A PSO-BPSO technique for hybrid power generation system sizing
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TLA.2020.9111671 http://hdl.handle.net/11449/200607 |
Resumo: | The Particle Swarm Optimization (PSO) algorithm has been widely used in the field of optimization mainly due to its easy implementation, robustness, fast convergence, and low computational cost. However, due to its continuous nature, the PSO cannot be applied directly to real-life problems such as hybrid energy generating systems (HEGS) sizing, which contain continuous and discrete decision variables. In this context, the present work proposes the combination of the original version of the PSO with the binary version of the same algorithm (BPSO) for the sizing of HEGS. The transfer function is the main difference between these two algorithms. In this paper, an S-type transfer function is used to map the continuous space into a discrete space. All components of the HEGS are modeled and simulated during the optimization process. The net present value is defined as the unique objective function. The state of charge (SOC) of the batteries is the main constraint. The proposed PSO-BPSO is used for sizing hybrid power generating systems in the Galapagos Islands in Ecuador. Results show that the best configuration for the studied case is a hybrid system with solar panels, batteries, and diesel generators. Configurations that contain only photovoltaic panels and batteries imply a higher cost due to the oversizing of the battery bank. The proposed PSO-BPSO algorithm revealed to be a simple and powerful tool for efficient energy systems sizing. |
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Repositório Institucional da UNESP |
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A PSO-BPSO technique for hybrid power generation system sizingHybrid generation energy systemsMathematical modelingOptimal sizingPSO-BPSOThe Particle Swarm Optimization (PSO) algorithm has been widely used in the field of optimization mainly due to its easy implementation, robustness, fast convergence, and low computational cost. However, due to its continuous nature, the PSO cannot be applied directly to real-life problems such as hybrid energy generating systems (HEGS) sizing, which contain continuous and discrete decision variables. In this context, the present work proposes the combination of the original version of the PSO with the binary version of the same algorithm (BPSO) for the sizing of HEGS. The transfer function is the main difference between these two algorithms. In this paper, an S-type transfer function is used to map the continuous space into a discrete space. All components of the HEGS are modeled and simulated during the optimization process. The net present value is defined as the unique objective function. The state of charge (SOC) of the batteries is the main constraint. The proposed PSO-BPSO is used for sizing hybrid power generating systems in the Galapagos Islands in Ecuador. Results show that the best configuration for the studied case is a hybrid system with solar panels, batteries, and diesel generators. Configurations that contain only photovoltaic panels and batteries imply a higher cost due to the oversizing of the battery bank. The proposed PSO-BPSO algorithm revealed to be a simple and powerful tool for efficient energy systems sizing.São Paulo State University UNESP College of Engineering of Guaratinguetá Department of Energy Laboratory of Optimization Energy Systems (LOSE) Institute of Bioenergy Research (IPBEN)GIDTEC - Mechanical Engineering Department Universidad Politécnica SalesianaEderal Center of Technological Education Celso Suckow da Fonseca (CEFET/RJ)São Paulo State University UNESP College of Engineering of Guaratinguetá Department of Energy Laboratory of Optimization Energy Systems (LOSE) Institute of Bioenergy Research (IPBEN)Universidade Estadual Paulista (Unesp)Universidad Politécnica SalesianaEderal Center of Technological Education Celso Suckow da Fonseca (CEFET/RJ)Llerena-Pizarro, Omar [UNESP]Proenza-Perez, NestorTuna, Celso Eduardo [UNESP]Silveira, Jose Luz [UNESP]2020-12-12T02:11:05Z2020-12-12T02:11:05Z2020-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1362-1370http://dx.doi.org/10.1109/TLA.2020.9111671IEEE Latin America Transactions, v. 18, n. 8, p. 1362-1370, 2020.1548-0992http://hdl.handle.net/11449/20060710.1109/TLA.2020.91116712-s2.0-85086462287Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Latin America Transactionsinfo:eu-repo/semantics/openAccess2024-07-01T19:30:00Zoai:repositorio.unesp.br:11449/200607Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:41:47.690647Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A PSO-BPSO technique for hybrid power generation system sizing |
title |
A PSO-BPSO technique for hybrid power generation system sizing |
spellingShingle |
A PSO-BPSO technique for hybrid power generation system sizing Llerena-Pizarro, Omar [UNESP] Hybrid generation energy systems Mathematical modeling Optimal sizing PSO-BPSO |
title_short |
A PSO-BPSO technique for hybrid power generation system sizing |
title_full |
A PSO-BPSO technique for hybrid power generation system sizing |
title_fullStr |
A PSO-BPSO technique for hybrid power generation system sizing |
title_full_unstemmed |
A PSO-BPSO technique for hybrid power generation system sizing |
title_sort |
A PSO-BPSO technique for hybrid power generation system sizing |
author |
Llerena-Pizarro, Omar [UNESP] |
author_facet |
Llerena-Pizarro, Omar [UNESP] Proenza-Perez, Nestor Tuna, Celso Eduardo [UNESP] Silveira, Jose Luz [UNESP] |
author_role |
author |
author2 |
Proenza-Perez, Nestor Tuna, Celso Eduardo [UNESP] Silveira, Jose Luz [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidad Politécnica Salesiana Ederal Center of Technological Education Celso Suckow da Fonseca (CEFET/RJ) |
dc.contributor.author.fl_str_mv |
Llerena-Pizarro, Omar [UNESP] Proenza-Perez, Nestor Tuna, Celso Eduardo [UNESP] Silveira, Jose Luz [UNESP] |
dc.subject.por.fl_str_mv |
Hybrid generation energy systems Mathematical modeling Optimal sizing PSO-BPSO |
topic |
Hybrid generation energy systems Mathematical modeling Optimal sizing PSO-BPSO |
description |
The Particle Swarm Optimization (PSO) algorithm has been widely used in the field of optimization mainly due to its easy implementation, robustness, fast convergence, and low computational cost. However, due to its continuous nature, the PSO cannot be applied directly to real-life problems such as hybrid energy generating systems (HEGS) sizing, which contain continuous and discrete decision variables. In this context, the present work proposes the combination of the original version of the PSO with the binary version of the same algorithm (BPSO) for the sizing of HEGS. The transfer function is the main difference between these two algorithms. In this paper, an S-type transfer function is used to map the continuous space into a discrete space. All components of the HEGS are modeled and simulated during the optimization process. The net present value is defined as the unique objective function. The state of charge (SOC) of the batteries is the main constraint. The proposed PSO-BPSO is used for sizing hybrid power generating systems in the Galapagos Islands in Ecuador. Results show that the best configuration for the studied case is a hybrid system with solar panels, batteries, and diesel generators. Configurations that contain only photovoltaic panels and batteries imply a higher cost due to the oversizing of the battery bank. The proposed PSO-BPSO algorithm revealed to be a simple and powerful tool for efficient energy systems sizing. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T02:11:05Z 2020-12-12T02:11:05Z 2020-08-01 |
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/10.1109/TLA.2020.9111671 IEEE Latin America Transactions, v. 18, n. 8, p. 1362-1370, 2020. 1548-0992 http://hdl.handle.net/11449/200607 10.1109/TLA.2020.9111671 2-s2.0-85086462287 |
url |
http://dx.doi.org/10.1109/TLA.2020.9111671 http://hdl.handle.net/11449/200607 |
identifier_str_mv |
IEEE Latin America Transactions, v. 18, n. 8, p. 1362-1370, 2020. 1548-0992 10.1109/TLA.2020.9111671 2-s2.0-85086462287 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IEEE Latin America Transactions |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1362-1370 |
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_ |
1808129235339968512 |