Estimating Wake Effects Between Wind Plants for Capacity Expansion Modeling

In August 2022, the US Congress passed the Inflation Reduction Act (IRA). This law intended to accelerate U.S. decarbonization, clean energy manufacturing, and deployment of new power and end-use technologies. NREL has examined possible scenarios for growth by 2050 resulting from the IRA and other emissions reduction drivers (Gagnon et al. 2022). Here we explore the possible wind turbine wake effects from two of these scenarios. As wind farms extract momentum from the atmosphere, they may generate “wake effects” (decreases in downwind wind speeds), which undermine downwind wind turbines’ power generation. Depending on atmospheric conditions, wakes have been observed to extend 50 km downwind or further, suggesting that wake effects between wind farms should be considered in power systems modeling for lower carbon futures . These between-farm wake effects are particularly important when wind farms are clustered near transmission lines or in regions of considerable wind resources.

We focus on a domain in the Southern Great Plains due to the extensive deployment in this region rising from the strong and consistent wind resource. The two scenarios for wind development assessed both have increased wind deployment in the region, but span a range of wind turbine densities to inform how wake effects might change with increasing wind. The lower deployment scenario deploys 9,175 turbines or ~ 50 GW (Figure 1b), while the high-wind scenario deploys 17,504 turbines or ~ 95 GW in our 2-km horizontal resolution WRF modeling domain (Figure 1e). Current (August 2023) deployment in the domain is ~ 23 GW (Hoen et al., 2023). All turbines are defined to be 5.44 MW, with a hub-height of 120 m and a rotor diameter of 175 m, with a power curve (Figure 1a) and a thrust coefficient curve (Figure 1d) that taper off at fast winds for reduced noise operation for a specific power of 226 W m-2 per turbine. We use calendar year 2017 as a proxy for a standard year because the 2017 average wind resource is typical of the mean wind resource over 2009-2020. We carry out three sets of WRF simulations: one with no wind farms, one “high” case, and one “mid” case using the Fitch wind farm parameterization (Fitch et al., 2012) following best practices for mesoscale wake modeling (Tomaszewski and Lundquist, 2020).

The resulting wakes depend on inflow wind speed and atmospheric stability. An example of the simulated wake effects for the mid-case is shown in Figure 1c, and for the higher deployment scenario in Figure 1f. The presentation will summarize the wake effects on wind speed, overall power production, and local changes in temperature and moisture in the vicinity of the turbines.

References

Fitch, A. C., J. B. Olson, J. K. Lundquist, J. Dudhia, A. K. Gupta, J. Michalakes, and I. Barstad, 2012: Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model. Monthly Weather Review, 140, 3017–3038, https://doi.org/10.1175/MWR-D-11-00352.1.

Gagnon, Pieter, Maxwell Brown, Dan Steinberg, Patrick Brown, Sarah Awara, Vincent Carag, Stuart Cohen, Wesley Cole, Jonathan Ho, Sarah Inskeep, Nate Lee, Trieu Mai, Matthew Mowers, Caitlin Murphy, and Brian Sergi. 2022. 2022 Standard Scenarios Report: A U.S. Electricity Sector Outlook. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A40-84327. https://www.nrel.gov/docs/fy23osti/84327.pdf.

Hoen, B.D., Diffendorfer, J.E., Rand, J.T., Kramer, L.A., Garrity, C.P., and Hunt, H.E., 2018, United States Wind Turbine Database v6.0 (May 31, 2023): U.S. Geological Survey, American Clean Power Association, and Lawrence Berkeley National Laboratory data release, https://doi.org/10.5066/F7TX3DN0.

Tomaszewski, J. M. and Lundquist, J. K.: Simulated wind farm wake sensitivity to configuration choices in the Weather Research and Forecasting model version 3.8.1, Geosci. Model Dev., 13, 2645–2662, https://doi.org/10.5194/gmd-13-2645-2020, 2020.