trading because of its characteristic of solving complicated interactions between provider and receiver. In the present work, we have considered a coalition based cooperative game theory framework whose objective is to maximize the total profit of the coalition. The simulation framework of this mechanism has been tested on a local energy community with 100 households having 50 consumers and 50 prosumers creating a win-win approach for both consumers and prosumers (users able to generate and consume simultaneously). Various trading scenarios have been proposed in this paper depending on geographical location, maximum energy demand, and maximum energy generated. These trading scenarios have been tested on a low voltage model to check their feasibility for a real network. The best operational performance priority at each timeslot with solar PV and community storage has also been analysed.en
dc.description.sponsorshipScience Foundation Ireland (SFI/12/RC/2289 P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMalik, S., Duffy, M., Thakur, S., Hayes, B. and Breslin, J. G. (2020) 'Cooperative game theory based peer to peer energy trading algorithm', 12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2020), Online, 9 - 12 November, pp. 135-142. doi: 10.1049/icp.2021.1241en
dc.identifier.doi10.1049/icp.2021.1241en
dc.identifier.endpage142en
dc.identifier.isbn978-1-83953-524-6
dc.identifier.startpage135en
dc.identifier.urihttps://hdl.handle.net/10468/12211
dc.language.isoenen
dc.publisherInstitution of Engineering and Technology (IET)en
dc.relation.ispartof12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2020), Online, 9 - 12 November
dc.relation.urihttp://medpower2020.org/
dc.relation.urihttps://digital-library.theiet.org/content/conferences/10.1049/icp.2021.1241
dc.rights© 2020, Institution of Engineering and Technology (IET).en
dc.subjectCoalitionsen
dc.subjectCommunity batteryen
dc.subjectCooperative game theoryen
dc.subjectEnergy tradingen
dc.subjectPeer to peeren
dc.titleCooperative game theory based peer to peer energy trading algorithmen
dc.typeConference itemen
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