Wake‐effect aware optimal online control of wind farms: An explicit solution

Wake effects impose significant aerodynamic interactions among wind turbines. To improve the wind farm operating performance, practical wind farm online control considering wake effects becomes very important. To achieve online optimal wind farm control while responding to grid demands, this paper proposes a novel optimal wind farm supervisory control (SC) model and its explicit solutions. From the controller modelling perspective, the two major wind farm operating modes, the maximum power point tracking mode and the set‐point tracking mode, are first analysed and unified in one optimisation model while considering wake effects. In this way, wind farm power production and rotor kinetic energy reserve can be simultaneously considered to conveniently modify the operation mode in response to different grid demands. Aside from controller modelling, the collocation method is first introduced to address the online application problem of such wake‐effect aware optimal WF control. Although a few optimisation algorithms have been proposed to find the optimum offline, online optimal control is still challenging because of the computational complexity brought by wake model non‐linearity and non‐convexity. The proposed collocation method explicitly approximates the optimal solutions to the proposed supervisory control model, through which only a direct algebraic operation is required for online optimal control instead of repeated optimisations. Case studies are carried out on different wind farms under various wind conditions, showing that the wind farm power production potential and releasable power reserve are improved compared to traditional greedy control in both modes. The accuracy of the collocation method is verified. A detailed analysis of the wind farm production capacity under different wind speeds and directions is also provided.