4.5 Discussion
In this section, we discuss the link between scientific knowledge evolution and the technological trajectory of wind turbines (Section 4.5.1) and then derive the implications for the literature on environmental innovation (Section 4.5.2).
4.5.1 Linking scientific knowledge evolution and technological trajectory
Overall, our results point to a close connection between scientific knowledge evolution and the technological trajectory of wind turbines. On the one hand, the analysis of scientific knowledge follows previous studies based on technological knowledge to confirm the importance of knowledge diversity for environmental innovation (e.g. Dechezleprêtre et al., 2013; Ghisetti et al., 2015; Horbach et al., 2013; Nemet and Johnson, 2012). As shown in the critical path (see Figure 4.2), scientific knowledge accumulation followed a trend of increasing heterogeneity with more distant knowledge areas combined and a larger diversity of research foci over time. This path of knowledge accumulation is well aligned with the technological changes on wind turbine design derived from up-scaling22 (IEA, 2013; Wilson, 2012). The sequential shifts on the focus of scientific knowledge development reflect changes in operational dynamics and management methods mainly driven by increases in wind turbine size. Following up-scaling, simulation and calculation models (e.g. large eddy simulations, ABL and wake modelling) have evolved to capture the complexity of wind regimes, and improve the accuracy of performance forecasts and safety estimations (Quarton,
22 Upscaling refers to increases on the size of wind turbines and wind farms. The size of wind turbines has increased from an average of 75kW and 17m of rotor diameter in the 1980s to 2 MW and 100m in 2012 (EWEA, 2014). Wind farms have grown from an average nameplate capacity of 37 MW in the 1980s to 120 MW in 2012 (DOE, 2014), with offshore projects expected to reach capacities between 500-1000 MW (Sieros et al., 2012).
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1998; Sieros et al., 2012). In fact, changes in wind power production derived from up-scaling have made the need for reliable control methods even more pressing (Garcia-Sanz et al., 2011).
On the other hand, the path of scientific knowledge evolution has shed new light on the focus of technological change where non-physical parts appear to have a prominent role in the technological trajectory of wind turbines. Next to the traditional components of wind turbine architecture, scientific knowledge puts a strong weight on technologies related to data collection and modelling (see Figure 4.5). Data collection and modelling are important research fields for product and project development (before wind turbine development and deployment) as well as for ongoing operational forecasting (after installation).
Correspondingly, the analysis of hubs and authorities shows scientific knowledge around these topics to have recently gained weight – accounting for 30% in the network with cut year 2010 and 50% for the one with cut year 2015.
These results reinforce the idea that the focus of innovation changes in a sequential manner following a product’s life-cycle. Previous studies mapping the technological trajectory of wind turbines point to shifts on the focus of innovation between components and sub-components of the product architecture (e.g. Arvesen and Hertwich, 2012; Huenteler et al., 2015). Yet, our results suggest a further step where the focus of innovation surpasses the limits of product architecture to move to a ‘hidden structure’ of complementary management and forecasting technologies. At least two facts related to the wind power industry help to explain this path. First, the fact that wind power has achieved maturity as an energy source which enables further development of modelling and simulations due to the availability of historical data on power production. Second, the expansion of wind power in terms of wind farms size and offshore installations which creates the need to adapt previous developed technologies to these new settings.
79 Figure 4.5. Research topics stylized distribution following development stage and materiality
Indeed, the advancement of modelling and simulation technologies is considered one of the most promising areas for further improvement of wind turbine performance. The main reason is their capability to enhance operation monitoring by facilitating (preventive) maintenance activities and early detection of catastrophic failures thereby improving turbine availability, power output and cost performance (Sheng et al., 2013). At the same time, product development is also favoured as accurate and reliable monitoring offers valuable data to improve component design, equipment operation and control strategies (García Márquez et al., 2012; Hameed et al., 2010). An additional incentive to invest in modelling and simulation technologies comes from the much lower upfront costs involved in their development when compared to the cost of parts replacement. In the U.S., for instance, wind turbine condition monitoring systems have an average market price of 16.000 USD, compared to parts replacement costs which can go up to millions – e.g. 2.3 million USD for a rotor (Yang et al., 2012).
4.5.2 Environmental innovation and knowledge dynamics: implications for the literature
The fact that data collection and modelling have emerged only after large scale diffusion of wind turbines suggests a particular dynamics of environmental innovation. Based on natural resources forecasting, such as solar irradiance and wind regimes, environmental innovation suffers with limited historical data on performance of comparable product designs. For wind
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turbines, a dominant design was established in the mid-80s and from there until the 2010s technological evolution had been focused on product architecture, essentially on improving components’ design with the goal of reducing costs and improving power generation performance (Bergek and Jacobsson, 2003; Bolinger and Wiser, 2012; Braun et al., 2010).
From the 2010s, wind turbine technologies matured and the innovation focus shifted, at least partially, towards increasing performance through complementary, non-material parts, i.e., data gathering and simulation (Hameed et al., 2010; Yang et al., 2012). This seems to be specific of such an emergent technology as the reciprocal interdependence between modelling and changes in wind turbine design are dependent on their previous deployment to generate real data to feed modelling. Moreover, the need of simulation accuracy gained force mainly with the unfolding grid integration issues (Holttinen et al., 2011; IEA, 2014; Peihong et al., 2012). A possible explanation is that, as it is characteristic of environmental innovations, wind power was developed upon strong policy support. For countries with the largest wind power capacity, subsidies have guaranteed a minimum return on wind power investment which in some cases has weakened the link between power output and profitability (Hitaj et al., 2014; Mir-Artigues and del Río, 2014). Arguably, this might have worked to reduce market pressure to develop technologies dedicated to wind forecast and monitoring.
Two additional characteristics of environmental innovations in the energy sector may have contributed for this path of technological development in wind turbines: the strong link to external knowledge and the high complexity of deployment. Modelling techniques as well as data gathering mechanisms are dependent on breakthrough innovations from basic science and not necessarily directly related to wind power or the energy sector in general (Davidsson et al., 2014). This entails a further hurdle as basic science is concentrated in a few countries and the disconnection with the energy system hinders efficient policy support targeting.
These conditions add dependence of environmental innovation on external research areas, as well as of the importance of inter-sectoral knowledge spillovers. At the same time, wind turbines functioning needs to have harmonized interactions with a number of external systems (e.g. natural environment, grid standards, mix of energy sources, demand characteristics, market structure). This variety of interfaces creates exceptional challenges in performance forecasting as deployment takes place in highly diverse terrains, and is connected to largely diverse power systems.
Last but not least, an additional lesson from our study comes from a methodological standpoint. The focus on scientific publications had a crucial role in the results found here
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due to appropriability issues particular to wind turbines. Current modelling techniques to assess wind turbine behaviour and impact on the power system are proprietary and manufacturer specific and mostly restricted by wind turbine manufacturer’s non-disclosure agreements (Singh et al., 2011). Hence, most modelling techniques are not patented as it is common practice among wind turbines manufacturers to avoid releasing the information required to patent23. By contrast, the scientific publications analysed here have provided access to enough data to map the evolution of modelling and data gathering technologies related to wind turbines. Mainly two factors are important to explain the difference of contents between patents and scientific publications in this case. First, publications tend to reveal information before it is patented as the process of publishing is less costly and quicker than patenting (Hicks, 1995; Nelson, 2009). Second, since less than 15% of the authors of the articles analysed here were affiliated to a wind turbine manufacturer, the non-disclosure agreements from such companies may have had limited impact on this study. A main implication is that using scientific publications may entail a strong advantage compared to patents depending on the appropriability system around the technology studied. Thus, future research on knowledge dynamics could benefit from adapting data sources to different appropriability regimes, as well as from combining scientific publications and patents.