UC Berkeley

Wind energy is the leading renewable technology in the U.S., generating over 10% of utility-scale electricity in recent years. Rapid growth in wind energy installations has made modeling and prediction of atmospheric boundary layer (ABL) wind speeds and the associated turbulence critical for wind turbine siting, resource assessment, and operational power forecasting. A number of modeling challenges currently exist, such as representing the impact of terrain on wind turbine wakes and capturing small-scale turbulence in stably-stratified conditions. Many low-fidelity wind turbine simulation methods fail to incorporate topography and struggle to account for dynamic flow behavior. In this dissertation, results are presented using high-fidelity large-eddy simulation (LES), which captures the dynamic and turbulent behavior of ABL winds, providing a framework to simulate a wide variety of turbulent atmospheric phenomena with a wind turbine parameterization to understand turbine-airflow interactions.

First, high-resolution simulations of the 2017 Perdigão field campaign in Portugal are conducted. The Perdigão site consists of two parallel ridges with a wind turbine located on top of one of the ridges. Both convective and stable atmospheric conditions are simulated to understand how the wind turbine wake behaves in complex terrain in two representative flow regimes. For the convective case study, flow recirculation in the lee of the ridge occurred, thus deflecting the wake upwards. For the stable case study, the wake deflected downwards following the terrain due to a mountain wave that occurred. The vertical behavior of the wind turbine wake can be detrimental to downwind turbines; however, this vertical behavior is not accounted for in current wind farm design wake models. These case studies demonstrate the dependence of the wind turbine wake behavior on terrain-induced flow phenomena, which, in-turn, depend on the thermal stratification of the atmosphere.

The stable case study from Perdigão is then studied in more depth to better understand both the ambient and wind turbine wake turbulence characteristics. Novel derived measurements of the turbulence dissipation rate are available from the field campaign, providing an opportunity to further examine the spatial structure of turbulence predicted by the model. Additionally, in this study, the dynamic reconstruction model (DRM) LES turbulence closure is used to better represent smaller-scale turbulence. The DRM closure more accurately predicts turbulence metrics, including the turbulence dissipation rate, most notably upwind of the major topographic features. After the flow passes over the first ridge, the differences between the DRM and a standard eddy-viscosity closure are small close to the surface, although the DRM closure does better predict the turbulence dissipation rate in the upper atmosphere in this region. Because the DRM closure is not a standard eddy-viscosity closure, negative turbulence dissipation rate or the backscatter of energy from smaller scales to larger scales is predicted; however, backscatter cannot be derived from Perdigão measurements due to the experimental setup and analysis methods used, thus leaving validation of this aspect for future work.

Next, a range of idealized stable boundary layer (SBL) conditions are modeled in support of the American Wake Experiment (AWAKEN) field campaign to address: (1) the effect of wind turbines on SBL development, and (2) the effect of intermittent turbulence on wind farm performance. In weak SBL conditions, turbulence is continuous and easier to simulate. With the intermittent turbulence that occurs in strongly stable conditions, only the DRM closure can resolve realistic turbulence. For all SBL conditions simulated, the wind farm significantly impacts wind speeds and thermal structure well downwind (greater than 30 rotor diameters or 2.4 km) of the farm. Wind speeds in the wakes are reduced, and the increased mixing as a result of the wakes weakens the stable stratification in the boundary layer.

Finally, simulations are performed of a real case study of intermittent turbulence observed during the AWAKEN field campaign. The intermittent turbulence event is determined to be driven by a nocturnal mesoscale convective system (MCS). The MCS results in a cold pool, which radiates outwards as a density current. This density current perturbs the SBL, thus inducing gravity waves. The structure of the simulated gravity waves is found to be especially sensitive to the parameterization of cloud and precipitation processes (microphysics). The gravity waves have very strong effects on the flow in the upper atmosphere; however, closer to the surface where there is additional ambient turbulence and turbulence generated by wakes, the effect of the waves is more nuanced. Notably, the waves induce local wind direction variation, which leads to fluctuations in the power output as various turbines within the farm are subjected to the wakes of nearby turbines.

The findings presented in this dissertation provide insight into wind farm performance in a broad range of atmospheric conditions by incorporating both terrain effects and thermal stratification. Specifically, these conditions include dynamic turbulent phenomena that current wind farm design tools are unable to capture. The advances in this dissertation related to high-resolution LES reveal novel and complex relationships between wind turbines and the atmosphere that can significantly improve wind farm power predictions at large.