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Experience Rates of Low-Carbon Domestic Heating Technologies in the United Kingdom

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  • Renaldi, Renaldi

    (Department of Engineering Science, University of Oxford)

  • Hall, Richard

    (Energy Transitions Ltd.)

  • Jamasb, Tooraj

    (Department of Economics, Copenhagen Business School)

  • Roskilly, Anthony P.

    (Department of Engineering, Durham University)

Abstract

This paper presents the experience curves of low-carbon domestic heating technologies in the United Kingdom between 2010 and 2019. The deployment of these technologies has been acknowledged as one of the main actions toward decarbonising the heating sector. In the UK, several deployment oriented policies have been implemented, such as the Renewable Heat Incentive (RHI). In this study, we focus on the following domestic heating technologies: air-source heat pumps, ground-source heat pumps, solar thermal collectors, and biomass boilers. Condensing combination gas boilers are also included to act as the baseline/incumbent technology. Using UK installation cost data for 2010 to 2019, we found that low-carbon heating technologies had experience rates of; air source heat pumps -2.3± 5%, ground source heat pumps -0.8±4%, biomass boilers 0.1±2%, and solar thermal 13±5%, all significantly lower than the re-ported learning rates of similar technologies in the literature. Furthermore, we found that gas boilers have reached the floor price at approximately £30/kW. The resulting experience rates can be used in energy economics models and to inform policymakers in developing further deployment programs.

Suggested Citation

  • Renaldi, Renaldi & Hall, Richard & Jamasb, Tooraj & Roskilly, Anthony P., 2020. "Experience Rates of Low-Carbon Domestic Heating Technologies in the United Kingdom," Working Papers 16-2020, Copenhagen Business School, Department of Economics.
  • Handle: RePEc:hhs:cbsnow:2020_016
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    as
    1. Matteson, Schuyler & Williams, Eric, 2015. "Residual learning rates in lead-acid batteries: Effects on emerging technologies," Energy Policy, Elsevier, vol. 85(C), pages 71-79.
    2. Sovacool, Benjamin K. & Martiskainen, Mari, 2020. "Hot transformations: Governing rapid and deep household heating transitions in China, Denmark, Finland and the United Kingdom," Energy Policy, Elsevier, vol. 139(C).
    3. Barnes, Jake & Bhagavathy, Sivapriya Mothilal, 2020. "The economics of heat pumps and the (un)intended consequences of government policy," Energy Policy, Elsevier, vol. 138(C).
    4. Nemet, Gregory F. & Lu, Jiaqi & Rai, Varun & Rao, Rohan, 2020. "Knowledge spillovers between PV installers can reduce the cost of installing solar PV," Energy Policy, Elsevier, vol. 144(C).
    5. Qiu, Yueming & Anadon, Laura D., 2012. "The price of wind power in China during its expansion: Technology adoption, learning-by-doing, economies of scale, and manufacturing localization," Energy Economics, Elsevier, vol. 34(3), pages 772-785.
    6. O. Schmidt & A. Hawkes & A. Gambhir & I. Staffell, 2017. "The future cost of electrical energy storage based on experience rates," Nature Energy, Nature, vol. 2(8), pages 1-8, August.
    7. Tooraj Jamasb, 2007. "Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 51-72.
    8. Nemet, Gregory F., 2009. "Interim monitoring of cost dynamics for publicly supported energy technologies," Energy Policy, Elsevier, vol. 37(3), pages 825-835, March.
    9. Johan Lilliestam & Marc Melliger & Lana Ollier & Tobias S. Schmidt & Bjarne Steffen, 2020. "Understanding and accounting for the effect of exchange rate fluctuations on global learning rates," Nature Energy, Nature, vol. 5(1), pages 71-78, January.
    10. Heuberger, Clara F. & Rubin, Edward S. & Staffell, Iain & Shah, Nilay & Mac Dowell, Niall, 2017. "Power capacity expansion planning considering endogenous technology cost learning," Applied Energy, Elsevier, vol. 204(C), pages 831-845.
    11. Hanna, Richard & Leach, Matthew & Torriti, Jacopo, 2018. "Microgeneration: The installer perspective," Renewable Energy, Elsevier, vol. 116(PA), pages 458-469.
    12. Grubb,Michael & Jamasb,Tooraj & Pollitt,Michael G. (ed.), 2008. "Delivering a Low Carbon Electricity System," Cambridge Books, Cambridge University Press, number 9780521888844.
    13. Lowes, Richard & Woodman, Bridget & Fitch-Roy, Oscar, 2019. "Policy change, power and the development of Great Britain's Renewable Heat Incentive," Energy Policy, Elsevier, vol. 131(C), pages 410-421.
    14. Amro Elshurafa & Shahad Albardi & Carlo Andrea Bollino, 2017. "Estimating the Learning Curve of Solar PV Balance-of-Systems for Over 20 Countries," Discussion Papers ks-2017--dp015, King Abdullah Petroleum Studies and Research Center.
    15. Neij, Lena, 2008. "Cost development of future technologies for power generation--A study based on experience curves and complementary bottom-up assessments," Energy Policy, Elsevier, vol. 36(6), pages 2200-2211, June.
    16. Zheng, Cheng & Kammen, Daniel M., 2014. "An innovation-focused roadmap for a sustainable global photovoltaic industry," Energy Policy, Elsevier, vol. 67(C), pages 159-169.
    17. Noah Kittner & Felix Lill & Daniel M. Kammen, 2017. "Energy storage deployment and innovation for the clean energy transition," Nature Energy, Nature, vol. 2(9), pages 1-6, September.
    18. Connor, Peter M. & Xie, Lei & Lowes, Richard & Britton, Jessica & Richardson, Thomas, 2015. "The development of renewable heating policy in the United Kingdom," Renewable Energy, Elsevier, vol. 75(C), pages 733-744.
    19. MacGillivray, Andrew & Jeffrey, Henry & Winskel, Mark & Bryden, Ian, 2014. "Innovation and cost reduction for marine renewable energy: A learning investment sensitivity analysis," Technological Forecasting and Social Change, Elsevier, vol. 87(C), pages 108-124.
    20. Wei, Max & Smith, Sarah Josephine & Sohn, Michael D., 2017. "Non-constant learning rates in retrospective experience curve analyses and their correlation to deployment programs," Energy Policy, Elsevier, vol. 107(C), pages 356-369.
    21. Hsieh, I-Yun Lisa & Pan, Menghsuan Sam & Chiang, Yet-Ming & Green, William H., 2019. "Learning only buys you so much: Practical limits on battery price reduction," Applied Energy, Elsevier, vol. 239(C), pages 218-224.
    22. Renaldi, R. & Kiprakis, A. & Friedrich, D., 2017. "An optimisation framework for thermal energy storage integration in a residential heat pump heating system," Applied Energy, Elsevier, vol. 186(P3), pages 520-529.
    23. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    24. Abu-Bakar, Siti Hawa & Muhammad-Sukki, Firdaus & Ramirez-Iniguez, Roberto & Munir, Abu Bakar & Mohd Yasin, Siti Hajar & Mallick, Tapas Kumar & McLennan, Campbell & Abdul Rahim, Ruzairi, 2014. "Financial analysis on the proposed renewable heat incentive for residential houses in the United Kingdom: A case study on the solar thermal system," Energy Policy, Elsevier, vol. 65(C), pages 552-561.
    25. Robert Gross & Richard Hanna, 2019. "Path dependency in provision of domestic heating," Nature Energy, Nature, vol. 4(5), pages 358-364, May.
    26. Weiss, Martin & Dittmar, Lars & Junginger, Martin & Patel, Martin K. & Blok, Kornelis, 2009. "Market diffusion, technological learning, and cost-benefit dynamics of condensing gas boilers in the Netherlands," Energy Policy, Elsevier, vol. 37(8), pages 2962-2976, August.
    27. Renaldi, Renaldi & Friedrich, Daniel, 2019. "Techno-economic analysis of a solar district heating system with seasonal thermal storage in the UK," Applied Energy, Elsevier, vol. 236(C), pages 388-400.
    28. Adams, P.W.R. & Lindegaard, K., 2016. "A critical appraisal of the effectiveness of UK perennial energy crops policy since 1990," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 188-202.
    29. Herrando, María & Markides, Christos N., 2016. "Hybrid PV and solar-thermal systems for domestic heat and power provision in the UK: Techno-economic considerations," Applied Energy, Elsevier, vol. 161(C), pages 512-532.
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    Cited by:

    1. Edoardo Ruffino & Bruno Piga & Alessandro Casasso & Rajandrea Sethi, 2022. "Heat Pumps, Wood Biomass and Fossil Fuel Solutions in the Renovation of Buildings: A Techno-Economic Analysis Applied to Piedmont Region (NW Italy)," Energies, MDPI, vol. 15(7), pages 1-25, March.

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    More about this item

    Keywords

    Experience curves; Learning curves; Experience rates; Low-carbon heating; Heat decarbonisation;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • P18 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Energy; Environment
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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