Balancing Authority Area Model and its Application to the Design of Adaptive AGC Systems

In this paper, we develop a reduced-order model for synchronous generator dynamics via selective modal analysis. Then, we use this reduced-order model to formulate a balancing authority (BA) area dynamic model. Next, we use the BA area model to design an adaptive automatic generation control (AGC) scheme, with self-tuning gain, that decreases the amount of regulation needed and potentially reduces the associated costs. In particular, we use the BA area model to derive a relationship between the actual frequency response characteristic (AFRC) of the BA area, the area control error, the system frequency, and the total generation. We make use of this relationship to estimate the AFRC online, and set the frequency bias factor equal to the online estimation. As a result, the AGC system is driven by the exact number of MW needed to restore the system frequency and the real power interchange to the desired values. We demonstrate the proposed ideas with a single machine infinite bus, the 9-bus 3-machine Western Electricity Coordinating Council (WECC), and a 140-bus 48-machine systems.

[1]  C. Concordia,et al.  Tie-Line Power and Frequency Control of Electric Power Systems [includes discussion] , 1953, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems.

[2]  E. Davison,et al.  On "A method for simplifying linear dynamic systems" , 1966 .

[3]  José Ignacio Pérez Arriaga,et al.  Selective modal analysis with applications to electric power systems , 1981 .

[4]  F. Schweppe,et al.  Selective Modal Analysis with Applications to Electric Power Systems, PART I: Heuristic Introduction , 1982, IEEE Transactions on Power Apparatus and Systems.

[5]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[6]  N. Hatziargyriou,et al.  A Flexible AGC Algorithm for the Hellenic Interconnected System , 1989, IEEE Power Engineering Review.

[7]  L. H. Fink,et al.  Understanding automatic generation control , 1992 .

[8]  Peter W. Sauer,et al.  Power System Dynamics and Stability , 1997 .

[9]  Youmin Zhang,et al.  A revisit to block and recursive least squares for parameter estimation , 2004, Comput. Electr. Eng..

[10]  V. Knazkins,et al.  Load modeling using the Ornstein-Uhlenbeck process , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[11]  Dingguo Chen,et al.  Extended term dynamic simulation for AGC with smart grids , 2011, 2011 IEEE Power and Energy Society General Meeting.

[12]  Tao Yu,et al.  Stochastic Optimal Relaxed Automatic Generation Control in Non-Markov Environment Based on Multi-Step $Q(\lambda)$ Learning , 2011, IEEE Transactions on Power Systems.

[13]  Joseph H. Eto,et al.  Use of Frequency Response Metrics to Assess the Planning and Operating Requirements for Reliable Integration of Variable Renewable Generation , 2011 .

[14]  M. York,et al.  Smart Automatic Generation Control , 2012, 2012 IEEE Power and Energy Society General Meeting.

[15]  S. Mishra,et al.  Maiden application of bacterial foraging-based optimization technique in multiarea automatic generation control , 2012, 2012 IEEE Power and Energy Society General Meeting.

[16]  Goran Andersson,et al.  IMPROVED FREQUENCY BIAS FACTOR SIZING FOR NON-INTERACTIVE CONTROL , 2012 .

[17]  Peter W. Sauer,et al.  Online estimation of power system actual frequency response characteristic , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[18]  Peter W. Sauer,et al.  Automatic Generation Control and Its Implementation in Real Time , 2014, 2014 47th Hawaii International Conference on System Sciences.

[19]  Joe H. Chow,et al.  Power System Toolbox , 2017 .