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Energy Demand Response in a Food-Processing Plant: A Deep Reinforcement Learning Approach

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Listed:
  • Philipp Wohlgenannt

    (Josef Ressel Centre for Intelligent Thermal Energy Systems, Illwerke vkw Endowed Professorship for Energy Efficiency, Energy Research Centre, Vorarlberg University of Applied Sciences, Hochschulstrasse 1, 6850 Dornbirn, Austria
    Faculty of Engineering and Science, University of Agder, Jon Lilletuns vei 9, 4879 Grimstad, Norway)

  • Sebastian Hegenbart

    (Department of Engineering and Technology, Vorarlberg University of Applied Sciences, Hochschulstrasse 1, 6850 Dornbirn, Austria
    Digital Factory Vorarlberg GmbH, Hochschulstrasse 1, 6850 Dornbirn, Austria)

  • Elias Eder

    (Josef Ressel Centre for Intelligent Thermal Energy Systems, Illwerke vkw Endowed Professorship for Energy Efficiency, Energy Research Centre, Vorarlberg University of Applied Sciences, Hochschulstrasse 1, 6850 Dornbirn, Austria)

  • Mohan Kolhe

    (Faculty of Engineering and Science, University of Agder, Jon Lilletuns vei 9, 4879 Grimstad, Norway)

  • Peter Kepplinger

    (Josef Ressel Centre for Intelligent Thermal Energy Systems, Illwerke vkw Endowed Professorship for Energy Efficiency, Energy Research Centre, Vorarlberg University of Applied Sciences, Hochschulstrasse 1, 6850 Dornbirn, Austria)

Abstract

The food industry faces significant challenges in managing operational costs due to its high energy intensity and rising energy prices. Industrial food-processing facilities, with substantial thermal capacities and large demands for cooling and heating, offer promising opportunities for demand response (DR) strategies. This study explores the application of deep reinforcement learning (RL) as an innovative, data-driven approach for DR in the food industry. By leveraging the adaptive, self-learning capabilities of RL, energy costs in the investigated plant are effectively decreased. The RL algorithm was compared with the well-established optimization method Mixed Integer Linear Programming (MILP), and both were benchmarked against a reference scenario without DR. The two optimization strategies demonstrate cost savings of 17.57% and 18.65% for RL and MILP, respectively. Although RL is slightly less efficient in cost reduction, it significantly outperforms in computational speed, being approximately 20 times faster. During operation, RL only needs 2ms per optimization compared to 19s for MILP, making it a promising optimization tool for edge computing. Moreover, while MILP’s computation time increases considerably with the number of binary variables, RL efficiently learns dynamic system behavior and scales to more complex systems without significant performance degradation. These results highlight that deep RL, when applied to DR, offers substantial cost savings and computational efficiency, with broad applicability to energy management in various applications.

Suggested Citation

  • Philipp Wohlgenannt & Sebastian Hegenbart & Elias Eder & Mohan Kolhe & Peter Kepplinger, 2024. "Energy Demand Response in a Food-Processing Plant: A Deep Reinforcement Learning Approach," Energies, MDPI, vol. 17(24), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6430-:d:1548622
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    References listed on IDEAS

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    1. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
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    3. Saffari, Mohammad & de Gracia, Alvaro & Fernández, Cèsar & Belusko, Martin & Boer, Dieter & Cabeza, Luisa F., 2018. "Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV," Applied Energy, Elsevier, vol. 211(C), pages 604-616.
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