Optimal combined wake and active power control of large-scale wind farm considering available power

The high-order nonlinear complex characteristics of traditional static wake model (SWM) restrict the fast optimization of wake effect. Therefore, this paper introduces a dynamic wake model (DWM) to describe the time-varying wake characteristics of wind turbines (WTs) with low computational cost. The traditional SWM is linearized to derive the wake wind speed sensitivity coefficients, which represents the sensitivity of wake wind speed deficit with respect to the active power reference. Considering the natural propagation characteristics of wake effect, a wake delay function is added to realize the future wind speed prediction of different locations of the wind farm. And a joint control strategy of wake and active power based on model predictive control (MPC) is proposed to optimize the power contribution of each WT to minimize wake effects, and maximize total available power. Thus, the increased available power is able to satisfy the power demand from Transmission System Operator (TSO) for ancillary services and enhance power support capability. The control performance of the proposed control strategy is evaluated by simulations under constant and varying incoming wind speed. A dynamic wake model is proposed to describe the time-varying wake characteristic and reduce computational complexity. And an MPC-based control scheme is used to achieve maximum available power of wind farm by reducing the wind speed deficit caused by wake effect. The increased available power is able to satisfy the power demand from Transmission System Operator for ancillary services and enhance power support capability.image