The impact of forested landscapes on wind parks

Forests are a predominant landscape in northern regions. Although they pose challenging conditions due to increased turbulence they produce in the wind, wind parks continue to be built in these areas due to various reasons. During the assessment of the wind potential of a site, the quality of the wind assessment is restricted by the complexity of the computational tools that are employed, which are often built under the principle of producing fast results at the price of accuracy in the simulation.

Since the effects of the forest, the terrain and the atmospheric stratification are inherently non-linear, a comprehensive approach with a more sophisticated tool is necessary to evaluate the integrated impact of these features on the production and performance of the turbines in a park. Therefore, this project proposes to assimilate different elements that shape wind flow over forested areas and evaluate their impact on wind power generation. This will permit to model with high accuracy the flow over these regions and to evaluate how variations in the forest such as leaf density, tree height and distribution, or changes in terrain elevation affect how individual turbines perform and generate power during the atmospheric changes along the day and during different seasons.

Main objective: to evaluate the aggregated effects on production and wake interaction of a forested landscape along the diurnal cycle
To attain this goal, we propose to fulfill these specific objectives:

I. To present a methodology suitable for the evaluation the wind characteristics above a
realistically forested and complex terrain during idealized diurnal cycles, based on LES with OpenFOAM.

II. To evaluate the effects of (I) on wind turbine wakes. For this, the expansion coefficients of wakes as well as their recovery rates will be calculated for the different setups.

III. Compare the wake simulations using the LES-methodology to wakes using RANS to establish the degree to which RANS approximations decrease the accuracy of the results.