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Experience curve development and cost reduction disaggregation for fuel cell markets in Japan and the US


  • Wei, Max
  • Smith, Sarah J.
  • Sohn, Michael D.


Technology learning rates can be dynamic quantities as a technology moves from early development to piloting and from low volume manufacturing to high volume manufacturing. This work describes a generalizable technology analysis approach for disaggregating observed technology cost reductions and presents results of this approach for one specific case study (micro-combined heat and power fuel cell systems in Japan). We build upon earlier reports that combine discussion of fuel cell experience curves and qualitative discussion of cost components by providing greater detail on the contributing mechanisms to observed cost reductions, which were not quantified in earlier reports. Greater standardization is added to the analysis approach, which can be applied to other technologies. This paper thus provides a key linkage that has been missing from earlier literature on energy-related technologies by integrating the output of earlier manufacturing cost studies with observed learning rates to quantitatively estimate the different components of cost reduction including economies of scale and cost reductions due to product performance and product design improvements. This work also provides updated fuel cell technology price versus volume trends from the California Self-Generation Incentive Program, including extensive data for solid-oxide fuel cells (SOFC) reported here for the first time. The Japanese micro-CHP market is found to have a learning rate of 18% from 2005 to 2015, while larger SOFC fuel cell systems (200kW and above) in the California market are found to have a flat (near-zero) learning rate, and these are attributed to a combination of exogenous, market, and policy factors.

Suggested Citation

  • Wei, Max & Smith, Sarah J. & Sohn, Michael D., 2017. "Experience curve development and cost reduction disaggregation for fuel cell markets in Japan and the US," Applied Energy, Elsevier, vol. 191(C), pages 346-357.
  • Handle: RePEc:eee:appene:v:191:y:2017:i:c:p:346-357
    DOI: 10.1016/j.apenergy.2017.01.056

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    References listed on IDEAS

    1. Barbieri, Enrico Saverio & Spina, Pier Ruggero & Venturini, Mauro, 2012. "Analysis of innovative micro-CHP systems to meet household energy demands," Applied Energy, Elsevier, vol. 97(C), pages 723-733.
    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. Kopanos, Georgios M. & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "Energy production planning of a network of micro combined heat and power generators," Applied Energy, Elsevier, vol. 102(C), pages 1522-1534.
    4. 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.
    5. MacGillivray, Andrew & Jeffrey, Henry & Winskel, Mark & Bryden, Ian, 2014. "Innovation and cost reduction for marine renewable energy: A learning investment sensitivity analysis," Technological Forecasting and Social Change, Elsevier, vol. 87(C), pages 108-124.
    6. Desroches, Louis-Benoit & Garbesi, Karina & Kantner, Colleen & Van Buskirk, Robert & Yang, Hung-Chia, 2013. "Incorporating experience curves in appliance standards analysis," Energy Policy, Elsevier, vol. 52(C), pages 402-416.
    7. Peter Thompson, 2012. "The Relationship between Unit Cost and Cumulative Quantity and the Evidence for Organizational Learning-by-Doing," Journal of Economic Perspectives, American Economic Association, vol. 26(3), pages 203-224, Summer.
    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. Kannan, R., 2009. "Uncertainties in key low carbon power generation technologies - Implication for UK decarbonisation targets," Applied Energy, Elsevier, vol. 86(10), pages 1873-1886, October.
    11. Winskel, Mark & Markusson, Nils & Jeffrey, Henry & Candelise, Chiara & Dutton, Geoff & Howarth, Paul & Jablonski, Sophie & Kalyvas, Christos & Ward, David, 2014. "Learning pathways for energy supply technologies: Bridging between innovation studies and learning rates," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 96-114.
    12. Reichelstein, Stefan & Yorston, Michael, 2013. "The prospects for cost competitive solar PV power," Energy Policy, Elsevier, vol. 55(C), pages 117-127.
    13. Trappey, Amy J.C. & Trappey, Charles V. & Liu, Penny H.Y. & Lin, Lee-Cheng & Ou, Jerry J.R., 2013. "A hierarchical cost learning model for developing wind energy infrastructures," International Journal of Production Economics, Elsevier, vol. 146(2), pages 386-391.
    14. 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.
    15. Narbel, Patrick André & Hansen, Jan Petter, 2014. "Estimating the cost of future global energy supply," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 91-97.
    16. 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.
    17. Kalkuhl, Matthias & Edenhofer, Ottmar & Lessmann, Kai, 2012. "Learning or lock-in: Optimal technology policies to support mitigation," Resource and Energy Economics, Elsevier, vol. 34(1), pages 1-23.
    18. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    19. Aguilera, Roberto F., 2014. "Production costs of global conventional and unconventional petroleum," Energy Policy, Elsevier, vol. 64(C), pages 134-140.
    20. 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.
    21. Criqui, P. & Mima, S. & Menanteau, P. & Kitous, A., 2015. "Mitigation strategies and energy technology learning: An assessment with the POLES model," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 119-136.
    22. Hu, Xiaosong & Johannesson, Lars & Murgovski, Nikolce & Egardt, Bo, 2015. "Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus," Applied Energy, Elsevier, vol. 137(C), pages 913-924.
    23. Koo, Jamin & Park, Kyungtae & Shin, Dongil & Yoon, En Sup, 2011. "Economic evaluation of renewable energy systems under varying scenarios and its implications to Korea's renewable energy plan," Applied Energy, Elsevier, vol. 88(6), pages 2254-2260, June.
    24. Wang, Yong-Peng & Liu, Xian-Wei & Li, Wen-Wei & Li, Feng & Wang, Yun-Kun & Sheng, Guo-Ping & Zeng, Raymond J. & Yu, Han-Qing, 2012. "A microbial fuel cell–membrane bioreactor integrated system for cost-effective wastewater treatment," Applied Energy, Elsevier, vol. 98(C), pages 230-235.
    25. Jiao, Kui & Park, Jaewan & Li, Xianguo, 2010. "Experimental investigations on liquid water removal from the gas diffusion layer by reactant flow in a PEM fuel cell," Applied Energy, Elsevier, vol. 87(9), pages 2770-2777, September.
    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. Narbel, Patrick A. & Hansen, Jan Petter, 2014. "Estimating the cost of future global energy supply," Discussion Papers 2014/14, Norwegian School of Economics, Department of Business and Management Science.
    28. Schoots, K. & Kramer, G.J. & van der Zwaan, B.C.C., 2010. "Technology learning for fuel cells: An assessment of past and potential cost reductions," Energy Policy, Elsevier, vol. 38(6), pages 2887-2897, June.
    29. Wang, Yun & Chen, Ken S. & Mishler, Jeffrey & Cho, Sung Chan & Adroher, Xavier Cordobes, 2011. "A review of polymer electrolyte membrane fuel cells: Technology, applications, and needs on fundamental research," Applied Energy, Elsevier, vol. 88(4), pages 981-1007, April.
    30. Nuytten, Thomas & Claessens, Bert & Paredis, Kristof & Van Bael, Johan & Six, Daan, 2013. "Flexibility of a combined heat and power system with thermal energy storage for district heating," Applied Energy, Elsevier, vol. 104(C), pages 583-591.
    31. Tang, Yong & Yuan, Wei & Pan, Minqiang & Li, Zongtao & Chen, Guoqing & Li, Yong, 2010. "Experimental investigation of dynamic performance and transient responses of a kW-class PEM fuel cell stack under various load changes," Applied Energy, Elsevier, vol. 87(4), pages 1410-1417, April.
    32. Li, Sheng & Zhang, Xiaosong & Gao, Lin & Jin, Hongguang, 2012. "Learning rates and future cost curves for fossil fuel energy systems with CO2 capture: Methodology and case studies," Applied Energy, Elsevier, vol. 93(C), pages 348-356.
    33. Candelise, Chiara & Winskel, Mark & Gross, Robert J.K., 2013. "The dynamics of solar PV costs and prices as a challenge for technology forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 96-107.
    34. Barelli, L. & Bidini, G. & Gallorini, F. & Ottaviano, A., 2012. "Dynamic analysis of PEMFC-based CHP systems for domestic application," Applied Energy, Elsevier, vol. 91(1), pages 13-28.
    35. de Wit, Marc & Junginger, Martin & Faaij, André, 2013. "Learning in dedicated wood production systems: Past trends, future outlook and implications for bioenergy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 417-432.
    36. van der Zwaan, Bob & Rivera-Tinoco, Rodrigo & Lensink, Sander & van den Oosterkamp, Paul, 2012. "Cost reductions for offshore wind power: Exploring the balance between scaling, learning and R&D," Renewable Energy, Elsevier, vol. 41(C), pages 389-393.
    37. Park, Jae Wan & Jiao, Kui & Li, Xianguo, 2010. "Numerical investigations on liquid water removal from the porous gas diffusion layer by reactant flow," Applied Energy, Elsevier, vol. 87(7), pages 2180-2186, July.
    38. Bazilian, Morgan & Onyeji, Ijeoma & Liebreich, Michael & MacGill, Ian & Chase, Jennifer & Shah, Jigar & Gielen, Dolf & Arent, Doug & Landfear, Doug & Zhengrong, Shi, 2013. "Re-considering the economics of photovoltaic power," Renewable Energy, Elsevier, vol. 53(C), pages 329-338.
    39. 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.
    40. Talavera, D.L. & Pérez-Higueras, P. & Ruíz-Arias, J.A. & Fernández, E.F., 2015. "Levelised cost of electricity in high concentrated photovoltaic grid connected systems: Spatial analysis of Spain," Applied Energy, Elsevier, vol. 151(C), pages 49-59.
    41. Qiu, Yueming & Anadon, Laura D., 2012. "The price of wind power in China during its expansion: Technology adoption, learning-by-doing, economies of scale, and manufacturing localization," Energy Economics, Elsevier, vol. 34(3), pages 772-785.
    42. Siderius, Hans-Paul, 2013. "The role of experience curves for setting MEPS for appliances," Energy Policy, Elsevier, vol. 59(C), pages 762-772.
    43. Cong, Rong-Gang, 2013. "An optimization model for renewable energy generation and its application in China: A perspective of maximum utilization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 17(C), pages 94-103.
    44. 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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    4. repec:eee:appene:v:225:y:2018:i:c:p:1022-1032 is not listed on IDEAS
    5. repec:eee:enepol:v:107:y:2017:i:c:p:356-369 is not listed on IDEAS


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