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The highly fluctuated renewable generations and electric vehicles haveundergone tremendous growth in recent years. The majority of them are connectedto the grid via power electronic devices, resulting in wide variation rangesfor several key parameters in the frequency response model (FRM) such as systeminertia 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 powerdisturbances are used to identify the FRM key parameters offline. Second, theoffline identification results and the normal operation data prior to theoccurrence of the disturbance are used to train the online probabilityestimation model of the FRM key parameters. Third, the online estimationresults of the FRM key parameters are used as the input, and the modelpredictive-based AGC signal optimization method is developed based ondistributionally robust optimization (DRO) technology. Case studies conductedon the IEEE 118-Bus System show that the proposed AGC method outperforms thewidely utilized PI-based control method in terms of performance and efficiency.

Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model