The economic effects of these wake interactions are significant enough to emerge in publicly-available Energy Information Agency monthly power production data. We have identified groups of upwind, downwind, and control wind farms in several states, and can correlate these wake effects with monthly-averaged wind direction data from local ASOS stations. We find statistically significant declines in generation for first-built sites once neighboring wind farms are installed, and these declines vary with the alignment of the average wind direction and the orientation between upwind and downwind wind farms. For our Texas cluster of wind farms, strongest wake effects appear in January, typically, with weakest wake effects in September.
The physical mechanism for these observed economic impacts is demonstrated with mesoscale numerical weather prediction simulations. Using the Weather Research and Forecasting (WRF) model and the WRF Wind Farm Parameterization (WFP), we simulate three cases to quantify wake effects: one containing the “upwind”, “downwind”, and “control” wind farms; one containing the “downwind” and “control” wind farms only; and one without wind farms at all. Each scenario is simulated daily throughout the months of January and September in 2013, in which the prevailing winds create strong and weak wake impacts, respectively. The three cases allow us to isolate the impacts that the upwind wind farm’s wake poses for the power production of the downwind wind farm, corroborating the econometric analysis from the EIA data. The hourly temporal resolution of the mesoscale simulations reveals how simulated wind farm wakes vary as a function of wind speed, wind direction, and atmospheric stability, with the largest effects during stable conditions and when the wind farms are aligned with the wind direction.
Finally, we comment on how local, state, and federal regulations do not reflect the reality of observed economic and physical wake impacts.