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Facilitating static firm frequency response with aggregated networks of commercial food refrigeration systems

Author

Listed:
  • Postnikov, A.
  • Albayati, I.M.
  • Pearson, S.
  • Bingham, C.
  • Bickerton, R.
  • Zolotas, A.

Abstract

Aggregated electrical loads from massive numbers of distributed retail refrigeration systems could have a significant role in frequency balancing services. To date, no study has realised effective engineering applications of static firm frequency response to these aggregated networks. Here, the authors present a novel and validated approach that enables large scale control of distributed retail refrigeration assets. The authors show a validated model that simulates the operation of retail refrigerators comprising centralised compressor packs feeding multiple in-store display cases. The model was used to determine an optimal control strategy that both minimised the engineering risk to the pack during shut down and potential impacts to food safety. The authors show that following a load shedding frequency response trigger the pack should be allowed to maintain operation but with increased suction pressure set-point. This reduces compressor load whilst enabling a continuous flow of refrigerant to food cases. In addition, the authors simulated an aggregated response of up to three hundred compressor packs (over 2 MW capacity), with refrigeration cases on hysteresis and modulation control. Hysteresis control, compared to modulation, led to undesired load oscillations when the system recovers after a frequency balancing event. Transient responses of the system during the event showed significant fluctuations of active power when compressor network responds to both primary and secondary parts of a frequency balancing event. Enabling frequency response within this system is demonstrated by linking the aggregated refrigeration loads with a simplified power grid model that simulates a power loss incident.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:22
    DOI: 10.1016/j.apenergy.2019.113357
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    References listed on IDEAS

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    1. Qadrdan, Meysam & Cheng, Meng & Wu, Jianzhong & Jenkins, Nick, 2017. "Benefits of demand-side response in combined gas and electricity networks," Applied Energy, Elsevier, vol. 192(C), pages 360-369.
    2. Muhssin, Mazin T. & Cipcigan, Liana M. & Sami, Saif Sabah & Obaid, Zeyad Assi, 2018. "Potential of demand side response aggregation for the stabilization of the grids frequency," Applied Energy, Elsevier, vol. 220(C), pages 643-656.
    3. S. Ehsan Shafiei & Henrik Rasmussen & Jakob Stoustrup, 2013. "Modeling Supermarket Refrigeration Systems for Demand-Side Management," Energies, MDPI, vol. 6(2), pages 1-21, February.
    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. Bradley, Peter & Coke, Alexia & Leach, Matthew, 2016. "Financial incentive approaches for reducing peak electricity demand, experience from pilot trials with a UK energy provider," Energy Policy, Elsevier, vol. 98(C), pages 108-120.
    6. Kremers, Enrique & González de Durana, José Marı´a & Barambones, Oscar, 2013. "Emergent synchronisation properties of a refrigerator demand side management system," Applied Energy, Elsevier, vol. 101(C), pages 709-717.
    7. Rosa Morales González & Shahab Shariat Torbaghan & Madeleine Gibescu & Sjef Cobben, 2016. "Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation," Energies, MDPI, vol. 9(7), pages 1-24, July.
    8. 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.
    9. Fayazbakhsh, M.A. & Bagheri, F. & Bahrami, M., 2015. "An inverse method for calculation of thermal inertia and heat gain in air conditioning and refrigeration systems," Applied Energy, Elsevier, vol. 138(C), pages 496-504.
    10. Cheng, Meng & Sami, Saif Sabah & Wu, Jianzhong, 2017. "Benefits of using virtual energy storage system for power system frequency response," Applied Energy, Elsevier, vol. 194(C), pages 376-385.
    11. Braun, M.R. & Altan, H. & Beck, S.B.M., 2014. "Using regression analysis to predict the future energy consumption of a supermarket in the UK," Applied Energy, Elsevier, vol. 130(C), pages 305-313.
    12. Grünewald, Philipp & Torriti, Jacopo, 2013. "Demand response from the non-domestic sector: Early UK experiences and future opportunities," Energy Policy, Elsevier, vol. 61(C), pages 423-429.
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    Cited by:

    1. 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.
    2. Nina Strobel & Daniel Fuhrländer-Völker & Matthias Weigold & Eberhard Abele, 2020. "Quantifying the Demand Response Potential of Inherent Energy Storages in Production Systems," Energies, MDPI, vol. 13(16), pages 1-22, August.

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