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

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dc.contributor.authorArias, Nataly Banol
dc.contributor.authorLopez, Juan C.
dc.contributor.authorRider, Marcos J.
dc.contributor.authorFranco, John Fredy [UNESP]
dc.contributor.authorIEEE
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-11-30T13:46:16Z
dc.date.available2022-11-30T13:46:16Z
dc.date.issued2021-01-01
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 (C&CG) 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.affiliationUniv Estadual Campinas, UNICAMP, Sch Elect & Comp Engn, Campinas, SP, Brazil
dc.description.affiliationSao Paulo State Univ UNESP, Sch Energy Engn, Rosana, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ UNESP, Sch Energy Engn, Rosana, SP, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2018/23617-7
dc.description.sponsorshipIdFAPESP: 2019/01906-0
dc.description.sponsorshipIdFAPESP: 2015/21972-6
dc.format.extent6
dc.identifierhttp://dx.doi.org/10.1109/PowerTech46648.2021.9494898
dc.identifier.citation2021 Ieee Madrid Powertech. New York: Ieee, 6 p., 2021.
dc.identifier.doi10.1109/PowerTech46648.2021.9494898
dc.identifier.urihttp://hdl.handle.net/11449/237837
dc.identifier.wosWOS:000848778000148
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartof2021 Ieee Madrid Powertech
dc.sourceWeb of Science
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
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee

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