Renewables software developer ENIAN has been awarded half a million pounds to develop a new cost predicting algorithm.
It’s hoped the “smart grant”, which is distributed by Innovate UK to “the best commercially viable innovative or disruptive ideas”, will accelerate the uptake of green energy.
ENIAN will collaborate with the University of Edinburgh’s School of Engineering and the Data Lab over 19 months to develop and test the cost-of-interconnection prediction algorithm (CIPA), with the aim to digitise, automate and enhance the way that project planners estimate the cost of connecting a new power plant to the nearest available grid.
The project will start in early December and will run until May 2022.
Currently grid connection costs are some of the most difficult to predict but make up a significant share of the total costs for generating new power.
However, as the need for the grid to adopt more renewable energy sources increases, so to does the requirement rapid, data-driven estimates to give project managers certainty.
© Supplied by ENIANPhillip Bruner, chief executive of ENIAN, said: “The highly variable but also significant costs of interconnection are some of the most critical to understand from an early stage.
“We’ve done a lot of research on what causes commercial solar and wind power plants to fail. It’s often the case that developers get caught off guard by grid constraints or runaway costs. Thanks to Innovate UK, with machine learning and open access data, we can unlock a new cost-saving capability for the UK energy sector that will help accelerate the path to net zero.”
Daniel Friedrich, from the University of Edinburgh’s School of Engineering, added: “We’re excited to continue the successful collaboration with ENIAN in this cutting-edge Innovate UK funded project which can make a real difference for the drive to net zero.
“This transition requires a massive increase in distributed renewable generation which needs to be fed into the grid and transported to the demand centres.
“We will use our expertise in network power flow models, geographic information-based systems and data-driven algorithms to streamline this process and to help unlock the full potential of the renewable energy sector in the UK.”
Gillian Docherty, chief executive at the Data Lab, said: “Never before has it been more pertinent for data-informed solutions to be brought to the energy market. With the ever-increasing need for energy supply as global populations rise, it is incredibly important that we scale our supplies in a clean and clever way, leveraging cutting edge data science to guide our approach.”