Large wind farms are expected to influence local and regional atmospheric circulations. As wind energy deployment grows, the potential for interactions between wind farms and for downwind impacts of wind farms must be addressed. An open-source representation of wind farms within the mesoscale numerical weather prediction model, WRF, has been implemented and distributed by a university-NREL-NCAR-NOAA team. This parameterization can be a useful tool to assess downwind impacts of wind farms. In addition to representing the increase of surface roughness due to the wind farm, the parameterization incorporates an increase of turbulent kinetic energy generated within turbine wakes.
This presentation will explain the physics incorporated in the WRF Wind Farm Parameterization (WRF-WFP). Simulations allow us to quantify the impact of a wind farm on an atmospheric boundary layer through a daily cycle. The presence of a wind farm covering 10x10km has a significant impact on the local atmospheric flow and on regions up to 60 km downwind at night. At night, near-surface warming ~ 0.5 K is observed. Other impacts on wind profiles, turbulent kinetic energy, surface heat fluxes, and boundary-layer height will be presented.
Assessments of impacts of wind farms on global climate typically represent wind farms with a simpler approach, an increase in aerodynamic roughness length. Studies employing this simpler method have found near-surface temperature changes of 1-2K over wind farms. We directly compare the two approaches, and find nearly the opposite wake structure between the two methods. Sensible heat fluxes are exaggerated in the simpler approach, leading to much greater changes in temperatures. We conclude that the increased surface roughness approach is not an appropriate option for representing wind farms or exploring their impacts.
Potential field campaign designs for validating the WRF-WFP will be discussed.