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Energy Management of Refrigeration Systems with Thermal Energy Storage Based on Non-Linear Model Predictive Control

Author

Listed:
  • Guillermo Bejarano

    (Departamento de Ingeniería, Escuela Técnica Superior de Ingeniería, Universidad Loyola Andalucía, Avda. de las Universidades s/n, 41704 Dos Hermanas, Spain)

  • João M. Lemos

    (INESC-ID—Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa, Instituto Superior Técnico, University Lisboa, Rua Alves Redol, 9, 1000-029 Lisbon, Portugal)

  • Javier Rico-Azagra

    (Departamento de Ingeniería Eléctrica, Universidad de la Rioja, Calle San José de Calasanz, 31, 26004 Logroño, Spain)

  • Francisco R. Rubio

    (Departamento de Ingeniería de Sistemas y Automática, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain)

  • Manuel G. Ortega

    (Departamento de Ingeniería de Sistemas y Automática, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain)

Abstract

This work addresses the energy management of a combined system consisting of a refrigeration cycle and a thermal energy storage tank based on phase change materials. The storage tank is used as a cold-energy buffer, thus decoupling cooling demand and production, which leads to cost reduction and satisfaction of peak demand that would be infeasible for the original cycle. A layered scheduling and control strategy is proposed, where a non-linear predictive scheduler computes the references of the main powers involved (storage tank charging/discharging powers and direct cooling production), while a low-level controller ensures that the requested powers are actually achieved. A simplified model retaining the dominant dynamics is proposed as the prediction model for the scheduler. Economic, efficiency, and feasibility criteria are considered, seeking operating cost reduction while ensuring demand satisfaction. The performance of the proposed strategy for the system with energy storage is compared in simulation with that of a cycle without energy storage, where the former is shown to satisfy challenging demands while reducing the operating cost by up to 28%. The proposed approach also shows suitable robustness when significant uncertainty in the prediction model is considered.

Suggested Citation

  • Guillermo Bejarano & João M. Lemos & Javier Rico-Azagra & Francisco R. Rubio & Manuel G. Ortega, 2022. "Energy Management of Refrigeration Systems with Thermal Energy Storage Based on Non-Linear Model Predictive Control," Mathematics, MDPI, vol. 10(17), pages 1-27, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3167-:d:905621
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    References listed on IDEAS

    as
    1. Oró, E. & de Gracia, A. & Castell, A. & Farid, M.M. & Cabeza, L.F., 2012. "Review on phase change materials (PCMs) for cold thermal energy storage applications," Applied Energy, Elsevier, vol. 99(C), pages 513-533.
    2. Pedro Macieira & Luis Gomes & Zita Vale, 2021. "Energy Management Model for HVAC Control Supported by Reinforcement Learning," Energies, MDPI, vol. 14(24), pages 1-14, December.
    3. Mosaffa, A.H. & Garousi Farshi, L. & Infante Ferreira, C.A. & Rosen, M.A., 2014. "Advanced exergy analysis of an air conditioning system incorporating thermal energy storage," Energy, Elsevier, vol. 77(C), pages 945-952.
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