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

The Weather Research and Forecasting (WRF) model has been extensively used for wind energy modeling applications and resource assessment. Yet as it stands, despite a plethora of physics schemes available to the user, only one scheme is currently available to parameterize the effects of wind turbines (WT) on the atmospheric flow, and quantify electricity generation from the wind resource. Here, we introduce a high resolution simulation using two different WT parameterizations; (1) the Fitch parameterization that is included in WRF releases, and (2) the recently developed Explicit Wake Parameterization (EWP) scheme. The key difference between these two WT parameterizations is that while the Fitch scheme applies a (local) drag force and additional turbulent kinetic energy (TKE) to all model grid cells that intersect the turbine rotor, the EWP scheme parameterizes the unresolved wake expansion within the grid-cell and applies a grid-cell averaged drag force and additional TKE is introduced solely from the enhanced vertical shear due to the WT wake(s).

The simulation reported herein is for a domain centered on the highest density of current WT deployments in the contiguous US. In order to directly compare the two WT schemes, three identical inner domains (resolved at 4 km) (no WT, WT Fitch, WT EWP) are within the same coarse domain (12 km) run with no WT parameterization operating. Explicit USGS 2014 wind turbine locations and rated capacity are used to represent wind turbines in the model. The 20 most-frequently used wind turbine types were parameterized explicitly using the manufacturers’ power and thrust curves while the remainder used an average power curve scaled to the rated capacity for each turbine.

WRF v3.8.1 is used to simulate the entire calendar year of 2008 for this configuration. The analyses presented here are seasonal unless otherwise stated. In order to maintain seasonal integrity, the simulation starts with a month of spin up in November 2007, and simulates the period 1 Dec 2007 – 1 Dec 2008. The year 2008 was selected as it was a good representative year for near neutral climate conditions. Indeed, after a moderate La-Nina in the first half of the year, ENSO-neutral conditions developed by July-August. In the continental US, air temperature was only 0.1 C above the twentieth-century (1901 – 2000) mean.

The outer model domain resolution is 12 km with 150 × 150 cells, with a nested 4 km domain of 247 × 205 cells. This nested domain resolution captures the wind climate for Iowa and surrounding Midwest states at high resolution, and is also appropriate for operation of the WT parameterizations. There are 41 vertical levels up to a model top of 50 hPa, 18 of these levels are in the first 1 km of the atmosphere, to suitably capture the planetary boundary layer (PBL). The lateral boundary conditions are updated 6 hourly, with ERA-Interim data, and Real Time Global (RTG) SST analyses provide initial conditions. The key physics settings include the Eta microphysics scheme, rapid radiative transfer scheme for longwave radiation, and Dudhia for shortwave, Revised Monin-Obhukov similarity scheme for the surface layer physics, the Noah land surface model, Mellor-Yamada-Nakanishi-Niino PBL scheme, and the Kain-Fritsch cumulus parameterization. Convection is explicitly resolved in the 4 km domain.

Pairwise analyses are applied to diagnose the downstream effects and climate impact of the WT. In line with previous studies by the authors, climate impacts are confined to the summer months and are generally minimal. It is shown that on average, use of the EWP nested results in faster recovery of full WT array wakes. This in turn leads to smaller climate impacts and reduced array-array interactions, which at a system-wide scale lead to summertime capacity factors (i.e. the electrical power produced relative to nameplate installed capacity) that are 2-6% higher than those from the more commonly applied ‘Fitch’ parameterization.

Vertical profiles of WT wakes from both WT parameterizations downwind of the WT installations are also analyzed. It has been suggested that the expansion of the WT installed capacity may result in ‘wind theft’, that is, downstream WT may experience disturbed atmospheric flow, thereby leading to reduced system-wide efficiency. By analyzing the downstream vertical profiles of the wakes, we show that wind theft could potentially be mitigated by placing downstream WT at a higher hub height than the upwind WT, such that the WT operate in undisturbed atmospheric flow, thus improving system-wide efficiency of the wind resource.