Abstract
The 2021 Texas power crisis has highlighted the vulnerability of the power
system under wind extremes, particularly with the increasing penetration of
energy resources that depend on weather conditions (e.g., wind energy). The
current wind power forecast models do not effectively consider the impact of
such extreme weather events. In the present study, we provide a fast and robust
statistical model developed using ten years of utility-scale turbine data at
the Eolos Wind Energy Research Station to forecast the icing losses under such
weather conditions. This model covers different cold climate impacts, including
precipitation icing, frost contamination, and low-temperature effect. This
model has been assessed using three large-scale (larger than 100 MW) wind farm
data involving turbines with different capacities and from different
manufacturers across multiple geographic regions. Notably, the model has been
used to predict the wind power losses in entire Texas (larger than 91% of total
wind installation in Texas) during 2021 Texas power crisis. The proposed model
can be easily integrated into the existing wind farm and power grid operations,
allowing the power system operators can develop more appropriate and pinpointed
plans to balance the severe and sudden energy deficits and increase the system
integrity under winter extremes.