In addition to the promotion of electricity generation from wind turbines (WT) as stipulated in the Renewable Energy Sources Act (EEG), the designation of wind priority and wind suitability areas, is particularly important for future development. Unit-specific modeling with WiSTl aims to determine an optimal configuration for wind farms in these designated areas. Three components must be considered in this process. First, the specific shape of the area affects the potential power density. Second, the power density varies with the type of turbine used, and third, the power generation depends on the wind supply at the particular site.
Model – Wind Farm Configuration
For the three influencing parameters mentioned above, input data such as published or requested geodata (partly as shapefile or as PDF file) of the regional planning associations on the designated WT areas are used, as well as specific turbine characteristics and site-specific total load hours.
The configuration modeling explicitly considers the specific rotor diameter of the chosen wind turbine type, determining the necessary distance between the turbines. The wind farms are aligned according to the main wind direction (assumption: southwest) and constructed in the typical elliptical shape, as shown in Figure 1 on the left. Based on variable parameters, the grid of the imaginary wind farm is shifted (translation) or rotated up to -30° and +30° (rotation) (see Figure 1 center). The potential yield is used to evaluate the turbine configuration. This is calculated using  and the characteristic curves of a wind turbine. With the sum of the total load hours per configuration, the best configuration of the wind farm is determined (see figure 1 right).
The choice of parameters influences the quality as well as the duration of the modeling. Several computation runs were performed on a test data set with different parameter configurations to determine the best configuration with an acceptable computation time for the entire federal territory. The step size of the translation in x- and y-direction, as well as that of the rotation, were varied. A choice of small steps for the translation and large ones for the rotation, and vice versa, provided good results with a favorable runtime. Therefore, a step size for the rotation of 2 ° and a translation of 10 % of the ellipse semi-axes were used for this application.
In the following use case, the energy density is determined on a county level throughout Germany using different turbine types and currently designated (non-existing) wind priority and wind suitability areas. The modeling is based on three turbine types. An Enercon E-115 (power 3 MW, rotor diameter 115.7 m, hub height from 92 m to 149 m) is used for low wind sites. For inland sites, an Enercon E-101 (power 3 MW, rotor diameter 101 m, hub height 99 to 149 m) and for high wind sites, an Enercon E-82 E3 (power 3 MW, rotor diameter 82 m, hub height from 78 to 138 m) are used. Hub heights for the calculations are set at 100 m, 120 m, and 140 m. The results of modeling each turbine type are shown in Figure 2 in the first three maps as energy density at the county level. Regions with high wind generation have high energy density. The fourth column selected a site-specific turbine type based on local wind conditions. Here, the highest energy density of over 200 GWh/km² is now shown in Schleswig-Holstein. The low values below 70 GWh/km² in Saxony-Anhalt can be compensated by building even higher WTs. Additional height restrictions like “10-h” reduce the total load hours and thus the energy density significantly.
 Deutscher Wetterdienst: Digitale Weibulldaten der Windgeschwindigkeit für gesamt Deutschland im 200-m-Raster. Offenbach 2012