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DNV GL earns highest score in E.ON’s wind flow modelling ‘blind’ test

Leading global energy advisory and testing authority demonstrates knack for atmospheric flow characteristics and accuracy in wind power computer modelling.

DNV GL (uniting Garrad Hassan, KEMA, DNV, GL Renewables Certification) 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 techniques to boost accuracy further.
E.ON’s blind test comprised 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,” said Matthew Meyers, head of wind yield assessment at E.ON. “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.”
According to Jean-François Corbett, head of computational fluid dynamics (wind) at DNV GL, the company has analysed hundreds of wind farm sites to date.

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Energy efficiency  •  Energy infrastructure  •  Wind power