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Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events

Author

Listed:
  • Bruno Mota

    (GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, P-4200-072 Porto, Portugal
    Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

  • Luis Gomes

    (GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, P-4200-072 Porto, Portugal
    Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

  • Pedro Faria

    (GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, P-4200-072 Porto, Portugal
    Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

  • Carlos Ramos

    (GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, P-4200-072 Porto, Portugal
    Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

  • Zita Vale

    (Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

  • Regina Correia

    (SISTRADE—Software Consulting, S.A., 4250-380 Porto, Portugal)

Abstract

The scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines and machine limitations. With innovative focus, the main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost in the production line. For that, an approach based on genetic algorithms is proposed and implemented. The use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers allow the user to manage its consumption according to energy prices and energy availability. The proposed solution takes into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response events by shifting its production, by using the flexibility of production lines. A case study using real production data that represents a textile industry is presented, where the tasks for six days are scheduled. During the week, a demand response event is launched, and the proposed algorithm shifts the consumption by changing task orders and machine usage.

Suggested Citation

  • Bruno Mota & Luis Gomes & Pedro Faria & Carlos Ramos & Zita Vale & Regina Correia, 2021. "Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events," Energies, MDPI, vol. 14(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:462-:d:481553
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    References listed on IDEAS

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    Cited by:

    1. Mota, Bruno & Faria, Pedro & Vale, Zita, 2022. "Residential load shifting in demand response events for bill reduction using a genetic algorithm," Energy, Elsevier, vol. 260(C).

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