Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview
Abstract
:1. Introduction
 Modern AGC techniques in present and in future smart power systems that can incorporate renewable energy sources, different fast energy storage devices, HVDC, and FACTS devices.
 Intelligent and pattern recognitionbased AGC techniques that can handle nonlinearities, parametric variations, uncertain states in demand identifications, and dynamics of the different loads. Furthermore, different virtual inertial controllers (VIC), which can support and improve the inertial response of renewable energybased AGC systems.
 AGC schemes in different configurations of microgrids including standalone single area and multiple area microgrids and support the integration of nondispatchable and high intermittent distributed generation sources.
 AGC techniques in smart grids, which incorporate and improve different features including demandside response, data forgery attacks, and twoway communications.
 Efficient AGC models in a deregulated framework that can enhance the economic efficiency and stability of the restructured power market.
 AGC techniques in conjunction with the Economic Dispatch (ED) factor to improve its economic efficiency.
 Industrial practices of different AGC models around the world to explore and analyze different issues related to its practical implementation in the field.
 The developmental history of AGC models in traditional and renewable energy power systems is explored, which considers various constraints in performing the frequency control analysis. These constraints include generation rate constraint (GRC) and governor dead band (GDB) nonlinearities, parametric variations, inertial response, time delay problems, observability of state variables, and other stability issues.
 The general concept of AGC models in a multiarea interconnected power system is explored and different objective functions, which are based on several criteria and used to eliminate the area control errors, are presented from the literature.
 A stateoftheart study of AGC schemes, focused on classical and modern control theories, is presented for current and future smart power systems. Furthermore, various intelligent AGC schemes based on fuzzy logic and artificial neural networks are explored and various soft computing control algorithms are comprehensively analyzed. All these control methods are critically compared using the tabular method showing their merits and demerits.
 The article addresses several frequency management systems integrating small and large renewable energy sources into the power grid for frequency regulation purposes. Further, a comprehensive literature review on AGC strategies incorporating various energy storage systems (ESSs), HVDC interconnections, and FACTS devices is provided.
 A detailed overview of the AGC schemes in various microgrid configurations is presented and summarized for comparison in a tabulated form. Further, AGC approaches integrating various aspects of the smart grid are illustrated.
 The concept of a deregulated power system is addressed and the application and challenges associated with AGC implementation in different contract scenarios are presented.
 Different AGC schemes in conjunction with economic dispatch are reviewed from the literature and a detailed overview of worldwide AGC practices is provided to explore the industrial applications of AGC.
2. History of the Power System AGC Models
3. AGC Operation and Objective Functions
4. Power System AGC Models
4.1. Single Area Power Systems
4.2. Two Area Power Systems
4.3. Three Area Power Systems
4.4. Four Area Power Systems
4.5. Mth Area Power Systems
5. AGC Classification Based on Controller Organizations
5.1. Centralized Controllers
5.2. Decentralized Controllers
5.3. TwoLevel and MultiLevel Controllers
6. AGC Classifications Based on the Control Methods
6.1. Classical Control Methods
6.2. Optimal and Suboptimal Control Methods
6.3. Adaptive, SelfTuning, and Model Reference Control Methods
6.4. Variable Structure and Sliding Mode Control Methods
6.5. Robust Control Methods
6.6. Model Predictive Control (MPC) Methods
6.7. Digital Control Methods (DCMs)
7. Intelligent and Soft Computing Control Methods
7.1. Intelligent Control Methods
7.1.1. Fuzzy Logic Control (FLC)
7.1.2. Artificial Neural Network (ANN) Control
7.2. Soft Computing Control Methods
7.2.1. Genetic Algorithm (GA)
7.2.2. Particle Swarm Optimization (PSO)
7.2.3. Firefly Algorithm (FA)
7.2.4. Artificial Bee Colony (ABC) Algorithm
7.2.5. Differential Evolution (DE) Algorithm
7.2.6. Bacterial Foraging Optimization (BFO) Algorithm
7.2.7. BatInspired Algorithm
7.2.8. Quasi Oppositional Harmony Search (QOHS) Algorithm
7.2.9. Teaching LearningBased Optimization (TLBO)
7.2.10. Cuckoo Search Algorithm (CSA)
7.2.11. Grey Wolf Optimizer (GWO) Algorithm
7.2.12. Other Computing Control Methods
8. AGC Incorporating ESSs, FACTs Devices and HVDC Link
8.1. AGC Incorporating Energy Storage Systems (ESS)
8.2. AGC Incorporating FACTS Devices
8.3. AGC with HVDC Link
9. AGC in Renewable Energy Generation Systems
10. AGC in Microgrids and Smart Grids
10.1. AGC in Microgrids
10.1.1. Single Area StandAlone MG Systems (SSAMGS)
10.1.2. Multiple Areas StandAlone MG Systems (MSAMGS)
10.2. AGC in Smart Grids
11. AGC in Deregulated Power Systems
12. AGC and Economic Dispatch (ED)
13. Worldwide AGC Practices
14. Future Scope of Work
 Explore AI techniques to train the AGC algorithm for activation of optimum reserves to secure the operation of the power system with largescale integration of RESs.
 Explore and include more constraint coefficients like transmission line congestions into the objective functions to make the system more efficient in the practical scenario.
 Explore adaptive and robust control methods for the AGC to effectively handle system parametric variations.
 Explore various control techniques for AGC to perfectly predict the load and forecast the weather in large and smallscale renewable energybased power systems.
 An indepth study on state estimation for AGC in realtime is required to effectively deal with the packet loss problems in the communication process.
 Susceptibilities of various AGC schemes to cyberattacks should need to be explored further.
15. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Acronym  Definition  Acronym  Definition 

AGC  Automatic generation control  FES  Flywheel energy storage 
TSO  Transmission system operator  ISO  Independent system operator 
AE  Aquaelectrolyzer  LTI  Linear timeinvariant 
SMES  Super magnetic energy storage system  RFC  Reliability first corporation (us) 
RESs  Renewable energy sources  GRC  Generation rate constrains 
BESS  Battery energy storage systems  GDB  Governor dead band 
UPFC  Unified power flow controller  DISCOs  Distribution companies 
MLCS  Modified load control scheme  GENCOs  Generation companies 
DGs  Distributed generation sources  SERC  Southeastern electric reliability council (us) 
FLC  Fuzzy logic control  TRANSCOs  Transmission companies 
MTSA  Multiple tabu search algorithm  DPM  Disco participation matrix 
IPFC  Interline power flow controller  ITAE  Integral time multiplied by absolute error 
LMI  Linear matrix inequalities  AGPM  Augmented participation matrix 
IAE  Integral of absolute error  EVs  Electrical vehicles 
LTI  Linear timeinvariant system  NPCC  Northeast power coordinating council (na) 
GNN  Generalized neural network  SISO  Single inputsingle output 
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Ref.  AGCO&OF  AGCCO  AGCC& MCM  AGCI&SCCM  AGCESS  AGC FACTS  AGCHVDCS  AGCLREGS  AGCMG & DG  AGCSG  AGCDPS  AGCED  AGCWWP 

[1]  ✗  ✓  ✓  ✓  ✓  ✓  ✓  ✗  ✗  ✗  ✓  ✓  ✗ 
[2]  ✗  ✓  ✓  ✓  ✓  ✓  ✓  ✗  ✗  ✗  ✓  ✗  ✗ 
[3]  ✗  ✗  ✓  ✗  ✓  ✗  ✓  ✗  ✗  ✗  ✓  ✗  ✗ 
[4]  ✗  ✓  ✓  ✓  ✓  ✓  ✓  ✗  ✗  ✗  ✗  ✗  ✗ 
[5]  ✓  ✓  ✓  ✗  ✗  ✓  ✓  ✗  ✓  ✓  ✗  ✗  ✗ 
[6]  ✗  ✗  ✓  ✓  ✓  ✓  ✗  ✗  ✗  ✗  ✓  ✓  ✗ 
[7]  ✗  ✗  ✓  ✓  ✓  ✗  ✓  ✓  ✓  ✗  ✗  ✗  ✗ 
[8]  ✓  ✗  ✓  ✗  ✗  ✗  ✗  ✓  ✓  ✗  ✗  ✗  ✗ 
[9]  ✓  ✓  ✓  ✓  ✗  ✗  ✓  ✓  ✓  ✗  ✓  ✗  ✗ 
[10]  ✗  ✗  ✗  ✗  ✗  ✗  ✗  ✓  ✓  ✗  ✗  ✗  ✗ 
[11]  ✗  ✗  ✗  ✗  ✓  ✗  ✓  ✓  ✗  ✗  ✗  ✗  ✗ 
[12]  ✗  ✗  ✓  ✗  ✓  ✗  ✓  ✗  ✗  ✗  ✓  ✗  ✗ 
[13]  ✗  ✓  ✓  ✗  ✗  ✗  ✓  ✓  ✓  ✓  ✓  ✗  ✗ 
OR  ✓  ✓  ✓  ✓  ✓  ✓  ✓  ✓  ✓  ✓  ✓  ✓  ✓ 
Sr. No  Control Method  Main Advantages  Drawbacks 

1  Classical Control Methods 


2  Optimal and Suboptimal Control Methods 


3  Adaptive Control Methods 


4  Variable Structure Control Method 


5  Robust Control Methods 


6  Model Predictive control methods 


7  Digital Control Methods 


8  Fuzzy logicbased control methods 


9  ANNbased Control Methods 


10  Neurofuzzy based Control Methods 


Ref.  Power System Configuration  Controller Approach  Operating Scenarios  Peak Overshoot  Settling Time  

[80]  Two area nonreheat thermal power system  DE based 2DOFPID regulator  Controller comparisons with other types:  $\Delta {f}_{1}$  $\Delta {f}_{2}$  $\Delta {P}_{tie12}$  $\Delta {f}_{1}$  $\Delta {f}_{2}$  $\Delta {P}_{tie12}$ 
2DOFPID  0.0144  0.00598  0.00711  11.1  7.2  13.8  
PID  0.0251  0.0172  0.0083  24.8  24.1  23.3  
[26]  Two area nonreheat thermal power system  ACO based PID regulator  Comparisons of objective functions:  
ISE  1.1 × 10^{−6}  0.0002  0.0001  29.62  37.20  50.41  
ITSE  0.0016  0.0001  0.0005  25.69  25.43  45.54  
IAE  3.66 × 10^{−6}  0.0000  0.0001  26.29  32.12  37.64  
ITAE  0.001  0.0000  0.0005  23.69  32.12  33.63  
[59]  Two area reheat thermal, hydro, gas and nuclear power plant  TLBO based AGC system with output feedback SMC  Controller comparisons with other types:  
SMC with output feedback with TLBO  0.001  2.242 × 10^{−4}  2.883 × 10^{−5}  1.3  1.46  1.05  
SMC with output feedback with DE  0.0018  6.389 × 10^{−4}  7.680 × 10^{−5}  1.4  1.9  1.1  
SMC with output feedback with PSO  0.0016  4.301 × 10^{−4}  8.050 × 10^{−5}  1.52  1.54  1.24  
[84]  Two area reheat thermal, hydro, gas and nuclear power plant  LUSTLBO based Fuzzy PID controller  Performance evaluations:  
Without ACDC tielines  0.000551  0.000219  0.0000826  5.26  2.96  2.36  
With ACDC tielines  0.000280  0.000208  0.0001353  1.85  4.14  2.55 
Power System Configurations  Controller Approach  Operating Scenarios  Peak Over Shoot  Settling Time  

Three area power system with hydrothermal sources  BFA based IDD and FDID regulator  Controller comparisons  $\Delta {f}_{1}$  $\Delta {f}_{2}$  $\Delta {f}_{3}$  $\Delta {P}_{tie12}$  $\Delta {P}_{tie13}$  $\Delta {P}_{tie23}$  $\Delta {f}_{1}$  $\Delta {f}_{2}$  $\Delta {f}_{3}$  $\Delta {P}_{tie12}$  $\Delta {P}_{tie13}$  $\Delta {P}_{tie23}$ 
BFAIDD  0.02142  0.01335  0.01081  0.00113  0.00117  60.26  64.81  65.47  61.00  73.63  
BFAFIDD  0.01554  0.01071  0.00742  0.00059  0.00069  49.75  52.76  55.21  51.37  58.75  
Three area nonreheat thermal power system with nonlinearities  CSA based 2DOFID controller  Use of FACTS devices  
SSSC  0.0013  0.0019  0.0012  0.0002  0  0  33.9  24.28  30.61  51.12  40.42  29.12  
TCSC  0.00004  0.01071  0.00742  0.0002  0  0  33.06  34.46  33.28  51.13  40.20  34.35  
TCPS  0.0012  0.0013  0.0004  0  0  0  31.7  33.36  29.21  50.97  40.23  34.32  
IPFC  0  0  0  0  0  0  28.53  23.37  26.03  34.56  40.07  29.12  
Three area nonreheat thermal power system with nonlinearities  GWO based PID controller  Use of ESs  0.007231  0.006335  0.006087  0.001814  0.001761  0.0008671  34.81  35.56  32.35  42.84  41.67  38.82 
WithSTTP  
WithoutSTTP  0.01432  0.01213  0.006886  0.001026  0.0007518  0.001709  24.47  23.35  21.81  21.26  24.11  32.97  
Five area reheat thermal power system  FFA based PID controller  Controller comparisons  $AC{E}_{1}$  $AC{E}_{2}$  $AC{E}_{3}$  $AC{E}_{4}$  $AC{E}_{5}$  $AC{E}_{1}$  $AC{E}_{2}$  $AC{E}_{3}$  $AC{E}_{4}$  $AC{E}_{5}$  
GAPID  0.00071  0.00063  0.0006  0.00089  0.0006  17.84  26.63  17.19  24.33  17.19  
PSOPID  0.00042  0.00065  0.00042  0.00078  0.00042  16.33  22.7  16.54  23.23  16.33  
FFAPID  0.00038  0.00075  0.00038  0.00085  0.00038  13.53  21.8  14.55  22.77  13.79 
Ref.  Type of Power System  Areas  Energy Generation Sources  Additional Devices  Controller Approach 

[32]  Traditional  2  Thermal, hydro, windDiesel  AC/DC link  IPSO based PID 
[33]  Deregulated  2  Wind, hydro, thermal, gas  TCPS, CES  ISE based I 
[34]  Deregulated  2  Thermal  IPFC + RFB  BFO based I and FL 
[36]  Traditional  3  Thermal  TCPS, UPFC  CSA based 2DOFIDD 
[37]  Traditional  3  Solar thermal, thermal    GWO based I, PI, PID 
[38]  Traditional  1,3,5  Thermal    QOHS based PID/IDD 
[76]  Traditional  2  Thermal, wind power    HTGA based PID 
[40]  Traditional  4  Thermal, hydro    BBBC based IT2FPID 
[93]  Traditional  3  Thermal, hydro, gas  TCSC, TCPS, SSSC  IPSO based I 
[99]  Traditional  3  Windthermal    PO2DOFPID 
[100]  Deregulated  2  Hydro, thermal, wind  TCPS, SMES  CRPSO based I 
[101]  Traditional  2  Wind, thermal, hydro  TCPS, SMES  MWO based FuzzyPIDF 
Ref.  Type of Power System  Areas  Energy Generation Sources  Additional Devices  Controller Approach 

[79]  SSAMGS  1  DEG, WTG  FESS, FC  ABC based fuzzyPID 
[85]  SSAMGS & MSAMGS  1,2  Wind, MH, and BG,    NQOSO based PID 
[87]  SSAMGS  1  WTG, PV  DLC, FC  GA based PID 
[102]  SSAMGS  1  DEG, WT, PV,  FC, BESS, FESS  ABC based TSMC 
[103]  SSAMGS  1  PV, WTG, DEG,  AE, FC, BESS  PI control 
[104]  SSAMGS  1  WTG, DEG, PV  BESS, FESS  LADR control 
[105]  SSAMGS  1  Wind, PV, DEG  BESS, SMES  PSO based ANN 
[106]  MSAMGS  2  WTG and PV  SMES, BES  SSO based PID 
[107]  MSAMGS  2  MT, PV DEG  FC, BESS  ISSO based typeII fuzzy PID 
[108]  MSAMGS  3  WTG, PV, PTC  ESS  MBA based 2DOFPID 
Ref.  Power System Configuration  Controller Approach  Operating Scenarios  Over Shoots  Settling Times  

[33]  Two area power system with thermal, hydro and gas power plants in coordination with CES/DFIG and TCPS  Integral controller  Contract scenarios:  $\Delta {f}_{1}$  $\Delta {f}_{2}$  $\Delta {P}_{tie12}$  $\Delta {f}_{1}$  $\Delta {f}_{2}$  $\Delta {P}_{tie12}$ 
Unilateral contract
Bilateral contract Contract with violation  0.03279 0.0756 0.07567  0.029 0.07932 0.079  0.0038 0.01114 0.01123  50.22 50.05 94.3  47.31 49.01 87.03  90.2 65.56 116.1  
[35]  Two area multiple units reheat thermal and, gas power plant with GRCs and GDBs  MSCAFPID based AGC regulator  Contract scenarios:  
Unilateral contract
Bilateral contract Contract with violation  0.000517 0.00291 0.004255  0.000471 0.004555 0.003285  0.000311 0 0  2.073 6.273 3.731  6.461 4.555 2.398  4.98 1.837 0.642  
[55]  Two area multiple units nonreheat thermal system with HVDC  WGA based SAFPID Controller  Contract scenarios:  
Unilateral contract
Bilateral contract Contract with violation  0.000135 0.0000813 0.0003905  0.0000662 0.0000813 0.0003529  0.0000329 0.0001680 0.0001680  0.57 2.29 2.96  5.07 2.29 3.47  3.94 5.07 5.68  
[34]  Two area multiple units reheat thermal power system with IPFC and RFB  BFO based Integral Controller  Controller comparisons:  
Integral controller
Integral controller with IPFC Integral controller with IPFC and RFBs  0.321 0.204 0.148  0.224 0.112 0.082  0.081 0.059 0.042  16.69 6.27 4.98  15.48 5.47 4.72  14.46 7.46 6.12 
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Ullah, K.; Basit, A.; Ullah, Z.; Aslam, S.; Herodotou, H. Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview. Energies 2021, 14, 2376. https://doi.org/10.3390/en14092376
Ullah K, Basit A, Ullah Z, Aslam S, Herodotou H. Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview. Energies. 2021; 14(9):2376. https://doi.org/10.3390/en14092376
Chicago/Turabian StyleUllah, Kaleem, Abdul Basit, Zahid Ullah, Sheraz Aslam, and Herodotos Herodotou. 2021. "Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview" Energies 14, no. 9: 2376. https://doi.org/10.3390/en14092376