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

Detalhes bibliográficos
Autor(a) principal: Arias, Nataly Banol
Data de Publicação: 2021
Outros Autores: Lopez, Juan C., Rider, Marcos J., Fredy Franco, John [UNESP]
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/11449/229309
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 (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.
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spelling 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 (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.University of Campinas (UNICAMP) School of Electrical and Computing Engineering, CampinasSão Paulo State University (UNESP) School of Energy Engineering, RosanaSão Paulo State University (UNESP) School of Energy Engineering, RosanaUniversidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (UNESP)Arias, Nataly BanolLopez, Juan C.Rider, Marcos J.Fredy Franco, John [UNESP]2022-04-29T08:31:50Z2022-04-29T08:31:50Z2021-06-28info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/PowerTech46648.2021.94948982021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings.http://hdl.handle.net/11449/22930910.1109/PowerTech46648.2021.94948982-s2.0-85112382001Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedingsinfo:eu-repo/semantics/openAccess2024-08-06T18:56:12Zoai:repositorio.unesp.br:11449/229309Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-06T18:56:12Repositório 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.
Fredy Franco, John [UNESP]
author_role author
author2 Lopez, Juan C.
Rider, Marcos J.
Fredy Franco, John [UNESP]
author2_role 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.
Fredy Franco, John [UNESP]
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 (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.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-28
2022-04-29T08:31:50Z
2022-04-29T08:31:50Z
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, PowerTech 2021 - Conference Proceedings.
http://hdl.handle.net/11449/229309
10.1109/PowerTech46648.2021.9494898
2-s2.0-85112382001
url http://dx.doi.org/10.1109/PowerTech46648.2021.9494898
http://hdl.handle.net/11449/229309
identifier_str_mv 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings.
10.1109/PowerTech46648.2021.9494898
2-s2.0-85112382001
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
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_ 1808128184693030912

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