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Model predictive control technologies for efficient and flexible power consumption in refrigeration systems

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  • Hovgaard, Tobias Gybel
  • Larsen, Lars F.S.
  • Edlund, Kristian
  • Jørgensen, John Bagterp

Abstract

Considerable amounts of energy are consumed in supermarket refrigeration systems worldwide. Due to the thermal capacity of refrigerated goods and the rather simplistic control most commonly applied, there is a potential for distributing the system load over time in a more cost-optimal way. In this paper we describe a novel economic-optimizing Model Predictive Control (MPC) scheme that reduces operating costs by utilizing the thermal storage capabilities. A nonlinear optimization tool to handle a non-convex cost function is utilized for simulations with validated scenarios. In this way we explicitly address advantages from daily variations in outdoor temperature and electricity prices. Secondly, we formulate a new cost function that enables the refrigeration system to contribute with ancillary services to the balancing power market. This involvement can be economically beneficial for the system itself, while crucial services can be delivered to a future flexible and intelligent power grid (Smart Grid). Furthermore, we discuss a novel incorporation of probabilistic constraints and Second Order Cone Programming (SOCP) with economic MPC. A Finite Impulse Response (FIR) formulation of the system models allows us to describe and handle model as well as prediction uncertainties in this framework. This means we can demonstrate means for robustifying the performance of the controller.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:44:y:2012:i:1:p:105-116
    DOI: 10.1016/j.energy.2011.12.007
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    1. Finn, P. & Fitzpatrick, C. & Connolly, D. & Leahy, M. & Relihan, L., 2011. "Facilitation of renewable electricity using price based appliance control in Ireland’s electricity market," Energy, Elsevier, vol. 36(5), pages 2952-2960.
    2. Andersson, S.-L. & Elofsson, A.K. & Galus, M.D. & Göransson, L. & Karlsson, S. & Johnsson, F. & Andersson, G., 2010. "Plug-in hybrid electric vehicles as regulating power providers: Case studies of Sweden and Germany," Energy Policy, Elsevier, vol. 38(6), pages 2751-2762, June.
    3. Partovi, Farzad & Nikzad, Mehdi & Mozafari, Babak & Ranjbar, Ali Mohamad, 2011. "A stochastic security approach to energy and spinning reserve scheduling considering demand response program," Energy, Elsevier, vol. 36(5), pages 3130-3137.
    4. Pina, André & Silva, Carlos & Ferrão, Paulo, 2012. "The impact of demand side management strategies in the penetration of renewable electricity," Energy, Elsevier, vol. 41(1), pages 128-137.
    5. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
    6. Shayesteh, E. & Yousefi, A. & Parsa Moghaddam, M., 2010. "A probabilistic risk-based approach for spinning reserve provision using day-ahead demand response program," Energy, Elsevier, vol. 35(5), pages 1908-1915.
    7. Blarke, Morten B. & Dotzauer, Erik, 2011. "Intermittency-friendly and high-efficiency cogeneration: Operational optimisation of cogeneration with compression heat pump, flue gas heat recovery, and intermediate cold storage," Energy, Elsevier, vol. 36(12), pages 6867-6878.
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    9. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    10. 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.
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