Modelling of a CFD Microscale Model and Its Application in Wind Energy Resource Assessment

Abstract

Abstract. The prediction of a wind farm near the wind turbines has a significant effect on the safety as well as economy of wind power generation. To assess the wind resource distribution within a complex terrain, a computational fluid dynamics (CFD) based wind farm forecast microscale model is developed. The model uses the Reynolds Averaged Navier-Stokes (RANS) model to characterize the turbulence. By using the results of Weather Research and Forecasting (WRF) mesoscale weather forecast model as the input of the CFD model, a coupled model of CFD-WRF is established. A special method is used for the treatment of the information interchange on the lateral boundary between two models. This established coupled model is applied in predicting the wind farm near a wind turbine in Hong Gang-zi, Jilin, China. The results from this simulation are compared to real measured data. On this basis, the accuracy and efficiency of turbulence characterization schemes are discussed. It indicates that this coupling system is easy to implement and can make these two separate models work in parallel. The CFD model coupled with WRF has the advantage of high accuracy and fast speed, which makes it valid for the wind power generation.

Forecasting wind speed events at a utility-scale wind farm using a WRF-ANN model

“…These techniques are categorised into physical, statistical and hybrid models. The physical models include dynamical downscaling (Heikkila et al, 2011), as well as Computational Fluid Dynamics (CFD) models (Bilal et al, 2016;Yue et al, 2016). The practicality of dynamical downscaling for wind power forecasting applications is limited due to intensive computational expense, as well as by the parameterisations which are implemented to model sub-grid scale processes (Carlini et al, 2016).…”