  Wake steering, the intentional yaw misalignment of certain turbines in an
array, has demonstrated potential as a wind farm control approach to increase
collective power. Existing algorithms optimize the yaw misalignment angle
set-points using steady-state wake models and either deterministic frameworks,
or optimizers which account for wind direction and yaw misalignment variability
and uncertainty. Wake models rely on parameterizations of physical phenomena in
the mean flow field, such as the wake spreading rate. The wake model parameters
are uncertain and vary in time at a wind farm depending on the atmospheric
conditions, including turbulence intensity, stability, shear, veer, and other
atmospheric features. In this study, we develop a yaw set-point optimization
approach which includes model parameter uncertainty, in addition to wind
condition variability and uncertainty. The optimization is tested in open-loop
control numerical experiments using utility-scale wind farm operational data
for which the set-point optimization framework with parameter uncertainty has a
statistically significant impact on the wind farm power production for certain
wind turbine layouts at low turbulence intensity, but the results are not
significant for all layouts considered nor at higher turbulence intensity. The
set-point optimizer is also tested for closed-loop wake steering control of a
model wind farm in large eddy simulations of a convective atmospheric boundary
layer. The yaw set-point optimization with model parameter uncertainty improved
the robustness of the closed-loop wake steering control to increases in the yaw
controller update frequency. Increases in wind farm power production were not
statistically significant due to the high ambient power variability in the
turbulent, convective ABL.
