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

Author

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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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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Rasmussen, Lisa Buth & Bacher, Peder & Madsen, Henrik & Nielsen, Henrik Aalborg & Heerup, Christian & Green, Torben, 2016. "Load forecasting of supermarket refrigeration," Applied Energy, Elsevier, vol. 163(C), pages 32-40.
    2. Biegel, Benjamin & Hansen, Lars Henrik & Stoustrup, Jakob & Andersen, Palle & Harbo, Silas, 2014. "Value of flexible consumption in the electricity markets," Energy, Elsevier, vol. 66(C), pages 354-362.
    3. 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.
    4. Kong, Xiaobing & Liu, Xiangjie & Lee, Kwang Y., 2015. "Nonlinear multivariable hierarchical model predictive control for boiler-turbine system," Energy, Elsevier, vol. 93(P1), pages 309-322.
    5. Gonçalves, Ivo & Gomes, Álvaro & Henggeler Antunes, Carlos, 2019. "Optimizing the management of smart home energy resources under different power cost scenarios," Applied Energy, Elsevier, vol. 242(C), pages 351-363.
    6. Carreiro, Andreia M. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2017. "Energy management systems aggregators: A literature survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1160-1172.
    7. Biegel, Benjamin & Westenholz, Mikkel & Hansen, Lars Henrik & Stoustrup, Jakob & Andersen, Palle & Harbo, Silas, 2014. "Integration of flexible consumers in the ancillary service markets," Energy, Elsevier, vol. 67(C), pages 479-489.
    8. 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.
    9. Kirchem, Dana & Lynch, Muireann Á & Casey, Eoin & Bertsch, Valentin, 2019. "Demand response within the energy-for-water-nexus: A review," Papers WP637, Economic and Social Research Institute (ESRI).
    10. Cappers, Peter & MacDonald, Jason & Goldman, Charles & Ma, Ookie, 2013. "An assessment of market and policy barriers for demand response providing ancillary services in U.S. electricity markets," Energy Policy, Elsevier, vol. 62(C), pages 1031-1039.
    11. Sun, Kai & Tseng, Chen-Ting & Shan-Hill Wong, David & Shieh, Shyan-Shu & Jang, Shi-Shang & Kang, Jia-Lin & Hsieh, Wei-Dong, 2015. "Model predictive control for improving waste heat recovery in coke dry quenching processes," Energy, Elsevier, vol. 80(C), pages 275-283.

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