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

• Published in: Journal of applied meteorology and climatology Vol. 59; no. 3; pp. 333 - 362
• Main Authors: Shepherd, T. J., Barthelmie, R. J., Pryor, S. C.
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 01.03.2020
• Subjects: Accuracy; Alternative energy sources; Arrays; Climate; Climate change; Climatic conditions; Computer simulation; Deployment; Downstream effects; Electric power; Electricity; Feasibility studies; Laboratories; Measurement techniques; Observational studies; Parameterization; Sensitivity; Simulation; Turbine engines; Turbines; Velocity; Wakes; Weather forecasting; Wind effects; Wind farms; Wind power; Wind power generation; Wind turbines; United States > US; Iowa
• DOI: 10.1175/JAMC-D-19-0135.1
• ISSN: 1558-8424

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.