Addressing the Challenge

06 Jul 2016

Determining wind conditions in forests dominated by coniferous trees

In North America, the installation of wind turbines in rural areas has increased dramatically in recent years, especially in Canada, where the installed wind energy capacity has increased by an average of 23 percent annually over the past five years. Canada is covered by 42 percent forest, and more than 50 percent of those trees are of the coniferous type, so the challenge of determining wind conditions in forests becomes an important task for the wind industry. In other parts of North America, wind project developers are being pushed to locate wind projects in rural and forested areas, as a response to lack of space, and rising land prices for favorable locations. The ever increasing wind turbine hub height has rendered it possible to deploy wind turbines in forests. Besides acquiring the needed land at lower prices, wind projects in forests are expected to be installed further from larger residential areas, which reduce the risk of social opposition, due to less exposure of the negative aspects of wind turbines such as noise emissions and flicker effects. A range of scholars have thus revealed the great potential of deploying wind turbines in forests. 

The risks involved with wind project development in forested areas
Notwithstanding the promising developments, several researchers have examined the risks of estimating the wind resources in forested areas. This trend in risk studies has been proven to follow the development of installed capacity in a country, suggesting that more wind projects overall equals more wind projects in forests.1 The majority of risk studies focus on fatigue loads, turbulence intensities, and loss in energy production, all caused by the change in wind conditions above forests. In general wind literature, several researches have investigated the estimations of roughness lengths and displacement heights of different tree types, which has been suggested as one of the approaches to control the risk of unpredictable wind conditions above forests. This article presents the key results from a study focusing on finding a uniform approach for determining the roughness lengths and displacement height for forests dominated by coniferous trees. The equation below displays the function of the displacement height and the roughness length when estimating wind resources. 

(1) Where Umean is the mean wind speed at a certain height, z. u* is the friction velocity,  is the von Kármán constant (0.40), Z the height above ground level, d is the displacement height, and Z0 is the roughness length. Being aware of the average mean wind speed at different heights makes it possible to simulate the relationship between roughness length and displacement height, when having access to the full wind profile at a given location. 

Testing approaches to reduce the risk of poor estimations of wind conditions above forests
In this study the wind profiles were based on 12 meteorological measurement masts located in the middle of forests dominated by coniferous, with tree heights varying from 3 meters at one site to 28 meters at another. This variety in tree heights made it possible to verify the final output for usage in different locations with different tree heights.

A total of 24 configurations combining approaches for roughness length and displacement height were tested. A regression analysis was then used to reveal the configuration matching the actual conditions measured at the 12 meteorological masts. The combination of A+3 provided the best fit when testing the relationship between the wind profiles estimated with this approach, and the ones measured at the 12 measurement masts, with a median delta average on 5.5% for the wind speed at 100 meters above ground level. 

It can therefore be concluded that the combination of a roughness length of 0.3*(tree height-displacement height) combined with a displacement height of 2* tree height /3 is considered the best approach for determining wind speeds above forests dominated by coniferous trees following (1).      

Peter Enevoldsen is an Industrial PhD fellow at Siemens Wind Power A/S

Siemens Wind Power | http://www.siemens.com

1 Enevoldsen, 2016


Volume: 2016 July/August