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Optimal Sizing of Renewable Energy Communities: A Multiple Swarms Multi-Objective Particle Swarm Optimization Approach

dc.contributor.authorFaria, João
dc.contributor.authorMarques, Carlos
dc.contributor.authorPombo, José
dc.contributor.authorMariano, Sílvio
dc.contributor.authorCalado, M. do Rosário
dc.date.accessioned2024-01-09T16:12:56Z
dc.date.available2024-01-09T16:12:56Z
dc.date.issued2023-11-01
dc.description.abstractRenewable energy communities have gained popularity as a means of reducing carbon emissions and enhancing energy independence. However, determining the optimal sizing for each production and storage unit within these communities poses challenges due to conflicting objectives, such as minimizing costs while maximizing energy production. To address this issue, this paper employs a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with multiple swarms. This approach aims to foster a broader diversity of solutions while concurrently ensuring a good plurality of nondominant solutions that define a Pareto frontier. To evaluate the effectiveness and reliability of this approach, four case studies with different energy management strategies focused on real-world operations were evaluated, aiming to replicate the practical challenges encountered in actual renewable energy communities. The results demonstrate the effectiveness of the proposed approach in determining the optimal size of production and storage units within renewable energy communities, while simultaneously addressing multiple conflicting objectives, including economic viability and flexibility, specifically Levelized Cost of Energy (LCOE), Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR). The findings also provide valuable insights that clarify which energy management strategies are most suitable for this type of community.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/en16217227pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/13893
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)pt_PT
dc.relationAI based Market Model for Renewable Energy Communities with Storage Sharing
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectRenewable energy community (REC)pt_PT
dc.subjectEnergy management strategiespt_PT
dc.subjectMulti-objective optimization algorithmpt_PT
dc.subjectMulti-swarm MOPSOpt_PT
dc.subjectEnergy storage systemspt_PT
dc.subjectEnergy storage sharingpt_PT
dc.titleOptimal Sizing of Renewable Energy Communities: A Multiple Swarms Multi-Objective Particle Swarm Optimization Approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAI based Market Model for Renewable Energy Communities with Storage Sharing
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F151349%2F2021/PT
oaire.citation.issue21pt_PT
oaire.citation.titleEnergiespt_PT
oaire.citation.volume16pt_PT
person.familyNameDomingos Faria
person.familyNamePombo
person.familyNamePinto Simões Mariano
person.familyNameCalado
person.givenNameJoão Pedro
person.givenNameJose
person.givenNameSílvio José
person.givenNameM. do Rosário
person.identifier.ciencia-id9618-F7B7-046E
person.identifier.ciencia-id7615-8E00-8084
person.identifier.ciencia-id541F-E2B4-D66D
person.identifier.ciencia-id9115-032B-370B
person.identifier.orcid0000-0001-5011-2201
person.identifier.orcid0000-0002-8727-0067
person.identifier.orcid0000-0002-6102-5872
person.identifier.orcid0000-0002-5206-487X
person.identifier.ridN-6834-2013
person.identifier.ridN-6809-2013
person.identifier.scopus-author-id34977533800
person.identifier.scopus-author-id35612517200
person.identifier.scopus-author-id9338016700
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationef235a52-d9cf-4108-a643-44226f973f58
relation.isAuthorOfPublicationcce2060a-24b8-441b-8896-cb4d0b3d3e83
relation.isAuthorOfPublicationcdbb9afc-4123-45ca-a946-89bafda7ab68
relation.isAuthorOfPublication321aefdd-cd1f-4dd6-878e-c904b3ef89ab
relation.isAuthorOfPublication.latestForDiscovery321aefdd-cd1f-4dd6-878e-c904b3ef89ab
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