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 in wide variation ranges for several key parameters in the frequency response model (FRM), such as system...

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Published in:IEEE transactions on power systems Vol. 37; no. 6; pp. 4605 - 4617
Main Authors: Liu, Likai, Hu, Zechun, Mujeeb, Asad
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automatic control
Automatic generation control
Control methods
Damping
Data models
distributionally robust optimization
Electric vehicles
Electronic devices
Estimation
Frequency control
Frequency response
Mathematical models
model predictive control
Optimization
Parameter identification
Predictive models
probability estimation
Proportional integral derivative
Regulation
Turbines
Uncertainty
ISSN:0885-8950, 1558-0679
Online Access:Get full text
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Abstract 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 in wide variation ranges for several key parameters in the frequency response model (FRM), such as system inertia and load damping factors. This paper proposes an automatic generation control (AGC) method considering the uncertainties of these key parameters. 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 right before the large power 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 optimization method is developed based on distributionally robust optimization (DRO) theory. Case studies conducted on the IEEE 118-bus system show that the proposed AGC method outperforms the widely utilized PI-based and PID-based control methods in terms of performance and efficiency.
AbstractList 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 in wide variation ranges for several key parameters in the frequency response model (FRM), such as system inertia and load damping factors. This paper proposes an automatic generation control (AGC) method considering the uncertainties of these key parameters. 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 right before the large power 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 optimization method is developed based on distributionally robust optimization (DRO) theory. Case studies conducted on the IEEE 118-bus system show that the proposed AGC method outperforms the widely utilized PI-based and PID-based control methods in terms of performance and efficiency.
Author Mujeeb, Asad
Liu, Likai
Hu, Zechun
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SubjectTerms Automatic control
Automatic generation control
Control methods
Damping
Data models
distributionally robust optimization
Electric vehicles
Electronic devices
Estimation
Frequency control
Frequency response
Mathematical models
model predictive control
Optimization
Parameter identification
Predictive models
probability estimation
Proportional integral derivative
Regulation
Turbines
Uncertainty
Title Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model
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