Hierarchical MPC–QP Control for Primary Frequency Regulation of a Wind–Storage Hybrid System with Intra-Farm Power Optimization

High penetration of wind power increases the need for coordinated frequency support from wind farms and battery energy storage systems (BESSs). To address insufficient power coordination, state-of-charge (SOC) deviation, and suboptimal intra-farm allocation of frequency-regulation power, this paper proposes a hierarchical MPC–QP control strategy for primary frequency regulation of a wind–storage hybrid system. At the upper level, model predictive control (MPC) minimizes system frequency deviation and a composite frequency-regulation cost index subject to wind-farm power, BESS power, and SOC constraints, thereby determining optimal regulation-power references for the wind farm and BESS. At the lower level, quadratic programming (QP) minimizes wind-farm regulation-power tracking error and equivalent torque fluctuations to achieve differentiated power allocation among equivalent turbine clusters. In a representative 1 h simulation with continuously varying wind speed and random load disturbances, the proposed strategy reduces the RMS frequency deviation, maximum absolute frequency deviation, RMS SOC deviation, and cumulative regulation cost index by 24.20%, 24.53%, 93.86%, and 84.72%, respectively, compared with PI control. Compared with equal allocation, it reduces the RMS wind-farm power-tracking error and RMS equivalent torque fluctuation by 87.93% and 15.52%, respectively. Three additional stochastic realizations demonstrate consistent improvements in the selected performance indices, further supporting the effectiveness of the proposed hierarchical strategy under the investigated simulation conditions.