Optimization of Automatic Generation Control and Economic Load Dispatch for Two Area Six Unit Interconnected Power System

Zwayyer, Faeq J.; Abbood, Afaneen A.; Hussein, Jasim F.

doi:10.30684/etj.v39i10.2158

	Optimization of Automatic Generation Control and Economic Load Dispatch for Two Area Six Unit Interconnected Power System
Engineering and Technology Journal
Article 15, Volume 39, Issue 10, 2021, Pages 1610-1624 PDF (1.01 M)
Document Type: Research Paper
DOI: 10.30684/etj.v39i10.2158
Authors
Faeq J. Zwayyer^* ; Afaneen A. Abbood; Jasim F. Hussein
Electrical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
Abstract
This paper proposes an integrated Economic Load Dispatch (ELD) and Automatic Generation Control (AGC) for interconnected power systems. Based on their participation factor determined from the economic load dispatch calculation, each unit shares the total change in the same control region. In this study, two control areas are considered. Three thermal units are located in each control area. An integral controller (I) is only used for the AGC mechanism's secondary controller and is used for the primary controller for the ELD mechanism. An Improved Grey Wolf Optimizer (IGWO) technique is used to evaluate the optimum parameters of the integral controllers for primary and secondary controllers. An integral time square error (ITSE) has been used as the objective function to tune the suitability of the proposed controller gains. The simulation results demonstrate that the integrated AGC with ELD has the superiority in reducing the overshoot and fast steady state compared with AGC only.
Highlights
Coordination of automatic generation control and economic dispatch lowers running cost. Power systems interconnecting is more economic than systems individual operation. Novel optimization algorithms are more accurate in tuning the gains of the controllers. Without automatic generation control, steady state frequency deviation is not zero.
Keywords
Automatic Generation Control; Multi-area multi-unit; Area Control Error; Grey Wolf Optimizer; Proportional Integral Controller; Economic dispatch

References
[1] J. Wood and B.F. Wollenberg, Power generation, operation and control, John Wiley & sons, Inc., USA, (2014). [2] D. Das, Electrical power systems, New Age International Publishers, New Delhi, (2006). [3] H. Saadat, Power System Analysis, McGraw-Hill Inc., USA, (1999). [4] Fister Jr, et al, A brief review of nature inspired algorithms for optimization, arXiv preprint arXiv:1307.4186, 1-7, (2013). [5] Talbi, El-Ghazali, Metaheuristics: from design to implementation, (Vol. 74): John Wiley & Sons, (2009). [6] Sörensen, Kenneth, Metaheuristics—the metaphor exposed, International Transactions in Operational Research, 22, 1, (2015) 3-18. [7] Glover, Fred, Tabu search—part I, ORSA Journal on computing, 1, 3, (1989), 190-206. [8] Glover, Fred, Tabu search—part II, ORSA Journal on computing, 2, 1, (1990), 4-32. [9] Lourenço, et al., Iterated local search Handbook of metaheuristics, 320-353,Springer, (2003). [10] Hasançebi, et al., Adaptive dimensional search: a new metaheuristic algorithm for discrete truss sizing optimization, Computers & Structures, 154, (2015), 1-16. [11] Zang, Hongnian, et al., A review of nature-inspired algorithms, Journal of Bionic Eng., 7, 4, (2010), S232-S2. [12] Holland, John H, Genetic algorithms, scientific American, 267, 1, (1992), 66-73. [13] Koza, John R., Genetic programming, (1997). [14] Storn, Rainer, & Price, Kenneth, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, Journal of global optimization, 11, 4, (1997), 341-359. [15] E. Osman K, & E. Ibrahim., A new optimization method: big bang–big crunch. Advances in Eng. Software, 37, 2, (2006), 106-111. [16] Rashedi, Esmat, et al, GSA: a gravitational search algorithm, Information sciences, 179, 13, (2009), 2232-2248. [17] Kaveh, A, & Talatahari, S., A novel heuristic optimization method: charged system search. Acta Mechanica, 213, 3-4, (2010), 267-289. [18] Kaveh, A, & Khayatazad, M., A new meta-heuristic method: ray optimization, Computers & structures, 112, (2012), 283-294. [19] Hatamlou, Abdolreza., Black hole: A new heuristic optimization approach for data clustering. Information sciences, 222, (2013), 175-184. [20] Zhao, Weiguo, et al., Atom search optimization and its application to solve a hydrogeological parameter estimation problem, Knowledge-Based Systems, 163, (2019), 283-304. [21] Hashim, et al., Henry gas solubility optimization: A novel physics-based algorithm, Future Generation Computer Systems, 101, (2019), 646-667. [22] Eberhart, Russell, & Kennedy, James, A new optimizer using particle swarm theory, Paper presented at the MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, (1995). [23] Karaboga, Dervis, & Basturk, Bahriye., A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of global optimization, 39, 3, (2007), 459-471. [24] G. A. Hossein, & A. A. Hossein., Krill herd: a new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17,12, (2012), 4831-4845. [25] M. Seyedali, et al., Grey wolf optimizer. Advances in Eng. Software, 69, (2014), 46-61. [26] Mirjalili, Seyedali, & Lewis, Andrew, The whale optimization algorithm. Advances in Eng. Software, 95, (2016), 51-67. [27] Askarzadeh, Alireza., A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm, Computers & Structures, 169, (2016), 1-12. [28] Heidari, et al., Harris hawks optimization: Algorithm and applications, Future generation computer systems, 97, (2019), 849-872. [29] Chen, Xu, et al., Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation, Applied energy, 212, (2018), 1578-1588. [30] F. Hossam, et al., Optimizing feed forward neural networks using krill herd algorithm for e-mail spam detection, Paper presented at the IEEE Jordan Conference on Applied Electrical Eng. and Computing Technologies (AEECT), (2015). [31] T. Thaer, et al., Binary Harris Hawks Optimizer for High-Dimensional, Low Sample Size Feature Selection Evolutionary Machine Learning Techniques, 251-272, Springer, (2020). [32] Zamani, et al., Feature selection based on whale optimization algorithm for diseases diagnosis, International Journal of Computer Science and Information Security, 14, 9, (2016) a, 1243-1247. [33] Mirjalili, et al., Grey wolf optimizer. Advances in Eng. Software, 69, (2014), 46-61. [34] Mirjalili, Seyedali., How effective is the Grey Wolf optimizer in training multi-layer perceptron’s, Applied Intelligence, 43,1, (2015), 150-161. [35] Song, et al., Grey wolf optimizer for parameter estimation in surface waves. Soil Dynamics and Earthquake Eng., 75, (2015), 147-157. [36] Saremi, et al., Evolutionary population dynamics and grey wolf optimizer. Neural Computing and Applications, 26, 5, (2015), 1257-12. [37] Faris, Hossam, et al., An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowledge-Based Systems, 154, (2018), 43-67. [38] Song, Xianhai, et al., Grey wolf optimizer for parameter estimation in surface waves. Soil Dynamics and Earthquake Eng., 75, (2015), 147-157. [39] AL-Hayali W. S., Small Perturbation Stability Improvement of Electric Power System Using Neural Network, Dept. of Education Technology Eng., Univ. of Technology, Ph.D, Thesis, (2003). [40] N. Kaur, Analysis of AGC Using Conventional and Fuzzy Logic Controller, Electrical and Instrumentation Dept., Thapar Univ., M.Sc. Thesis, June, (2008). [41] O. I. Elgerd, Electric Energy System Theory- An Introduction, McGraw-Hill Hill, (2019). [42] H. Altas and J. Neyens, A Fuzzy Logic Load- Frequency controller for Power System, Int, Symposium on Math, Method of Eng., MME- 06, Cankaya Univ., Turkey, (2006). [43] K. Praserwong and N. Mithulananthan, Conventional and Fuzzy Logic Controllers at Generator Location for Low Frequency Oscillation Damping, International Journal of Electrical and Electronics Eng., 3, 11, (2009). [44] H. A. Asghar, & P. Parham, An efficient modified grey wolf optimizer with Lévy flight for optimization tasks. Applied Soft Computing, 60, (2017), 115-134. [45] MacNulty, et.al, A proposed ethogram of large-carnivore predatory behavior, exemplified by the wolf. Journal of Mammalogy, 88, 3, (2007), 595-605. [46] Y. Arya, Automatic generation control of two-area electrical power systems via optimal fuzzy classical controller, J. Franklin Inst. 355, 5, (2018), 2662–2688. [47] Y. Arya, Improvement in automatic generation control of two-area electric power systems via a new fuzzy aided optimal PIDN-FOI controller, ISA Trans. 80, (2018), 475–490. [48] Y. Arya, Impact of ultra-capacitor on automatic generation control of electric energy systems using an optimal FFOID controller, Int. J. Energy Res. 43, 14, (2019), 8765–8778. [49] Y. Arya, A new optimized fuzzy FOPI-FOPD controller for automatic generation control of electric power systems, J. Franklin Inst. 356, 11, (2019), 5611–5629. [50] Y. Arya, AGC performance enrichment of multi-source hydrothermal gas power systems using new optimized FOFPID controller and redox flow batteries, Energy, 127, (2017), 704–715. [51] A. A. Alkhazraji, I. salim, an algorithm for automatic generation control of Iraqi power system using fuzzy logic technique, Diyala Journal of Eng. Sciences, 09, 03, September, (2016). [52] A. A. Al-Khazragy & M. Abdulkareem Hasan, The Application of Neural Network on the Contingency Analysis of Iraqi Super Grid Network, Eng. & Tech. Journal, 28, 1, (2010). [53] A. A. Abood & A. J. Hameed, Iraqi Super Grid Network State Estimation Using PSO Technique, Journal of Univ. of Thi Qar, 10, 2, June, (2015). [54] N. Shahraki, et al., An Improved Grey Wolf Optimizer for Solving Eng. Problems, Expert Systems with Applications, 113917, (2020). [55] Houssein, E. H., et al., Lévy flight distribution: A new metaheuristic algorithm for solving Eng. optimization problems, Eng. Applications of Artificial Intelligence, 94, (2020), 103731. [56] A. A. Al-Khazragy and A. H. Mohammed, A Proposed Algorithm for Reactive Power & Voltage Coordination in Distribution Systems Using Fuzzy Technique, Eng. and Technology Journal , 27, 12, (2009), 2447-2464. [57] A. A. Al-Khazragy and, M. A. Hasan, The Application of Neural Network on The Contingency Analysis of Iraqi Super Grid Network, Eng. and Technology Journal , 28, 1, (2010), 199-213.
Statistics Article View: 554 PDF Download: 649