Artículo
A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms
Autor/es | Gómez Jiménez, Javier
Chicaiza Salazar, William David Escaño González, Juan Manuel Bordons Alba, Carlos |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2023 |
Fecha de depósito | 2023-08-30 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | Unión Europea. Horizonte 2020 Ministerio de Ciencia e Innovación (MICIN). España Agencia Estatal de Investigación. España |
Identificador del proyecto | 958339
PID2019-104149RB-I00 10.13039/501100011033 |
Cita | 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. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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RE_2023_Gomez_A-renewable_OA.pdf | 2.739Mb | [PDF] | Ver/ | |