Wind farm resolution enhancing through meso-microscale coupled method with complex terrain

Abstract

Accurate simulation of wind energy resources requires effective coupling of mesoscale and microscale models to resolve both atmospheric dynamics and site-specific turbine effects. However, scale mismatches often introduce significant errors into the modeling process. This study proposes a hierarchical meso-microscale coupled method that leverages the Wind Driven Optimization (WDO) algorithm to optimize dynamic inflow wind profiles derived from mesoscale WRF simulations. By improving the vertical distribution of wind speed at microscale inflow boundaries, the method enhances the physical consistency and accuracy of large-eddy simulations in complex terrain environments. Validation results show that WDO-Optimized profiles consistently outperform traditional polynomial and swarm-based methods in reducing velocity field errors across various temporal scales. This approach offers a robust and computationally efficient framework for improving wind field prediction accuracy, contributing to more reliable wind energy resource assessments and turbine layout planning in heterogeneous atmospheric conditions.