dc.creator | Gómez Jiménez, Javier | es |
dc.creator | Chicaiza Salazar, William David | es |
dc.creator | Escaño González, Juan Manuel | es |
dc.creator | Bordons Alba, Carlos | es |
dc.date.accessioned | 2023-08-30T10:02:16Z | |
dc.date.available | 2023-08-30T10:02:16Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Gómez Jiménez, J., Chicaiza Salazar, W.D., Escaño González, J.M. y Bordons Alba, C. (2023). A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms. Renewable Energy, 215, 118933. https://doi.org/10.1016/j.renene.2023.118933. | |
dc.identifier.issn | 0960-1481 | es |
dc.identifier.uri | https://hdl.handle.net/11441/148563 | |
dc.description | This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). | es |
dc.description.abstract | 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. | es |
dc.format | application/pdf | es |
dc.format.extent | 12 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Renewable Energy, 215, 118933. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Genetic algorithms | es |
dc.subject | Energy optimisation | es |
dc.subject | Renewable energy | es |
dc.subject | Manufacturing process | es |
dc.subject | Production scheduling | es |
dc.title | A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | 958339 | es |
dc.relation.projectID | PID2019-104149RB-I00 | es |
dc.relation.projectID | 10.13039/501100011033 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S096014812300839X | es |
dc.identifier.doi | 10.1016/j.renene.2023.118933 | es |
dc.contributor.group | Universidad de Sevilla. TEP116: Automática y Robótica Industrial | es |
dc.journaltitle | Renewable Energy | es |
dc.publication.volumen | 215 | es |
dc.publication.initialPage | 118933 | es |
dc.contributor.funder | Unión Europea. Horizonte 2020 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |