IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v38y2010i6p2887-2897.html
   My bibliography  Save this article

Technology learning for fuel cells: An assessment of past and potential cost reductions

Author

Listed:
  • Schoots, K.
  • Kramer, G.J.
  • van der Zwaan, B.C.C.

Abstract

Fuel cells have gained considerable interest as a means to efficiently convert the energy stored in gases like hydrogen and methane into electricity. Further developing fuel cells in order to reach cost, safety and reliability levels at which their widespread use becomes feasible is an essential prerequisite for the potential establishment of a 'hydrogen economy'. A major factor currently obviating the extensive use of fuel cells is their relatively high costs. At present we estimate these at about 1100 [euro](2005)/kW for an 80Â kW fuel cell system but notice that specific costs vary markedly with fuel cell system power capacity. We analyze past fuel cell cost reductions for both individual manufacturers and the global market. We determine learning curves, with fairly high uncertainty ranges, for three different types of fuel cell technology - AFC, PAFC and PEMFC - each manufactured by a different producer. For PEMFC technology we also calculate a global learning curve, characterised by a learning rate of 21% with an error margin of 4%. Given their respective uncertainties, this global learning rate value is in agreement with those we find for different manufacturers. In contrast to some other new energy technologies, R&D still plays a major role in today's fuel cell improvement process and hence probably explains a substantial part of our observed cost reductions. The remaining share of these cost reductions derives from learning-by-doing proper. Since learning-by-doing usually involves a learning rate of typically 20%, the residual value for pure learning we find for fuel cells is relatively low. In an ideal scenario for fuel cell technology we estimate a bottom-line for specific (80Â kW system) manufacturing costs of 95 [euro](2005)/kW. Although learning curves observed in the past constitute no guarantee for sustained cost reductions in the future, when we assume global total learning at the pace calculated here as the only cost reduction mechanism, this ultimate cost figure is reached after a large-scale deployment about 10 times doubled with respect to the cumulative installed fuel cell capacity to date.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:enepol:v:38:y:2010:i:6:p:2887-2897
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301-4215(10)00028-5
    Download Restriction: Full text for ScienceDirect subscribers only

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

    References listed on IDEAS

    as
    1. Sagar, Ambuj D. & van der Zwaan, Bob, 2006. "Technological innovation in the energy sector: R&D, deployment, and learning-by-doing," Energy Policy, Elsevier, vol. 34(17), pages 2601-2608, November.
    2. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    3. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    4. 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.
    5. Tribe, M. A. & Alpine, R. L. W., 1986. "Scale economies and the "0.6 rule"," Engineering Costs and Production Economics, Elsevier, vol. 10(4), pages 271-278, March.
    6. 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.
    7. Lipman, Timothy E. & Edwards, Jennifer L. & Kammen, Daniel M., 2004. "Fuel cell system economics: comparing the costs of generating power with stationary and motor vehicle PEM fuel cell systems," Energy Policy, Elsevier, vol. 32(1), pages 101-125, January.
    8. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    9. John F. Muth, 1986. "Search Theory and the Manufacturing Progress Function," Management Science, INFORMS, vol. 32(8), pages 948-962, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Berggren, Christian & Magnusson, Thomas, 2012. "Reducing automotive emissions—The potentials of combustion engine technologies and the power of policy," Energy Policy, Elsevier, vol. 41(C), pages 636-643.
    2. repec:eee:renene:v:115:y:2018:i:c:p:1281-1293 is not listed on IDEAS
    3. Niko Jaakkola, 2013. "Putting OPEC Out of Business," OxCarre Working Papers 099, Oxford Centre for the Analysis of Resource Rich Economies, University of Oxford.
    4. Siskos, Pelopidas & Capros, Pantelis & De Vita, Alessia, 2015. "CO2 and energy efficiency car standards in the EU in the context of a decarbonisation strategy: A model-based policy assessment," Energy Policy, Elsevier, vol. 84(C), pages 22-34.
    5. Basrawi, Mohamad Firdaus Bin & Yamada, Takanobu & Nakanishi, Kimio & Katsumata, Hideaki, 2012. "Analysis of the performances of biogas-fuelled micro gas turbine cogeneration systems (MGT-CGSs) in middle- and small-scale sewage treatment plants: Comparison of performances and optimization of MGTs," Energy, Elsevier, vol. 38(1), pages 291-304.
    6. 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, pages 346-357.
    7. Hardman, Scott & Shiu, Eric & Steinberger-Wilckens, Robert & Turrentine, Thomas, 2017. "Barriers to the adoption of fuel cell vehicles: A qualitative investigation into early adopters attitudes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 166-182.
    8. Engelen, Peter-Jan & Kool, Clemens & Li, Ye, 2016. "A barrier options approach to modeling project failure: The case of hydrogen fuel infrastructure," Resource and Energy Economics, Elsevier, vol. 43(C), pages 33-56.
    9. Nakata, Toshihiko & Sato, Takemi & Wang, Hao & Kusunoki, Tomoya & Furubayashi, Takaaki, 2011. "Modeling technological learning and its application for clean coal technologies in Japan," Applied Energy, Elsevier, pages 330-336.
    10. Julien Brunet & Jean-Pierre Ponssard, 2016. "Policies and Deployment for Fuel Cell Electric Vehicles An Assessment of the Normandy Project," Working Papers hal-01366205, HAL.
    11. repec:eee:enepol:v:110:y:2017:i:c:p:447-460 is not listed on IDEAS
    12. Densing, Martin & Turton, Hal & Bäuml, Georg, 2012. "Conditions for the successful deployment of electric vehicles – A global energy system perspective," Energy, Elsevier, vol. 47(1), pages 137-149.
    13. Fukui, Rokuhei & Greenfield, Carl & Pogue, Katie & van der Zwaan, Bob, 2017. "Experience curve for natural gas production by hydraulic fracturing," Energy Policy, Elsevier, vol. 105(C), pages 263-268.
    14. van der Zwaan, Bob & Keppo, Ilkka & Johnsson, Filip, 2013. "How to decarbonize the transport sector?," Energy Policy, Elsevier, vol. 61(C), pages 562-573.
    15. Arias-Gaviria, Jessica & van der Zwaan, Bob & Kober, Tom & Arango-Aramburo, Santiago, 2017. "The prospects for Small Hydropower in Colombia," Renewable Energy, Elsevier, vol. 107(C), pages 204-214.

    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:enepol:v:38:y:2010:i:6:p:2887-2897. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.