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A Consumer Trustworthiness Rate for Participation in Demand Response Programs

2020Journal article Open access
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dc.contributor.authorSilva, Cátia
dc.contributor.authorFaria, Pedro
dc.contributor.authorVale, Zita
dc.date.accessioned2022-01-11T15:17:55Z
dc.date.available2022-01-11T15:17:55Z
dc.date.issued2020
dc.description.abstractLocal energy communities with information from the real-time market may improve the market operation but also increase the complexity of the management problem thanks to the uncertainty associated with the actual response of these resources. For instance, consumers with price knowledge may change their power consumption to lower-cost periods. The authors present a model to deal with uncertainty from the point of view of the Aggregator: apply reliability rates to each consumer according to their actual response in events of Demand Response (DR). The consumers with higher rates are chosen to participate in the energy market. To compute the final rate, three different independent rates are used: Historical rate with past information, Cut-rate from the response in the actual period and the Last Day Rate which is the final reliability rate from the previous day. In the present paper, the influence of each independent rate, through the weight used, is studied.pt_PT
dc.description.sponsorshipThis work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224) and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UID/EEA/00760/2019, CEECIND/02887/2017, and SFRH/BD/144200/2019pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.ifacol.2020.12.1825pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/19392
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationCEECIND/02887/2017pt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relationEffective DR gathering and deployment for intensive renewable integration using aggregation and machine learning
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S2405896320324368?via%3Dihubpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectDemand Responsept_PT
dc.subjectUncertaintypt_PT
dc.subjectAggregatorpt_PT
dc.subjectSmart Gridspt_PT
dc.subjectOptimizationpt_PT
dc.titleA Consumer Trustworthiness Rate for Participation in Demand Response Programspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardTitleEffective DR gathering and deployment for intensive renewable integration using aggregation and machine learning
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F00760%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F144200%2F2019/PT
oaire.citation.endPage12601pt_PT
oaire.citation.issue2pt_PT
oaire.citation.startPage12596pt_PT
oaire.citation.titleIFAC-PapersOnLinept_PT
oaire.citation.volume53pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFaria
person.familyNameVale
person.givenNamePedro
person.givenNameZita
person.identifier632184
person.identifier.ciencia-idB212-2309-F9C3
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-5982-8342
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-5824-2012
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.typearticlept_PT
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