Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

Abstract

Abstract. Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of windturbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

Simulated wind farm wake sensitivity to configuration choices in the Weather Research and Forecasting model version 3.8.1

“…During this period, a lack of major synoptic events allowed strong nightly nocturnal low-level jets to occur (Vanderwende et al, 2015). Lee and Lundquist (2017a) found that WRF performed well in capturing the timing, intensity, and position of these low-level jets. Furthermore, the wind turbines operated without curtailment, and the instruments were online collecting data, making this period ideal for a model sensitivity and performance evaluation.…”

“…Past studies have begun evaluating this sensitivity in the WRF WFP. Lee and Lundquist (2017a) note that a ∼12-m vertical resolution is necessary to reproduce observed power production, while Mangara et al (2019) find that the wake dynamics simulated by the WFP are more sensitive to horizontal resolution than vertical resolution. Xia et al (2019) find differences in the WFP solution of surface temperature changes depending on the inclusion of the turbine-generated TKE term.…”

“…Here we expand upon and synthesize the work of Lee and Lundquist (2017a) generally flat terrain and a vegetated surface of corn and soybeans and featured an operating wind farm northeast of the city of Ames, Iowa. In the 2013 CWEX campaign, seven surface flux stations, a radiometer, three profiling lidars, and a scanning lidar were deployed within and around this wind farm to explore the interaction of multiple wakes in a range of atmospheric stability conditions (Vanderwende et al, 2015;Bodini et al, 2017;Sanchez Gomez and Lundquist, 2019).…”

Micrometeorological impacts of offshore wind farms as seen in observations and simulations

“…We use the wind farm parameterization (WFP) of Fitch et al (2012) to simulate the interaction between atmosphere and wind farms. The WFP extracts kinetic energy from the mean flow and acts as a source of turbulence, depending on the thrust and power coefficients of the installed turbines in the model (Fitch et al 2012, Jiménez et al 2015, Lee and Lundquist 2017. Therefore, the WFP interacts with the planetary boundary layer scheme of Nakanishi and Niino (2004) that we use in all three domains.…”

One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data

“…The vertical direction is discretized with 100 grid cells in all the domains. The grid points are clustered near the surface and in the rotor region where the resolution is between 7 and 12 m. Lee et al 21 showed that a resolution of 12 m near the surface is sufficient to obtain good agreement with the observed wind speed, wind direction, and power production. The turbines are modeled as a momentum sink (drag) and turbulent kinetic energy source as in Fitch et al 2 The drag generated by the turbine in a grid cell is given by…”