Agent-based modeling of peer-to-peer energy trading in a smart grid environment

dc.contributor.authorSilva, Pedro
dc.contributor.authorGough, Matthew
dc.contributor.authorSantos, Sérgio F.
dc.contributor.authorHome-Ortiz, Juan M.
dc.contributor.authorShafie-khah, M.
dc.contributor.authorCatalão, João P. S.
dc.contributor.authorOsório, Gerardo J.
dc.date.accessioned2022-02-01T12:24:11Z
dc.date.available2022-02-01T12:24:11Z
dc.date.issued2021-09
dc.description.abstractEnd users have become active participants in local electricity market transactions because of the growth of the smart grid concept and energy storage systems (ESS). This participation is optimized in this article using a stochastic two-stage model considering the day-ahead and real-time electricity market data. This model optimally schedules the operation of a Smart Home (SH) to meet its energy demand. In addition, the uncertainty of wind and photovoltaic (PV) generation is considered along with different appliances. In this paper, the participation of an EV (electric vehicle), together with the battery energy storage systems, which allow for the increase in bidirectional energy transactions are considered. Demand Response (DR) programs are also incorporated which consider market prices in real-time and impact the scheduling process. A comparative analysis of the performance of a smart home participating in the electricity market is carried out to determine an optimal DR schedule for the smart homeowner. The results show that the SH’s participation in a real-time pricing scheme not only reduces the operating costs but also leads to better than expected profits. Moreover, total, day-ahead and real-time expected profits are better in comparison with existing literature. The objective of this paper is to analyze the SH performance within the electrical market context so as to increase the system’s flexibility whilst optimizing DR schedules that can mitigate the variability of end-users generation and load demand.pt_PT
dc.identifier.citationSilva, P., Osório, G. J., Gough, M., Santos, S. F., Home-Ortiz, J. M., Shafie-khah, M., & Catalão, J. P. S. (2021). Agent-based modeling of peer-to-peer energy trading in a smart grid environment. In Proceedings of the 21th IEEE International Conference on Environment and Electrical Engineering and 5th IEEE Industrial and Commercial Power Systems Europe (EEEIC 2021 / I&CPS Europe 2021), Bari, Italy, 7-10 September 2021 (pp. 1-6). doi: 10.1109/EEEIC/ICPSEurope51590.2021.9584767. Disponível no Repositório UPT, http://hdl.handle.net/11328/3917pt_PT
dc.identifier.doi10.1109/EEEIC/ICPSEurope51590.2021.9584767pt_PT
dc.identifier.isbn978-1-6654-3613-7
dc.identifier.urihttp://hdl.handle.net/11328/3917
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversionhttps://doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584767pt_PT
dc.rightsrestricted accesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectEnergy management systempt_PT
dc.subjectEnergy storage systempt_PT
dc.subjectDemand responsept_PT
dc.subjectInternet of thingspt_PT
dc.subjectSmart homept_PT
dc.subjectSmart gridpt_PT
dc.subjectStochastic programmingpt_PT
dc.titleAgent-based modeling of peer-to-peer energy trading in a smart grid environmentpt_PT
dc.typejournal articlept_PT
degois.publication.firstPage1pt_PT
degois.publication.lastPage6pt_PT
degois.publication.locationBari, Italy, 7-10 September, 2021pt_PT
degois.publication.title21th IEEE International Conference on Environment and Electrical Engineering and 5th IEEE Industrial and Commercial Power Systems Europe (EEEIC 2021 / I&CPS Europe 2021)pt_PT
dspace.entity.typePublicationen
person.affiliation.nameREMIT – Research on Economics, Management and Information Technologies
person.familyNameOsório
person.givenNameGerardo J.
person.identifier.ciencia-idBD19-D0AD-65DB
person.identifier.gsidt13DoaMAAAAJ
person.identifier.orcid0000-0001-8328-9708
person.identifier.ridC-3616-2014
person.identifier.scopus-author-id54783251300
relation.isAuthorOfPublication7ce5da40-610d-4361-a87f-5cbdfe392256
relation.isAuthorOfPublication.latestForDiscovery7ce5da40-610d-4361-a87f-5cbdfe392256

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