Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets

Página do item simplificado
dc.contributor.authorArias, Nataly Banol
dc.contributor.authorLopez, Juan C.
dc.contributor.authorRider, Marcos J.
dc.contributor.authorFredy Franco, John [UNESP]
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-29T08:31:50Z
dc.date.available2022-04-29T08:31:50Z
dc.date.issued2021-06-28
dc.description.abstractHigh penetration of electric vehicles (EVs) triggers challenges and opportunities for distribution system operators. Inverter-based EV chargers with active/reactive power control can be used to coordinate the EV fleet's charging process while providing local volt/var regulation. This paper proposes an adaptive robust programming model for the charging scheduling of EV fleets that exploits their capability to locally support the grid via reactive power control. The proposed model aims at maximizing the aggregator's revenue while considering the worst-case scenario in terms of active power losses at the supporting grid. Operational constraints of unbalanced three-phase distribution networks under demand uncertainty are also enforced. The proposed robust model is a min-max problem that can be linearized and solved using a column-and-constraint generation (CCG) method. Tests performed in a 25-node distribution system illustrate the EV fleet's capacity to support the grid while minimizing the total energy not supplied.en
dc.description.affiliationUniversity of Campinas (UNICAMP) School of Electrical and Computing Engineering, Campinas
dc.description.affiliationSão Paulo State University (UNESP) School of Energy Engineering, Rosana
dc.description.affiliationUnespSão Paulo State University (UNESP) School of Energy Engineering, Rosana
dc.identifierhttp://dx.doi.org/10.1109/PowerTech46648.2021.9494898
dc.identifier.citation2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings.
dc.identifier.doi10.1109/PowerTech46648.2021.9494898
dc.identifier.scopus2-s2.0-85112382001
dc.identifier.urihttp://hdl.handle.net/11449/229309
dc.language.isoeng
dc.relation.ispartof2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings
dc.sourceScopus
dc.subjectAdaptive robust optimization
dc.subjectaggregators
dc.subjectdistribution systems
dc.subjectelectric vehicle fleets
dc.subjectlinear programming
dc.subjectreactive power control
dc.titleAdaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleetsen
dc.typeTrabalho apresentado em evento

Arquivos

Coleções

Artigos - Turismo - Rosana