Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles

Detalhes bibliográficos
Autor(a) principal: Zandrazavi, Seyed Farhad [UNESP]
Data de Publicação: 2022
Outros Autores: Guzman, Cindy Paola, Pozos, Alejandra Tabares, Quiros-Tortos, Jairo, Franco, John Fredy [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.energy.2021.122884
http://hdl.handle.net/11449/223094
Resumo: Microgrids (MGs) contribute to the integration of renewable energy-based distributed generation (DG) units and electric vehicles (EVs) in a smart, secure, sustainable, and economic fashion. However, the unbalanced nature of MGs along with the probabilistic nature of renewable energy, electricity prices, and EV demand complicate the energy management process. To overcome that challenge, a stochastic multi-objective optimization model for grid-connected unbalanced MGs is proposed here to minimize the total operational cost and the voltage deviation. The epsilon-constraint method and fuzzy satisfying approach are used to solve the multi-objective optimization problem and to obtain compromise solutions. Uncertainties are considered by employing the roulette wheel mechanism for generating scenarios regarding renewable energy generations, EV charging demands, electric loads, and electricity prices. In addition, to avoid adopting infeasible and impractical solutions, a three-phase power flow is integrated in the proposed model. The proposed method is assessed in a modified IEEE 34-bus test system consisting of EVs, battery systems, wind turbine units, photovoltaic units, and diesel generators. The results show the effectiveness and benefits of the proposed model for handling uncertainties while minimizing both operational cost and voltage deviation index and providing more realistic and reliable solutions that can be applied by MG operators.
id UNSP_6d15eccd3baffd624da3b22d4ec87809
oai_identifier_str oai:repositorio.unesp.br:11449/223094
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehiclesElectric vehiclesMicrogridsMulti-objective optimizationRenewable energyStochastic optimizationMicrogrids (MGs) contribute to the integration of renewable energy-based distributed generation (DG) units and electric vehicles (EVs) in a smart, secure, sustainable, and economic fashion. However, the unbalanced nature of MGs along with the probabilistic nature of renewable energy, electricity prices, and EV demand complicate the energy management process. To overcome that challenge, a stochastic multi-objective optimization model for grid-connected unbalanced MGs is proposed here to minimize the total operational cost and the voltage deviation. The epsilon-constraint method and fuzzy satisfying approach are used to solve the multi-objective optimization problem and to obtain compromise solutions. Uncertainties are considered by employing the roulette wheel mechanism for generating scenarios regarding renewable energy generations, EV charging demands, electric loads, and electricity prices. In addition, to avoid adopting infeasible and impractical solutions, a three-phase power flow is integrated in the proposed model. The proposed method is assessed in a modified IEEE 34-bus test system consisting of EVs, battery systems, wind turbine units, photovoltaic units, and diesel generators. The results show the effectiveness and benefits of the proposed model for handling uncertainties while minimizing both operational cost and voltage deviation index and providing more realistic and reliable solutions that can be applied by MG operators.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)ASCRS Research FoundationFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Electrical Engineering São Paulo State UniversityDepartment of Systems and Energy University of Campinas (UNICAMP)Department of Industrial Engineering Los Andes UniversitySchool of Electrical Engineering University of Costa RicaSchool of Energy Engineering São Paulo State UniversityDepartment of Electrical Engineering São Paulo State UniversitySchool of Energy Engineering São Paulo State UniversityCAPES: 001FAPESP: 2015/21972–6FAPESP: 2017/02831–8FAPESP: 2018/08008-4FAPESP: 2018/20990–9Universidade Estadual Paulista (UNESP)Universidade Estadual de Campinas (UNICAMP)Los Andes UniversityUniversity of Costa RicaZandrazavi, Seyed Farhad [UNESP]Guzman, Cindy PaolaPozos, Alejandra TabaresQuiros-Tortos, JairoFranco, John Fredy [UNESP]2022-04-28T19:48:31Z2022-04-28T19:48:31Z2022-02-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.energy.2021.122884Energy, v. 241.0360-5442http://hdl.handle.net/11449/22309410.1016/j.energy.2021.1228842-s2.0-85121591731Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEnergyinfo:eu-repo/semantics/openAccess2022-04-28T19:48:31Zoai:repositorio.unesp.br:11449/223094Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:38:11.298937Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles
title Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles
spellingShingle Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles
Zandrazavi, Seyed Farhad [UNESP]
Electric vehicles
Microgrids
Multi-objective optimization
Renewable energy
Stochastic optimization
title_short Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles
title_full Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles
title_fullStr Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles
title_full_unstemmed Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles
title_sort Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles
author Zandrazavi, Seyed Farhad [UNESP]
author_facet Zandrazavi, Seyed Farhad [UNESP]
Guzman, Cindy Paola
Pozos, Alejandra Tabares
Quiros-Tortos, Jairo
Franco, John Fredy [UNESP]
author_role author
author2 Guzman, Cindy Paola
Pozos, Alejandra Tabares
Quiros-Tortos, Jairo
Franco, John Fredy [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Universidade Estadual de Campinas (UNICAMP)
Los Andes University
University of Costa Rica
dc.contributor.author.fl_str_mv Zandrazavi, Seyed Farhad [UNESP]
Guzman, Cindy Paola
Pozos, Alejandra Tabares
Quiros-Tortos, Jairo
Franco, John Fredy [UNESP]
dc.subject.por.fl_str_mv Electric vehicles
Microgrids
Multi-objective optimization
Renewable energy
Stochastic optimization
topic Electric vehicles
Microgrids
Multi-objective optimization
Renewable energy
Stochastic optimization
description Microgrids (MGs) contribute to the integration of renewable energy-based distributed generation (DG) units and electric vehicles (EVs) in a smart, secure, sustainable, and economic fashion. However, the unbalanced nature of MGs along with the probabilistic nature of renewable energy, electricity prices, and EV demand complicate the energy management process. To overcome that challenge, a stochastic multi-objective optimization model for grid-connected unbalanced MGs is proposed here to minimize the total operational cost and the voltage deviation. The epsilon-constraint method and fuzzy satisfying approach are used to solve the multi-objective optimization problem and to obtain compromise solutions. Uncertainties are considered by employing the roulette wheel mechanism for generating scenarios regarding renewable energy generations, EV charging demands, electric loads, and electricity prices. In addition, to avoid adopting infeasible and impractical solutions, a three-phase power flow is integrated in the proposed model. The proposed method is assessed in a modified IEEE 34-bus test system consisting of EVs, battery systems, wind turbine units, photovoltaic units, and diesel generators. The results show the effectiveness and benefits of the proposed model for handling uncertainties while minimizing both operational cost and voltage deviation index and providing more realistic and reliable solutions that can be applied by MG operators.
publishDate 2022
dc.date.none.fl_str_mv 2022-04-28T19:48:31Z
2022-04-28T19:48:31Z
2022-02-15
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/10.1016/j.energy.2021.122884
Energy, v. 241.
0360-5442
http://hdl.handle.net/11449/223094
10.1016/j.energy.2021.122884
2-s2.0-85121591731
url http://dx.doi.org/10.1016/j.energy.2021.122884
http://hdl.handle.net/11449/223094
identifier_str_mv Energy, v. 241.
0360-5442
10.1016/j.energy.2021.122884
2-s2.0-85121591731
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Energy
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_ 1808129538879651840