Experience curve development and cost reduction disaggregation for fuel cell markets in Japan and the US
Section snippets
Introduction-fuel cells in stationary applications
Fuel cells are both a longstanding and emerging technology for stationary and transportation applications, and their future use may be critical for the deep decarbonization of global energy systems. For example fuel cell (FC) systems are being considered for a range of stationary and specialty transport applications due to their ability to provide reliable power with cleaner direct emissions profiles than fossil fuel combustion-based systems. Existing and emerging applications include primary
Japan’s Micro-CHP program and the California SGIP program
In this section we highlight some of the key similarities and differences in the two regions’ fuel cell programs. We discuss the implications of these findings together with the empirically derived cost reduction results in the Discussion section below.
Both case studies produce systems with fuel cells as an energy provider and in both cases there were initially generous subsidies from the government, which are now declining. Comparison of the two programs shows marked differences in many areas:
Data Collection and cost reduction disaggregation approach
Data from California’s SGIP program is drawn from a publically available database of information managed by the California Center for Sustainable Energy [73]. Cost data from this database represents total eligible system costs and thus includes non-fuel cell “balance of plant” or “balance of system” costs such as power electronics and air handling subsystems as well as installation costs. SGIP installed costs included systems that have been approved and/or constructed and are split into
Summary of results
Table 2 has a summary of experience curve learning rates. A learning rate of 18% is observed in Japan for fuel cell micro-CHP from 2005 to 2015 (Fig. 3) versus a fairly flat learning rate for fuel cells systems in California (Fig. 4). A learning rate in the range of 0–5% is quoted in Table 2 for three different fuel cell technologies. The average learning rate for the three technologies in Fig. 4 is 3.9%, but all three of the three technologies (SOFC, PAFC, and MCFC) have a very poor fit and
Discussion
Some key considerations and differences in the Japan and California case are described in this section including development, market, technology, and policy factors.
The target annual volume in 2020 and 2030 for Japan’s micro CHP provides more “market certainty” in the Japan case, where both fuel cell providers and supply chain vendors can count on a certain overall market volume trend and associated total revenue projection. From Table 4 above, the declination is subsidies have been steeper in
Conclusions
Learning rates are not static and evolve with technology learning, policy environments, and market changes. Here, we have described a promising further approach for integrating cost analysis studies with observed learning rates that can be a framework to be applied to other technologies and countries. Using this approach provide quantitative estimates for contributing mechanisms for cost reductions in Japanese stationary fuel cell market that could not be revealed in earlier qualitative
Acknowledgements
This work was funded by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy, and conducted at Lawrence Berkeley National Laboratory under Contract No. DE-AC02-05CH11231.
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