A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).

Detalles Bibliográficos
Autores: Gómez Jiménez, Javier, Chicaiza Salazar, William David, Escaño González, Juan Manuel, Bordons Alba, Carlos
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/148563
Acceso en línea:https://hdl.handle.net/11441/148563
https://doi.org/10.1016/j.renene.2023.118933
Access Level:acceso abierto
Palabra clave:Genetic algorithms
Energy optimisation
Renewable energy
Manufacturing process
Production scheduling
id ES_0c679eeebb39a0e586491f381fe3f2b6
oai_identifier_str oai:idus.us.es:11441/148563
network_acronym_str ES
network_name_str España
repository_id_str
spelling A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithmsGómez Jiménez, JavierChicaiza Salazar, William DavidEscaño González, Juan ManuelBordons Alba, CarlosGenetic algorithmsEnergy optimisationRenewable energyManufacturing processProduction schedulingThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).This article presents the formulation of the optimisation of a manufacturing process, through genetic algorithms, managing the generation and demand of energy in a factory at periodic moments of time. The strategy manages to minimise the daily energy cost and maximise the use of installed renewable energy, also taking advantage of potential battery banks. A time series with a 24-hour horizon of energy production from renewable sources and the electricity supply prices provided by the electricity market operator has been considered. Furthermore, in the simulations, scenarios with different battery capacities have been tested, which has allowed a preliminary study to be carried out for the installation of the electrical storage bank. The results presented in this work show that 6% of energy costs can be saved per day, compared to the current management decided by the manufacturing plant operators.ElsevierIngeniería de Sistemas y AutomáticaTEP116: Automática y Robótica IndustrialUnión Europea. Horizonte 2020Ministerio de Ciencia e Innovación (MICIN). EspañaAgencia Estatal de Investigación. España2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/148563https://doi.org/10.1016/j.renene.2023.118933reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésRenewable Energy, 215, 118933.958339PID2019-104149RB-I0010.13039/501100011033https://www.sciencedirect.com/science/article/pii/S096014812300839Xinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1485632026-06-17T12:51:07Z
dc.title.none.fl_str_mv A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms
title A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms
spellingShingle A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms
Gómez Jiménez, Javier
Genetic algorithms
Energy optimisation
Renewable energy
Manufacturing process
Production scheduling
title_short A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms
title_full A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms
title_fullStr A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms
title_full_unstemmed A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms
title_sort A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms
dc.creator.none.fl_str_mv Gómez Jiménez, Javier
Chicaiza Salazar, William David
Escaño González, Juan Manuel
Bordons Alba, Carlos
author Gómez Jiménez, Javier
author_facet Gómez Jiménez, Javier
Chicaiza Salazar, William David
Escaño González, Juan Manuel
Bordons Alba, Carlos
author_role author
author2 Chicaiza Salazar, William David
Escaño González, Juan Manuel
Bordons Alba, Carlos
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
TEP116: Automática y Robótica Industrial
Unión Europea. Horizonte 2020
Ministerio de Ciencia e Innovación (MICIN). España
Agencia Estatal de Investigación. España
dc.subject.none.fl_str_mv Genetic algorithms
Energy optimisation
Renewable energy
Manufacturing process
Production scheduling
topic Genetic algorithms
Energy optimisation
Renewable energy
Manufacturing process
Production scheduling
description This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/148563
https://doi.org/10.1016/j.renene.2023.118933
url https://hdl.handle.net/11441/148563
https://doi.org/10.1016/j.renene.2023.118933
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Renewable Energy, 215, 118933.
958339
PID2019-104149RB-I00
10.13039/501100011033
https://www.sciencedirect.com/science/article/pii/S096014812300839X
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403282458279936
score 15,300719

Similares en LA Referencia