Investigating the impact of temporal wind-speed variations on wind power modeling using a dynamically coupled meso–microscale simulation framework

Accurate day-ahead wind power forecasting is crucial for peak shaving and load balancing in distribution grids. Capturing the short-term transient wind speeds can further improve the wind power forecasting accuracy. This study introduces a dynamically coupled simulation framework that integrates the mesoscale Weather Research and Forecasting (WRF) model with the microscale computational fluid dynamics software—OpenFOAM (Open Field Operation and Manipulation), named WRF-OpenFOAM Coupled Simulation System—WOCSS. WOCSS uses online coupling to construct a coupling interface at each OpenFOAM time step, thereby greatly reducing the temporal resolution mismatch between the WRF and OpenFOAM. Compared to WRF alone, WOCSS can accurately resolve the acceleration and deceleration of wind speeds, enhancing the wind field modeling across various heights. Compared to the traditional “snapshot” method, its higher temporal resolution made WOCSS outperforms the conventional “snapshot” method in modeling wind speeds at different heights, in particular, wind speeds that non-linearly varied with time. When the wind speed shows significant nonlinear variations, the difference of hourly average power output predicted by WOCSS and the “snapshot” method can be as large as 52.2% of the wind turbine's rated power capacity. This finding highlights the critical impact of temporal wind-speed variations on wind power modeling and underscores the significance of WOCSS for accurate wind energy forecasting, which enables more efficient day-ahead energy management.