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Complex Large-Scale Energy Resource Management Optimization Considering Demand Flexibility

2020Conference object Open access
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dc.contributor.authorCanizes, Bruno
dc.contributor.authorSoares, João
dc.contributor.authorLezama, Fernando
dc.contributor.authorVale, Zita
dc.date.accessioned2021-03-04T18:18:51Z
dc.date.available2021-03-04T18:18:51Z
dc.date.issued2020
dc.description.abstractAs renewable energy sources penetration is increasing in the power distribution network, an energy aggregator can provide a highly flexible generation and demand as required by the smart grid paradigm. However, this energy aggregator entity needs adequate decision support tools to overcome the complex challenges and deal with a number of energy resources. So, the energy resource management is crucial for the aggregator, to increase the profits, reduce the operation costs, reduce the carbon footprint and also to improve the system stability. Thus, this paper proposes a model for a large-scale energy resource scheduling problem of aggregators in a smart grid. Also, it is compared the performance of five evolutionary algorithms to solve this kind of problem. A realistic case study is performed using a real distribution network in Zaragoza, Spain. The results show that load flexibility can contribute to the profitability improvement of the aggregators' entities.pt_PT
dc.description.sponsorshipThis work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224) and from National Funds through the FCT Portuguese Foundation for Science and Technology, under Project UIDB/00760/2020. Joao Soares is supported by FCT CEECIND/02814/2017 grantpt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/CEC48606.2020.9185617pt_PT
dc.identifier.isbn978-1-7281-6929-3
dc.identifier.urihttp://hdl.handle.net/10400.22/17287
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9185617pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectDemand responsept_PT
dc.subjectDifferential evolutionpt_PT
dc.subjectDistribution networkspt_PT
dc.subjectElectric vehiclept_PT
dc.subjectEvolutionary computationpt_PT
dc.subjectLoad flexibilitypt_PT
dc.subjectSmart gridpt_PT
dc.titleComplex Large-Scale Energy Resource Management Optimization Considering Demand Flexibilitypt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceGlasgow, UKpt_PT
oaire.citation.endPage8pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2020 IEEE Congress on Evolutionary Computation (CEC)pt_PT
person.familyNameCanizes
person.familyNameSoares
person.familyNameLezama
person.familyNameVale
person.givenNameBruno
person.givenNameJoão
person.givenNameFernando
person.givenNameZita
person.identifier1043580
person.identifier632184
person.identifier.ciencia-idA411-F561-E922
person.identifier.ciencia-id1612-8EA8-D0E8
person.identifier.ciencia-idE31F-56D6-1E0F
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-9808-5537
person.identifier.orcid0000-0002-4172-4502
person.identifier.orcid0000-0001-8638-8373
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridI-3492-2017
person.identifier.ridA-6945-2017
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35408699300
person.identifier.scopus-author-id35436109600
person.identifier.scopus-author-id36810077500
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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