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Benefits of smart control of hybrid heat pumps: An analysis of field trial data

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  • Sun, Mingyang
  • Djapic, Predrag
  • Aunedi, Marko
  • Pudjianto, Danny
  • Strbac, Goran

Abstract

Smart hybrid heat pumps have the capability to perform smart switching between electricity and gas by employing a fully-optimized control technology with predictive demand-side management to automatically use the most cost-effective heating mode across time. This enables a mechanism for delivering flexible demand-side response in a domestic setting. This paper conducts a comprehensive analysis of the fine-grained data collected during the world’s first sizable field trial of smart hybrid heat pumps to present the benefits of the smart control technology. More specifically, a novel flexibility quantification framework is proposed to estimate the capability of heat pump demand shifting based on preheating. Within the proposed framework, accurate estimation of baseline heat demand during the days with interventions is fundamentally critical for understanding the effectiveness of smart control. Furthermore, diversity of heat pump demand is quantified across different numbers of households as an important input into electricity distribution network planning. Finally, the observed values of the Coefficient of Performance (COP) have been analyzed to demonstrate that the smart control can optimize the heat pump operation while taking into account a variety of parameters including the heat pump output water temperature, therefore delivering higher average COP values by maximizing the operating efficiency of the heat pump. Finally, the results of the whole-system assessment of smart hybrid heat pumps demonstrate that the system value of smart control is between 2.1 and 5.3 £ bn/year.

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  • Sun, Mingyang & Djapic, Predrag & Aunedi, Marko & Pudjianto, Danny & Strbac, Goran, 2019. "Benefits of smart control of hybrid heat pumps: An analysis of field trial data," Applied Energy, Elsevier, vol. 247(C), pages 525-536.
  • Handle: RePEc:eee:appene:v:247:y:2019:i:c:p:525-536
    DOI: 10.1016/j.apenergy.2019.04.068
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    6. Parrish, Bryony & Hielscher, Sabine & Foxon, Timothy J., 2021. "Consumers or users? The impact of user learning about smart hybrid heat pumps on policy trajectories for heat decarbonisation," Energy Policy, Elsevier, vol. 148(PB).
    7. D’Ettorre, F. & Banaei, M. & Ebrahimy, R. & Pourmousavi, S. Ali & Blomgren, E.M.V. & Kowalski, J. & Bohdanowicz, Z. & Łopaciuk-Gonczaryk, B. & Biele, C. & Madsen, H., 2022. "Exploiting demand-side flexibility: State-of-the-art, open issues and social perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
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    11. Wang, Jidong & Liu, Jianxin & Li, Chenghao & Zhou, Yue & Wu, Jianzhong, 2020. "Optimal scheduling of gas and electricity consumption in a smart home with a hybrid gas boiler and electric heating system," Energy, Elsevier, vol. 204(C).
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    14. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).

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