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Bidding in local electricity markets with cascading wholesale market integration

2021-04Journal article Open access
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dc.contributor.authorLezama, Fernando
dc.contributor.authorSoares, João
dc.contributor.authorFaia, Ricardo
dc.contributor.authorVale, Zita
dc.contributor.authorKilkki, Olli
dc.contributor.authorRepo, Sirpa
dc.contributor.authorSegerstam, Jan
dc.date.accessioned2021-09-17T11:25:13Z
dc.date.available2021-09-17T11:25:13Z
dc.date.issued2021-04
dc.description.abstractLocal electricity markets are a promising idea to foster the efficiency and use of renewable energy at the distribution level. However, as such a new concept, how these local markets will be designed and integrated into existing market structures, and make the most profit from them, is still unclear. In this work, we propose a local market mechanism in which end-users (consumers, small producers, and prosumers) trade energy between peers. Due to possible low liquidity in the local market, the mechanism assumes that end-users fulfill their energy demands through bilateral contracts with an aggregator/retailer with access to the wholesale market. The allowed bids and offers in the local market are bounded by a feed-in tariff and an aggregator tariff guaranteeing that end-users get, at most, the expected cost without considering this market. The problem is modeled as a multi-leader single-follower bi-level optimization problem, in which the upper levels define the maximization of agent profits. In contrast, the lower level maximizes the energy traded in the local market. Due to the complexity of the matter, and lack of perfect information of end-users, we advocate the use of evolutionary computation, a branch of artificial intelligence that has been successfully applied to a wide variety of optimization problems. Throughout three different case studies considering end-users with distinct characteristics, we evaluated the performance of four different algorithms and assessed the benefits that local markets can bring to market participants. Results show that the proposed market mechanism provides overall costs improvements to market players of around 30–40% regarding a baseline where no local market is considered. However, the shift to local markets in energy procurement can affect the conventional retailer/aggregator role. Therefore, innovative business models should be devised for the successful implementation of local markets in the future.pt_PT
dc.description.sponsorshipThis work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066), from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020. Joao Soares has received support from National funds through (FCT) under grant CEECIND/02814/2017. Ricardo Faia has received support under the PhD grant SFRH/BD/133086/2017 from National Funds through (FCT).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.ijepes.2021.107045pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/18406
dc.language.isoengpt_PT
dc.publisherElsevierpt_PT
dc.relationSmart Distribution Grid: a Market Driven Approach for the Next Generation of Advanced Operation Models and Services
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0142061521002842?via%3Dihubpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectBi-level optimizationpt_PT
dc.subjectEvolutionary computationpt_PT
dc.subjectLocal electricity marketspt_PT
dc.subjectRenewable energypt_PT
dc.subjectWholesale marketpt_PT
dc.titleBidding in local electricity markets with cascading wholesale market integrationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleSmart Distribution Grid: a Market Driven Approach for the Next Generation of Advanced Operation Models and Services
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/771066/EU
oaire.citation.startPage107045pt_PT
oaire.citation.titleInternational Journal of Electrical Power & Energy Systemspt_PT
oaire.citation.volume131pt_PT
oaire.fundingStreamH2020
person.familyNameLezama
person.familyNameSoares
person.familyNameFaia
person.familyNameVale
person.givenNameFernando
person.givenNameJoão
person.givenNameRicardo Francisco Marcos
person.givenNameZita
person.identifier1043580
person.identifier78FtZwIAAAAJ
person.identifier632184
person.identifier.ciencia-idE31F-56D6-1E0F
person.identifier.ciencia-id1612-8EA8-D0E8
person.identifier.ciencia-id9B12-19F6-D6C7
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8638-8373
person.identifier.orcid0000-0002-4172-4502
person.identifier.orcid0000-0002-1053-7720
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-6945-2017
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id36810077500
person.identifier.scopus-author-id35436109600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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