Estimating annual energy production of wake mixing control strategies including comparisons to wake steering

Abstract. This study presents an estimation of the annual energy production (AEP) associated with active wake mixing (AWM) control strategies in a wind farm. To achieved this, we first conduct a series of high-fidelity large eddy simulations (LES) of a wind farm for various turbine layouts and control parameters. These simulations extend previous findings from two-turbine studies to a larger array of wind turbines, demonstrating the effectiveness of AWM in enhancing power generation, particularly in geometrically aligned wind farms situated in stable atmospheric boundary layers. The results indicate that while the conventional pulse method leads to the best performance for second-row turbines, the helix method leads to greater improvements in power generation for third-row turbines. Second, a framework for estimating the AEP associated with AWM strategies is developed within the FLOw Redirection and Induction in Steady-state (FLORIS) toolkit, using a new empirical Gaussian wake model. The FLORIS parameters are calibrated to the LES data and an optimization routine is established for determining the optimal use of AWM in a wind farm for maximizing AEP. AEP estimates are provided using Weibull data from the New York Bight for multiple turbine layouts and blade pitching amplitudes. Third, the AEP gains from wake mixing are compared to those from wake steering using the yaw optimization routines in FLORIS. The power performance is similar for both control methods, generally leading to power gains of 1 % to 3 % for the wind conditions where active wake mixing or steering is used, which translates to AEP gains that are mostly less than 1 % for the wind farm and control parameters considered in this study.