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

Javier Gómez, William D. Chicaiza, Juan M. Escaño and Carlos Bordons

Renewable Energy, 2023, vol. 215, issue C

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.

Keywords: Genetic algorithms; Energy optimisation; Renewable energy; Manufacturing process; Production scheduling (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:215:y:2023:i:c:s096014812300839x

DOI: 10.1016/j.renene.2023.118933

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