Sensitivity of Wind Turbine Array Downstream Effects to the Parameterization Used in WRF

Abstract

The Weather Research and Forecasting (WRF) Model has been extensively used for wind energy applications, and current releases include a scheme that can be applied to examine the effects of wind turbine arrays on the atmospheric flow and electricity generation from wind turbines. Herein we present a high-resolution simulation using two different wind farm parameterizations: 1) the “Fitch” parameterization that is included in WRF releases and 2) the recently developed Explicit Wake Parameterization (EWP) scheme. We compare the schemes using a single yearlong simulation for a domain centered on the highest density of current turbine deployments in the contiguous United States (Iowa). Pairwise analyses are applied to diagnose the downstream wake effects and impact of wind turbine arrays on near-surface climate conditions. On average, use of the EWP scheme results in small-magnitude wake effects within wind farm arrays and faster recovery of full WT array wakes. This in turn leads to smaller impacts on near-surface climate variables and reduced array–array interactions, which at a systemwide scale lead to summertime capacity factors (i.e., the electrical power produced relative to nameplate installed capacity) that are 2%–3% higher than those from the more commonly applied Fitch parameterization. It is currently not possible to make recommendations with regard to which wind farm parameterization exhibits higher fidelity or to draw inferences with regard to whether the relative performance may vary with prevailing climate conditions and/or wind turbine deployment configuration. However, the sensitivities documented herein to the wind farm parameterization are of sufficient magnitude to potentially influence wind turbine array siting decisions. Thus, our research findings imply high value in undertaking combined long-term high-fidelity observational studies in support of model validation and verification.

Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains

“…Lower mean wind speeds are simulated for all heights during the summer months. This is consistent with the seasonality observed capacity factors for wind farms within Iowa that also exhibit a summer minimum [54]. Monthly mean wind speed profiles also imply lower shear during summer (Figure 3b).…”

“…In the summer months when conditions are most unstable (convective) and wind speeds are lowest [46], the eastern sites tend to experience high shear more frequently. The regional flow is generally dominated by southeasterly and southwesterly flow in winter and spring and dominated by southwesterly flow in summer [54]. The LLJ is more frequently associated with north/south wind directions and is more common in the northeast of the domain in spring [31] and may account for the high frequency of extreme shear in March and at night.…”

Review of Mesoscale Wind-Farm Parametrizations and Their Applications

“…Besides the different magnitudes, the spatial distribution of TKE also differs between the WFPs. The Fitch approach shows high TKE values within the farm at all levels, while the EWP approach shows a reduction below the rotor due to the extraction of kinetic energy and an increase downwind and above (Volker et al 2015;Shepherd et al 2020). Higher TKE values at the upper parts and above the rotor agree better with lidar observations (Lee and Lundquist 2017b) and LES results (Porté-Agel et al 2020).…”

“…3.2.2). The recovery length has been found to be shorter for the EWP parametrization than for the Fitch parametrization over land (Pryor et al 2018a(Pryor et al , 2020Shepherd et al 2020), whereas over water Volker et al (2015) found the opposite in ideal simulations. An analysis of in situ aircraft measurements indicates that wake recoveries are for all stability conditions mostly exponential but can also be approximately linear (Cañadillas et al 2020).…”

“…The EWP approach shows a more linear reduction within the farm, while the Fitch approach shows a more exponential decrease. In addition, the maximum wind-speed deficit is simulated differently in the EWP and the Fitch parametrization with the EWP parametrization tending to show smaller wake effects (Volker et al 2015;Pryor et al 2018a;Shepherd et al 2020;Pryor et al 2020; not visible in Fig. 12).…”