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Model Predictive Control with Binary Quadratic Programming for the Scheduled Operation of Domestic Refrigerators

Author

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  • Mohammad Reza Zavvar Sabegh

    (School of Engineering, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK)

  • Chris Bingham

    (School of Engineering, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK)

Abstract

The rapid proliferation of the ‘Internet of Things’ (IoT) now affords the opportunity to schedule the operation of widely distributed domestic refrigerator and freezers to collectively improve energy efficiency and reduce peak power consumption on the electrical grid. To accomplish this, the paper proposes the real-time estimation of the thermal mass of each refrigerator in a network using on-line parameter identification, and the co-ordinated (ON-OFF) scheduling of the refrigerator compressors to maintain their respective temperatures within specified hysteresis bands commensurate with accommodating food safety standards. A custom model predictive control (MPC) scheme is devised using binary quadratic programming to realize the scheduling methodology which is implemented through IoT hardware (based on a NodeMCU). Benefits afforded by the proposed scheme are investigated through experimental trials which show that the co-ordinated operation of domestic refrigerators can i) reduce the peak power consumption as seen from the perspective of the electrical power grid (i.e., peak load levelling), ii) can adaptively control the temperature hysteresis band of individual refrigerators to increase operational efficiency, and iii) contribute to a widely distributed aggregated load shed for demand side response purposes in order to aid grid stability. Importantly, the number of compressor starts per hour for each refrigerator is also bounded as an inherent design feature of the algorithm so as not to operationally overstress the compressors and reduce their lifetime. Experimental trials show that such co-ordinated operation of refrigerators can reduce energy consumption by ~30% whilst also providing peak load levelling, thereby affording benefits to both individual consumers as well as electrical network suppliers.

Suggested Citation

  • Mohammad Reza Zavvar Sabegh & Chris Bingham, 2019. "Model Predictive Control with Binary Quadratic Programming for the Scheduled Operation of Domestic Refrigerators," Energies, MDPI, vol. 12(24), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4649-:d:295375
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    References listed on IDEAS

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    1. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    2. Edorta Carrascal & Izaskun Garrido & Aitor J. Garrido & José María Sala, 2016. "Optimization of the Heating System Use in Aged Public Buildings via Model Predictive Control," Energies, MDPI, vol. 9(4), pages 1-20, March.
    3. Postnikov, A. & Albayati, I.M. & Pearson, S. & Bingham, C. & Bickerton, R. & Zolotas, A., 2019. "Facilitating static firm frequency response with aggregated networks of commercial food refrigeration systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Ibrahim M. Saleh & Andrey Postnikov & Corneliu Arsene & Argyrios C. Zolotas & Chris Bingham & Ronald Bickerton & Simon Pearson, 2018. "Impact of Demand Side Response on a Commercial Retail Refrigeration System," Energies, MDPI, vol. 11(2), pages 1-18, February.
    5. Harrington, Lloyd & Aye, Lu & Fuller, Bob, 2018. "Impact of room temperature on energy consumption of household refrigerators: Lessons from analysis of field and laboratory data," Applied Energy, Elsevier, vol. 211(C), pages 346-357.
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