Clas-Otto Wene, who was on leave of absence from the chair of Energy Systems Technology, at Chalmers University of Technology, Göteborg, Sweden in 2000 assembled this text. He concluded that existing data (2000) show that experience curves provide a rational and systematic methodology to describe the historical development and performance of technologies. He used them to assess the prospects for future improvements in the performance of a technology. The curves show that cumulative production for the market reduces prices. Assessments of future prospects are therefore particularly important in developing deployment policies for environmentally friendly technologies.
He uses the example of photovoltaics to demonstrate the use of the experience curve methodology for policy analysis indicating how learning acquired through cumulative production reduces the cost of photovoltaic modules. For photovoltaic systems to compete against central power stations, the cost of modules show the break-even point and learning investments for photovoltaic modules with a progress ratio of 80%.
The shaded area in one of the figures indicated the remaining "learning investments" to reach the break-even point. The figure also shows changes in the break-even point for progress ratios of 78% and 82% indicating this has to be brought down to 0.5 US$/Wp, indicated by the “Fossil fuel
alternative” line in the diagram.
Updating and expanding the experience-curve analysis in Figure 1.1 until 1997 lowers the progress ratio for the modules to 80%. Extrapolating the experience curve over future cumulative production levels of modules shows that to break even with fossil fuel technology requires cumulative production of about 200 GW. However, there are niche markets that may act as stepping stones for the descent to break even with currently cost-efficient technologies.
The use of such niche markets in designing technology policy will be discussed in Chapter 3. The experience curve shows the investment necessary to make a technology, such as PV, competitive, but it does not forecast when the technology will break-even. The time of break-even depends on deployment rates, which the decision-maker can influence through policy. With historical annual growth rates of 15%, photovoltaic modules will reach break-even point around the year 2025. Doubling the rate of growth will move the break-even point 10 years ahead to 2015.
Investments will be needed for the ride down the experience curve, that is for the learning efforts which will bring prices to the break-even point. An indicator for the resources required for learning is the difference between actual price and break-even price, i.e., the additional costs for the technology compared with the cost of the same service from technologies which the market presently considers cost-efficient. We will refer to these additional costs as learning investments.
More detailed calculations of learning investments therefore require databases with time series not only of technology prices and installations, but also of market prices and interest rates. Throughout this book the author assumed that the break-even price stays constant during the whole learning period.
This assumption usually provides a good first approximation to estimate the magnitude of learning investments for the technologies discussed here. Compare, for instance, our assumption of a constant break-even price with the curve for supercritical coal in Figure 1.5. means that they are investments in learning to make the technology cost-efficient, after which they will be recovered as the technology continues to improve.
The remaining learning investments for photovoltaic modules are indicated by the shaded triangle in Figure 1.4. The sum of all future learning investments needed to bring module technology to the breakeven point indicated in the figure is 60 billion US$. This is a substantial investment in learning, considering the learning investments of 3-4 billion US$ made in PV modules until 1998. The challenge is to put policies in place which mobilize resources on the market for these investments.
Public demonstration program and subsidies can only seed this process. The learning investments do include the cost of research and development activities carried out by the commercial market actors, who ultimately have to recover those costs through market revenues. Means of obtaining learning investments, for instance through electricity feed laws or by stimulating niche markets, will be discussed in Chapter 3.
Learning investments are primarily provided through market mechanisms, and they always involve commercial actors on the market. There may be some overlap between learning investments and government expenditures for research, development and demonstration (RD&D), because experimental or demonstration plants may be financed from the public RD&D budget (IEA/OECD, 1997). In specific cases, involving smaller programs, government expenditures may be a substantial part of total learning investments. However, for major technologies such as photovoltaics, wind power, biomass, or heat pumps, resources provided through the market dominate the learning investments. Government deployment programs may still be needed to stimulate these investments. The government expenditures for these programs will be included in the learning investments.
Although there may be occasional overlaps, we find that the terms “learning investments” and “public RD&D” refer to different parts of the resources needed for technology learning. It is therefore not surprising that until today the RD&D spending on photovoltaics by the IEA
countries was twice as large as the learning investments.
Public RD&D
Riding on the Experience Curve
Spending can initiate and support the initial stages in developing a major new technology. If the technology appears valuable and marketable, deployment may start and learning investments will then provide an increasing share of the resources for technology improvements. We thus expect learning investments to become the dominant resource for later stages in technology development, where the objectives are to overcome cost barriers and make the technology commercial.
Figure 1.4 indicates some of the uncertainties in our estimates of breakeven points and learning investments. A progress ratio of 80% represents a best fit to available information. But this information is also consistent with progress ratios of 78% and 82%, which would indicate break-even at 150 GW and 600 GW and learning investments of 40 billion US$ and 120 billion US$.
The uncertainties in the estimates of break-even point and learning investments are enhanced by the extrapolation of the experience curve over more than two orders of magnitude. These uncertainties will be resolved as experience about photovoltaics accumulates. Beside numerical precision, an extrapolation must consider also other sources of uncertainty, which will be considered in the next chapter. For instance, it is important to measure the experience curve under stable market conditions.
Like all forecasting methodologies, experience curves have to be used with prudence. One of the basic messages of this book is that the experience curve methodology should be embedded in a continuous process of policy analysis and evaluation, where it will serve as an interactive tool for developing effective strategies to make environmentally friendly technologies available to the energy system.
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