Sensitivity of wind farm wake steering strategies to analytical wake models

Abstract

The aerodynamic interactions between wind turbines arranged in farm layout lead to annual energy production losses ranging from 10% to 30%. Wake steering represents a promising strategy in wind farm control for power loss mitigation. The purpose of this work is to assess the sensitivity of optimal wake steering strategies to both analytical wake model choice and optimisation parameters. Using the FLOw Redirection and Induction in Steady State (FLORIS) framework, different wake models are employed to optimise a 4 × 4 farm layout for power maximisation. Model comparison findings indicate significant discrepancies in absolute power predictions for optimal set-points, as well as in optimal decision variables, with different or even opposite optimal yaw angle settings. Initialisation sensitivity results show that solutions corresponding to local extrema lead to potential power losses up to 14% compared to the global maximum for power production. Moreover, wind farm power function is observed to be multi-modal and discontinuous, suggesting that care must be taken when using gradient-based methods in wake steering optimisation.