Wind Farm Icing Loss Forecast Pertinent to Winter Extremes

TL;DR

This study develops a fast, robust statistical model to forecast wind farm icing losses during winter extremes, improving power system resilience amid severe weather events like the 2021 Texas crisis.

Contribution

The paper introduces a novel statistical model that accurately predicts wind power losses due to icing in cold climates, validated across multiple large-scale wind farms and applicable for real-time grid management.

Findings

Model accurately predicts icing losses during winter extremes.

Validated across multiple regions and turbine types.

Can be integrated into existing power system operations.

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.