Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1049/gtd2.12026 http://hdl.handle.net/11449/208740 |
Resumo: | This work presents a novel strategy, designed from the distribution system operator viewpoint, aimed at estimating the hosting capacity in electric distribution systems when controllable plug-in electric vehicles are in place. The strategy seeks to determine the maximum wind-based distributed generation penetration by coordinating, on a forecast basis, the dispatch of electric vehicle aggregators, the operation of voltage regulation devices, and the active and reactive distributed generation power injections. Different from previous works, the proposed approach leverages controllable features of electric vehicles taking into account technical electric vehicle characteristics, driving behaviour of electric vehicle owners, and electric vehicle energy requirements to accomplish their primary purpose. The presented strategy is formulated as a two-stage stochastic mixed-integer linear programming problem. The first stage maximises the distributed generation installed capacity, while the second stage minimises the energy losses during the planning horizon. Probability density functions are used to describe the uncertainties associated with renewable distributed generation, conventional demand, and electric vehicle driving patterns. Obtained results show that controlling the power dispatched to electric vehicle aggregators can increase the distributed generation hosting capacity by up to 15% (given a 40% electric vehicle penetration), when compared to an uncontrolled electric vehicle approach. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregatorsThis work presents a novel strategy, designed from the distribution system operator viewpoint, aimed at estimating the hosting capacity in electric distribution systems when controllable plug-in electric vehicles are in place. The strategy seeks to determine the maximum wind-based distributed generation penetration by coordinating, on a forecast basis, the dispatch of electric vehicle aggregators, the operation of voltage regulation devices, and the active and reactive distributed generation power injections. Different from previous works, the proposed approach leverages controllable features of electric vehicles taking into account technical electric vehicle characteristics, driving behaviour of electric vehicle owners, and electric vehicle energy requirements to accomplish their primary purpose. The presented strategy is formulated as a two-stage stochastic mixed-integer linear programming problem. The first stage maximises the distributed generation installed capacity, while the second stage minimises the energy losses during the planning horizon. Probability density functions are used to describe the uncertainties associated with renewable distributed generation, conventional demand, and electric vehicle driving patterns. Obtained results show that controlling the power dispatched to electric vehicle aggregators can increase the distributed generation hosting capacity by up to 15% (given a 40% electric vehicle penetration), when compared to an uncontrolled electric vehicle approach.Electrical Engineering Department São Paulo State University (UNESP)Centre for Urban Energy Ryerson UniversityElectrical Engineering Department São Paulo State University (UNESP)Universidade Estadual Paulista (Unesp)Ryerson UniversityQuijano, Darwin A. [UNESP]Melgar-Dominguez, Ozy D. [UNESP]Sabillon, CarlosVenkatesh, BalaPadilha-Feltrin, Antonio [UNESP]2021-06-25T11:18:15Z2021-06-25T11:18:15Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article359-370http://dx.doi.org/10.1049/gtd2.12026IET Generation, Transmission and Distribution, v. 15, n. 2, p. 359-370, 2021.1751-86951751-8687http://hdl.handle.net/11449/20874010.1049/gtd2.120262-s2.0-85107332291Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIET Generation, Transmission and Distributioninfo:eu-repo/semantics/openAccess2024-07-04T19:06:35Zoai:repositorio.unesp.br:11449/208740Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:32:41.992873Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators |
title |
Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators |
spellingShingle |
Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators Quijano, Darwin A. [UNESP] |
title_short |
Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators |
title_full |
Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators |
title_fullStr |
Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators |
title_full_unstemmed |
Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators |
title_sort |
Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators |
author |
Quijano, Darwin A. [UNESP] |
author_facet |
Quijano, Darwin A. [UNESP] Melgar-Dominguez, Ozy D. [UNESP] Sabillon, Carlos Venkatesh, Bala Padilha-Feltrin, Antonio [UNESP] |
author_role |
author |
author2 |
Melgar-Dominguez, Ozy D. [UNESP] Sabillon, Carlos Venkatesh, Bala Padilha-Feltrin, Antonio [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Ryerson University |
dc.contributor.author.fl_str_mv |
Quijano, Darwin A. [UNESP] Melgar-Dominguez, Ozy D. [UNESP] Sabillon, Carlos Venkatesh, Bala Padilha-Feltrin, Antonio [UNESP] |
description |
This work presents a novel strategy, designed from the distribution system operator viewpoint, aimed at estimating the hosting capacity in electric distribution systems when controllable plug-in electric vehicles are in place. The strategy seeks to determine the maximum wind-based distributed generation penetration by coordinating, on a forecast basis, the dispatch of electric vehicle aggregators, the operation of voltage regulation devices, and the active and reactive distributed generation power injections. Different from previous works, the proposed approach leverages controllable features of electric vehicles taking into account technical electric vehicle characteristics, driving behaviour of electric vehicle owners, and electric vehicle energy requirements to accomplish their primary purpose. The presented strategy is formulated as a two-stage stochastic mixed-integer linear programming problem. The first stage maximises the distributed generation installed capacity, while the second stage minimises the energy losses during the planning horizon. Probability density functions are used to describe the uncertainties associated with renewable distributed generation, conventional demand, and electric vehicle driving patterns. Obtained results show that controlling the power dispatched to electric vehicle aggregators can increase the distributed generation hosting capacity by up to 15% (given a 40% electric vehicle penetration), when compared to an uncontrolled electric vehicle approach. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T11:18:15Z 2021-06-25T11:18:15Z 2021-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1049/gtd2.12026 IET Generation, Transmission and Distribution, v. 15, n. 2, p. 359-370, 2021. 1751-8695 1751-8687 http://hdl.handle.net/11449/208740 10.1049/gtd2.12026 2-s2.0-85107332291 |
url |
http://dx.doi.org/10.1049/gtd2.12026 http://hdl.handle.net/11449/208740 |
identifier_str_mv |
IET Generation, Transmission and Distribution, v. 15, n. 2, p. 359-370, 2021. 1751-8695 1751-8687 10.1049/gtd2.12026 2-s2.0-85107332291 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IET Generation, Transmission and Distribution |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
359-370 |
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_ |
1808129216903905280 |