A parameterization scheme for the floating wind farm in a coupled atmosphere-wave model (COAWST v3.7)

Abstract

Coupling Weather Research and Forecasting (WRF) model with wind farm parameterization can be effective in examining the performance of large-scale wind farms. However, the current scheme is not suitable for floating wind turbines. In this study, a new scheme is developed for floating wind farm parameterization (FWFP) in the WRF model. The impacts of the side columns of a semi-submersible floating wind turbine on waves are firstly parameterized in the spectral wave model (SWAN) where the key idea is to consider both inertial and drag forces on side columns. A machine learning model is trained using results of idealized high-resolution SWAN simulations and then implemented in the WRF to form the FWFP. The difference between our new scheme and the original scheme in a realistic case is investigated using a coupled atmosphere-wave model. Results indicate that the original scheme underestimates the power output of the entire floating wind farm in the winter scenario. On average, the power output of a single turbine is underestimated by a maximum of 694 kW (12 %). The turbulent kinetic energy decreases within the wind farm, with the greatest drop of 0.4 m2 s-2 at the top of the turbine. This demonstrates that the FWFP is necessary for both predicting the power generated by floating wind farms and evaluating the impact of floating wind farms on the surrounding environment.