DNV GL comes out on top in E.ON's wind flow modelling blind test
DNV GL (uniting Garrad Hassan, KEMA, DNV, GL Renewables Certification), a leading global energy advisory and testing authority, today announced that it has achieved the highest score in a wind flow modelling blind test organised by E.ON, a leading power and gas company.
The blind test challenged six participants, including some of the most reputable consultancies in the global wind industry, to accurately predict the wind regime at eight wind farm sites.
The most attractive wind farm sites are often found at locations where the wind conditions are difficult to quantify. Examples include sites affected by atmospheric stability, exposed and hilly sites as well as wind farms in or near forests. An ongoing challenge for the wind industry is to accurately predict the variation of wind speed across such “complex” sites in order to determine a credible estimate for the energy output of the project in question. If these predictions can be made more accurately, even by a relatively small amount, it leads to a direct improvement in the financing conditions available to project developers - thereby reducing the cost of electricity delivered to the grid and improving returns.
DNV GL has been developing improved methods for predicting wind flow on complex sites for more than two decades and in recent years has developed cutting edge Computation Fluid Dynamics (CFD) techniques to boost accuracy further.
E.ON’s blind test comprised of wind flow modelling for 8 wind farm sites located across four countries: USA, UK, Spain and Sweden. Each location posed varying geographical and climatic challenges which add complexity to the modelling. This included atmospheric stability, forestry and complex terrains. Participants were asked to make their best predictions of the wind conditions at locations corresponding to existing measurement masts at each site. The predictions were then compared to the actual measurements; the smaller the difference, the higher the ranking in the blind test.
“Enabling greater accuracy in wind speed predictions helps to reduce financial risk. We’d like to congratulate DNV GL on achieving the best results across the sites relative to other models tested. This outcome is as much about knowledge of atmospheric flow characteristics as accuracy in computer modelling,” says Matthew Meyers, Head of Wind Yield Assessment at E.ON.
The DNV GL CFD team has analysed hundreds of wind farm sites to date. Jean-François Corbett, Head of CFD (wind) at DNV GL comments: “While our wind flow models are advanced, it is the expertise of our team which sets us apart. Anyone can produce a CFD-predicted wind speed; but in our view, modelled physics must be combined with qualified engineering judgment in order to produce results that are meaningful. This requires years of experience and learning from real data and real-world sites.”