Optimal control of wind farms requires accurate, yet computationally efficient models to capture the complex interactions between turbines. These interactions are dependent on atmospheric turbulence, wind direction changes, but also turbine properties which can be controlled. Existing control strategies often 1) neglect atmospheric effects such as wind veer, 2) rely on steady-state simulations or 3) do not optimise turbine actuation costs. This thesis addresses these limitations through the development of an optimisation-based control algorithm in a two-step process.

Firstly, the dynamic wind farm modelling and control package FLORIDyn is extended with veer-capable wake, turbulence and power calculation models. This enables the evaluation of the farm-wide power time traces, under veered conditions, as a function of yaw control inputs. These model additions are validated with Large Eddy Simulation data in single-, double-, and farm configurations. These comparisons show reasonable agreement with the high-fidelity simulations, and dependence of prediction accuracy on turbulence level estimation is highlighted. Secondly, a sparse control parametrisation is developed and integrated, making use of the new model in FLORIDyn. This optimisation-based approach jointly solves for the optimal actuation times and turbine yaws, making use of weather forecast information. Real-time numerical performance is achieved by using an efficient genetic algorithm as optimiser. Up to 6% in power gains are observed in strongly waked dynamic scenarios. When moving to veered conditions, these gains drop to 2%, quantifying the impact of veer on yaw control for power optimisation in wind farms.

Overall, this thesis establishes a framework for real-time wind farm optimisation that supports realistic atmospheric behaviour while remaining computationally tractable ... Read more