Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/PowerTech46648.2021.9494898 http://hdl.handle.net/11449/237837 |
Resumo: | High 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. |
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Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV FleetsAdaptive robust optimizationAggregatorsDistribution systemsElectric vehicle fleetsLinear programmingReactive power controlHigh 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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Estadual Campinas, UNICAMP, Sch Elect & Comp Engn, Campinas, SP, BrazilSao Paulo State Univ UNESP, Sch Energy Engn, Rosana, SP, BrazilSao Paulo State Univ UNESP, Sch Energy Engn, Rosana, SP, BrazilFAPESP: 2018/23617-7FAPESP: 2019/01906-0FAPESP: 2015/21972-6IeeeUniversidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (UNESP)Arias, Nataly BanolLopez, Juan C.Rider, Marcos J.Franco, John Fredy [UNESP]IEEE2022-11-30T13:46:16Z2022-11-30T13:46:16Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject6http://dx.doi.org/10.1109/PowerTech46648.2021.94948982021 Ieee Madrid Powertech. New York: Ieee, 6 p., 2021.http://hdl.handle.net/11449/23783710.1109/PowerTech46648.2021.9494898WOS:000848778000148Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2021 Ieee Madrid Powertechinfo:eu-repo/semantics/openAccess2022-11-30T13:46:16Zoai:repositorio.unesp.br:11449/237837Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-05-23T11:46:15.634403Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets |
title |
Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets |
spellingShingle |
Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets Arias, Nataly Banol Adaptive robust optimization Aggregators Distribution systems Electric vehicle fleets Linear programming Reactive power control |
title_short |
Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets |
title_full |
Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets |
title_fullStr |
Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets |
title_full_unstemmed |
Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets |
title_sort |
Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets |
author |
Arias, Nataly Banol |
author_facet |
Arias, Nataly Banol Lopez, Juan C. Rider, Marcos J. Franco, John Fredy [UNESP] IEEE |
author_role |
author |
author2 |
Lopez, Juan C. Rider, Marcos J. Franco, John Fredy [UNESP] IEEE |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual de Campinas (UNICAMP) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Arias, Nataly Banol Lopez, Juan C. Rider, Marcos J. Franco, John Fredy [UNESP] IEEE |
dc.subject.por.fl_str_mv |
Adaptive robust optimization Aggregators Distribution systems Electric vehicle fleets Linear programming Reactive power control |
topic |
Adaptive robust optimization Aggregators Distribution systems Electric vehicle fleets Linear programming Reactive power control |
description |
High 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-11-30T13:46:16Z 2022-11-30T13:46:16Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/PowerTech46648.2021.9494898 2021 Ieee Madrid Powertech. New York: Ieee, 6 p., 2021. http://hdl.handle.net/11449/237837 10.1109/PowerTech46648.2021.9494898 WOS:000848778000148 |
url |
http://dx.doi.org/10.1109/PowerTech46648.2021.9494898 http://hdl.handle.net/11449/237837 |
identifier_str_mv |
2021 Ieee Madrid Powertech. New York: Ieee, 6 p., 2021. 10.1109/PowerTech46648.2021.9494898 WOS:000848778000148 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2021 Ieee Madrid Powertech |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
6 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803045879506010112 |