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Likely market-penetrations of renewable-energy technologies

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  • Mackay, R.M
  • Probert, S.D

Abstract

The learning-curve concept is considered to be an important tool for predicting the future costs of renewable-energy technology systems. This paper sets out the underlying rationale for learning-curve theory and the potential for its application to renewable technologies, such as photovoltaic-module and wind-power generator technologies. An indication of the data requirements for carrying out learning-curve projections is given together with an assessment of the requirements necessary for an analysis to be undertaken of the application of learning curves to other renewable-energy technologies. The paper includes a cost comparison and a figure-of-merit criterion applicable to photovoltaic-module and wind-power-turbine technologies.

Suggested Citation

  • Mackay, R.M & Probert, S.D, 1998. "Likely market-penetrations of renewable-energy technologies," Applied Energy, Elsevier, vol. 59(1), pages 1-38, January.
    • Handle: RePEc:eee:appene:v:59:y:1998:i:1:p:1-38
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    Citations

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    Cited by:

    1. Malte Schwoon, 2006. "Learning-by-doing, Learning Spillovers and the Diffusion of Fuel Cell Vehicles," Working Papers FNU-112, Research unit Sustainability and Global Change, Hamburg University, revised Jun 2006.
    2. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    3. Kahouli, Sondès, 2011. "Effects of technological learning and uranium price on nuclear cost: Preliminary insights from a multiple factors learning curve and uranium market modeling," Energy Economics, Elsevier, vol. 33(5), pages 840-852, September.
    4. Yu, Yang & Li, Hong & Che, Yuyuan & Zheng, Qiongjie, 2017. "The price evolution of wind turbines in China: A study based on the modified multi-factor learning curve," Renewable Energy, Elsevier, vol. 103(C), pages 522-536.
    5. Loschel, Andreas, 2002. "Technological change in economic models of environmental policy: a survey," Ecological Economics, Elsevier, vol. 43(2-3), pages 105-126, December.
    6. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    7. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
    8. Kahouli-Brahmi, Sondes, 2009. "Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach," Ecological Economics, Elsevier, vol. 68(4), pages 1195-1212, February.
    9. Yi Zhou & Alun Gu, 2019. "Learning Curve Analysis of Wind Power and Photovoltaics Technology in US: Cost Reduction and the Importance of Research, Development and Demonstration," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    10. Sascha Samadi, 2016. "A Review of Factors Influencing the Cost Development of Electricity Generation Technologies," Energies, MDPI, vol. 9(11), pages 1-25, November.
    11. Rahimiyan, Morteza & Morales, Juan M. & Conejo, Antonio J., 2011. "Evaluating alternative offering strategies for wind producers in a pool," Applied Energy, Elsevier, vol. 88(12), pages 4918-4926.
    12. Purohit, Pallav & Kandpal, Tara C., 2005. "Renewable energy technologies for irrigation water pumping in India: projected levels of dissemination, energy delivery and investment requirements using available diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 9(6), pages 592-607, December.
    13. Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    14. Al-Ismaily, Hilal A. & Probert, Douglas, 1998. "Photovoltaic electricity prospects in oman," Applied Energy, Elsevier, vol. 59(2-3), pages 97-124, February.
    15. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    16. Blanco, María Isabel, 2009. "The economics of wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1372-1382, August.
    17. Malte Schwoon, 2006. "A Tool to Optimize the Initial Distribution of Hydrogen Filling Stations," Working Papers FNU-110, Research unit Sustainability and Global Change, Hamburg University, revised Jun 2006.

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