A collaboration between the School of Engineering, University spin-out ENIAN
and the Data Lab will revolutionise the way renewables projects predict grid
The Innovate UK grant worth half a million pounds will develop a
cost-of-interaction prediction algorithm to allow wind and solar developers to
assess the cost of grid connection at an early stage and avoid planning
Cutting-edge big data and machine learning will leverage expertise in
power-flow models and geographical information systems to create certainty in
renewable projects and drive the transition to net-zero.
"We've done a lot of research on what causes commercial solar and wind power
plants to fail", said Phillip Bruner, chief executive of ENIAN.
The algorithmwill help project managers to estimate previously-unexpected grid connection costs.
"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", said Daniel Friedrich from the University of Edinburgh
School of Engineering.