Available wind power assessment of wind farms for AGC frequency regulation using the sand cat swarm algorithm

Abstract

With the increasing integration of wind power into modern power systems, accurate quantification of a wind farm’s real-time regulation capability is essential for reliable participation in Automatic Generation Control (AGC) and grid frequency support. This paper proposes a comprehensive framework based on the concept of Available Wind Power (AWP) — defined as the maximum constant active power a turbine or wind farm can sustain over an AGC cycle without triggering protective actions such as Maximum Power Point Tracking (MPPT) re-engagement or overspeed pitch control. At the turbine level, AWP is formulated through a dynamic optimisation model that couples nonlinear electromechanical dynamics, rotor speed constraints, and aerodynamic characteristics. This definition is then extended to the wind farm scale by incorporating a physics-based Jensen wake model that accounts for multi-turbine wake superposition and spatial wind speed deficits, thereby capturing the complex inter-turbine interactions that limit aggregate power availability. To efficiently solve the resulting non-convex, coupled optimisation problem, the Sand Cat Swarm Optimisation (SCSO) algorithm is employed for optimal reference command allocation across turbines. Simulation results demonstrate that the proposed AWP-aware approach enables accurate, stability-preserving power dispatch under realistic wake conditions, significantly improving tracking performance, reducing power shortfalls, and enhancing the wind farm’s grid-support capability. The method provides a physically consistent and computationally tractable pathway for large-scale wind farms to participate reliably in AGC frequency regulation while maximising energy utilisation.