Downstream effects from contemporary wind turbine deployments

Abstract

The Long distance wake behind Horns Rev I studied using large eddy simulations and a wind turbine parameterization in WRF O Eriksson, M Baltscheffsky, S-P Breton et al. Resulting analyses indicate that for both WT parameterizations impacts on temperature, specific humidity, precipitation, sensible and latent heat fluxes from current wind turbine deployments are statistically significant only in summer, are of very small magnitude, and are highly localized. It is also shown that use of the relatively recently developed new explicit wake parameterization (EWP) results in faster recovery of full array wakes. This in turn leads to smaller climate impacts and reduced array-array interactions, which at a systemwide scale lead to higher summertime capacity factors (2-6% higher) than those from the more commonly applied 'Fitch' parameterization. Our research implies that further expansion of wind turbine deployments can likely be realized without causing substantial downstream impacts on weather and climate, or array-array interactions of a magnitude that would yield substantial decreases in capacity factors.

“Wind Theft” from Onshore Wind Turbine Arrays: Sensitivity to Wind Farm Parameterization and Resolution

“…Consistent with the summary above that compares the two wind farm parameterizations, prior research has also revealed important differences in wake profiles, downstream recovery and also gross capacity factors (CF) from the Fitch and EWP schemes (Pryor et al 2019;Volker et al 2015). For example, a numerical experiment describing the wind speed field inside and downwind of very large wind farms (10 5 km 2 ) with a WT spacing of 10.5D in three regions with distinct wind speed and roughness conditions showed wind speeds recovered to 2% above the maximum velocity deficit faster in EWP than Fitch (Volker et al 2017).…”

“…The mean and 90th-percentile wind speeds are derived from each of the raw time series from d02_noWT for the three different resolutions. For visualization, the resulting monthly wind speed time series U from all grid cells in the subdomain centered over the Pomeroy cluster are also fitted to Weibull distributions using maximum likelihood methods to derive the distribution parameters (A and k, and confidence intervals thereon) (Pryor et al 2004;Wilks 2011):…”

“…These WTs are allocated to the appropriate WRF grid cell ( Fig. 1) and are characterized using WT specific HH, D, power, and thrust curves as in our previous research (Pryor et al 2018a) for use with the two wind farm parameterizations. In all simulations, advection of TKE is enabled as in research conducted by Fitch (Fitch 2015;Fitch et al 2012Fitch et al , 2013 to allow for effective downstream propagation of the TKE produced by the wind turbine arrays.…”

“…In the first inner domain (d02_noWT) no WT parameterization is employed, in the second the real WT locations and specifications are used with the Fitch parameterization (d02_Fitch), and in the third the real WT locations and specifications are used with the EWP parameterization (d02_EWP). The decision to run three ''simultaneous'' inner domains was made in an attempt to avoid differences in independent simulations such as those presented in Pryor et al (2018b) that may arise from numerical sources (see discussion below). The number ordering of the inner domains was identical for all simulations presented herein and was d02_noWT, d02_Fitch, and then d02_ EWP.…”

Review of Mesoscale Wind-Farm Parametrizations and Their Applications

“…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).…”