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Cost development of future technologies for power generation--A study based on experience curves and complementary bottom-up assessments

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  • Neij, Lena

Abstract

Technology foresight studies have become an important tool in identifying realistic ways of reducing the impact of modern energy systems on the climate and the environment. Studies on the future cost development of advanced energy technologies are of special interest. One approach widely adopted for the analysis of future cost is the experience curve approach. The question is, however, how robust this approach is, and which experience curves should be used in energy foresight analysis. This paper presents an analytical framework for the analysis of future cost development of new energy technologies for electricity generation; the analytical framework is based on an assessment of available experience curves, complemented with bottom-up analysis of sources of cost reductions and, for some technologies, judgmental expert assessments of long-term development paths. The results of these three methods agree in most cases, i.e. the cost (price) reductions described by the experience curves match the incremental cost reduction described in the bottom-up analysis and the judgmental expert assessments. For some technologies, the bottom-up analysis confirms large uncertainties in future cost development not captured by the experience curves. Experience curves with a learning rate ranging from 0% to 20% are suggested for the analysis of future cost development.

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  • 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.
    • Handle: RePEc:eee:enepol:v:36:y:2008:i:6:p:2200-2211
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    1. Edward S. Rubin & Sonia Yeh & David A. Hounshell & Margaret R. Taylor, 2004. "Experience curves for power plant emission control technologies," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 2(1/2), pages 52-69.
    2. Colpier, Ulrika Claeson & Cornland, Deborah, 2002. "The economics of the combined cycle gas turbine--an experience curve analysis," Energy Policy, Elsevier, vol. 30(4), pages 309-316, March.
    3. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    4. Rubin, Edward S. & Yeh, Sonia & Antes, Matt & Berkenpas, Michael & Davison, John, 2007. "Use of experience curves to estimate the future cost of power plants with CO2 capture," Institute of Transportation Studies, Working Paper Series qt46x6h0n0, Institute of Transportation Studies, UC Davis.
    5. Sabine Messner, 1997. "Endogenized technological learning in an energy systems model," Journal of Evolutionary Economics, Springer, vol. 7(3), pages 291-313.
    6. Riahi, Keywan & Rubin, Edward S. & Taylor, Margaret R. & Schrattenholzer, Leo & Hounshell, David, 2004. "Technological learning for carbon capture and sequestration technologies," Energy Economics, Elsevier, vol. 26(4), pages 539-564, July.
    7. Yeh, Sonia & Rubin, Edward S., 2007. "A centurial history of technological change and learning curves for pulverized coal-fired utility boilers," Energy, Elsevier, vol. 32(10), pages 1996-2005.
    8. 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.
    9. Rubin, Edward S & Taylor, Margaret R & Yeh, Sonia & Hounshell, David A, 2004. "Learning curves for environmental technology and their importance for climate policy analysis," Energy, Elsevier, vol. 29(9), pages 1551-1559.
    10. Paul Joskow & Nancy L. Rose, 1985. "The Effects of Technological Change, Experience, and Environmental Regulation on the Construction Cost of Coal-Burning Generating Units," RAND Journal of Economics, The RAND Corporation, vol. 16(1), pages 1-17, Spring.
    11. Mooz, W.E., 1981. "Cost analysis for LWR power plants," Energy, Elsevier, vol. 6(3), pages 197-225.
    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. Neij, L, 1999. "Cost dynamics of wind power," Energy, Elsevier, vol. 24(5), pages 375-389.
    14. Nikolaos Kouvaritakis & Antonio Soria & Stephane Isoard, 2000. "Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 104-115.
    15. Martin B. Zimmerman, 1982. "Learning Effects and the Commercialization of New Energy Technologies: The Case of Nuclear Power," Bell Journal of Economics, The RAND Corporation, vol. 13(2), pages 297-310, Autumn.
    16. C. Harmon, 2000. "Experience Curves of Photovoltaic Technology," Working Papers ir00014, International Institute for Applied Systems Analysis.
    17. Lena Neij, 2004. "The development of the experience curve concept and its application in energy policy assessment," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 2(1/2), pages 3-14.
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