A coupled turbine-interaction wind farm parameterization in the Weather Research and Forecasting model. (1st May 2023) - Wu, Chunlei; Wang, Qiang; Luo, Kun; Fan, Jianren
Highlights: A novel wind farm parameterization is proposed for the mesoscale wind farm simulation. A sub-grid wind turbine interaction model improves the wind speed simulation. The wake length is shortened and the turbine-induced turbulence is reduced in the coupled model. The simulated wind power output is decreased by 1 % compared with the original model. Abstract: The wind farm parameterization in the mesoscale numerical weather prediction (NWP) model is a valuable approach to exploring the performance and atmospheric impact of the large-scale wind farm. However, the sub-grid interactions between turbines are not resolved in the current parameterization method. In this study, we propose an improved wind farm parameterization by coupling a novel sub-grid wind turbine interference model. It is incorporated in the state-of-the-art Weather Research and Forecasting (WRF) model. To verify the model, the coupled model is applied in a real offshore wind farm in China for comparing the model performances with the original parameterization. The findings demonstrate that the proposed approach improves the wind simulation and has minimal sensitivity to horizontal grid resolution, hence strengthening the model's robustness. Additionally, it is discovered that the sub-grid interaction reduces the turbine-produced turbulence and shortens the wake length by more than 30% compared to the original parameterization. Importantly, the power output prediction of turbine cells is modified, approximately resulting in a 1 % reduction of the entire wind farm relative to the original model. The effects of sub-grid turbine interaction are effectively captured in the proposed model and it is recommended for further wind farm simulations.