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dc.contributor.authorGil Marcelino, Carolina 
dc.contributor.authorMatos Cardoso Leite, Gabriel 
dc.contributor.authorDelgado, C.A.D.M.
dc.contributor.authorOliveira, L.B. de
dc.contributor.authorFialho Wanner, Elizabeth
dc.contributor.authorJiménez Fernández, Silvia 
dc.contributor.authorSalcedo Sanz, Sancho
dc.identifier.bibliographicCitationMarcelino, C.G. et al. 2021, "An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants", Expert Systems with Applications, vol. 185, art. no. 115638.
dc.description.abstractThis paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.en
dc.description.sponsorshipEuropean Commissionen
dc.description.sponsorshipAgencia Estatal de Investigaciónes_ES
dc.description.sponsorshipComunidad de Madrides_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)*
dc.subjectCascading hydro-power plant modelingen
dc.subjectMulti-objective optimizationen
dc.subjectSwarm intelligenceen
dc.subjectEnergy productionen
dc.titleAn efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plantsen
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.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.identifier.publicationtitleExpert Systems with Applications

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