Enhanced modelling rapidly predicts off-shore wind energy generation

Wind park

With access to HPC Midlands’ vast modelling capacity E.ON can create accurate and rapid predictions of off-shore wind energy generation and determine where to invest.

Challenge

Off-shore wind farms are recognised as having enormous potential to reduce reliance on fossil fuels and contribute significantly to UK renewable energy generation targets. However, they demand huge levels of investment and, if that is to be justified, there needs to be greater certainty about financial return and environmental impact.

Loughborough University has considerable expertise in wind energy generation and computational fluid dynamics (CFD) modelling. This specialist expertise and access to HPC Midlands infrastructure has supported E.ON to address the difficult to predict wind flows around large numbers of wind turbines.

How wakes – or tails of disturbed air between one turbine and the next – interact and affect power output from other turbines has historically been produced under and over predictions that need to be improved.

Solution

A team from Loughborough’s School of Civil and Building Engineering, led by Professor Malcolm Cook, is working with E.ON to use CFD to model air flows through large off-shore wind farms and to develop a simple, press-of-a-button method for predicting their energy yield.

The partnership has been able to address the key questions via CFD simulations of large domains, rotor modelling, and validation of modelling techniques against field data in order to develop a simplified, fast and reliable method of calculating the power production of a wind farm.

CFD simulation of large domains requires the complex computation of vast quantities of data – something that can only be achieved using a combination of HPC hardware and specialist software such as that supplied by Ansys for this project.

Impact

Early evidence indicates that the new models being developed could increase the accuracy of the energy yield prediction. Work on the simplified prediction model which has the potential to provide E.ON with a tremendous competitive edge as it will enable them to gather commercially important evidence more rapidly than their competitors.

“We have benefitted from the excellent academic expertise at Loughborough University and we look forward to extending this collaboration,” said Lionel Mazzella, Plant Modelling Team Leader at E.ON.