Abstract

This paper aims at providing a solution to optimum power flow (OPF) in considered power systems by using a flexible genetic algorithm (GA) model. The proposed approach finds the optimal setting of OPF control variables which include generator active output, generator bus voltages, transformer tap-setting and shunt devices with the objective function of minimizing the fuel cost. The proposed GA is modeled to be flexible for implementation to any power systems with the given system line, bus data, generator fuel cost parameter and forecasted load demand. The GA model has been analyzed and tested on the standard benchmark IEEE 30-bus system and two real time power systems which are an industrial park power system and a gold-copper mining power system both located in Indonesia. The results obtained outperform other approaches which are recently applied to the IEEE 30-bus system with the same control variable maximum & minimum limits and system data. Better results are also found when compared against the configurations used in the two real power systems. These superior results are achieved due to the robust and reliable algorithm of the proposed GA which utilizes the differential evaluation.

References

• Irfan mulyawan malik, Optimum power flow using flexible genetic algorithm model in practical power systems thesis for MASTER of Engineering 2010 NATIONAL UNIVERSITY of SINGAPURE.
• M. Syai'in, K. L. Lian, N. C. Yang and T. H. Chen, A distribution power flow using particle swarm optimization, IEEE Power and Energy Society General Meeting, pp. 1-7, 2012.
• P. Acharjee and S. K. Goswami, Chaotic particle swarm optimization based reliable algorithm to overcome the limitations of conventional power ow methods, Proc. of Power Systems Conference and Exposition, 2009.
• A. A. A. Esmin, G. Lambert-Torres and A. C. Z. de Souza, A hybrid particle swarm optimization applied to loss power minimization, IEEE Trans. on Power Syst. , vol. 20, pp. 859-866, 2005.
• R. Gämperle, S. D. Müller and P. Koumoutsakos, "A parameter study for differential evolution," Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, WSEAS Press, Interlaken, Switzerland, 2002, pp. 293-298.
• S. Das, A. Konar and U. K. Chakraborty, Two improved differential evolution schemes for faster global search, Proceedings of the 2005 conference on Genetic and evolutionary computation (2005), 991-998.
• V. L. Paucar and M. J. Rider, On the use of artificial neural networks for enhanced convergence of the load flow problem in power systems, Proc. of Intelligent Systems Applications to Power Systems, pp. 153-158, 2001.
• Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
• M. S. Osman, M. A. Abo-Sinna, A. A. Mousa, A solution to the optimal power flow using genetic algorithm, Applied Mathematics and Computation. 155 (2004) 391-405.
• G. Duan, Y. Yu, Problem-specific genetic algorithm for power transmission system planning, Electric Power Systems Research. 61 (2002) 41-50.
• F. Li, J. D Pilgrim, C. Dabeedin, A. Chebbo, R. K. Aggarwal, Genetic algorithms for optimal reactive power compensation on the national grid system. Power Systems, IEEE Transactions on, 20 (2005) 493-500.
• K. P. Dahal, S. J. Galloway, G. M. Burt, J. R. McDonald, Generation scheduling using genetic algorithm based hybrid techniques, Power Engineering, LESCOPE'01, Large Engineering Systems Conference (2001) 74 – 78.
• V. Miranda, D. Srinivasan, L. M. Proenca, Evolutionary computation in power systems, International Journal of Electrical Power and Energy System, 20(1998) 89-98.
• B. Stott, Review of load flow calculation methods, IEEE Proc. , vol. 62, pp. 916-929, 1974.
• M R Rashid, M E El-Hawary, Application of computational intelligent techniques for solving the revived optimum power flow problem, Electric Power System Research 79 (2009) 694-702.
• J. A. Momoh, R. Adapa, M. E. El-Hawary, A review of selected optimal power flow literature to 1993, part II: Newton, linear programming and interior point methods, IEEE Transactions on Power Systems Vol. 14 (1) (1999) 105–111.