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Optimal Bidding in Local Energy Markets using Evolutionary Computation

dc.contributor.authorLezama, Fernando
dc.contributor.authorSoares, João
dc.contributor.authorVale, Zita
dc.date.accessioned2021-09-22T13:28:03Z
dc.date.available2021-09-22T13:28:03Z
dc.date.issued2019
dc.description.abstractIncreased adoption of distributed resources and renewables in distribution networks has led to a significant interest in local energy transactions at lower levels of the energy supply chain. Local energy markets (LM) are expected to play a crucial part in guaranteeing the balance between generation and consumption and contribute to the reduction of carbon emissions. Besides, LMs aim at increasing the participation of small end-users in energy transactions, setting the stage for transactive energy systems. In this work, we explore the use of evolutionary algorithms (EAs) to solve a bi-level optimization problem that arises when trading energy in an LM. We compare the performance of different EAs under a realistic case study with nine agents trading energy in the day-ahead LM. Results suggest that EAs can provide solutions in which all agents can improve their profits. It is shown the advantages in terms of profits that an LM can bring to market participants, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition.pt_PT
dc.description.sponsorshipThis research has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145 FEDER-028983; by National Funds under FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/28983/2017(CENERGETIC) and UID/EEA/00760/2019; Joao Soares is supported by FCT under CEECIND/02814/2017 grant.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/ISAP48318.2019.9065976pt_PT
dc.identifier.isbn978-1-7281-3192-4
dc.identifier.urihttp://hdl.handle.net/10400.22/18476
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9065976pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/pt_PT
dc.subjectLocal energy marketspt_PT
dc.subjectEvolutionary Computationpt_PT
dc.titleOptimal Bidding in Local Energy Markets using Evolutionary Computationpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FEEI-EEE%2F28983%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F00760%2F2019/PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)pt_PT
oaire.fundingStream9471 - RIDTI
oaire.fundingStream6817 - DCRRNI ID
person.familyNameLezama
person.familyNameSoares
person.familyNameVale
person.givenNameFernando
person.givenNameJoão
person.givenNameZita
person.identifier1043580
person.identifier632184
person.identifier.ciencia-idE31F-56D6-1E0F
person.identifier.ciencia-id1612-8EA8-D0E8
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8638-8373
person.identifier.orcid0000-0002-4172-4502
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/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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