| Popis: |
As part of the UK’s energy system transition to a low-carbon electricity supply, decentralised energy sources such as small and medium scale wind turbines have become increasingly relevant. Decentralised energy generation has a central role in a proposed societal pathway to deliver a low-carbon energy system transition. Given the vast onshore wind energy potential of Great Britain, small and medium scale wind turbines will be a key part in this transition. With the introduction of the Feed-in Tariff (FIT) in April 2010, small and medium scale wind turbine deployment was expected to increase towards the technical potential of the technology, estimated to be up to 400,000 turbines. However, only 6,000 wind turbines have been installed in Great Britain since April 2010, highlighting there is still significant potential for small and medium scale wind turbine deployment. To fulfil this potential, an understanding of the influencing factors on previous wind turbine adoptions is required. A key part of this analysis is an investigation of the wind resource assessment methodology prescribed in the FIT policy. The Microgeneration Certification Scheme (MCS) is designed to offer a low-cost and quick scoping tool for prospective wind turbine installations. Analysis carried out in this work shows that long-term mean near surface wind speed predictions from the MCS method have a mean percentage error of 2.36 %. Over the same sample of 124 sites across Great Britain, a Boundary Layer Scaling (BLS) method, developed in this work, using UK wind map data offered wind speed predictions with a mean percentage error of 1.43 %. While these errors appear small, they equate, in the most extreme cases, to a difference of over £500 in annual FIT payments for a single wind turbine. While the MCS method is mandated in the FIT accreditation process, there is a risk that the potential financial returns of an installation can be severely miscalculated. Using the more accurate wind speed predictions available from the BLS model, it is possible to understand the influence of available wind resource on wind turbine adoption patterns. Throughout this work, wind turbine adoptions in Great Britain from 1995 until 2015 at both local authority and statistical geography resolution were analysed. Using a regression model, it is shown that wind resource explains up to 34 % of the spatial variance in adoption patterns. A threshold wind speed of 4.5 ms−1, above which wind turbine deployment is likely, was found in the current adoption patterns. These results highlight that while wind resource is an important factor, it is not the sole factor which influences wind turbine deployment in Great Britain. Previous literature has identified a number of socio-economic factors that have influenced adopters of other microgeneration technologies. Using a regression model and additional variables, such as land availability and agricultural statistics, it is possible to understand the influence of these socio-economic factors on wind turbine adoption patterns. The Socio-Economic and Resource (SER) model developed in this work highlights that wind turbine adoptions are more likely to occur in rural areas where wind resource, availability of land and prevalence of agriculture are high. Wind adopters are more likely to be older, hold degree-level qualifications and live in a detached home. This regression model however, only accounts for up to 65 % of the spatial variance in adoption patterns. This is an improvement over using only the resource model, however, there are still additional factors which influence wind turbine adoption patterns. The additional factors examined in this research were the influence from changes to the subsidy level of the FIT and the potential visibility of neighbouring turbines on adoption patterns. The visibility of neighbouring microgeneration installations has been cited as a factor which raises awareness in adopters, a factor particularly prevalent to wind turbines, which are highly visible to close neighbours. The influence of these factors was examined using a peer effects model in areas of high installations. The model shows that reductions in the FIT subsidy level have severely affected deployment. A peer effect from visible neighbouring turbines can be seen in these clusters of installations, however, it is secondary to the level of FIT subsidy available. In some clusters, evidence for a slow diffusion of wind turbines between peers was observed. Overall, the model indicates that the subsidy level available from the FIT was more influential than the visual peer effects. However, it is anticipated that this peer effect, will increase as deployment increases. In conclusion, the research has found that adoptions of wind turbines in Great Britain are influenced by a number of factors, namely available wind resource, rurality of turbine location, income of individual adopters and the subsidy level available for energy generation. These findings indicate that the small and medium scale wind turbine market in Great Britain is approaching a critical stage in its adoption lifecycle. Additionally, the results were used to develop a number of potential deployment estimates to understand where future growth in the market may occur. To meet these potential deployment estimates, there needs to be higher levels of deployment in order to help reduce capital costs. To achieve this future deployment, the levels of subsidy available from the FIT need to be maintained, in addition to the introduction of a BLS methodology in the FIT policy to facilitate more accurate financial assessments. A reduction in capital costs and maintaining of FIT subsidies will increase the number of sites which are financially viable for wind turbine installation. Potential new sites must still have a sufficient long-term mean wind resource of 4.5 ms−1 or above to be economically viable, highlighting the need for the introduction of the more accurate BLS methodology. If these conditions occur, deployment of small and medium scale wind turbines can increase towards the technical potential and play a central role in the transition to a low-carbon electricity market in Great Britain. |
