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What cost for photovoltaic modules in 2020? Lessons from experience curve models

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Author Info

  • Arnaud De La Tour

    (CERNA - Centre d'économie industrielle - MINES ParisTech - École nationale supérieure des mines de Paris)

  • Matthieu Glachant

    ()
    (CERNA - Centre d'économie industrielle - MINES ParisTech - École nationale supérieure des mines de Paris)

  • Yann Ménière

    ()
    (CERNA - Centre d'économie industrielle - MINES ParisTech - École nationale supérieure des mines de Paris)

Abstract

Except in few locations, photovoltaic generated electricity remains considerably more expensive than conventional sources. It is however expected that innovation and learning-bydoing will lead to drastic cuts in production cost in the near future. The goal of this paper is to predict the cost of PV modules out to 2020 using experience curve models, and to draw implications about the cost of PV electricity. Using annual data on photovoltaic module prices, cumulative production, R&D knowledge stock and input prices for silicon and silver over the period 1990 - 2011, we identify a experience curve model which minimizes the difference between predicted and actual module prices. This model predicts a 67% decrease of module price from 2011 to 2020. This rate implies that the cost of PV generated electricity will reach that of conventional electricity by 2020 in the sunniest countries with annual solar irradiation of 2000 kWh/year or more, such as California, Italy, and Spain.

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File URL: http://hal-ensmp.archives-ouvertes.fr/docs/00/82/65/16/PDF/I3WP_13-ME-03_v2.pdf
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Bibliographic Info

Paper provided by HAL in its series Working Papers with number hal-00805668.

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Date of creation: Mar 2013
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Handle: RePEc:hal:wpaper:hal-00805668

Note: View the original document on HAL open archive server: http://hal-ensmp.archives-ouvertes.fr/hal-00805668
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Related research

Keywords: Learning curve; solar photovoltaic energy; cost prediction;

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References

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  1. Isoard, Stephane & Soria, Antonio, 2001. "Technical change dynamics: evidence from the emerging renewable energy technologies," Energy Economics, Elsevier, vol. 23(6), pages 619-636, November.
  2. Branker, K. & Pathak, M.J.M. & Pearce, J.M., 2011. "A review of solar photovoltaic levelized cost of electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4470-4482.
  3. Antoine Dechezleprêtre & Matthieu Glachant & Ivan Hascic & Nick Johnstone & Yann Ménière, 2010. "Invention and transfer of climate change mitigation technologies on a global scale: a study drawing on patent data," Grantham Research Institute on Climate Change and the Environment Working Papers 17, Grantham Research Institute on Climate Change and the Environment.
  4. Arnaud De La Tour & Matthieu Glachant & Yann Ménière, 2011. "Innovation and international technology transfer: The case of the Chinese photovoltaic industry," Post-Print hal-00498578, HAL.
  5. Popp, David, 2004. "ENTICE: endogenous technological change in the DICE model of global warming," Journal of Environmental Economics and Management, Elsevier, vol. 48(1), pages 742-768, July.
  6. Antoine Dechezlepr�tre & Matthieu Glachant & Ivan Haščič & Nick Johnstone & Yann Ménière, 2011. "Invention and Transfer of Climate Change--Mitigation Technologies: A Global Analysis," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 5(1), pages 109-130, Winter.
  7. Ferioli, F. & Schoots, K. & van der Zwaan, B.C.C., 2009. "Use and limitations of learning curves for energy technology policy: A component-learning hypothesis," Energy Policy, Elsevier, vol. 37(7), pages 2525-2535, July.
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Cited by:
  1. Hallegatte, Stephane & Fay, Marianne & Vogt-Schilb, Adrien, 2013. "Green industrial policies : when and how," Policy Research Working Paper Series 6677, The World Bank.

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