Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm

Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-gen...

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Autores: Gil Marcelino, Carolina, Salcedo Sanz, Sancho|||0000-0002-4048-1676, Jiménez Fernández, Silvia|||0000-0002-2065-1754, Camacho Gómez, Carlos
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/47548
Acceso en línea:http://hdl.handle.net/10017/47548
https://dx.doi.org/10.3390/en14092443
Access Level:acceso abierto
Palabra clave:Generation scheduling
Hydro-power plants
Coral Reefs Optimization algorithm
Meta-heuristics
Bio-inspired algorithms
Energy efficiency
Informática
Energías Renovables/Energías Alternativas
Computer science
Alternative energies
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spelling Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithmGil Marcelino, CarolinaSalcedo Sanz, Sancho|||0000-0002-4048-1676Jiménez Fernández, Silvia|||0000-0002-2065-1754Camacho Gómez, CarlosGeneration schedulingHydro-power plantsCoral Reefs Optimization algorithmMeta-heuristicsBio-inspired algorithmsEnergy efficiencyInformáticaEnergías Renovables/Energías AlternativasComputer scienceAlternative energiesHydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem.This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.European CommissionMinisterio de Economía y CompetitividadComunidad de MadridMDPI20212021-04-25journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/47548https://dx.doi.org/10.3390/en14092443reponame: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 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/475482026-06-18T11:13:07Z
dc.title.none.fl_str_mv Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm
title Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm
spellingShingle Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm
Gil Marcelino, Carolina
Generation scheduling
Hydro-power plants
Coral Reefs Optimization algorithm
Meta-heuristics
Bio-inspired algorithms
Energy efficiency
Informática
Energías Renovables/Energías Alternativas
Computer science
Alternative energies
title_short Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm
title_full Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm
title_fullStr Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm
title_full_unstemmed Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm
title_sort Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm
dc.creator.none.fl_str_mv Gil Marcelino, Carolina
Salcedo Sanz, Sancho|||0000-0002-4048-1676
Jiménez Fernández, Silvia|||0000-0002-2065-1754
Camacho Gómez, Carlos
author Gil Marcelino, Carolina
author_facet Gil Marcelino, Carolina
Salcedo Sanz, Sancho|||0000-0002-4048-1676
Jiménez Fernández, Silvia|||0000-0002-2065-1754
Camacho Gómez, Carlos
author_role author
author2 Salcedo Sanz, Sancho|||0000-0002-4048-1676
Jiménez Fernández, Silvia|||0000-0002-2065-1754
Camacho Gómez, Carlos
author2_role author
author
author
dc.subject.none.fl_str_mv Generation scheduling
Hydro-power plants
Coral Reefs Optimization algorithm
Meta-heuristics
Bio-inspired algorithms
Energy efficiency
Informática
Energías Renovables/Energías Alternativas
Computer science
Alternative energies
topic Generation scheduling
Hydro-power plants
Coral Reefs Optimization algorithm
Meta-heuristics
Bio-inspired algorithms
Energy efficiency
Informática
Energías Renovables/Energías Alternativas
Computer science
Alternative energies
description Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem.This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-04-25
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/10017/47548
https://dx.doi.org/10.3390/en14092443
url http://hdl.handle.net/10017/47548
https://dx.doi.org/10.3390/en14092443
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 4.0 International
http://creativecommons.org/licenses/by/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 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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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