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dc.contributor.authorGil Marcelino, Carolina 
dc.contributor.authorSalcedo Sanz, Sancho 
dc.contributor.authorJiménez Fernández, Silvia 
dc.contributor.authorCamacho Gómez, Carlos 
dc.date.accessioned2021-04-27T13:49:45Z
dc.date.available2021-04-27T13:49:45Z
dc.date.issued2021-04-25
dc.identifier.bibliographicCitationMarcelino, Carolina G.; Camacho-Gómez, Carlos; Jiménez-Fernández, Silvia & Salcedo-Sanz, Sancho. 2021. "Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm", Energies, vol. 14, no. 9, art. no. 2443.
dc.identifier.issn1996-1073
dc.identifier.urihttp://hdl.handle.net/10017/47548
dc.description.abstractHydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem.This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.en
dc.description.sponsorshipEuropean Commissionen
dc.description.sponsorshipMinisterio de Economía y Competitividades_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/4.0/*
dc.subjectGeneration schedulingen
dc.subjectHydro-power plantsen
dc.subjectCoral Reefs Optimization algorithmen
dc.subjectMeta-heuristicsen
dc.subjectBio-inspired algorithmsen
dc.subjectEnergy efficiencyen
dc.titleOptimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithmen
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.updated2021-04-27T13:47:00Z
dc.relation.publisherversionhttps://doi.org/10.3390/en14092443
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.3390/en14092443
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/0000037179
dc.identifier.publicationtitleEnergies
dc.identifier.publicationvolume14
dc.identifier.publicationissue9


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