Robust multi-objective optimization of a renewable based hybrid power system
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.apenergy.2018.04.032 http://hdl.handle.net/11449/176207 |
Resumo: | This paper proposes a probabilistic simulation-based multi-objective optimization approach for dimensioning robust renewable based Hybrid Power Systems. The method integrates an Optimization Module based on a multi-objective Genetic Algorithm, an Uncertainty Module that uses Latin Hypercube Sampling method and Monte Carlo Simulation to generate uncertainty scenarios and a Simulation Module to simulate the power system under real operating conditions. Uncertainties considered include the renewable resources availability, the load demand, and the probability of the components’ failure. The performance of the proposed approach was assessed in a rural community of the Amazonian region of Brazil. Results show that a system configuration with the same level of reliability as in the deterministic scenario implies a higher economic cost; however, the configurations obtained probabilistically represent feasible robust solutions and guarantee a reliable source of generation. The proposed optimization method constitutes a useful decision making tool for dimensioning hybrid power systems that require both optimality and robustness. |
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Repositório Institucional da UNESP |
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Robust multi-objective optimization of a renewable based hybrid power systemGenetic AlgorithmHybrid power systems dimensioningProbabilistic simulationRenewable energyUncertaintyThis paper proposes a probabilistic simulation-based multi-objective optimization approach for dimensioning robust renewable based Hybrid Power Systems. The method integrates an Optimization Module based on a multi-objective Genetic Algorithm, an Uncertainty Module that uses Latin Hypercube Sampling method and Monte Carlo Simulation to generate uncertainty scenarios and a Simulation Module to simulate the power system under real operating conditions. Uncertainties considered include the renewable resources availability, the load demand, and the probability of the components’ failure. The performance of the proposed approach was assessed in a rural community of the Amazonian region of Brazil. Results show that a system configuration with the same level of reliability as in the deterministic scenario implies a higher economic cost; however, the configurations obtained probabilistically represent feasible robust solutions and guarantee a reliable source of generation. The proposed optimization method constitutes a useful decision making tool for dimensioning hybrid power systems that require both optimality and robustness.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Engineering Faculty UNESP–Univ Estadual Paulista Campus of Guaratinguetá Department of Electrical Engineering, Av. Ariberto P. da Cunha, 333 – GuaratinguetáInstitute of Bioenergy Research, IPBEN–UNESP GuaratinguetáEngineering Faculty Itajubá Federal University – UNIFEIEngineering Faculty UNESP–Univ Estadual Paulista Campus of Guaratinguetá Energy Department, Av. Ariberto P. da Cunha, 333 – GuaratinguetáEngineering Faculty UNESP–Univ Estadual Paulista Campus of Guaratinguetá Department of Electrical Engineering, Av. Ariberto P. da Cunha, 333 – GuaratinguetáEngineering Faculty UNESP–Univ Estadual Paulista Campus of Guaratinguetá Energy Department, Av. Ariberto P. da Cunha, 333 – GuaratinguetáUniversidade Estadual Paulista (Unesp)Itajubá Federal University – UNIFEIRoberts, Justo José [UNESP]Marotta Cassula, Agnelo [UNESP]Silveira, José Luzda Costa Bortoni, EdsonMendiburu, Andrés Z. [UNESP]2018-12-11T17:19:36Z2018-12-11T17:19:36Z2018-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article52-68application/pdfhttp://dx.doi.org/10.1016/j.apenergy.2018.04.032Applied Energy, v. 223, p. 52-68.0306-2619http://hdl.handle.net/11449/17620710.1016/j.apenergy.2018.04.0322-s2.0-850457323252-s2.0-85045732325.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplied Energy3,162info:eu-repo/semantics/openAccess2024-01-12T06:30:25Zoai:repositorio.unesp.br:11449/176207Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-01-12T06:30:25Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Robust multi-objective optimization of a renewable based hybrid power system |
title |
Robust multi-objective optimization of a renewable based hybrid power system |
spellingShingle |
Robust multi-objective optimization of a renewable based hybrid power system Roberts, Justo José [UNESP] Genetic Algorithm Hybrid power systems dimensioning Probabilistic simulation Renewable energy Uncertainty |
title_short |
Robust multi-objective optimization of a renewable based hybrid power system |
title_full |
Robust multi-objective optimization of a renewable based hybrid power system |
title_fullStr |
Robust multi-objective optimization of a renewable based hybrid power system |
title_full_unstemmed |
Robust multi-objective optimization of a renewable based hybrid power system |
title_sort |
Robust multi-objective optimization of a renewable based hybrid power system |
author |
Roberts, Justo José [UNESP] |
author_facet |
Roberts, Justo José [UNESP] Marotta Cassula, Agnelo [UNESP] Silveira, José Luz da Costa Bortoni, Edson Mendiburu, Andrés Z. [UNESP] |
author_role |
author |
author2 |
Marotta Cassula, Agnelo [UNESP] Silveira, José Luz da Costa Bortoni, Edson Mendiburu, Andrés Z. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Itajubá Federal University – UNIFEI |
dc.contributor.author.fl_str_mv |
Roberts, Justo José [UNESP] Marotta Cassula, Agnelo [UNESP] Silveira, José Luz da Costa Bortoni, Edson Mendiburu, Andrés Z. [UNESP] |
dc.subject.por.fl_str_mv |
Genetic Algorithm Hybrid power systems dimensioning Probabilistic simulation Renewable energy Uncertainty |
topic |
Genetic Algorithm Hybrid power systems dimensioning Probabilistic simulation Renewable energy Uncertainty |
description |
This paper proposes a probabilistic simulation-based multi-objective optimization approach for dimensioning robust renewable based Hybrid Power Systems. The method integrates an Optimization Module based on a multi-objective Genetic Algorithm, an Uncertainty Module that uses Latin Hypercube Sampling method and Monte Carlo Simulation to generate uncertainty scenarios and a Simulation Module to simulate the power system under real operating conditions. Uncertainties considered include the renewable resources availability, the load demand, and the probability of the components’ failure. The performance of the proposed approach was assessed in a rural community of the Amazonian region of Brazil. Results show that a system configuration with the same level of reliability as in the deterministic scenario implies a higher economic cost; however, the configurations obtained probabilistically represent feasible robust solutions and guarantee a reliable source of generation. The proposed optimization method constitutes a useful decision making tool for dimensioning hybrid power systems that require both optimality and robustness. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:19:36Z 2018-12-11T17:19:36Z 2018-08-01 |
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/10.1016/j.apenergy.2018.04.032 Applied Energy, v. 223, p. 52-68. 0306-2619 http://hdl.handle.net/11449/176207 10.1016/j.apenergy.2018.04.032 2-s2.0-85045732325 2-s2.0-85045732325.pdf |
url |
http://dx.doi.org/10.1016/j.apenergy.2018.04.032 http://hdl.handle.net/11449/176207 |
identifier_str_mv |
Applied Energy, v. 223, p. 52-68. 0306-2619 10.1016/j.apenergy.2018.04.032 2-s2.0-85045732325 2-s2.0-85045732325.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Applied Energy 3,162 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
52-68 application/pdf |
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_ |
1797790277539201024 |