Turbulence Intensity from ICON: A study of the potential for Wind Energy Applications

In addition to wind speed, turbulence intensity (TI) is a key atmospheric variable in the wind energy sector, as it affects both mechanical loads on wind turbines and their power production. Enhanced turbulence levels can increase structural fatigue and wear, while also influencing electricity generation. Consequently, reliable measurements and forecasts of wind speed and TI are essential for technical planning, safe operation, and accurate power yield forecasting for grid integration.

The German Meteorological Service (Deutscher Wetterdienst, DWD) runs the ICOsahedral Nonhydrostatic (ICON) model operationally as a numerical weather prediction model with horizontal grid size resolution of 2.1 km. This model provides wind data and subgrid-scale turbulent kinetic energy (TKE) using the TURBDIFF turbulence parameterization scheme. In parallel, Doppler Lidar (DL) systems are deployed at DWD's Lindenberg Meteorological Observatory to measure wind and turbulence profiles, including TKE, within the lowest 600 m of the atmospheric boundary layer. TI can be derived from both model output and observations by combining wind speed and TKE, enabling an evaluation of ICON with respect to wind-energy-relevant parameters.

In the presented study the model results for wind speed and TI from ICON simulations are compared with DL measurements over a five-day period with a typical summertime convective boundary layer evolution during daytime, while low level jets (LLJ) were observed during nighttime. Although the comparisons show reasonable overall agreement, it also becomes clear that uncertainties in both variables vary depending on atmospheric stratification.

In addition, the results of theoretical investigations into the potential benefits of using ICON forecasts of wind speed and ambient TI as inputs for wind energy power forecasts are presented. For this purpose, a performance model with a single turbine was used, which was driven with measured and simulated wind speed and TI in order to estimate the power output. The respective power outputs were compared with each other and the results suggest that incorporating TI information from ICON into wind power modelling can be advantageous, particularly under convective boundary-layer conditions. However, under stable stratification, the impact of simulated TI appears to be less significant, as uncertainties in LLJ forecasts can outweigh the effect of TI on electricity generation.

Although higher-resolution atmospheric models may better resolve ambient turbulence at rotor scales, their operational applicability is often limited by computational costs and data availability. This study therefore focuses on assessing whether freely available, operational ICON turbulence forecasts, which are available continuously and spatially consistently across Germany, can provide added value for wind energy applications under realistic practical constraints. The studies are limited to the investigation of ambient turbulence. Wake effects and turbine-turbine interactions, which will additionally occur in wind farms with more than one turbine, are not taken into account.