Element Energy

Feed-in Tariff

Element Energy was one of the main architects of the Feed–in Tariff. The goal of the FiT is to drive a step change in the growth of the small-scale renewables sector, while minimising the impact on electricity bills.  We believe that goal has been achieved, and our work has been well received both by the renewables industry and potential investors. Since April 2010, the Feed-in Tariff has caused a huge surge in interest and demand for small-scale renewables. New business models are evolving to take advantage of this growing market.

During 2009, we worked closely with DECC advising them on the high-level design of the Feed-in Tariff for small-scale renewables. Our advice was based on our knowledge of best practice across Europe. We later developed the complex predictive model that was used by DECC to determine the optimal tariffs payable under the Feed-in Tariff.

Having played a pivotal  role in the development of policy in this area for DECC and its predecessors BERR and the DTI, Element Energy is uniquely placed to offer business planning and modelling for companies looking to exploit the opportunities offered by the Feed-in Tariff.

As an example, we were engaged to advise an FTSE100 energy supplier on the effect of the growth of small-scale renewables on grid balancing. Initially, we quantified the impact using short-time-step modelling, and from this we constructed electricity export and import profiles for a range of small systems and building types. This enabled us to assess the changes in output across different times of day and seasons as a function of weather. Combining this with our FiT model of renewables deployment between 2010 and 2020, we have enabled the client to anticipate the effects of FiT-supported electricity on its balancing operation, and make better informed investment decisions to mitigate these effects.

We have also provided business development consultancy to suppliers of small-scale electricity technologies, using our knowledge of deployment rates and predicted cost reductions to assess likely sales and revenue growth.