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Quantitative study on long term global solar photovoltaic market

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  • Gan, Peck Yean
  • Li, ZhiDong

Abstract

This paper analyzes the relationship between the declines in solar photovoltaic (PV) module costs and cumulative production, silicon prices, supply−demand imbalance and the presence of lower-cost Chinese products in global PV market using learning curve model. State of market development and its connection with learning is also examined. Results indicate that learning effect is best measured when supplementing output with silicon prices in the analysis. Learning rate (LR) diminishes over the time periods examined, thereby suggesting the declining of progress as market reaches maturity. The outcomes from the learning curve analysis are subsequently applied to project future uptake of PV worldwide, module and electricity costs till 2035. Demand for PV is anticipated to remain robust with cumulative installed capacity worldwide projected to reach 659GW by 2035. At the same time, module cost is estimated to decline from $3.8/W in 2006 to $1.78/W in 2035, a reduction of over 50% relative to 2006 level. Unit cost of electricity from PV is predicted to be in the range of $0.13/kWh to $0.17/kWh by 2035 for the three scenarios analyzed.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:rensus:v:46:y:2015:i:c:p:88-99
    DOI: 10.1016/j.rser.2015.02.041
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    as
    1. Kim, Dong Wook & Chang, Hyun Joon, 2012. "Experience curve analysis on South Korean nuclear technology and comparative analysis with South Korean renewable technologies," Energy Policy, Elsevier, vol. 40(C), pages 361-373.
    2. Weiss, Martin & Patel, Martin K. & Junginger, Martin & Perujo, Adolfo & Bonnel, Pierre & van Grootveld, Geert, 2012. "On the electrification of road transport - Learning rates and price forecasts for hybrid-electric and battery-electric vehicles," Energy Policy, Elsevier, vol. 48(C), pages 374-393.
    3. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    4. Ek, Kristina & Söderholm, Patrik, 2010. "Technology learning in the presence of public R&D: The case of European wind power," Ecological Economics, Elsevier, vol. 69(12), pages 2356-2362, October.
    5. Lohwasser, Richard & Madlener, Reinhard, 2013. "Relating R&D and investment policies to CCS market diffusion through two-factor learning," Energy Policy, Elsevier, vol. 52(C), pages 439-452.
    6. Hernández-Moro, J. & Martínez-Duart, J.M., 2015. "Economic analysis of the contribution of photovoltaics to the decarbonization of the power sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1288-1297.
    7. Ibenholt, Karin, 2002. "Explaining learning curves for wind power," Energy Policy, Elsevier, vol. 30(13), pages 1181-1189, October.
    8. Hayward, Jennifer A. & Graham, Paul W., 2013. "A global and local endogenous experience curve model for projecting future uptake and cost of electricity generation technologies," Energy Economics, Elsevier, vol. 40(C), pages 537-548.
    9. Wand, Robert & Leuthold, Florian, 2011. "Feed-in tariffs for photovoltaics: Learning by doing in Germany?," Applied Energy, Elsevier, vol. 88(12), pages 4387-4399.
    10. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    11. 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.
    12. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    13. 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.
    14. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    15. Gao, Cuixia & Sun, Mei & Shen, Bo & Li, Ranran & Tian, Lixin, 2014. "Optimization of China's energy structure based on portfolio theory," Energy, Elsevier, vol. 77(C), pages 890-897.
    16. Zhang, Mingming & Zhou, Dequn & Zhou, Peng, 2014. "A real option model for renewable energy policy evaluation with application to solar PV power generation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 944-955.
    17. Gregory F. Nemet, 2006. "How well does Learning-by-doing Explain Cost Reductions in a Carbon-free Energy Technology?," Working Papers 2006.143, Fondazione Eni Enrico Mattei.
    18. Seel, Joachim & Barbose, Galen L. & Wiser, Ryan H., 2014. "An analysis of residential PV system price differences between the United States and Germany," Energy Policy, Elsevier, vol. 69(C), pages 216-226.
    19. Berglund, Christer & Soderholm, Patrik, 2006. "Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models," Energy Policy, Elsevier, vol. 34(12), pages 1344-1356, August.
    20. 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.
    21. Papineau, Maya, 2006. "An economic perspective on experience curves and dynamic economies in renewable energy technologies," Energy Policy, Elsevier, vol. 34(4), pages 422-432, March.
    22. Tooraj Jamasb, 2006. "Technical Change Theory and Learning Curves: Patterns of Progress in Energy Technologies," Working Papers EPRG 0608, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    23. Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
    24. Klaassen, Ger & Miketa, Asami & Larsen, Katarina & Sundqvist, Thomas, 2005. "The impact of R&D on innovation for wind energy in Denmark, Germany and the United Kingdom," Ecological Economics, Elsevier, vol. 54(2-3), pages 227-240, August.
    25. C. Harmon, 2000. "Experience Curves of Photovoltaic Technology," Working Papers ir00014, International Institute for Applied Systems Analysis.
    26. 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.
    27. Gan, Peck Yean & Komiyama, Ryoichi & Li, ZhiDong, 2013. "A low carbon society outlook for Malaysia to 2035," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 432-443.
    28. Hernández-Moro, J. & Martínez-Duart, J.M., 2013. "Analytical model for solar PV and CSP electricity costs: Present LCOE values and their future evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 119-132.
    29. Neij, Lena, 1997. "Use of experience curves to analyse the prospects for diffusion and adoption of renewable energy technology," Energy Policy, Elsevier, vol. 25(13), pages 1099-1107, November.
    30. Wang, Yu & Zhou, Sheng & Huo, Hong, 2014. "Cost and CO2 reductions of solar photovoltaic power generation in China: Perspectives for 2020," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 370-380.
    31. Söderholm, Patrik & Sundqvist, Thomas, 2007. "Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies," Renewable Energy, Elsevier, vol. 32(15), pages 2559-2578.
    32. 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.
    33. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
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