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dc.contributor.authorGil Marcelino, Carolina 
dc.contributor.authorAvancini, Joäo V.C.
dc.contributor.authorDelgado, C.A.D.M.
dc.contributor.authorFialho Wanner, Elizabeth
dc.contributor.authorJiménez Fernández, Silvia 
dc.contributor.authorSalcedo Sanz, Sancho 
dc.date.accessioned2021-10-29T13:59:39Z
dc.date.available2021-10-29T13:59:39Z
dc.date.issued2021-10-28
dc.identifier.bibliographicCitationMarcelino, C.G. et al. 2021, "Dynamic electric dispatch for wind power plants: a new automatic controller system using evolutionary algorithms", Sustainability, vol. 13, n. 21, art. no. 11924.
dc.identifier.issn2071-1050
dc.identifier.urihttp://hdl.handle.net/10017/49808
dc.description.abstractIn this paper, we use an evolutionary swarm intelligence approach to build an automatic electric dispatch controller for an offshore wind power plant (WPP). The optimal power flow (OPF) problem for this WPP is solved by the Canonical Differential Evolutionary Particle Swarm Optimization algorithm (C-DEEPSO). In this paper, C-DEEPSO works as a control system for reactive sources in energy production. The control operation takes place in a daily energy dispatch, scheduled into 15 min intervals and resulting in 96 operating test scenarios. As the nature of the optimization problem is dynamic, a fine-tuning of the initialization parameters of the optimization algorithm is performed at each dispatch interval. Therefore, a version of the C-DEEPSO algorithm has been built to automatically learn the best set of initialization parameters for each scenario. For this, we have coupled C-DEEPSO with the irace tool (an extension of the iterated F-race (I/F-Race)) by using inferential statistic techniques. The experiments carried out showed that the methodology employed here is robust and able to tackle this OPF-like modeling. Moreover, the methodology works as an automatic control system for a dynamic schedule operation.en
dc.description.sponsorshipEuropean Commissionen
dc.description.sponsorshipAgencia Estatal de Investigaciónes_ES
dc.description.sponsorshipComunidad de Madrides_ES
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherMDPI
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectOffshore wind poweren
dc.subjectOptimizationen
dc.subjectEnergy efficiencyen
dc.subjectEnergy resourcesen
dc.subjectClean energiesen
dc.titleDynamic electric dispatch for wind power plants: a new automatic controller system using evolutionary algorithmsen
dc.typeinfo:eu-repo/semantics/articleen
dc.subject.ecienciaInformáticaes_ES
dc.subject.ecienciaComputer scienceen
dc.subject.ecienciaEnergías Renovables/Energías Alternativases_ES
dc.subject.ecienciaAlternative energiesen
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Teoría de la Señal y Comunicacioneses_ES
dc.date.updated2021-10-29T13:59:01Z
dc.relation.publisherversionhttps://doi.org/10.3390/su132111924
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.3390/su132111924
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/754382/EU/GOT Energy Talent/GETen
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85887-C2-2-P/ES/NUEVOS ALGORITMOS HIBRIDOS DE INSPIRACION NATURAL PARA PROBLEMAS DE CLASIFICACION ORDINAL Y PREDICCION/en
dc.relation.projectIDinfo:eu-repo/grantAgreement/CAM//S2018%2FEMT4366/ES/PROgrama Microredes INTeligentes-CM/PROMINT-CMen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.uxxiAR/0000038230
dc.identifier.publicationtitleSustainability
dc.identifier.publicationvolume13
dc.identifier.publicationissue21


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