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Predictive Controller for Refrigeration Systems Aimed to Electrical Load Shifting and Energy Storage

Author

Listed:
  • Edoardo Di Mattia

    (Interdepartmental Research Center on Food Safety, Technology and Innovation (SITEIA.PARMA), University of Parma, Tecnopolo Padiglione 33, Campus Universitario, 43124 Parma, Italy)

  • Agostino Gambarotta

    (Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy
    Center for Energy and Environment (CIDEA), University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy)

  • Emanuela Marzi

    (Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy)

  • Mirko Morini

    (Interdepartmental Research Center on Food Safety, Technology and Innovation (SITEIA.PARMA), University of Parma, Tecnopolo Padiglione 33, Campus Universitario, 43124 Parma, Italy
    Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy
    Center for Energy and Environment (CIDEA), University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy)

  • Costanza Saletti

    (Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy)

Abstract

The need to reduce greenhouse gas emissions is leading to an increase in the use of renewable energy sources. Due to the aleatory nature of these sources, to prevent grid imbalances, smart management of the entire system is required. Industrial refrigeration systems represent a source of flexibility in this context: being large electricity consumers, they can allow large-load shifting by varying separator levels or storing surplus energy in the products and thus balancing renewable electricity production. The work aims to model and control an industrial refrigeration system used for freezing food by applying the Model Predictive Control technique. The controller was developed in Matlab ® and implemented in a Model-in-the-Loop environment. Two control objectives are proposed: the first aims to minimize total energy consumption, while the second also focuses on utilizing the maximum amount of renewable energy. The results show that the innovative controller allows energy savings and better exploitation of the available renewable electricity, with a 4.5% increase in its use, compared to traditional control methods. Since the proposed software solution is rapidly applicable without the need to modify the plant with additional hardware, its uptake can contribute to grid stability and renewable energy exploitation.

Suggested Citation

  • Edoardo Di Mattia & Agostino Gambarotta & Emanuela Marzi & Mirko Morini & Costanza Saletti, 2022. "Predictive Controller for Refrigeration Systems Aimed to Electrical Load Shifting and Energy Storage," Energies, MDPI, vol. 15(19), pages 1-22, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7125-:d:927816
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    References listed on IDEAS

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    1. Zhao, Lei & Cai, Wenjian & Ding, Xudong & Chang, Weichung, 2013. "Model-based optimization for vapor compression refrigeration cycle," Energy, Elsevier, vol. 55(C), pages 392-402.
    2. Yin, Xiaohong & Wang, Xinli & Li, Shaoyuan & Cai, Wenjian, 2016. "Energy-efficiency-oriented cascade control for vapor compression refrigeration cycle systems," Energy, Elsevier, vol. 116(P1), pages 1006-1019.
    3. Xu, Yun-Chao & Chen, Qun, 2013. "A theoretical global optimization method for vapor-compression refrigeration systems based on entransy theory," Energy, Elsevier, vol. 60(C), pages 464-473.
    4. Zhao, B.Y. & Zhao, Z.G. & Li, Y. & Wang, R.Z. & Taylor, R.A., 2019. "An adaptive PID control method to improve the power tracking performance of solar photovoltaic air-conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    5. Hovgaard, Tobias Gybel & Larsen, Lars F.S. & Edlund, Kristian & Jørgensen, John Bagterp, 2012. "Model predictive control technologies for efficient and flexible power consumption in refrigeration systems," Energy, Elsevier, vol. 44(1), pages 105-116.
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