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

Bruno Mota, Luis Gomes, Pedro Faria, Carlos Ramos, Zita Vale and Regina Correia
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Bruno Mota: GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, 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
Pedro Faria: GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, 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
Zita Vale: Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal
Regina Correia: SISTRADE—Software Consulting, S.A., 4250-380 Porto, Portugal

Energies, 2021, vol. 14, issue 2, 1-14

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.

Keywords: demand-side management; demand response; flexibility; genetic algorithm; production line; tasks scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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