Increasing wind farm efficiency by yaw control: Beyond ideal studies towards a realistic assessment

Abstract

Recent studies have demonstrated promising results for yaw control to mitigate wake interactions and optimize wind farm efficiency. These studies have been carried out for a single 'ideal' operating condition, typically assuming a fixed wind direction and speed. In a real scenario, the wind speed and direction change continuously over time, including cases in which yaw control does not provide any improvement. Thus, results from idealized studies cannot be generalized to estimate annual energy production (AEP) improvements in real scenarios with sufficient accuracy. In this paper, we provide a method to estimate the impact of yaw control on annual energy production, based on high-fidelity simulations. The stochastic procedure leverages high-fidelity numerical simulations to obtain a surrogate model with generalized Polynomial Chaos (gPC). For a single wind direction improvements of the order ∼ 10% are obtained; which is typical of an ideal estimate. On the other hand, the AEP gain is about 3% for the entire wind farm under realistic variable wind directions. The magnitude of the AEP gain is site-dependent. The proposed methodology provides an accurate tool useful for evaluating the value proposition of yaw control for wake steering.