Wind flow modelling is key to turbine planning

Since the Feed-in Tariffs (FiTs) were launched in April 2010, smaller scale wind projects have surged in popularity. In the six months from April to September 2012, more than 900 projects were registered with Ofgem, taking the total number of installations past the 3,300 mark. Many of those projects were added by farmers creating an extra income stream, either by investing in turbines or leasing their land to developers.

In October 2012, however, the returns farmers could hope for were hit when the Department for Energy and Climate Change (DECC) revised downwards the price for each kilowatt hour produced.

It means that now, more than ever, it is critical turbines are sited optimally to ensure returns aren’t eroded further and even moving a site a few metres can have a huge impact on yield. If an incentive was needed, a 0.2m/sec increase in average wind speed can produce an extra 100,000kWh/year of generation for a large on-farm turbine.

Wind velocity varies greatly across farmland. Hills, vegetation and farm buildings can affect the flow of air, which can reduce wind speed when compared to open areas without large obstacles.

Met masts (temporary towers of varying height, which are used to record wind speeds at a set location) have been the traditional method of collecting wind speed data, but they have drawbacks: they’re costly, can require planning consent and delay the development of a project while they accrue data.

Also, of course, they only measure wind speed while they’re operating, which, in small-scale scenarios, is usually one to three years. As turbines generate revenue all year round and wind speeds fluctuate each month, gaining an insight of inter-annual wind speeds is an important step towards making more accurate calculations of financial return.

There are alternative information sources to physical met masts. These include standardised national datasets such as numerical objective analysis of boundary layer (NOABL), which provides generic wind speed information across the United Kingdom through to more complex desktop models that simulate wind flow over particular fields or potential sites.

As they are computer-based, these methods provide a cost-effective balance between the need for detailed localised wind yield estimates, without the need for time-consuming on-site measurements.

A powerful desktop tool for modelling wind flow for small to medium-sized wind developments is Flowstar, which was developed by Cambridge Environmental Research Consultants (CERC) and is now used by ADAS and the Met Office Rural Environment Team to investigate potential wind resources.

The tool allows the wind characteristics at numerous potential sites to be analysed, which can help to identify the most efficient site on a farm. The driving data for Flowstar is five years of meteorological data, which is obtained from powerful Met Office computer models. Flowstar also takes into account the effect of altitude, local vegetation, buildings and the turbine height, all of which can affect wind and the ultimate return on investment.

As these are computer-based tools they can dramatically speed up the feasibility phase and ensure the most appropriate localised site is chosen. Ultimately they mean farmers can maximise the financial advantage of renewable energy sooner.

By combining historical meteorological data with site-specific analysis, farmers can obtain accurate wind speed data and ensure they locate wind turbines on sites with most financial reward.


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