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dc.contributor.authorPalacios García, Emilio
dc.contributor.authorMoreno-Muñoz, A.
dc.contributor.authorSantiago, Isabel
dc.contributor.authorMoreno-García, Isabel
dc.contributor.authorMilanés-Montero, María-Isabel
dc.date.accessioned2017-11-20T10:39:10Z
dc.date.available2017-11-20T10:39:10Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10396/15476
dc.description.abstractReduction of CO2 emissions is a main target in the future smart grid. This goal is boosting the installation of renewable energy resources (RES), as well as a major consumer engagement that seeks for a more efficient utilization of these resources toward the figure of ‘prosumers’. Nevertheless, these resources present an intermittent nature, which requires the presence of an energy storage system and an energy management system (EMS) to ensure an uninterrupted power supply. Moreover, network-related issues might arise due to the increasing power of renewable resources installed in the grid, the storage systems also being capable of contributing to the network stability. However, to assess these future scenarios and test the control strategies, a simulation system is needed. The aim of this paper is to analyze the interaction between residential consumers with high penetration of PV generation and distributed storage and the grid by means of a high temporal resolution simulation scenario based on a stochastic residential load model and PV production records. Results of the model are presented for different PV power rates and storage capacities, as well as a two-level charging strategy as a mechanism for increasing the hosting capacity (HC) of the network.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightshttps://creativecommons.org/licenses/by/4.0/es_ES
dc.sourceEnergies 10(10), 1488 (2017)es_ES
dc.subjectStochastic load modelinges_ES
dc.subjectSolar power generationes_ES
dc.subjectEnergy storagees_ES
dc.subjectHosting capacityes_ES
dc.subjectEnergy management systemes_ES
dc.titlePV Hosting Capacity Analysis and Enhancement Using High Resolution Stochastic Modelinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttp://dx.doi.org/10.3390/en10101488es_ES
dc.relation.projectIDGobierno de España. TEC2013-47316-C3-1-Pes_ES
dc.relation.projectIDGobierno de España. TEC2016-77632-C3-2-Res_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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