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dc.contributor.authorGarcía Cerezo, Álvaroes-ES
dc.contributor.authorBaringo Morales, Luises-ES
dc.contributor.authorGarcía González, Javieres-ES
dc.date.accessioned2025-07-10T14:19:07Z-
dc.date.available2025-07-10T14:19:07Z-
dc.date.issued2025-07-08es_ES
dc.identifier.issn0093-9994es_ES
dc.identifier.urihttps:doi.org10.1109TIA.2025.3587193es_ES
dc.identifier.urihttp://hdl.handle.net/11531/100531-
dc.descriptionArtículos en revistases_ES
dc.description.abstractes-ES
dc.description.abstractThis paper proposes a stochastic adaptive robust optimization approach to build the bidding curves of an aggregator managing a fleet of electric vehicles (EVs) participating in the day-ahead and intraday electricity markets. These bidding decisions are made hourly, one day in advance, within an uncertain environment. In this context, uncertainties comprise market prices, as well as driving requirements of EV users. These uncertainties are accounted for by using a set of scenarios and confidence bounds, respectively. In this way, this paper combines classic stochastic optimization techniques with adaptive robust optimization, realistically modeling multiple sources of uncertainty. EVs are equipped with vehicle-to-grid technology so that they can both buy and sell energy to the market. The resulting stochastic adaptive robust optimization problem is solved by using the column-and-constraint generation algorithm, which ensures the attainment of the optimal solution in a finite number of steps. Simulations are run by applying CPLEX under GAMS. A case study demonstrates the effectiveness of the proposed approach. Results show that the bidding decisions of the EV aggregator are sensitive to the uncertainty in driving requirements of EVs, which can be controlled through the uncertainty budget. This highlights the usefulness of the proposed approach to prevent the attainment of suboptimal bidding decisions. Moreover, the good performance of the algorithm in terms of obtaining the optimal solution with computational times lower than 6 min suggests potential for model expansion and increased complexity in future works.en-GB
dc.format.mimetypeapplication/octet-streames_ES
dc.language.isoen-GBes_ES
dc.sourceRevista: IEEE Transactions on Industry Applications, Periodo: 1, Volumen: En imprenta, Número: , Página inicial: 0, Página final: 0es_ES
dc.subject.otherInstituto de Investigación Tecnológica (IIT)es_ES
dc.titleA Stochastic Adaptive Robust Optimization Approach to Build Day-Ahead Bidding Curves for an EV Aggregatores_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.holderes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.keywordses-ES
dc.keywordsAdaptive robust optimization, aggregator, bidding strategy, electric vehicle, electricity market, stochastic programming, uncertaintyen-GB
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