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The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial

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  • Love, Jenny
  • Smith, Andrew Z.P.
  • Watson, Stephen
  • Oikonomou, Eleni
  • Summerfield, Alex
  • Gleeson, Colin
  • Biddulph, Phillip
  • Chiu, Lai Fong
  • Wingfield, Jez
  • Martin, Chris
  • Stone, Andy
  • Lowe, Robert

Abstract

Previous studies on the effect of mass uptake of heat pumps on the capability of local or national electricity grids have relied on modelling or small datasets to create the aggregated heat pump load profile. This article uses the UK Renewable Heat Premium Payment dataset, which records the electricity consumption of nearly 700 domestic heat pump installations every 2minutes, to create an aggregated load profile using an order of magnitude more sites than previously available. The aggregated profile is presented on cold and medium winter weekdays and weekends and is shown to contain two peaks per day, dropping overnight to around 40% of its peak. After Diversity Maximum Demand (ADMD) for the population of heat pumps is calculated as 1.7kW per site; this occurs in the morning, whereas the peak national grid demand occurs in the evening. Analysis is carried out on how heat pump ADMD varies with number of heat pumps in the sample. A simple upscaling exercise is presented to give a first order approximation of the increase in GB peak electricity demand with mass deployment of heat pumps. It is found that peak grid demand increases by 7.5GW (14%) with 20% of households using heat pumps. The effect of the same heat pump uptake on grid ramp rate is also discussed; this effect is found to be minor. Finally, a comparison of heat pump and gas boiler operation is given, discussing day and night time operation and mean and peak power at different external temperatures.

Suggested Citation

  • Love, Jenny & Smith, Andrew Z.P. & Watson, Stephen & Oikonomou, Eleni & Summerfield, Alex & Gleeson, Colin & Biddulph, Phillip & Chiu, Lai Fong & Wingfield, Jez & Martin, Chris & Stone, Andy & Lowe, R, 2017. "The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial," Applied Energy, Elsevier, vol. 204(C), pages 332-342.
    • Handle: RePEc:eee:appene:v:204:y:2017:i:c:p:332-342
    DOI: 10.1016/j.apenergy.2017.07.026
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    References listed on IDEAS

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    1. Luickx, Patrick J. & Helsen, Lieve M. & D'haeseleer, William D., 2008. "Influence of massive heat-pump introduction on the electricity-generation mix and the GHG effect: Comparison between Belgium, France, Germany and The Netherlands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(8), pages 2140-2158, October.
    2. Protopapadaki, Christina & Saelens, Dirk, 2017. "Heat pump and PV impact on residential low-voltage distribution grids as a function of building and district properties," Applied Energy, Elsevier, vol. 192(C), pages 268-281.
    3. Good, Nicholas & Zhang, Lingxi & Navarro-Espinosa, Alejandro & Mancarella, Pierluigi, 2015. "High resolution modelling of multi-energy domestic demand profiles," Applied Energy, Elsevier, vol. 137(C), pages 193-210.
    4. Johnston, D. & Lowe, R. & Bell, M., 2005. "An exploration of the technical feasibility of achieving CO2 emission reductions in excess of 60% within the UK housing stock by the year 2050," Energy Policy, Elsevier, vol. 33(13), pages 1643-1659, September.
    5. Barton, John & Huang, Sikai & Infield, David & Leach, Matthew & Ogunkunle, Damiete & Torriti, Jacopo & Thomson, Murray, 2013. "The evolution of electricity demand and the role for demand side participation, in buildings and transport," Energy Policy, Elsevier, vol. 52(C), pages 85-102.
    6. Veldman, Else & Gibescu, Madeleine & Slootweg, Han (J.G.) & Kling, Wil L., 2013. "Scenario-based modelling of future residential electricity demands and assessing their impact on distribution grids," Energy Policy, Elsevier, vol. 56(C), pages 233-247.
    7. Navarro-Espinosa, Alejandro & Mancarella, Pierluigi, 2014. "Probabilistic modeling and assessment of the impact of electric heat pumps on low voltage distribution networks," Applied Energy, Elsevier, vol. 127(C), pages 249-266.
    8. McKenna, R. & Hofmann, L. & Merkel, E. & Fichtner, W. & Strachan, N., 2016. "Analysing socioeconomic diversity and scaling effects on residential electricity load profiles in the context of low carbon technology uptake," Energy Policy, Elsevier, vol. 97(C), pages 13-26.
    9. Pudjianto, Danny & Djapic, Predrag & Aunedi, Marko & Gan, Chin Kim & Strbac, Goran & Huang, Sikai & Infield, David, 2013. "Smart control for minimizing distribution network reinforcement cost due to electrification," Energy Policy, Elsevier, vol. 52(C), pages 76-84.
    10. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    11. Wilson, I.A. Grant & Rennie, Anthony J.R. & Ding, Yulong & Eames, Philip C. & Hall, Peter J. & Kelly, Nicolas J., 2013. "Historical daily gas and electrical energy flows through Great Britain's transmission networks and the decarbonisation of domestic heat," Energy Policy, Elsevier, vol. 61(C), pages 301-305.
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