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

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