  The highly fluctuated renewable generations and electric vehicles have
undergone tremendous growth in recent years. The majority of them are connected
to the grid via power electronic devices, resulting in wide variation ranges
for several key parameters in the frequency response model (FRM) such as system
inertia and load damping factor. In this paper, an automatic generation control
(AGC) method considering the uncertainties of these key parameters is proposed.
First, the historical power system operation data following large power
disturbances are used to identify the FRM key parameters offline. Second, the
offline identification results and the normal operation data prior to the
occurrence of the disturbance are used to train the online probability
estimation model of the FRM key parameters. Third, the online estimation
results of the FRM key parameters are used as the input, and the model
predictive-based AGC signal optimization method is developed based on
distributionally robust optimization (DRO) technology. Case studies conducted
on the IEEE 118-Bus System show that the proposed AGC method outperforms the
widely utilized PI-based control method in terms of performance and efficiency.
