IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v62y2013icp341-348.html
   My bibliography  Save this article

Predicting the costs of photovoltaic solar modules in 2020 using experience curve models

Author

Listed:
  • de La Tour, Arnaud
  • Glachant, Matthieu
  • Ménière, Yann

Abstract

Except in few locations, photovoltaic generated electricity remains considerably more expensive than conventional sources. It is however expected that innovation and learning-by-doing 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.

Suggested Citation

  • de La Tour, Arnaud & Glachant, Matthieu & Ménière, Yann, 2013. "Predicting the costs of photovoltaic solar modules in 2020 using experience curve models," Energy, Elsevier, vol. 62(C), pages 341-348.
    • Handle: RePEc:eee:energy:v:62:y:2013:i:c:p:341-348
    DOI: 10.1016/j.energy.2013.09.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544213007883
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2013.09.037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bosetti, Valentina & Catenacci, Michela & Fiorese, Giulia & Verdolini, Elena, 2012. "The future prospect of PV and CSP solar technologies: An expert elicitation survey," Energy Policy, Elsevier, vol. 49(C), pages 308-317.
    2. Dechezlepretre, Antoine & Glachant, Matthieu & Hascic, Ivan & Johnstone, Nick & Meniere, Yann, 2009. "Invention and Transfer of Climate Change Mitigation Technologies on a Global Scale: A Study Drawing on Patent Data," Sustainable Development Papers 54361, Fondazione Eni Enrico Mattei (FEEM).
    3. Nemet, Gregory F., 2009. "Interim monitoring of cost dynamics for publicly supported energy technologies," Energy Policy, Elsevier, vol. 37(3), pages 825-835, March.
    4. Antoine Dechezleprêtre & 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.
    5. Yu, C.F. & van Sark, W.G.J.H.M. & Alsema, E.A., 2011. "Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 324-337, January.
    6. Kobos, Peter H. & Erickson, Jon D. & Drennen, Thomas E., 2006. "Technological learning and renewable energy costs: implications for US renewable energy policy," Energy Policy, Elsevier, vol. 34(13), pages 1645-1658, September.
    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.
    8. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    9. C. Harmon, 2000. "Experience Curves of Photovoltaic Technology," Working Papers ir00014, International Institute for Applied Systems Analysis.
    10. Paul L. Joskow, 2011. "Comparing the Costs of Intermittent and Dispatchable Electricity Generating Technologies," American Economic Review, American Economic Association, vol. 101(3), pages 238-241, May.
    11. Valentina Bosetti & Michela Catenacci & Giulia Fiorese & Elena Verdolini, 2012. "The Future Prospects of PV and CSP Solar Technologies," Review of Environment, Energy and Economics - Re3, Fondazione Eni Enrico Mattei, January.
    12. Kamp, Linda M. & Smits, Ruud E. H. M. & Andriesse, Cornelis D., 2004. "Notions on learning applied to wind turbine development in the Netherlands and Denmark," Energy Policy, Elsevier, vol. 32(14), pages 1625-1637, September.
    13. 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.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arnaud de La Tour & Matthieu Glachant & Yann Ménière, 2013. "What cost for photovoltaic modules in 2020? Lessons from experience curve models," Working Papers hal-00805668, HAL.
    2. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    3. 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.
    4. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    5. Sascha Samadi, 2016. "A Review of Factors Influencing the Cost Development of Electricity Generation Technologies," Energies, MDPI, vol. 9(11), pages 1-25, November.
    6. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    7. Strupeit, Lars & Neij, Lena, 2017. "Cost dynamics in the deployment of photovoltaics: Insights from the German market for building-sited systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 948-960.
    8. Hernandez-Negron, Christian G. & Baker, Erin & Goldstein, Anna P., 2023. "A hypothesis for experience curves of related technologies with an application to wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    9. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    10. Arnaud de La Tour & Matthieu Glachant, 2013. "How do solar photovoltaic feed-in tariffs interact with solar panel and silicon prices? An empirical study," Working Papers hal-00809449, HAL.
    11. Zhang, Da & Chai, Qimin & Zhang, Xiliang & He, Jiankun & Yue, Li & Dong, Xiufen & Wu, Shu, 2012. "Economical assessment of large-scale photovoltaic power development in China," Energy, Elsevier, vol. 40(1), pages 370-375.
    12. Gan, Peck Yean & Li, ZhiDong, 2015. "Quantitative study on long term global solar photovoltaic market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 88-99.
    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. Pfeiffer, Birte & Mulder, Peter, 2013. "Explaining the diffusion of renewable energy technology in developing countries," Energy Economics, Elsevier, vol. 40(C), pages 285-296.
    15. Elia, A. & Kamidelivand, M. & Rogan, F. & Ó Gallachóir, B., 2021. "Impacts of innovation on renewable energy technology cost reductions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    16. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    17. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng, 2022. "Effects of learning curve models on onshore wind and solar PV cost developments in the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    18. Spiros Papaefthimiou, Manolis Souliotis, and Kostas Andriosopoulos, 2016. "Grid parity of solar energy: imminent fact or future's fiction," The Energy Journal, International Association for Energy Economics, vol. 0(Bollino-M).
    19. Hayashi, Daisuke & Huenteler, Joern & Lewis, Joanna I., 2018. "Gone with the wind: A learning curve analysis of China's wind power industry," Energy Policy, Elsevier, vol. 120(C), pages 38-51.
    20. Biondi, Tommaso & Moretto, Michele, 2015. "Solar Grid Parity dynamics in Italy: A real option approach," Energy, Elsevier, vol. 80(C), pages 293-302.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:62:y:2013:i:c:p:341-348. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.