IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v251y2019ic22.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919310311
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113357?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Müller, Theresa & Möst, Dominik, 2018. "Demand Response Potential: Available when Needed?," Energy Policy, Elsevier, vol. 115(C), pages 181-198.
    3. Xu, Fangyuan & Wu, Wanli & Zhao, Fei & Zhou, Ya & Wang, Yongjian & Wu, Runji & Zhang, Tao & Wen, Yongchen & Fan, Yiliang & Jiang, Shengli, 2019. "A micro-market module design for university demand-side management using self-crossover genetic algorithms," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    4. Gillich, Aaron & Godefroy, Julie & Ford, Andy & Hewitt, Mark & L'Hostis, Jonathan, 2022. "Performance analysis for the UK's first 5th generation heat network – The BEN case study at LSBU," Energy, Elsevier, vol. 243(C).
    5. Dong, Zhe & Liu, Miao & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2019. "Automatic generation control for the flexible operation of multimodular high temperature gas-cooled reactor plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 11-31.
    6. 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.
    7. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    8. Jungsub Sim & Minsoo Kim & Dongjoo Kim & Hongseok Kim, 2021. "Cloud Energy Storage System Operation with Capacity P2P Transaction," Energies, MDPI, vol. 14(2), pages 1-13, January.
    9. Feuerriegel, Stefan & Neumann, Dirk, 2014. "Measuring the financial impact of demand response for electricity retailers," Energy Policy, Elsevier, vol. 65(C), pages 359-368.
    10. Barelli, L. & Bidini, G. & Bonucci, F. & Castellini, L. & Fratini, A. & Gallorini, F. & Zuccari, A., 2019. "Flywheel hybridization to improve battery life in energy storage systems coupled to RES plants," Energy, Elsevier, vol. 173(C), pages 937-950.
    11. Koltsaklis, Nikolaos E. & Nazos, Konstantinos, 2017. "A stochastic MILP energy planning model incorporating power market dynamics," Applied Energy, Elsevier, vol. 205(C), pages 1364-1383.
    12. Shunyong Yin & Jianjun Xia & Yi Jiang, 2020. "Characteristics Analysis of the Heat-to-Power Ratio from the Supply and Demand Sides of Cities in Northern China," Energies, MDPI, vol. 13(1), pages 1-14, January.
    13. Kovacic, Zora & Giampietro, Mario, 2015. "Empty promises or promising futures? The case of smart grids," Energy, Elsevier, vol. 93(P1), pages 67-74.
    14. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    15. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    16. 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.
    17. Frank Bruno & Martin Belusko & Edward Halawa, 2019. "CO 2 Refrigeration and Heat Pump Systems—A Comprehensive Review," Energies, MDPI, vol. 12(15), pages 1-39, August.
    18. 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.
    19. Pusceddu, Elian & Zakeri, Behnam & Castagneto Gissey, Giorgio, 2021. "Synergies between energy arbitrage and fast frequency response for battery energy storage systems," Applied Energy, Elsevier, vol. 283(C).
    20. Velvart, Joëlle & Dato, Prudence & Kuhlmey, Florian, 2022. "Tailored interventions in a major life decision: A home relocation discrete choice experiment," Working papers 2022/03, Faculty of Business and Economics - University of Basel.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:251:y:2019:i:c:22. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.