P2P Power Trading between Nanogrid Clusters Exploiting Electric Vehicles and Renewable Energy Sources

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dc.contributor.authorLee, Sangkeumko
dc.contributor.authorHar, Dongsooko
dc.contributor.authorJin, Hojunko
dc.contributor.authorNengroo, Sarvar Hussainko
dc.date.accessioned2021-12-14T06:48:07Z-
dc.date.available2021-12-14T06:48:07Z-
dc.date.created2021-12-07-
dc.date.created2021-12-07-
dc.date.created2021-12-07-
dc.date.created2021-12-07-
dc.date.issued2021-12-15-
dc.identifier.citationInternational Conference on Computational Science and Computational Intelligence (CSCI)-
dc.identifier.urihttp://hdl.handle.net/10203/290585-
dc.description.abstractP2P power trading addresses direct energy exchange between peers, thereby energy from small-scale distributed energy resources in households, workplaces, factories, and other locations is exchanged among neighborhood energy prosumers and consumers. A novel method for real-time P2P power trading between nanogrid clusters based on cooperative game theory is proposed in this paper. Cooperative P2P power trading is used as a powerful aid for a nanogrid cluster's power management involving electric vehicles and renewable energy sources (wind turbine and photovoltaic energy system). For the nanogrid clusters' power management, multi-objective optimization making use of relevant information obtained from the Internet of Things and from the time-varying production of hybrid wind power and PV power is carried out. As a result, cooperative P2P power trading between nanogrid clusters can save the electricity cost and amount of energy supplied from the grid, as compared to the stand-alone nanogrid clusters without P2P power trading.-
dc.languageEnglish-
dc.publisheramerican council on science and education-
dc.titleP2P Power Trading between Nanogrid Clusters Exploiting Electric Vehicles and Renewable Energy Sources-
dc.typeConference-
dc.identifier.wosid000832229300332-
dc.identifier.scopusid2-s2.0-85133944846-
dc.type.rimsCONF-
dc.citation.publicationnameInternational Conference on Computational Science and Computational Intelligence (CSCI)-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationLas Vegas, Nevada-
dc.identifier.doi10.1109/CSCI54926.2021.00349-
dc.contributor.localauthorHar, Dongsoo-
dc.contributor.nonIdAuthorLee, Sangkeum-
dc.contributor.nonIdAuthorJin, Hojun-
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