Presentations

Abstract

Atmospheric models are essential for wind energy projects, providing synthetic time series that capture site-specific wind flow characteristics. To achieve a profound understanding of the site's behavior, long-term data at a high temporal resolution is required. However, modeling the microscale for extended periods is computationally challenging. To address this, we present an alternative methodology that enhances a 20-year quasi-microscale Weather Research and Forecasting (WRF) simulation using statistical properties derived from a shorter but high-quality microscale Large-Eddy Simulation (LES). The resulting 10-minutal time series capture phenomena such as interannual variability and trends, as well as local effects like turbulence and atmospheric stability. By performing time-domain analysis on these comprehensive datasets, we can reduce uncertainties in site assessments and enable more accurate energy yield estimations, surpassing traditional techniques. We present the methodology and an extensive validation for over 300 sites around the world, including a study of the dependance of the metrics on terrain complexity. Results show a promising accuracy of 5.5% for the mean absolute wind speed bias, which remains largely unaffected by terrain complexity. This rigorous validation underscores the reliability and applicability of our approach in the field of wind energy assessment and development.