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Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response

2019Journal article Open access
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dc.contributor.authorSilva, Cátia
dc.contributor.authorFaria, Pedro
dc.contributor.authorVale, Zita
dc.date.accessioned2021-02-18T10:21:29Z
dc.date.available2021-02-18T10:21:29Z
dc.date.issued2019
dc.description.abstractDistributed energy resources can improve the operation of power systems, improving economic and technical efficiency. Aggregation of small size resources, which exist in large number but with low individual capacity, is needed to make these resources’ use more efficient. In the present paper, a methodology for distributed resources management by an aggregator is proposed, which includes the resources scheduling, aggregation and remuneration. The aggregation, made using a k-means algorithm, is applied to different approaches concerning the definition of tariffs for the period of a week. Different consumer types are remunerated according to time-of-use tariffs existing in Portugal. Resources aggregation and remuneration profiles are obtained for over 20.000 consumers and 500 distributed generation units. The main goal of this paper is to understand how the aggregation phase, or the way that is performed, influences the final remuneration of the resources associated with Virtual Power Player (VPP). In order to fulfill the proposed objective, the authors carried out studies for different time frames (week days, week-end, whole week) and also analyzed the effect of the formation of the remuneration tariff by considering a mix of fixed and indexed tariff. The optimum number of clusters is calculated in order to determine the best number of DR programs to be implemented by the VPPpt_PT
dc.description.sponsorshipThe present work was done and funded in the scope of Project GREEDI (ANI|P2020-17822) co-funded by Portugal 2020 "Fundo Europeu de Desenvolvimento Regional" (FEDER) through POCI, and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/en12071248pt_PT
dc.identifier.issn1996-1073
dc.identifier.urihttp://hdl.handle.net/10400.22/17031
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://www.mdpi.com/1996-1073/12/7/1248pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/pt_PT
dc.subjectClusteringpt_PT
dc.subjectDemand responsept_PT
dc.subjectDistributed generationpt_PT
dc.subjectSmart gridspt_PT
dc.titleMulti-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Responsept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F00760%2F2019/PT
oaire.citation.issue7pt_PT
oaire.citation.startPage1248pt_PT
oaire.citation.titleEnergiespt_PT
oaire.citation.volume12pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSilva
person.familyNameFaria
person.familyNameVale
person.givenNameCátia
person.givenNamePedro
person.givenNameZita
person.identifier632184
person.identifier.ciencia-id5318-DCFD-218D
person.identifier.ciencia-idB212-2309-F9C3
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8306-4568
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.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication5eebf2bb-32f6-4593-a536-6db611d531e8
relation.isAuthorOfPublication35e6a4ab-f644-4bc5-b6fc-9fd89c23d6c6
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