Sources of experience. Theoretical considerations and empirical observations from Danish wind energy technology

Per Dannemand AndersenSystems Analysis Department, Risoe National Laboratory,

P.O. Box 49, DK 4000 Roskilde, Denmarkper.dannemand@risoe.dk (pean@dtu.dk)

Reference to this paper should be made as follows: Andersen, P. D. (2004), ‘Sources of experience. Theoretical considerations and empirical observations from Danish wind energy technology’, Int. J. Energy Technology and Policy, Col: 2, issue: 1/2, pages: 33-51, 2004

Abstract: This article attempts to establish better understanding of processes within the black box of industrial learning. Through theoretical considerations and empirical observations this article also aims at finding the most true and fair expression of an experience curve diagram. The article concludes, that experience curves based on the cost of produced electricity are more true and fair than experience curves only based on cost of equipment. Furthermore, the article argues, that modern systems of innovation are too complicated to be modeled as linear systems. Policy advice based on experience curve analyses must be accompanied by a more advanced understanding of the whole innovation system and of the roles of the many actors involved.

Keywords: Experience curves, industrial learning, innovation theory, wind energy technology.

Bibliographic notes: Dr. Per Dannemand Andersen is head of the Technology Scenarios research programme at Risoe National Laboratory. He has been involved with almost all aspects of wind energy since 1986. His main interests are innovation and strategy studies as well as technology foresight.

1. Introduction

The concept of experience curves comprises the empirical observation that the cost of an industrially manufactured product decreases to a more or less constant percentage each time the cumulative volume of the product is doubled. This percentage is usually referred to as the Progress Rate (PR), and it is typically within the range of 80% - 90%. So, there are two dimensions: Technology and market. The y-axis of the experience curve usually expresses the industrial learning (or experience), usually as reduction in technology costs. The x-axis represents cumulative production of that technology on the market. T. P. Wright first reported the principles behind experience curves in a study of cost reductions in airplane production in America in the 1920s and 1930s [1]. The consultancy Boston Consulting Group (BCG) introduced experience curves in marketing strategy in the 1970s [2]. In both cases experience curve studies were affiliated with manufacturing industrial products.

The recent interest in experience curves for energy technologies aims at establishing models for the balance between public expenditure on R&D programs and market stimulation programs for new energy technologies not yet fully competitive on the marketplace [3], [4], [5]. To this end, a simple model for industrial learning to fit into econometric models is needed. Watanabe has introduced an innovative model for this, but the model tends to focus less on how industrial innovations actually take place [6]. Wene talks about the need for a “....simple model for the experience phenomenon. We use the fact that both learning and

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experience curves establish relations between the input and the output of a learning system” [7]. Wene then introduces a model for learning from cybernetics theory. Wene acknowledges, that such a basic “model does not make any hypothesis about the processing going on inside the learning system; in fact it considers this system as a black box for which only input and output can be observed” [8]. For econometric studies, this cybernetics-originated model might be adequate. Watanabe and Wene has elaborated on such a simple model and created an elegant and useful model for the interaction between public R&D, industrial R&D, production, and the technology stock created by such R&D – in his case photovoltaic technology. This model is also a central part of Wene’s considerations [9].

In other recent studies of experience curves for energy technology policy assessment, the learning system of firms and business sectors (systems of innovation) is also treated as a black box.

In my experience, after more than 17 years of professional work with technological innovation with wind energy, if the nature of the technology in question and the industrial structure around that technology is not correctly understood, development and use of experience curves will involve many pitfalls.

The rationale behind this paper is that much valuable insight can be gained from better understanding and modeling of learning in industrial systems. The aim of this article is to establish such an understanding on the basis of the framework of innovation theory and other related areas and on the basis of empirical evidence from the Danish wind energy industry. Also it is the aim of this paper, through theoretical considerations and empirical observations, to find the most true and fair expressions on the two dimensions of an experience curve diagram.

2. Theory on technology and innovation revisited

To open up the black box of industrial learning and innovation within the wind turbine industry it is necessary to revisit the literature’s theoretical considerations on technology and knowledge and their dynamic counterparts (or first order derivatives as engineers might express it) innovation (change of technology) and learning or experience (change of knowledge). The following chapter will outline the most important theoretical considerations in five areas: the relationship between technology and knowledge, useful taxonomies on technology and knowledge, taxonomies on how knowledge is created and stored, sources of knowledge, and finally theoretical considerations on technological trajectories.

2.1 Technology: artifact and knowledgeAccording to the New Encyclopedia Britannica (vol. 18), the term technology has a Greek origin (tekhné: art or craft and logos: word or speech) and is explained as “the means or activity by which man seeks to change or manipulate his environment”. Technology is the tools and techniques employed for carrying out the plans for achieving desired objectives. In classical economical and organizational theory this involves the transformation of raw materials into products. Technology in its widest meaning contains both some means (knowledge, techniques, organization, etc) and some objects (tools, results or products), but in order to make an operational technology model I will define technology more narrowly, so that technology has two sides: artifacts and knowledge. Considering technology as both artifacts and knowledge has also, for example, been applied by Layton [10].

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Because the original linguistic meaning of technology is closer related to knowledge than to artifacts, most parameters used for classifying technology deal with classifying the content of knowledge in technology ([11], [12], [13]), although, some students of technology and organization do primarily use the artifact part to classify technology [14]. The reason for the latter approach is obvious: it is much easier to detect or measure changes of artifacts than of knowledge. When analyzing experience curves for energy technology we also face this challenge, that learning and the industrial stock of experience are not measurable entities. Therefore, we need to focus on measurable entities such as mass or cost of the artifacts.

2.2 Technology and knowledge: Concept, process, utilizationAnother common classification is to view technology as either a product or a process. This has often been used by scholars looking at the artifact side of technology (i.e. organization sociologists as Woodward [15] and Perrow [16]. Scholars focusing on the knowledge part of technology tend to use other names, namely concept knowledge and manufacturing or process knowledge. The first refers to knowledge necessary to design or construct a certain artifact; for example making the blueprint and component lists of a wind turbine. The latter refers to knowledge applied to produce the actual artifact (wind turbine) from the blueprint and component lists.

Vincenti analyzed the historical development of flush riveting in the American aircraft industry, and he classified the required competence or knowledge as conceptual knowledge or “knowledge for design” and process knowledge or “knowledge for production”. Conceptual competence is based on the demands from both the function or use of the product (artifact), and its manufacture. Vincenti’s point is that an artifact (in his case a rivet) cannot be designed without considering how it is going to be manufactured [17].

Dealing with flush riveting technology Vincenti focuses mostly on knowledge on the design and production of artifacts (rivets), and not so much on knowledge related to using artifacts. However, when writing about learning, Rosenberg introduced this distinction using the terms of learning-by-doing and learning-by-using. “With respect to a given product, I want to distinguish between gains that are internal to the production process (doing) and the gains that are generated as a result of subsequent use of that product (using). For in an economy with complex new technology, there are essential aspects that are a function not of the experience involved in producing a product, but of its utilizations by the final user. This is particularly important in the case of capital goods” [18].

In the following I will use the terms manufacturing or process knowledge and utilization knowledge. If we use the example of a car, it takes some types of knowledge to make the car and some very different types of knowledge to use the car.

Knowledge on producing is split up by Vincenti into design knowledge and production knowledge, where design knowledge is based on either production requirements or strength requirements, ie. preparing the blueprint drawings. Production knowledge concerns how to translate the drawings into the actual devices or artifacts. We recognize the distinction between conceptual and manufacturing knowledge, and we also recognize the whole technology process in an organization - design, production, and utilization of a hardware artifact. From the above we are now able to set up a taxonomy on what knowledge is about. See figure 1.

Figure 1 to be inserted about here.

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2.3 Formalized, tacit, and embodied knowledgeWhen analyzing types of knowledge, Vincenti used the terms descriptive, prescriptive and tacit knowledge. “Descriptive knowledge, as the term suggests, describes things as they are. Prescriptive knowledge by contrast, prescribes how things should be to attain a desired end” [19]. Vincenti points out that both prescriptive knowledge and descriptive knowledge are explicit knowledge or codified knowledge. Tacit knowledge is, according to Vincenti: “the implicit, wordless, pictureless knowledge essential to engineering judgment and workers’ skills” [20]. When a new machine - for example a gearbox - is to be designed, one can acquire a textbook or some recommendations for gearboxes and design it accordingly. This is an example of codified knowledge. In practice, in earlier phases of the modern era wind energy, design engineers were often able design a wind turbine from scratch without any calculations or any use of books and recommendations. One such design engineer, interviewed in 1989, had made several turbines before, and the knowledge of how to do so had become routine for him [21]. This is an example of tacit knowledge. However, an essential question for him would be how deep into the process he should go. A wind turbine can be regarded as an assembly of standard components, and he believed that he would be able to put up the overall specifications, and then select standard components to suit them.

One can ask how far back in the production chain one has to go. A gearbox is primarily made of steel, but one does not need any knowledge of iron ores, steel mills, or steel production to make a gearbox. That is in contrast to industries in the former socialist economies. The largest Soviet manufacturer of wind turbines in the 1980’s was said to have 10,000 (sic) employees in its factory. The tradition here is that an enterprise produces nearly all components in-house from raw steel - even commodities such as nuts and bolts [22]. The point is that modern industrial enterprises use lots of knowledge without actually possessing it themselves. Hardware artifacts can be acquired and utilized by the organization, but not necessarily understood. An example could be power electronics bought from a vendor and installed in a wind turbine. To deal with this problem Rosenberg has introduced the terms: embodied knowledge and disembodied knowledge [23]. Rosenberg’s definitions of terms are a bit ambiguous, but they are connected to the knowledge acquired through learning in the early stages of an innovation process and then embodied in the product. Embodied knowledge is apparent as construction changes in an artifact, e.g. a wind turbine. Disembodied knowledge is apparent as changes in how an artifact is manufactured or used.

There is of course an overlap between the above forms of knowledge. An example of ambiguity could be the simplified rules for calculating the loads on wind turbines issued by the Test Station for Wind Turbines at Risoe National Laboratory - also called the “Load Paradigm”. This Load Paradigm was used by the Danish wind turbine industry during the 1980’s. The Load Paradigm consisted of six very simple formulas for the loads on wind turbines (forces and momentums in three directions), but they were based on theoretical and extensive practical insight in aerodynamics and structural dynamics of wind turbines. Therefore, the Load Paradigm can be regarded as embodying this insight, and we can also regard it as codified knowledge. Both observations can be useful, but in order to avoid misunderstandings, in the following I will only use embodied knowledge on knowledge embodied in hardware artifacts – such as gearboxes - and not on knowledge embodied in software systems, such as calculation rules.

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2.4 The relation between experience-based and science-based knowledgeOlder innovation literature often distinguishes between science-based and experience-based creation of knowledge. Around the middle of the 20th century a widely used classification of research and development activities in five groups was introduced: pure science, basic science, industrial research, development, and design [24]. Others have added production and marketing to this classification, linked the groups, and referred to this model as the "linear model of innovation". The policy implication of this linear model was that if public money was fuelled into the model at the top as pure science at universities and research organizations, the knowledge created would trickle down the model through strategic research and applied research in firms, and end as products on the market. The result would then be innovation, competitiveness, and prosperity for firms and nations. In this model, science-based learning takes place in the upper end of the model, within institutions for science and research or in research departments at large enterprises. Rosenberg refers to this as an exogenous activity [25]. Experience-based learning takes place at the other end of the model in enterprises' product development departments, in prototype workshops, or during industrial or artisan manufacturing. The linear model has been challenged ever since its appearance [26], but the critique took pace during the 1980s. Due to the increased understanding of industrial innovation processes, the linear model for innovation has been replaced by more advanced models. Stephen Kline and Rosenberg of Stanford University in the mid-1980s suggested an influential alternative model, the chain-linked model [27]. This model suggests a more complicated relationship between research, invention, innovation and production, and shows that at least six paths link a firms' knowledge pool to the central innovation process and its research activities. In contrast to the linear model of innovation, Kline does not think that the origin of industrial innovation is research activities. "Any modern technical person beginning a task in innovation will not turn first to research. On the contrary, one turns first to the current state of the art, then to personal knowledge about the governing principles of the field. After that, one goes to the literature, consults, and calls in leading experts. Only when all that does not suffice does one start research" [28]. Elsewhere, it has been demonstrated, that Kline’s statement is highly valid for the Danish wind turbine industry in the 1980s and 1990s [29]. The point is, that much valuable research-based learning has not been initiated from a vacuum of pure science, but from firms’ needs to solve problems.

Most recently, science sociologists have also made significant contributions on the interactions and linkages in science systems. These contributions have been labeled as the Mode1/Mode2 discussion [30, 31] and the Triple-Helix discussion [32]. It is argued, that that the relation between the scientific community and society changed dramatically during the end of the 20th century. Modern science or knowledge production has changed from being dominated by classical scientific disciplines at universities into a new mode of knowledge production that is problem driven, trans-disciplinary and often organizationally sited outside universities. This has been described as a transition from Mode 1 to Mode 2 conditions of knowledge production.

Knowledge production within the Danish wind turbine sector is almost archetypically Mode 2. In national research programs such as in Denmark and in other OECD/IEA countries, as well as in EU-funded research programs, emphasis in recent years has focused research agendas formulated on close interaction between industry, the scientific communities and policy makers in government energy administrations. To a very large extent, industry and government needs for research have set the agenda for research, but NGOs and trade organizations have also played important roles in agenda setting. Most government-funded

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research projects carried out within wind energy have been carried out in collaboration between universities and research institutions on the one side, and industry on the other. When most of the theory and considerations on experience curves were established, there was a linear understanding of the relationship between science and technology. No scientific research was involved in the improvements in productivity observed by Wright in the aircraft industry or in the Boston Consulting Group recommendations for marketing strategies. Both worked within a context of understanding where experience was gained at the lower end of the linear model and scientific results were gained in the upper end of the linear model. The point is, that scientific progress and technological innovation do not happen in a void or as a result of scientific research alone. The scientific agenda is often set by the needs of industry and governments, and their needs are created by practical experience with state-of-the-art technology. In industrial innovation, knowledge is created by both research and experience. It is difficult to quantify the impact on the "stock of knowledge" from these two sources.

Furthermore, it is difficult – or maybe impossible - to qualify what is actually a research activity and what is systematic collection of practical experience. When research and experience are defined according to where they take place, it is not that complicated to distinguish between the two types of learning. Science-based learning takes place at universities and "research laboratories" and experience-based learning takes place in industry. However, if the area of analysis is one organization or one "system-of-innovation" the differences become much more ambiguous.

In the first decade (the 1980s) of modern Danish wind technology, classical scientific research played a minor role compared to knowledge created through experience with generations of wind turbines. Even at research institutions such as Risoe National Laboratory much of the “research” activity concerned debugging industry’s prototype machines and collecting empirical evidence on the nature of wind [33]. The point is, that science-based learning and experience-based learning looks the same. Kline notes on this: ".. the knowledge and methods of science are used continuously in innovation whenever a question arises within any step in the processes along the central chain-of-innovation. If we don't have the information in our heads, we go to the literature of science and related fields. If that fails, we use “scientific” methods to do some form of research to solve the problem. The methods of science and technology largely overlap” [34]. For the purposes of this article, two implications can be drawn from this. Technology experience curves must be determined from contributions from both scientific research and from practical experience. A policy level implication of this has been noted by Wene: "A system that has no output will not learn, meaning, that a technology which is not produced and deployed cannot start ride down the experience curve. Technologies cannot become cost-efficient by laboratory R&D alone" [35].

The second implication of the considerations in this paragraph relates to the fact that the use of experience curves in models for policy advice such as described in current literature on experience curves for energy technologies seems to reflect a linear model of the system of innovation. At the one end we have public R&D policies and at the other end we have market support policies. As argued above, modern systems of innovation are too complicated to be modeled as a linear system. There might be other ways that public policy can influence the efficiency of the innovation system than subsidies for R&D and market support. Such models as Watanabe and Wene describe can be very useful, but when used in policy advice they

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must be accompanied by a more advanced understanding of the whole innovation system and the role of the different actors involved.

2.5 A model for the sources for knowledge and experienceOn the basis of the above terms and considerations, we are not able to construct a model of the sources for accumulated knowledge and experience.

In the core of the model we have the three types of knowledge in the industrial innovation process: Concept knowledge, process knowledge and utilization knowledge. This follows a generic model of a product’s major life cycle: design, manufacture and use.

Experience-based knowledge can also be created in three ways.

Firstly, through the process of developing and designing, the artifact design of a machine is carried out by using available knowledge. However, through this process new knowledge is also created, and this new knowledge is directly embodied into the design or redesign the machine. This could be improved knowledge on using computer codes for design and stress calculations. Of course targeted research activities creating codified knowledge are also used this way. However, we are not talking about research, but rather about the learning processes creating tacit knowledge affiliated with development and design.

Secondly, in the center of the model is the kind of learning Wright observed in the aircraft industry and the kind of learning that the BCG deals with. Learning through manufacturing will lead to improved or cheaper manufacturing of the same product. The product will not be exposed to visible changes due to these improvements, we a talking about disembodied knowledge. In wind turbine technology this could comprise better logistics in purchase and distribution, outsourcing production, improvements in the speed of welding robots, etc. Some of the experience with manufacturing might lead to suggestions for changes in the design of the manufactured product. This kind of new knowledge will be embodied in the product as artifact changes that will improve the manufacturability of the product.

Figure 2 to be inserted about here.

Thirdly, experience can be gained through using a product as pointed out in the above quotation from Rosenberg. Rosenberg might primarily focus on learning gained through the use of a machine, leading to improved or more efficient use of machine. This new knowledge is disembodied since it will not affect artifact changes of the machine. However, if the learning is linked to a feedback to the design of the machine, the new knowledge can be embodied in the machine. Several heads of R&D departments in Danish wind turbine manufacturers acknowledge that they receive lots of input in their new product development process from the firm's operation and maintenance (O&M) staff. In several firms, there is formalized knowledge exchange between R&D staff and O&M staff. This is also how experience from demonstration programs is fed into the model.

As indicated above, models of learning usually only include knowledge affiliated with design, manufacture and use of technology. But in recent years there has been an increasing focus on technology’s adverse life-cycle impact on the environment. This also includes environmental affects after end use. Therefore, an element could be added to the model

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concerning knowledge on dismantling and recycling. Wind turbines, just like other machinery, should be designed with recycling in mind. Alloys might not only be selected to fulfill certain stress and strain criteria, but also to fulfill a demand for recycling after end use. Also, components that are difficult to recycle, such as electrical and hydraulic cables, should be considered carefully. For the purposes of this article we need not include this type of knowledge.

It would be of great interest if we could describe and quantify each element of learning shown in fig. 2. In practice this is very difficult, partly due to the difficulty mentioned above in measuring changes in knowledge, and partly due to the fact that measuring changes in technology requires long time tracks of detailed statistical data that is considered highly confidential by industry. Nevertheless, the model gives us a tool for describing many of the elements and, as we shall see in the empirical part of this article, even quantify some of the elements.

2.6 Business clusters, technology paradigms and trajectoriesWith a disciplinary basis in evolutionary economics and political science, one line of studies treats technological trajectories explicitly. Here, terms such as natural trajectories, technological imperatives, technological regimes [36], innovation avenues [37], and technological trajectories [38] are used to describe different sides of the phenomenon. This deals with what we could call the trajectories and paradigms of whole industries or products, such as aircraft and tractors, but also wind turbines [39]. Islas has described the creation of a new technological paradigm of gas turbines based on this theoretical framework [40] and Garud and Karnøe have formulated a recent theoretical contribution on how such paradigms and paths are created [41].

Dosi defines "as technological trajectory the activity of technological process along the economic and technological trade-offs defined by a paradigm"42. Furthermore, Dosi states that: “continuous changes are often related to progress along a technological trajectory, while discontinuities are associated with the emergence of new paradigms”[43].

The original use of experience curves was based on analyses of such incremental changes within a certain technological paradigm and improved manufacturing of identical components. The recent use of the concept of experience curves for energy technologies differs from this in two important ways.

Firstly, the focus on putting technological innovation at a higher systemic level, such as the whole wind turbine industry. A wider definition of the paradigm includes what could be called the wind power innovation system or the Danish wind turbine industry cluster [44]. This cluster has been described in a recent report from the Danish Ministry of Trade and Industry [45]. The report defines three main groups within the cluster: The first group is the major wind turbine and blade manufacturers (today: Bonus, LM Glasfiber, NEG-Micon, Nordex and Vestas). The second group is large suppliers of components such as steel parts, electronics and software. Finally, the third group consists of smaller suppliers of various parts and components. I also find it important to include suppliers of specialized services to the cluster: banks (Ringkoebing Bank has specialized in financing wind power projects), insurance companies, transportation firms, science centers and universities (such as Danish Technical University, Aalborg University and Risoe National Laboratory), engineering and management consultancies, and so on. Also governmental administration and other public

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sector institutions are important to competitive conditions of the cluster. To this can be added foreign and international firms and organizations – competitors, customers and suppliers.

In the early years during the 1980s, most Danish wind turbines were assembled from a number of standard components such as gearboxes, generators, hydraulic motors for yaw systems, standard bearings for main shaft and yaw rim etc. Only blades and control systems were tailored specifically for the wind turbine industry. Because of the relatively small volume of the wind turbine industry, no vendor was able to tailor special components (e.g. bearings). However, as the total market volume increased, room has been made for specialized suppliers to the wind industry. Large international corporations such ABB, VALMET and FAG now focus much more on tailoring components for wind turbines. They also carry out targeted R&D on these components. Hence, a large portion of both science-based and experience-based knowledge has been created within the vendor companies and this knowledge is embodied in the components.

The implication of this is, that when the basis of an experience curve analysis is the whole wind turbine industry instead of just a single wind turbine type, it is very important to understand that a lot of codified and especially tacit knowledge is created everywhere within the cluster.

The discount rate of interest has a significant influence on electricity production costs and hence on the cost of electricity from wind turbines. In the early 1980s, banks and other financial institutions had less experience with wind projects and the discount rate was consequently above average compared to similar projects. However, as banks gained experience with wind projects they became able to offer lower relative discount rates. The same applies for insurances – as experience was gained, the financial risks became better understood, and insurance became cheaper. In this sense an important source of experience also comes from the financial sector, though the quantitative effect of this is difficult to estimate.

The second way recent use of experience curves for energy technologies differs from the early use of experience curves is connected to the magnitude of technological changes. When examining the use of experience curves for energy technologies, Wene focuses most of his analysis on radical changes in technology. Based on experience from photovoltaics he considers “how an R&D breakthrough, a major technical change or shift in production processes may appear in the experience curve” [46]. This contradicts Dosi’s observation cited above, indicating, that changes often are incremental and continuous along a technological trajectory.

The Danish wind turbine industry developed its technology within a certain paradigm. This paradigm is usually referred to as “The Danish wind turbine concept”. This is the upwind, three bladed, stall regulated, grid connected, and horizontal axle concept. Introduction of new technologies into wind energy technology such as pitch regulation of the power, variable speed, power electronics, etc. have been introduced as a series of incremental steps over several years. One firm, Vestas, introduced pitch-regulated turbines during the 1980’s, and in the 1990’s forms of variable speed were also introduced by Vestas.

It is well documented, that in the case of the Danish wind turbine industry, technological innovation has taken place as a series of incremental changes rather than a few radical

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changes [47]. Typically, a new generation of turbines has been designed conservatively with some extra safety margins on critical details. When the first experiences of the turbines were collected, extra safety margins could be narrowed. Typically, the experiences gained have not been embodied as reductions in material use (and cost) but rather as an increase the electricity output by means of slightly larger rotor diameter, changed pitch angle, etc. Manufacturing also allowed learning through manufacturing that could be disembodied in the turbines.

The implication of this is, that changes in experience curves for Danish wind technology often are explained by occurrences in the market (the x-axis) and not radical shifts in technology (y-axis).

3. Cutting costs – the y-axis in the experience curve diagram

From the early studies of Wright in 1920s and 1930s, decreases in costs have been the central issue in experience curve studies. However, cost-cutting can be defined in different ways. Looking back at the early use of experience curves, cost-cutting was associated with cutting manufacturing costs for exactly the same component. Much of recent literature on experience curves for energy technologies uses cost of equipment as measuring point – often measured as USD/kW of installed capacity [48].

This approach might give a flawed picture of the industrial learning, as will be argued in the following. At least for wind turbine technology, competition in the market place is not based on comparisons of cost of equipment from different manufactures. Competition is based on the equipment’s ability to produce electricity. Several issues indicate this.

Firstly, from the model of competence and learning (Fig. 2) it is obvious, that if we choose equipment cost in our experience curve we will only include learning that is embodied in the design of the machine or disembodied in the manufacturing process. Learning-by-using disembodied in utilization of turbines will not be included. This theoretical consideration suggests that if we are aiming at including improvements due to learning-by-using in our analysis of experience curves we need to choose a measuring point where this type of learning is included.

Secondly, it might be of interest to examine what the industry considers important in the market place. In a survey carried out by the Danish Association of Wind Turbines Manufacturers on the industry’s need for public-financed research, development and demonstration, a main conclusion was that the most important driving factor for technological development was the decrease the cost of energy. "Danish wind turbine manufacturers operate in markets characterized by very fierce competition.In the end, the most important competition parameter is the cost per kWh produced” [49]. Competition is not on the cost of equipment, but on the cost of the electricity produced by the equipment.

Figure 3 to be inserted about here

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Thirdly, wind turbines’ ability to produce electrical energy per installed unit of capacity has improved over time. See fig. 3. These improvements are primarily due to higher towers and better aerodynamic design. This improved efficiency of the technology will not be reflected in experience curves only based on the cost of equipment. A further source of change in the relationship between rated capacity and ability to produce electricity is the design decision on the relationship between rated capacity and the diameter of the rotor. Basically, the swept area of the rotor together with the rotors aerodynamic efficiency determines the electrical output. Over time, the optimum design decision has changed slightly. Today most wind turbine manufacturers use numerical optimization tools (large computer codes) to make these design decisions. The key design criterion for numerical optimization is minimum cost per kWh of produced electricity. As knowledge improves concerning design criteria, material properties, etc. so does the relationship between rotor diameter and rated capacity. This improved knowledge is always reflected in the machine’s ability to produce electricity but not always in the machine’s rated capacity.

3.1 Components of costs of wind generated electricityWhat then are the components of the costs of wind generated electricity? As a starting point one could consult the manufacturers. In an earlier quoted report from the Danish Wind Turbine Manufacturers Association, the following quote can also be found: ".. the cost per kWh produced (electricity) .. depends partly on the costs of the wind turbine itself, and partly on costs of foundations, erection, network connection, operation and maintenance. Last but not least the size and nature of local wind resources are of importance to the energy turnover. Furthermore, financing, guarantees, insurance and service are competition parameters to the manufacturers" [50]. This by and large reflects the elements in the cost function for cost of energy from wind turbines [51].

Total installation costsTotal installation costs comprise three components: 1) ex-works costs of the wind turbine (this usually also comprises transport and installation), 2) additional costs on the site including purchase of land (if not land is rented). In this analysis cost of land is excluded from analysis of learning and experience.

The additional costs comprise items such as foundations, electrical installations, grid connection, electronic surveillance (via phone or internet), financial costs (initial costs of financial packages, financing during the construction period, etc.), civil engineering planning and consulting, roads, and other less important costs.

Operation and maintenance costsOperational and maintenance costs comprise a number of components: insurance, regular maintenance, repair, spare parts, administration, land lease (if not on bought land). To this list of cost components, depreciation and interest on loans can be added. As mentioned above the total economy of a wind turbine project is very dependent of the cost of money.

Local wind resourcesOf course the is no learning affiliated with the wind resources, but computer tools for micro-siting the wind turbines within a site have improved. A number of resource assessment tools have been developed during the 1990s, and there is still room to improve these tools.

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5. Experience curve comparisons

The empirical data for the following considerations are defined in another paper presented in this special issue of IJETP [52]. And from this data it is possible to develop a variety of experience curves and analyze these curves from different perspectives. For the purposes of this article, it is only of interest to compare different definitions of cost reduction (the y-axis in the experience curve graph). The following comparisons are all based on the cumulated installations of wind turbine capacity in MW in Denmark on the x-axis.

For the y-axis we will consider cases of how to express the learning in the wind energy technology. One can say that we consider four points of measurement.

Case a) Ex-works unit price of turbines. Based on the time track for average ex-works costs of a progress ratio can be determined to PR = 91% (R2 = 0.94).

Case b) Total installation cost. When ex-works turbine prices and additional costs are added the result is the total project installation cost. Based on the cumulated installations of wind turbine capacity in MW in Denmark and the average total investment cost, a progress ratio can be determined as PR = 90% (R2 = 0.92). Hence almost no additional learning effects can be detected from the additional cost components.

Case c) Specific cost of electricity (or specific ex-works costs). In cases a) and b) only experience embodied as reduction of equipment cost is included. As argued earlier in this article, we also need to include experience embodied as larger electricity production to get a true and fair presentation of the industry’s experience curve. The specific ex-works costs actually reflect experience embodied as both from declining equipment costs and improved production. When an experience curve is determined by use of the time track for specific ex-works costs as defined above, the progress ratio PR = 86 (R2 = 0.97). This figure is significantly different from the progress ratio in case a).

Case d) Levelised cost of electricity (estimated electricity production costs). The most true and fair measure of the overall improvement rate for wind power might be the development of production costs of electricity for actual wind turbine projects. Here, both embodied and disembodied learning, fed into all three types of knowledge (conceptual, manufacturing and utilization) in our model in figure 1, are included in the measurement. Not only experience gained within wind turbine manufacturing firms is included, but also experience gained throughout the whole business cluster of the Danish wind turbine industry. The information needed for this calculation is very difficult to find, but I have estimated the average cost of electricity from wind turbines in Denmark based on statistical data for the different types of costs [53]. Hence, learning effects from better micro-siting and financial packages are not included, as these are fixed figures in the estimation. When an experience curve is developed based on the estimated electricity production cost, the progress ration PR = 83% (R2 = 0.97). Again a significant difference compared with the other three cases. The difference can be explained by more reliable technology needing less service and maintenance.

Figure 4 to be inserted about here.

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The reliability of this result has been checked in several ways. The same relationship between the progress ratios in the four cases is also found if cumulative units of turbines are used instead of cumulative installed capacity on the x-axis of the experience curves. The same relationship also results from only analyzing the last 5 years of the 20 years of data. If installations in Denmark are replaced by Danish firms’ global installations, the same relationship appears.

This empirical evidence clearly supports the conclusions from the theoretical considerations earlier in the article.

6. Conclusions

The aim of this article was to establish a better understanding on the basis of innovation theory and other areas of social science. Through theoretical considerations this article has opened up the black box of the learning systems of the firms in the wind energy industry and the wind energy business cluster as a whole. The article’s observations and conclusions based on theoretical considerations have been supported by empirical examples and evidence.

From organization theory, and technology sociology innovation theory we have obtained some basic definitions of technology and learning. Recent studies of experience curves for energy technologies are often based on the concepts of learning-by-doing and learning-by-using with a reference to Nathan Rosenberg. This article has elaborated this slightly with other contributions from innovation theory and suggested taxonomy for knowledge with three elements: conceptual knowledge, process knowledge, and utilization knowledge. Furthermore, the article has argued that the concepts of embodied and disembodied knowledge can be included to help understand how new knowledge (or learning) affects the technology. The article has described a more detailed relationship between knowledge and learning and its different forms. A model for this has been developed (figure 2), although, a description of the role of different actors is not included in the model. An Actor Network Theory approach might be a very useful tool for understanding actors’ roles in the system of innovation, but this is outside the scope of this article [54].

With elements from innovation theory, and recent contributions to science sociology this article has analyzed the relationship between science-based learning and experience-based learning. It has been argued that technology’s experience curves must be determined from contributions from both scientific research and from practical experience. Modern systems of innovation are too complicated to be modeled as a linear system. There might be other ways that public policy can influence the efficiency of the innovation system than subsidies for R&D and market support, but this requires a more advanced understanding of the whole innovation system and the role of the different actors involved.

The area of evolutionary economy has contributed with the concepts of technological paradigms and technological trajectories. This has contributed to understanding technological innovation as continuous improvements of a technological concept and that real shifts in wind energy technology do not happen overnight or from year to year, but rather they happen over a decade or more. Furthermore we have introduced the concepts of business clusters to explain, that the learning is not solely affiliated with a process going on within a single wind turbine firm but rather with processes of the whole business cluster of the Danish wind

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turbine industry, its suppliers, its customers, public authorities, etc.

The most important conclusion is, that experience curves based on the cost of produced electricity are more true and fair than experience curves only based on the cost of equipment. The progress ratio for wind turbines in Denmark based on ex-works turbine costs has been determined at 91%. In this progress ratio, effects from improvements in the efficiency of the turbines are not included; neither are effects from much of the activities outside the manufacturing industry. When an experience curve is based on the estimated cost of electricity, the progress ratio can be determined at 83%. In this case experience gained throughout the whole business cluster is included and therefore also effects from improvements in the technology’s efficiency and operation & maintenance. I am aware of the

1 Wright, T. (1936), Factors Affecting the Cost of Airplanes, Journal of the Aeronautical Sciences, February 1935, Vol 32 Boston Consulting Group (1972), Perspectives on Experience, Boston, MA3 Colpier, U. C. and Debora Cornland (2002), The Economics of Combined Cycle gas turbine – an experience curve analysis, Energy Policy, vol. 30, p309-316.4 Seebregts, Ad, Tom Kram, Gerrit Jan Schaeffer, Alexandra Bos, (2000), Endogenous learning and technology clustering: Analysis with MARKAL model of the Western European energy system, Intl. J. Global Energy Issues, Vol.14, Nos.1-4.5 McDonald, A. and Leo Schrattenholzer (2001), Learning rates for energy technologies, Energy Policy, vol. 29, p255-261.6 See: Wene, Clas-Otto (2000), Experience Curves for Energy Technology Policy, OECD/IEA, Paris7 Wene, 2000.8 Wene, 2000, p289 Wene, 2000, p2910 Layton, T. T. (1974), Technology as Knowledge. Technology and culture, vol.15, no.1, p.31-41.11 Perrow, Charles (1974), A framework for the comparative analysis of organizations. American Sociological Review (April 1967): p194-20812 Vincenti, Walther G. (1984), Technological Knowledge without Science: The Innovation of Flush Riveting in American Airplanes, ca 1930-ca. 1950. Technology and Culture 25(July):540-57613 Rosenberg, Nathan (1982), Inside the Black Box: Technology and Economics. Cambridge University Press, Cambridge14 Woodward, Joan (1965), Industrial Organization: Behavior and Control. Oxford University Press.15 Woodward, 1965.16 Perrow,1974, p194-208.17 Vincenti, 1984.18 Rosenberg, 1982, p12219 Vincenti, 1984, p573. Underlinings are added by this author.20 Vincenti, 1984, p57421 Dannemand Andersen (1993) En analyse of den teknologiske innovation i dansk vindmølleindustri. Samfundslitteratur, Copenhagen, PhD-Serie 93-322 Dannemand Andersen, 1993.23 Rosenberg, 1982.24 E.g. Burns, Tom and G. M. Stalker (1961), Management of Innovation. Tavistock Publications, London25 Rosenberg (1982)26 e.g. Klein, Burton (1958), A Radical Proposal for R. and D., Fortune (Time), May 1958, p11227 Kline, Stephen J. and Nathan Rosenberg (1986), An Overview of Innovation. In: R. Landau and N. Rosenberg (eds): The Positive Sum Strategy. Harnessing Technology for Economic Growth. National Academy Press, Washington D.C. See also: Kline, Stephen (1985), Innovation is not a Linear Process. Research Management, July-August 1985.28 Kline, Stephen (1985), Innovation is not a Linear Process. Research Management, July-August 1985. p41. See also Clark, Kim B. & Robert H. Hayes (1988), Recapturing America’s Manufacturing Heritage, California Management Review, Vol.30, No.4, Summer 1988.29 Dannemand Andersen, 1993.30 Gibbons, M., Limoges, Nowotny, Schwartzman, Scott & Trow (1994): The new production of knowledge, Sage, London

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fact that empirical data and time tracks of equipment costs are much easier to acquire than similar data based on cost of electricity. However, in experience curve studies used as policy advice some considerations on this matter must be carried out to avoid false advice.

Another important conclusion is connected to the recent use of experience curves in models for learning systems used for policy advise on public support for R&D and market stimulation. These models seem to be based on a linear system of innovation. This article has argued that modern systems of innovation are too complicated to be modeled as a linear system. Policy advice based on such systems must be accompanied by a more advanced understanding of the whole innovation system and of the various roles of the large numbers of actors involved.31 Nowotny, H, Scott & Gibbons (2001), Rethinking science. Knowledge and the Public in an age of uncertainty, Cambridge: Polity Press32 Etzkowitz, H. and L. Leydesdorff (2000), The dynamics of innovation: from National Systems and ”Mode 2” to a Triple Helix of university-industry-government relations, Research Policy, Vol. 29, pp. 109-12333 An interesting analogy can be found as “the artisan mode of production” in Clark, Kim B. & Robert H. Hayes (1988), Recapturing America’s Manufacturing Heritage, California Management Review, Vol.30, No.4, Summer 1988. Another useful analogy can be drawn between the Danish wind industry and industry-oriented research in the 1970s and 1980’s and the American aircraft industry and NACA’s role herein between 1920 and 1940. See: Constant, E. (1980), The origins of the turbojet revolution. The Johns Hopkins University Press, Baltimore and London..34 Kline, 1985, p44.35 Wene, 2000, p28.36 Nelson, Richard R. and Sidney G. Winter (1977), In Search of Useful Theory of Innovation. Research Policy, Vol.6 (1977), pp36-76.37 Sahel, Devandra (1985), Technological Guideposts and Innovation Avenues, Research Policy (North-Holland), Vol. 14, pp61-82.38 Dosi, Giovanni (1982) Technological Paradigms and Technological Trajectories. Research Policy, Vol.11, pp147-162. See also: Dosi, Giovanni (1988), Sources, Procedures, and Microeconomic Effects of Innovation.Journal of Economical Literature, Vol. XXVI (September 1988), pp1120-1171)39 Karnøe, Peter (1991), Dansk vindmølleindustri - en overraskende international succes. Samfundslitteratur Copenhagen.40 Islas, J. (1999), The gas turbine: A new technological paradigm in electricity generation, Technology Forecasting and Social Change, Vol 60, p129-148.41 Garud, R. and P. Karnøe (2001), Path Dependence and Creation, Lawrence Erlbaum Associates, London.42 Dosi, 1988, p1128.43 Dosi, 1982, p147.44 For an analysis of systems of innovation and business clusters see Lundvall, B.-A., (1988), Innovation as an Interactive Process - from User-Producer Interaction to National Systems of Innovation', in Dosi, G. et al. (eds.): Technology and Economic Theory, London: Pinter Publishers. See also Porter, Michael. E. (1990), The Competitive Advantage of Nations, The Free Press, New York.45 Danish Ministry of Trade and Industry (2001), A new Economy and its New Clusters, Danish Agency for Trade and Industry, Copenhagen. http://www.efs.dk/publikationer/rapporter/klynge_eng/ren.htm46 Wene, 2000, p3347 See Redlinger, R.Y.; Dannemand Andersen, P.; Morthorst, P.E., (2002) Wind energy in the 21st century: Economics, policy, technology, and the changing electricity industry. Palgrave. See also: Karnøe, P. (1995), Institutional Interpretations and Explanations of Differences in American and Danish Approaches to Innovation, In: Scott, W. Richard and S. Christensen: The Institutional Construction of Organisations, Sage Publications, London.48 For a variety of technologies see: McDonald, A. and Leo Schrattenholzer (2001), Learning rates for energy technologies, Energy Policy, vol. 29, p255-261. For hydrogen technologies see Rogner, H.-H. (1998), Hydrogen technologies and the technology learning curve, Int. J. Hydrogen Energy, Vol. 23, No. 9, p833-840.49 Krohn, Søren (ed.) 1995, Vindkraft forskning og udvikling. Vindmølleindustrien syn på offentlige forsknings- og udviklingsfremme, Danish Wind Turbine Manufacturers Association, p8, author’s translation.50 Krohn, 1995, p8 (author’s translation).51 Tande. J. O. & Raymond Hunter (eds.) Recommended practices for Wind Turbine Testing: 2. Estimation of cost of energy from wind energy conversion systems. Recommended practice submittet to the executive

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Acknowledgements

Most of the theoretical considerations in this article stem from Dannemand Andersen (1993) and from a variety of internal reports and notes in Danish prepared between 1989 and 1995 for the Danish Energy Agency. The elaboration of this material and further collection and analyses of empirical data have been made possible by the ENERGIE project EXTOOL (NNE5-2000-00010).

I acknowledge valuable input to this article from our EXTOOL partners Lena Neij from Lund University and Michael Durstewitz from ISET/Kassel University and from my colleagues at Risoe National Laboratory Poul Erik Morthorst, Helge Larsen., Klaus Skytte and Stine Grenaa Jensen.

Committee52 Neij L., P. Dannemand Andersen, M. Durstewitz, Experience curves for wind power, This issue of Int. J. Energy Technology and Policy. See also: Neij, L, P. Dannemand Andersen, M. Durstewitz, P. Helby, M. Hoppe-Kilpper, P. E. Morthorst (2003), Experience curves: a tool for energy policy programmes assessment. Lund University, Lund.53 This is further elaborated in : Neij, L, P. Dannemand Andersen, M. Durstewitz, P. Helby, M. Hoppe-Kilpper, P. E. Morthorst (2003), Experience curves: a tool for energy policy programmes assessment. Lund University, Lund. Se also Redlinger, R.Y.; Dannemand Andersen, P.; Morthorst, P.E., (2002) pp73-85.54 See Latour, B. (1987), Science in Action: How to follow scientists and engineers through society. Harvard University Press. Cambridge, Mass.

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Making knowledge Concept knowledge

Knowledge of how an artifact is designed and constructed (blueprints)

Manufacturing knowledge

Knowledge of how an artifact is manufactured according to blueprints and description

Utilization knowledge Knowledge of how an artifact is used

Figure 1. A taxonomy for what knowledge is about.

Figure 2. A model for knowledge and learning.

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Figure 3. Development in the ability of Danish wind turbines to produce electrical energy (measured in annual production in kWh) per unit of installed generator capacity (measured in kW) – often referred to as “full load hours”.

Type of cost applied on the y-axis of the experience curves

Progress ratio

Types of experience included

Organizational boundary

a) Ex-works prices of wind turbines.

91% Experience embodied and disembodied as cost reduction of equipment

Manufacturing industry

b) Total installation costs. 90% Experience embodied and disembodied as cost reduction of equipment and installations

The whole business cluster except that related to operations

c) Specific electricity cost (or specific) ex-works prices.

86% Experience embodied and disembodied as cost reduction of equipment and as improvements in efficiency

Manufacturing industry

d) Levelised cost of electricity. 83% Experience embodied and disembodied as cost reduction of equipment and installation, as improvements in efficiency and disembodied utilization experience

The whole business cluster

Figure 4. Progress ratios for four definitions of the y-axis of Danish wind turbine technology.

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0

500

1000

1500

2000

2500

3000

1975 1980 1985 1990 1995 2000 2005

Full

load

hou

rs in

kW

h/kW

References and endnotes

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