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

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

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.

Suggested Citation

  • Gómez, Javier & Chicaiza, William D. & Escaño, Juan M. & Bordons, Carlos, 2023. "A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms," Renewable Energy, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s096014812300839x
    DOI: 10.1016/j.renene.2023.118933
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    References listed on IDEAS

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    1. Mohammad T. Taghavifard, 2012. "Scheduling Cellular Manufacturing Systems Using ACO and GA," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 3(1), pages 48-64, January.
    2. Yun, Lingxiang & Li, Lin & Ma, Shuaiyin, 2022. "Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment," Applied Energy, Elsevier, vol. 310(C).
    3. Ruiz Duarte, José Luis & Fan, Neng & Jin, Tongdan, 2020. "Multi-process production scheduling with variable renewable integration and demand response," European Journal of Operational Research, Elsevier, vol. 281(1), pages 186-200.
    4. Rodríguez-García, Javier & Álvarez-Bel, Carlos & Carbonell-Carretero, José-Francisco & Alcázar-Ortega, Manuel & Peñalvo-López, Elisa, 2016. "A novel tool for the evaluation and assessment of demand response activities in the industrial sector," Energy, Elsevier, vol. 113(C), pages 1136-1146.
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    Cited by:

    1. Akshay Ranade & Javier Gómez & Andrew de Juan & William D. Chicaiza & Michael Ahern & Juan M. Escaño & Andriy Hryshchenko & Olan Casey & Aidan Cloonan & Dominic O’Sullivan & Ken Bruton & Alan McGibney, 2024. "Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications," Energies, MDPI, vol. 17(8), pages 1-28, April.

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