From lidar scans to roughness maps for wind resource modeling in forested areas

Abstract

Abstract. Applying erroneous roughness lengths can have a large impact on the estimated performance of wind turbines, particularly in forested areas. In this study, a new method called the Objective Roughness Approach (ORA), which converts tree height maps created using airborne lidar scans to roughness maps suitable for wind modeling, is evaluated via crosspredictions between different anemometers at a complex forested site with seven tall meteorological masts using the Wind Atlas Analysis and Application program (WAsP). The cross-predictions were made using ORA maps created at four spatial 5 resolutions and from four freely available roughness maps based on land-use classifications. The validation showed that the use of ORA maps resulted in a closer agreement with observational data for all investigated resolutions compared to the land-use maps. Further, when using the ORA maps, the risk of making large errors (> 25 %) in predicted power density was reduced by 40-50 % compared to satellite based products with the same resolution. The results could be further improved for highresolution ORA maps by adding the displacement height. The improvement when using the ORA maps came down to two 10 factors, first they had a higher roughness length for forests, which was confirmed to by increasing the forest roughness value of the land-use based maps to the value of the ORA map, and second, due to the higher resolution of the ORA data, since the ORA maps with the highest resolution had the largest reduction in mean absolute errors.

Socio-technical constraints in German wind power planning: An example of the failed interdisciplinary challenge for academia

“…For the development of the socio-technical wind atlas, only constraints that are mentioned in multiple articles (>1) will be included. Forests are not viewed as a general constraint for wind power development, as previous studies have indicated how to overcome this challenge (Enevoldsen, 2018;Floors, et al, 2018).…”