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dc.contributor.authorWogrin, Sonjaes-ES
dc.contributor.authorTejada Arango, Diego Alejandroes-ES
dc.contributor.authorPineda Morente, Salvadores-ES
dc.contributor.authorMorales González, Juan Migueles-ES
dc.date.accessioned2019-09-11T03:17:45Z
dc.date.available2019-09-11T03:17:45Z
dc.date.issued2019-09-26es_ES
dc.identifier.urihttp://hdl.handle.net/11531/40650
dc.descriptionCapítulos en libroses_ES
dc.description.abstractes-ES
dc.description.abstractIn this paper we compare two cutting-edge timeperiod aggregation methodologies for power system models that consider both renewables and storage technologies: the chronological time-period clustering; and, the enhanced representative period approach. Such methodologies are used in order to reduce the computational burden of highly complex optimization models while not compromising the quality of the results. With this paper, we identify which method works best, and under which conditions, in order to reproduce the outcomes of the hourly benchmark model.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoen-GBes_ES
dc.publisherUniversidade do Porto; Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência (Oporto, Portugal)es_ES
dc.rightses_ES
dc.rights.uries_ES
dc.sourceLibro: 2nd International Conference on Smart Energy Systems and Technologies - SEST 2019, Página inicial: 1-6, Página final:es_ES
dc.subject.otherInstituto de Investigación Tecnológica (IIT)es_ES
dc.titleWhat time-period aggregation method works best for power system operation models with renewablesand storage?es_ES
dc.typeinfo:eu-repo/semantics/bookPartes_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses_ES
dc.keywordses-ES
dc.keywordspower system models, clustering, time-period aggregationen-GB


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