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

Wind Farm Icing Loss Forecast Pertinent to Winter Extremes