[Objectives] Against the backdrop of high-proportion renewable energy integration

the uncertainty in wind and solar power output poses challenges to the safe and stable operation of the power grid. Compressed air energy storage (CAES)

with its high flexibility and adjustable rotational speed characteristics

can rapidly respond to grid power commands and effectively suppress fluctuations. However

traditional control strategies struggle to adapt to the multi-variable and complex operating conditions of CAES

resulting in suboptimal power tracking performance. To address this issue

a power tracking control strategy for CAES based on neural network PID is proposed. [Methods] First

a turbine-side dynamic model is constructed to characterize the dynamic relationships between reactive power and excitation voltage

as well as between active power and mass flow rate. Subsequently

a control framework based on neural network PID is designed to achieve precise power tracking control without overshoot through online adaptive parameter tuning. Finally

the response performance of this strategy in power tracking is validated through MATLAB/Simulink simulation. [Results] Compared to traditional PID control

dynamic active power deviation decreases by 22.7% and reactive power deviation reduces to 0.66% under continuous load fluctuations.[Conclusions] The proposed strategy achieves rapid

overshoot-free tracking of power command signals

providing a practical power tracking control solution for power grids with high-proportion renewable energy.