Evolutionary Based Optimal Power Flow Solution For Load Congestion Using PRNG

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-8
Year of Publication : 2021
Authors : Vijaya Bhaskar K, Ramesh S, Chandrasekar P


MLA Style: Vijaya Bhaskar K, Ramesh S, Chandrasekar P  "Evolutionary Based Optimal Power Flow Solution For Load Congestion Using PRNG" International Journal of Engineering Trends and Technology 69.8(2021):225-236. 

APA Style: Vijaya Bhaskar K, Ramesh S, Chandrasekar P. Evolutionary Based Optimal Power Flow Solution For Load Congestion Using PRNG International Journal of Engineering Trends and Technology, 69(8),225-236.

This paper presents a solution for optimal power flow by considering the random nature of load variations in a regulated electricity network. The algorithms are based on an evolutionary approach. Namely, Improved Learner Performance-based Behaviour algorithm (ILPB), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), and Harris Hawks Algorithm (HHO) are attempted to identify the best solution under random load variations. The concept of a pseudo-random number generator is used to represent the variations in load. The IEEE-30 and IEEE- 118 bus standard systems are considered in addition to the practical 62-bus Indian utility system to evaluate the performance of the algorithms. The systems are assessed with different objectives such as total fuel cost, total active power losses, total voltage deviation, and voltage stability index to achieve the optimal solution for the power flow problem. The purpose of all algorithms is to obtain the optimal solution by minimizing the fitness functions. Based on the optimal value of the solution and convergence characteristics of the test systems, the effectiveness and robustness of the algorithms are compared under random load conditions and definite raise and fall-off load conditions.

[1] Carpentier. M., Contribution à l’ ´ Etude du Dispatching ´ Economique. Bull. de la Soc. Fran. des ´ Elec., 8 (1962) 431– 447.
[2] Zohrizadeh. F., Josz. C., Jin. M., Madani. R., Lavaei. J. and Sojoudi. S., A Survey on Conic Relaxations of Optimal Power Flow Problem. European Journal of Operational Research, (2020).
[3] Lin. J., Li. V. O., Leung. K.C., and Lam. A. Y., Optimal power flow with power flow routers. IEEE Transactions on Power Systems, 32(1) (2017) 531–543.
[4] Saha. A., Das. P.,&Chakraborty. A. K., Water evaporation algorithm: A new metaheuristic algorithm towards the solution of optimal power flow. Engineering Science and Technology, an International Journal, 20(6) (2017) 1540–1552.
[5] A. Santos, G.R.M. Da Costa, Optimal-power-flow solution by Newton’s method applied to an augmented Lagrangian function, IEE Proceedings- IET, 142 (1995) 33–36.
[6] E.P. De Carvalho, A. dos Santos, T.F. Ma, Reduced gradient method combined with augmented Lagrangian and barrier for the optimal power flow problem, Appl. Math. Comput, 200 (2008) 529–536.
[7] J.A. Momoh, M.E. El-Hawary, R. Adapa, A review of selected optimal power flow literature to 1993. Part II: Newton, linear programming and interior-point methods, IEEE Trans. Power Syst. 14 (1999) 105–111.
[8] Ebeed. M., Kamel. S., &Jurado. F., Optimal Power Flow Using Recent Optimization Techniques. Classical and Recent Aspects of Power System Optimization, (2018) 157–183.
[9] Rahman, C. M., & Rashid, T. A., A new evolutionary algorithm: Learner performance-based behavior algorithm. Egyptian Informatics Journal, (2020). doi:10.1016/j.eij.2020.08.003
[10] Blanco. A., Delgado. M., &Pegalajar. M. C., A real-coded genetic algorithm for training recurrent neural networks. Neural Networks, 14(1) (2001) 93–105.
[11] Mirjalili. S. &Lewis.A. The whale optimization algorithm. Adv. Eng. Softw,95 (2016) 51–67.
[12] Jiang. T., Zhang. C., Zhu. H., Gu.J.,& Deng. G., Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm. Mathematics, 6(11) (2018) 220.
[13] Mirjalili. S., Mirjalili. S. M.,& Lewis. A., Grey Wolf Optimizer. Advances in Engineering Software, 69 (2014) 46–61.
[14] Panda. M., & Das. B., Grey Wolf Optimizer and Its Applications: A Survey. Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, (2019) 179–194.
[15] Guha. D., Roy. P. K.,& Banerjee. S., Load frequency control of interconnected power system using grey wolf optimization. Swarm and Evolutionary Computation, 27 (2016) 97–115.
[16] Saremi. S., Mirjalili. S. Z., &Mirjalili, S. M., Evolutionary population dynamics and grey wolf optimizer. Neural Computing and Applications, 26(5) (2014) 1257–1263.
[17] Heidari. A. A., Mirjalili. S., Faris. H., Aljarah.I., Mafarja. M., & Chen. H., Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, (2019).
[18] Bairathi.D.,&Gopalani.D., A Novel Swarm Intelligence Based Optimization Method: Harris’ Hawk Optimization. Intelligent Systems Design and Applications, (2019) 832–842.
[19] Moayedi. H., Abdullahi. M. M., Nguyen. H.,& Rashid. A. S. A., Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils. Engineering with Computers, (2019).
[20] Dodge. Y., A Natural Random Number Generator. International Statistical Review / Revue Internationale de Statistique, 64(3) (1996) 329.

Grey Wolf Optimization; Harris Hawks Optimization; Improved Learner Performance-based Behaviour algorithm; Optimal power flow; Whale Optimization Algorithm