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dc.contributor.authorGerres, Timoes-ES
dc.contributor.authorLukszo, Zofiaes-ES
dc.date.accessioned2017-12-21T15:53:02Z-
dc.date.available2017-12-21T15:53:02Z-
dc.date.issued16/05/2017es_ES
dc.identifier.urihttp://hdl.handle.net/11531/24709-
dc.descriptionCapítulos en libroses_ES
dc.description.abstractes-ES
dc.description.abstractAn increasing share of renewable energy sources (RES), such as wind and solar generation, is traded on the electricity markets. The volatility and forecast uncertainty of RES production cause market imbalances and hinder the transition towards emission-free electricity generation. Flexible load scheduling in the form of charging electric vehicles (EVs) offers an opportunity to counterbalance RES variability and forecast uncertainty and thereby enables a higher share of renewables. A stochastic linear multistage optimisation approach is presented to explore the benefits of combining RES productionand flexible EV charging in the form of a hybrid aggregator trading on the liberalised electricity market. Model results show that the hybrid aggregator is able to minimise its imbalance requirements, but uses RES production for charging only if such is financially optimal. Further research is required to explore the market impact of the hybrid aggregator and test its feasibility.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoen-GBes_ES
dc.publisherUniversità della Calabria (Falerna, Italia)es_ES
dc.rightses_ES
dc.rights.uries_ES
dc.sourceLibro: 14th IEEE International Conference on Networking, Sensing and Control - ICNSC 2017, Página inicial: , Página final:es_ES
dc.subject.otherInstituto de Investigación Tecnológica (IIT)es_ES
dc.titleMitigating production uncertainties of renewables by adjustable load schedulinges_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.keywordsRES market trading, controlled EV charging, load scheduling, stochastic optimisation, RES uncertaintyen-GB
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