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Economic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models

dc.creatorMontoya O.D.
dc.creatorGil-González W.
dc.creatorGrisales-Norena L.
dc.creatorOrozco-Henao C.
dc.creatorSerra F.
dc.date.accessioned2020-03-26T16:41:28Z
dc.date.available2020-03-26T16:41:28Z
dc.date.issued2019
dc.identifier.citationMontoya O.D., Gil-González W., Grisales-Norena L., Orozco-Henao C. y Serra F. (2019) Economic dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models. Energies; Vol. 12, Núm. 23
dc.identifier.issn19961073
dc.identifier.urihttps://hdl.handle.net/20.500.12585/9253
dc.description.abstractThis paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which is the main contribution of this paper. An artificial neural network was employed for the short-term prediction of available renewable energy from wind and photovoltaic sources. The NLP model was solved by using the general algebraic modeling system (GAMS) to implement a 30-node test feeder composed of four renewable generators and three batteries. Simulation results demonstrate that the cost reduction for a daily operation is drastically affected by the operating conditions of the BESS, as well as the type of load model used. © 2019 MDPI AG. All rights reserved.eng
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherMDPI AG
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85076239314&doi=10.3390%2fen12234494&partnerID=40&md5=5aad556cf10a550cccf8bae524f3e92d
dc.sourceScopus2-s2.0-85076239314
dc.titleEconomic Dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models
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datacite.rightshttp://purl.org/coar/access_right/c_abf2
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.3390/en12234494
dc.subject.keywordsArtificial neural networks
dc.subject.keywordsBattery energy storage system
dc.subject.keywordsEconomic dispatch problem
dc.subject.keywordsBattery storage
dc.subject.keywordsCost reduction
dc.subject.keywordsData storage equipment
dc.subject.keywordsElectric batteries
dc.subject.keywordsElectric machine theory
dc.subject.keywordsNeural networks
dc.subject.keywordsNonlinear programming
dc.subject.keywordsScheduling
dc.subject.keywordsBattery energy storage systems
dc.subject.keywordsEconomic dispatch problems
dc.subject.keywordsOperating condition
dc.subject.keywordsOperational periods
dc.subject.keywordsPhotovoltaic sources
dc.subject.keywordsRenewable generators
dc.subject.keywordsShort term prediction
dc.subject.keywordsVoltage dependent load models
dc.subject.keywordsElectric load dispatching
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.ccAtribución-NoComercial 4.0 Internacional
dc.identifier.instnameUniversidad Tecnológica de Bolívar
dc.identifier.reponameRepositorio UTB
dc.type.spaArtículo
dc.identifier.orcid56919564100
dc.identifier.orcid57191493648
dc.identifier.orcid55791991200
dc.identifier.orcid55488549400
dc.identifier.orcid37104976300


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