Measurements show that wind speeds just upstream of a wind farm decrease relative to locations farther away after the turbines are turned on. Computational fluid dynamics (CFD) simulations point to wind-farm-scale blockage as the primary cause of these slowdowns, the magnitude of which call into question long-held assumptions regarding the upstream influence of wind turbines. Many wind energy prediction procedures in use today still use these assumptions and ignore blockage effects, resulting in an over-prediction bias that pervades the entire wind farm.
There are two sources of bias: power curves and turbine interaction models (often referred to as wake models). In this project we developed a complementary set of wind farm flow models designed to reliably predict the impact of blockage and turbine interaction effects in general on wind farm energy production and even measured power curves. The set includes a high-fidelity RANS CFD model and an efficient engineering model.
The project activities lead to:
- An advanced RANS CFD model
- Validation of the model and related sensitivity studies
- Development of more efficient models based on RANS results
- Integration of models like WindFarmer: Analyst
With these models, you can better understand the energy yield at your wind farm and reduce investment risk.