A global experience curve for wind energy M. Junginger*, A. Faaij Department of Science, Technology and Society. Copernicus Institute, Utrecht University. Padualaan 14, 3584 CH Utrecht, The Netherlands. * Corresponding author. Tel. 030-2537613/00, fax 030-2537601, M.Junginger@chem.uu.nl Abstract In order to forecast the technological development and cost of wind turbines and the production costs of wind electricity, frequent use is made of the so-called experience curve concept. Experience curves of wind turbines are generally based on data describing the development of national markets, which cause a number of methodological difficulties when applied for global assessments. To analyze global wind energy price development more adequately, we compose a global experience curve. We present and discuss a new approach of establishing a global experience curve and thus a global progress ratio for the investment cost of wind farms. Results show that global progress ratios for wind farms may lie between 77-85% (with an average of 81%), which is significantly more optimistic than progress ratios applied in most current scenario studies and integrated assessment models. Combining the progress ratios with three different growth scenarios for global cumulative wind capacity results in possible cost reductions of 43-75% in 2020 compared to current turnkey wind farm investment prices. This is equivalent to a decrease in electricity production costs from 6.6 to 2.1 – 4.0 €ct/kWh. Thus, it can be concluded that a large fraction of the electricity produced by wind farms may be able to directly compete with electricity from fossil fuels by 2020. Keywords: Experience curve, wind energy, technological learning, wind farm investment cost, cost of electricity Introduction and objective Over the last twenty years, the specific turnkey prices of wind farms have dropped significantly as the global installed wind capacity has increased from a few hundred MW to about 32 GW. However, wind electricity is not yet entirely competitive with electricity from fossil fuel sources. Thus, it is of interest whether (and at what speed) further cost reductions may be expected. One way of analysing the cost reductions is the experience curve concept. In this top- down approach, costs decline by a fixed percentage with every cumulative doubling of capacity (e.g. a decline of 15% with a cumulative doubling of capacity equals a progress ratio of 85%). The concept of experience curves has been applied widely within the energy technologies area. Recent examples are PV modules , combined cycle gas turbines  or fuel cells . An overview of studies concerning energy technologies is given by McDonald and Schrattenholzer . Especially for the wind energy sector, experience curves have been devised for Denmark , Germany , the United States , and other countries [8-10]. However, in contrast to other renewable energy technologies, the cost reductions of wind turbines, wind farms and wind electricity have so far only been measured on a national basis, i.e. against the national installed capacity. This approach involves a number of possible pitfalls. For example, national policy measures may influence both local prices and the annually capacity added . Both effects may strongly influence the progress ratio, but are not directly related to technological learning. Both methodological problems may possibly be overcome when using worldwide data. The objective of this work is to set up a new approach to establish a global experience curve and determine a global progress ratio for wind farms. In combination with different growth scenarios for global cumulative capacity, we also indicate possible cost reduction margins for investment costs and subsequently cost of wind electricity. Experience curve theory and methodological setup Typically, the costs of a technology come down with increased experience, i.e. increased penetration of the technology. This phenomenon has been observed numerous times in history, starting with serial production of airplanes at the beginning of the last century. One special empirical observation is that costs tend to decline a fixed percentage with every cumulative doubling of capacity. One way of analyzing the cost reductions is the experience curve concept. In this methodology, costs decline by a fixed percentage with every cumulative doubling of capacity. A basic experience curve can be expressed as: CCum = C0 * Cumb (1) PR = 2b (3) log CCum = log C0 + b log Cum (2) LR = 1 – PR (4) CCum Cost per unit C0 Cost of the first unit produced Cum Cumulative (unit) production b experience index PR Progress ratio LR Learning rate The progress ratio (PR) is a parameter that expresses the rate at which costs decline each time the cumulative production doubles. For example, a progress ratio of 0.8 (= 80%) equals a learning rate of 0.2 (20%) and thus a 20% cost decrease for each doubling of the cumulative capacity. The definition of the ‘unit’ may vary: in many cases a unit is a product (for example a car or an airplane). In relation to energy technologies, more often the unit is the capacity of an energy technology (e.g. the capacity of solar modules or steam turbines) or the amount of electricity produced by a technology. In the remainder of this article we focus on installed wind farm capacity, i.e. the turnkey prices of wind farms against the global cumulative installed capacity of wind farms. For such a global experience curve, we must make sure that it actually concerns a reasonably homogeneous learning system, i.e. that technological innovations are exchanged between different actors, and that the resulting technology is available in the entire geographical area. Technological development: The global wind market is dominated by seven turbine manufacturers: Vestas, Enercon, NEG Micon, GE Wind (formerly Enron Wind), Gamesa, Bonus and Nordex. With the exception of Enercon1, these manufacturers also use similar technology (horizontal axis wind turbines, variable speed, pitch regulated, utilizing a gearbox and a asynchronous generator). Global diffusion: In the year 2002, the seven largest wind turbine producers (see previous paragraph) together held a global market share of approximately 78%. Over 80% of this worldwide wind-based power generation capacity is currently installed in Germany, Spain, Denmark, the U.S.A. and India . The remaining 20% is installed in at least 50 countries all over the world, mainly in Europe. Thus, as the big manufacturing companies deliver turbines all over the world and use the same basic technology concepts, we can conclude that the knowledge and technology present in these companies is applied on a global level, and that there is to a large extent “global learning”. The additional investment costs of wind farms (e.g. foundations and grid-connections) are more subject to local learning. Yet, many wind farms are built by the wind turbine producers (i.e. delivering turnkey wind farms) and thus ‘export’ knowledge on these components as well. Furthermore, in order to qualify as suitable, the data should meet the following requirements : • Price of wind farms should originate stem from competitive market environments to avoid effects of price distortions (caused e.g. by governmental support measures) • Markets should also be open for imports from all major manufacturers, with little or no obligations to involve local producers • Data must be available over sufficiently long time period (at least five years or longer) Data selection UK data for small wind farms: Given the criteria set in the previous section, data from the UK seemed to be well suitable. First of all, in the UK, the NFFO/SRO-system required the electricity supply companies in the UK to provide a proportion of their supply from renewable energy sources. From 1991-1999, several bidding rounds were held, where project developers could subscribe. Bids were assessed on a competitive basis and an upper threshold was selected after a predetermined deadline. While this system failed to stimulate a large capacity expansion (in contrast to the German feed-in tariff system), the cost of electricity dropped strongly from 1991-1999 , . Due to this competitive bidding system, it is likely that turbine manufacturers offered turbines at low profit margins. Second, the United Kingdom does not have a domestic wind turbine industry, apart from some plants manufacturing components of wind turbines from international manufacturers. Most turbines had to be imported, with the possible exception of certain components, e.g. towers. From 1991-2001, more than twelve international manufacturers installed wind turbines in the UK, Vestas having the largest share . Thus, it is likely that the turbines reflect world market prices. Third, over the last decade about 500 MW of capacity have been installed (with annual additions 1 Enercon and a few other minor producers use a direct-drive ring generator, eliminating the requirement of a gearbox. A drawback of this concept is the higher weight of the ring generator. varying between 10-80 MW) , thus providing sufficient data. Most existing wind parks built between 1995-1999 utilize wind turbines between 500-700 kW. More recently, also large turbines up to 2.5 MW have been installed. The average size of the wind farms is rather small (4-7 MW), and thus the data from the UK may be seen as representative for small wind parks. Spanish data from large wind farms: A shortcoming of the UK data is the relatively small average plant size. Specific investment costs tend to decline with increasing wind farm size as the share of grid-connection, project planning and other overhead costs become smaller. Therefore, we also attempted to find data for large-scale wind farms. Possible data sources for relatively large-scale wind parks may be either the USA or Spain, which both have an number wind farms of over 100 MW capacity. As not enough wind farm price data over an extended period of time could be found for American wind farms, Spain was chosen as a second source of data. In contrast to the UK, Spain hosts some of the largest wind farms in the world, while the average wind farm size is about 20 MW. The operating capacity in Spain at the beginning of 2003 was about 4100 MW , while the total technical potential is estimated to be approximately 15,000 MW . However, the requirements regarding competitive and open market are not entirely met. To support renewable energy technologies, Spain uses a feed-in tariff. Feed-in systems have been known to discourage cost reductions in case the tariff paid is higher than the actual costs of electricity production. Especially since 1998, rather favorable feed-in tariffs have been in place in Spain. We therefore chose only to regard data up to 1998. Second, a number of national turbine manufacturers dominate the market. While the market is open to foreign manufacturers, there are over 40 facilities for the production of turbines and components in Spain, which makes adaptation of the national production capacity to new technological developments more difficult and more costly . As little other data on large wind farms are available, we used Spanish data for incorporating large-scale wind farms in composing a global experience curve, taking into account the limitations described above. For both experience curve based on British and Spanish data, the conversion from nominal to real prices was carried out using the advanced economies GDP deflator of the IMF . The choice for the advanced economies deflator was deemed the most appropriate when looking at global wind prices development. Results and discussion Using data from the UK and Spain as substitute for the worldwide cost reduction of wind power plants, two experience curves for small-scale and large-scale wind farms were devised (Figure 1). Both result in progress ratios of 81-82%. As expected, the Spanish investment costs are lower than British investment costs. This is likely due to the larger average wind farm size in Spain, allowing on average for cheaper turbines and a relatively smaller share of other investment costs. Figure 1 Global experience curves for wind farms, using data from British and Spanish wind farms. All data are adjusted for inflation using the advanced economies GDP deflator . The Spanish data runs from 1990- 1998. The data set for the UK runs from 1992-2001. Sensitivity analysis revealed that when the national GDP-deflator is used to correct for inflation (instead of the GDP deflator of the advanced economies), progress ratios may be lower, between 77-80%. Also, the chosen time frame and possible policy measures can be of influence. When using Spanish data from 1990-2001, the progress ratio becomes 85%. Thus, it is clear that the progress ratio may vary somewhat depending on the chosen calculation method. Therefore we chose to use the range of 77-85% as uncertainty interval, with an average of 81%. These progress ratios are combined with three different growth scenarios (see Figure 2). In the strong growth scenario, a continuing increase in global installed wind farm capacity is assumed, identical to the growth rates in the ‘wind force 12’ study by EWEA . This capacity would be sufficient to satisfy 12% of the world’s electricity demand in 2020. On the other hand, in the stagnant growth scenario, annual growth rates decline to 4%, indicating an almost saturated market in 2020. The difference in installed capacity between these two scenarios is a factor four. The medium growth scenario is chosen in between these two. Next, the most three scenarios are combined with the three progress ratios, thereby revealing two extreme combinations of 250 and 575 €/kW, and a best guess of 405 €/kW (see also Figure 3). Cross-combining the strong growth scenario with a pessimistic PR and vice versa yields investment costs of 422 and 408 €/kW respectively. Compared to current turnkey investment costs (of approximately 1000 €/kW), this indicates an average possible price reduction of about 60%, with margins of 43-75% reduction. 10000 1100 Wind farm turnkey investment prices (€/kW) Realized capacity Cumulative installed global capacity (GW) 1000 Strong growth, 25-15% / year 900 1000 Moderate growth, 25-10% / year 800 Stagnant capacity growth Stagnant growth, 25-4% / year PR = 85% 700 100 600 Moderate capacity growth 500 Strong capacity PR = 81% 10 growth, PR = 77% 400 300 1 200 1990 1995 2000 2005 2010 2015 2020 2001 2006 2011 2016 2021 Time (years) Time (years) Figure 2 Growth scenarios for global installed wind Figure 3 Development of wind farm turnkey capacity. investment costs over time, depending on the average global growth rate of installed capacity and on the progress ratio. Finally, based on these turnkey investment costs and assuming an economic lifetime of 15 years, an average capacity factor of 25%, and a real discount rate of 10%, electricity production costs may decrease from current 6.6 to 2.1 –4.0 €ct/kWh in 2020. It must be emphasised that this analysis can only be used and interpreted for general cost trends. Specific cost of wind electricity do depend on site conditions such as average wind speeds, accessibility, varying O&M costs et cetera. Also other issues have not been taken into account, such as the potential additional costs associated with the large- scale introduction of intermittent energy sources (e.g. electricity storage capacity or grid fortifications). In addition, the results depend to large degree on the chosen assumptions regarding progress ratios and capacity growth scenarios. Yet, even under low capacity growth rates and pessimistic progress ratios, significant cost reductions are achieved. Conclusions Regarding the methodology, it was shown that the choice of time frame, geographical area, GDP-deflator et cetera can cause significant differences in the resulting progress ratios. Even though we narrowed down this range to 81- 82% using the most appropriate GDP-deflators and time frames, we prefer not to determine a single PR for wind farms. Rather we would like to suggest to limit the range between 77-85% (with an average of 81%) and to use this uncertainty interval for different scenarios. Regarding the outcome of the scenario analysis, it is evident that estimates may range substantially depending on capacity growth rates and progress ratio. Yet, even under low capacity growth rates and pessimistic progress ratios, significant cost reductions may be achieved. We conclude that by 2020 a large fraction of the electricity produced by wind farms may be able to directly compete with electricity from fossil fuels. Acknowledgements This research was carried out within the framework of the ‘Stimuleringsprogramma Energieonderzoek’, which was set up by the Netherlands Organization for Scientific Research (NWO) and Novem (Nederlandse Onderneming voor Energie en Milieu). The financial support from NWO / Novem is gratefully acknowledged. References 1. Harmon C, Experience Curves of Photovoltaic Technology. IIASA: Laxenburg, Austria, 2000; p. 22. 2. Claeson Colpier U and Cornland D. The economics of the combined cycle gas turbine. An experience curve analysis. Energy Policy 2002; 30(4):309-316. 3. Tsuchiya H. Fuel Cell Cost Study by Learning Curve. In: Proceedings of the International Energy Workshop 2002. Stanford University, USA, 2002. 4. McDonald A and Schrattenholzer L. Learning rates for energy technologies. Energy policy 2001; 29(4):255-261. 5. Neij L, Cost dynamics of wind power. Energy 1999; 24(5):375-389. 6. Durstewitz M and Hoppe-Kilpper M. 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