Document Type : Research Paper

Authors

Electrical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.

Abstract

The traditional concepts and practices of power systems are superimposed by economic market management. So OPF has become complex, and classical optimization methods were used to solve OPF effectively. But, in recent years, Artificial Intelligence methods (GA, etc.) have emerged that can solve highly complex OPF problems. In this work two algorithms, were used for the solution of dynamic optimal power flow (OPF) problem taking the transmission losses and the cost of generation as the main constraints. Both algorithms were tested on a 14-bus IEEE test system. The contingency analysis was considered in the application of the algorithms. Additionally, a comparison was made between the two algorithms. The obtained results showed the effectiveness of the GA algorithm over the traditional algorithm

Graphical Abstract

Highlights

  • Genetic Algorithm and Newton Raphson (NR) based approach to Optimal Power Flow problem has been presented.
  • Both algorithms were tested on a 14-bus IEEE test system.
  • The Genetic Algorithm Optimization (GAs) is very efficient in solving the OPF problem

Keywords

[1] S. A. Soman, S. A. Khaparde, Shubha Pandit, Computational Methods for Large Sparse Power Systems Analysis:An Object-Oriented Approach, Book, The Springer International Series in Engineering and Computer Science, (2002).
[2] Montather Fadhil Meteb," Particle Swarm Optimization (PSO) Based Optimal Power Flow For The Iraqi EHV Network", A Thesis Submitted to the College of Engineering University of Baghdad, (2012).
[3] Suresh, K, Optimal Power Flow Of An Interconnected Power System Using Linear Programming And Artificial Neural Network, Department Of Electrical And Electronic Engineering Bharath University Chennai – India (2011).
[4] K.S. Verma, Optimal Power Flow using Genetic Algorithm and Particle Swarm Optimization, IOSR Journal of Engineering (IOSRJEN), 2 (2012) 46-49.
[5] Jinendra Rahul, Yagvalkya Sharma, and Dinesh Birla, Reduction of Transmission Losses based on Optimal Power Flow using Genetic Algorithm, Int. J. Comput. & Technol. (IJCT), 2 (2012).
[6] G. A. Ajenikoko, O. E. Olabode, and A. E. Lawal, Application of Firefly Optimization Technique for Solving Convex Economic Load Dispatch of Generation on Nigerian 330 kV, 24-BUS Grid System, Eur. J. Eng. Res. & Sci. (EJERS), 3 (2018).
[7] Attia, Yusuf. A. Al-Turki & Hussein F. Soliman, Genetic Algorithm-Based Fuzzy Controller for Improvingthe A. F Dynamic Performance of Self-Excited Induction Generator, Arabian J. Sci. & Eng. (AJSE), 37 (2012) 665-682.
[8]  Duman , A Modified Moth Swarm Algorithm Based on an Arithmetic Crossover for Constrained Optimization and Optimal Power Flow Problems, IEEE Trans. Power Systems, 6 (2017) 45394-45416.
[9] Andreas Venzke, Lejla Halilbasic, Uros Markovic, Gabriela Hug, and Spyros Chatzivasileiadis, Convex Relaxations of Chance Constrained AC Optimal Power Flow, IEEE Transactions on power systems, 33 (2017) 2829-2841.
[10] Andreas Venzke, Lejla Halilbasic, Uros Markovic, Gabriela Hug, and Spyros Chatzivasileiadis, Convex Relaxations of Chance Constrained AC Optimal Power Flow, IEEE Transactions on power systems, 33 (2017) 2829-2841.
[11] A.Singh, H.D.Singh, and V.Singh, Optimal Power Flow Solution of Transmission Line Network of Electric power System using Genetic Algorithm Technique,  Int. Res. J. Eng. & Technol. (IRJET), 5 (2018) 56-72.
[12] E.Davoodi, E. Babaei, B. Mohammadi-ivatloo and M. Rasouli, A Novel Fast Semidefinite Programming-Based Approach for Optimal Reactive Power Dispatch, IEEE Transactions on Industrial Informatics, 16 (2019) 288-298.
[13] J. Kardos, D. Kourounis and O. Schenk, Two-Level Parallel Augmented Schur Complement Interior-Point Algorithms for the Solution of Security Constrained Optimal Power Flow Problems, IEEE Transactions on power systems, 35 (2019) 1340-1350.
[14] Z. D. Mihai D. Rotaru and Jan K. Sykulski, Kriging Assisted Surrogate Evolutionary Computation to Solve Optimal Power Flow Problems, IEEE Transactions on power systems, 35 (2019) 831-839.
[15] V. V. Mehtre and A. S. Newton Raphson Single and Multiple Variable Methods to Obtain the Solutions of Linear and Non-Linear Equations, IEEE Transactions on power systems, 3 (2019).
[16] Join Richard Horton. On a comparison of Newton–Raphson solvers for power flow problems, IEEE Trans. Power Apparatus and Systems, 360 (2019) 157-169.
[17] Hadi Saadat, Power System Analysis, 2nd Edition, McGraw-Hill, Inc. (2004).
[18] D.G. Luenberger, Introduction to Linear and Nonlinear Programming, 2nd edn, Addison-Wesley, New York, (1984).
[19] D.I. Sun, B. Ashley, B. Brewer, A. Hughes and W.F. Tinney, Optimal Power Flow By Newton Approach, IEEE Trans. Power Apparatus and Systems, 103 (1984) 2864–2880.
[20] H. J., Adaptation in Natural and Artificial Systems, MIT Press, (1975).
[21] Samir Sami Mahmood, Genetic Algorithm Based Load Flow Solution Problem of Electrical Power Systems, A Thesis Submitted to the College of Engineering University of Baghdad, (2008).