An active primary frequency regulation strategy for grid integrated wind farms based on model predictive control

Abstract

The increasing penetration level of wind power can reduce the dependency on fossil fuels, but it is accompanied with challenges such as the jeopardized dynamic stability of the frequency of power grid. As an effective method to improve frequency dynamic stability, primary frequency regulation (PFR) is conventionally based on the feedback of measured frequency, by which means it is conducted through analyzing historical information. By taking the predictive information into account, this paper introduces an active PFR (APFR) strategy for grid integrated wind farms (WFs) to enhance control performance of PFR. According to small-signal models of power system integrated with WFs based on conventional PFR and APFR, the state-space predictive models are established to obtain the predictive states. Then, to take active measures to deal with the electrical load disturbance, the predicted frequency is optimized in a receding horizon period by adjusting the PFR power reference of WFs flexibly. Moreover, a finite terminal weighting matrix based lyapunov function is presented to prove the asymptotic stability of the closed-loop model predictive control system. Finally, extensive case studies based on the standard test systems are performed to validate the effectiveness of the theoretical analysis and the superiority of APFR strategy.