An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and...

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Autores: Gil Marcelino, Carolina, Matos Cardoso Leite, Gabriel|||0000-0002-1486-346X, Delgado, C.A.D.M., Oliveira, L.B. de, Fialho Wanner, Elizabeth, Jiménez Fernández, Silvia|||0000-0002-2065-1754, Salcedo Sanz, Sancho|||0000-0002-4048-1676
Formato: artículo
Fecha de publicación:2021
País:España
Recursos:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/49807
Acesso em linha:http://hdl.handle.net/10017/49807
https://dx.doi.org/10.1016/j.eswa.2021.115638
Access Level:acceso abierto
Palavra-chave:Cascading hydro-power plant modeling
Multi-objective optimization
Swarm intelligence
MESH
Energy production
Informática
Energías Renovables/Energías Alternativas
Computer science
Alternative energies
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spelling An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plantsGil Marcelino, CarolinaMatos Cardoso Leite, Gabriel|||0000-0002-1486-346XDelgado, C.A.D.M.Oliveira, L.B. deFialho Wanner, ElizabethJiménez Fernández, Silvia|||0000-0002-2065-1754Salcedo Sanz, Sancho|||0000-0002-4048-1676Cascading hydro-power plant modelingMulti-objective optimizationSwarm intelligenceMESHEnergy productionInformáticaEnergías Renovables/Energías AlternativasComputer scienceAlternative energiesThis paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.European CommissionAgencia Estatal de InvestigaciónComunidad de MadridElsevier20212021-12-15journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/49807https://dx.doi.org/10.1016/j.eswa.2021.115638reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)InglésengEuropean Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 754382 GOT Energy TalentAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TIN2017-85887-C2-2-P NUEVOS ALGORITMOS HIBRIDOS DE INSPIRACION NATURAL PARA PROBLEMAS DE CLASIFICACION ORDINAL Y PREDICCIONComunidad de Madrid http://dx.doi.org/10.13039/100012818 Not available S2018%2FEMT4366 PROgrama Microredes INTeligentes-CMopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/498072026-06-18T11:13:07Z
dc.title.none.fl_str_mv An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants
title An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants
spellingShingle An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants
Gil Marcelino, Carolina
Cascading hydro-power plant modeling
Multi-objective optimization
Swarm intelligence
MESH
Energy production
Informática
Energías Renovables/Energías Alternativas
Computer science
Alternative energies
title_short An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants
title_full An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants
title_fullStr An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants
title_full_unstemmed An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants
title_sort An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants
dc.creator.none.fl_str_mv Gil Marcelino, Carolina
Matos Cardoso Leite, Gabriel|||0000-0002-1486-346X
Delgado, C.A.D.M.
Oliveira, L.B. de
Fialho Wanner, Elizabeth
Jiménez Fernández, Silvia|||0000-0002-2065-1754
Salcedo Sanz, Sancho|||0000-0002-4048-1676
author Gil Marcelino, Carolina
author_facet Gil Marcelino, Carolina
Matos Cardoso Leite, Gabriel|||0000-0002-1486-346X
Delgado, C.A.D.M.
Oliveira, L.B. de
Fialho Wanner, Elizabeth
Jiménez Fernández, Silvia|||0000-0002-2065-1754
Salcedo Sanz, Sancho|||0000-0002-4048-1676
author_role author
author2 Matos Cardoso Leite, Gabriel|||0000-0002-1486-346X
Delgado, C.A.D.M.
Oliveira, L.B. de
Fialho Wanner, Elizabeth
Jiménez Fernández, Silvia|||0000-0002-2065-1754
Salcedo Sanz, Sancho|||0000-0002-4048-1676
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Cascading hydro-power plant modeling
Multi-objective optimization
Swarm intelligence
MESH
Energy production
Informática
Energías Renovables/Energías Alternativas
Computer science
Alternative energies
topic Cascading hydro-power plant modeling
Multi-objective optimization
Swarm intelligence
MESH
Energy production
Informática
Energías Renovables/Energías Alternativas
Computer science
Alternative energies
description This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-12-15
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/49807
https://dx.doi.org/10.1016/j.eswa.2021.115638
url http://hdl.handle.net/10017/49807
https://dx.doi.org/10.1016/j.eswa.2021.115638
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 754382 GOT Energy Talent
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TIN2017-85887-C2-2-P NUEVOS ALGORITMOS HIBRIDOS DE INSPIRACION NATURAL PARA PROBLEMAS DE CLASIFICACION ORDINAL Y PREDICCION
Comunidad de Madrid http://dx.doi.org/10.13039/100012818 Not available S2018%2FEMT4366 PROgrama Microredes INTeligentes-CM
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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