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Economic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimization

dc.contributor.authorGil-González, Walter
dc.contributor.authorMontoya, Oscar Danilo
dc.contributor.authorGrisales-Noreña, Luis F.
dc.contributor.authorCruz-Peragón, Fernando
dc.contributor.authorAlcalá, Gerardo
dc.coverage.spatialCartagena de Indias
dc.date.accessioned2020-08-31T21:37:13Z
dc.date.available2020-08-31T21:37:13Z
dc.date.issued2020-04-03
dc.date.submitted2020-08-31
dc.identifier.citationGil-González, W.; Montoya, O.D.; Grisales-Noreña, L.F.; Cruz-Peragón, F.; Alcalá, G. Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization. Energies 2020, 13, 1703.spa
dc.identifier.issn1996-1073
dc.identifier.urihttps://hdl.handle.net/20.500.12585/9356
dc.description.abstractA convex mathematical model based on second-order cone programming (SOCP) for the optimal operation in direct current microgrids (DCMGs) with high-level penetration of renewable energies and battery energy storage systems (BESSs) is developed in this paper. The SOCP formulation allows converting the non-convex model of economic dispatch into a convex approach that guarantees the global optimum and has an easy implementation in specialized software, i.e., CVX. This conversion is accomplished by performing a mathematical relaxation to ensure the global optimum in DCMG. The SOCP model includes changeable energy purchase prices in the DCMG operation, which makes it in a suitable formulation to be implemented in real-time operation. An energy short-term forecasting model based on a receding horizon control (RHC) plus an artificial neural network (ANN) is used to forecast primary sources of renewable energy for periods of 0.5h. The proposed mathematical approach is compared to the non-convex model and semidefinite programming (SDP) in three simulation scenarios to validate its accuracy and efficiencyeng
dc.format.extent15 páginas
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.sourceEnergies; Vol. 13, Núm. 7 (2020)spa
dc.titleEconomic dispatch of renewable generators and BESS in DC microgrids using second-order cone optimizationspa
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datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.identifier.urlhttps://www.mdpi.com/1996-1073/13/7/1703
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.identifier.doi10.3390/en13071703
dc.subject.keywordsSecond-order cone programmingspa
dc.subject.keywordsEconomic dispatch problem
dc.subject.keywordsArtificial neural networks;
dc.subject.keywordsBattery energy storage system
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessspa
dc.rights.ccAtribución-NoComercial 4.0 Internacional*
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio UTBspa
dc.type.spaArtículospa
dc.audienceInvestigadoresspa
dc.publisher.sedeCampus Tecnológicospa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.publisher.disciplineIngeniería Eléctricaspa


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