Design and Implementation of Power System Optomization Using Particles Swarm Algorithms for Addressing the Economic Dispatch Problem

This research is aimed to design a power system using particle swarm algorithm (PSA) and test its efficiency on a standard IEEE bus bar system. The algorithm has been modified for power generating system and successfully demonstrates and provides optimization results for six generating units . Reducion in fuel cost by distributing load among the generating units in  inter connected bus bar system is the core area of this research. The losses in power transmission as well as generation are also minimized. Moreover, the PSA coding was executed using MATLAB and the  graphs are shown for comparison with other optimization techniques. The results obtained using PSA were compared with other optimization techniques and PSA was found comparatively better than other techniques. PSA has been found to be reliable for power system optimization and hence suitable for practical purposes.

1. [1]     D. K. &. I. Nagrath, Modern Power System Analysis, New Delhi: Tata Mc Graw Hill, 2003, pp. 258-280.
2. [2]     J. Zhu, Optimization of Power System Operation, Hoboken, New Jersy: John Wiley & Son.inc, 2009.
3. [3]     S. S. &. V. V. V. Karthikeyan, "A New Approach To The Solution of Economic Load Dispatch Using Particle Swarm Optimization with Simulated Annealing," International Journal on Computational Science& Application (IJCSA), vol. 3, no. 3, pp. 37-41, June 3 2013.
4. [4]     Mohn Faazi, Othman,Rubiyan,Mayzuki Khalid, "Solving Economic Load DispatchUsing Particle Swarm Optimization," Journal of Theoritical &Applied InformationTechnology, vol. 46, no. 2, pp. 526-535, 31st-Dec-2012.
5. [5]     S. SARANGI, "Particle Swarm Optimization Apllied To Economic load Dispatch," a thesis submitted in partial fulfillment of the requirements for the degree ofmaster of technology in power control and drives , 2009.
6. [6]     S. Talukder, "Mathematical Modelling and Applicationsof Particle Swarm Optimization," Master’s Thesis Mathematical Modelling and Simulation, Feb 2011.
7. [7]     D. V.Selvi, "Comparative Analysis of Ant Colonyand Particle Swarm Optimization Techniques," International Journal of Computer Applications , vol. 5, no. 4, pp. 1-5, August 2010.
8. [8]     Y. Saber, "Economic dispatch using particle swarm optimization with bacterial foraging effect," Electrical Power and Energy Systems, vol. 34 , p. 38–46, 2012.
9. [9]     SUREKHA P, Dr.S.SUMATHI , “Solving Economic Load Dispatch problems using Differential Evolution with Opposition Based Learning”, WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS ; Issue 1, Volume 9, January 2012
10. [10]  H. M. D. ,. M. P. &. B. K. P. Kamlesh Kumai Vishwakama, "Simulated Annealing For Solving Economic LOad Dispatch Problem with Valve Point Loading Dispatch Problem," International Journal of Engineering, Science & Technology, vol. 4, no. 4, pp. 60-72, 2012
11. [11]  N. A. R. ,. A. B. H. M. Mohd Noor Abbullah, "Efficient Evolution Particle Swarm Optimization Approach For Non Convex Economic Load Dispatch Problem," vol. 89, no. 2a, pp. 139-143, 2013.
12. [12]  V. P. R. Amita Mahor, "Economic dispatch using particle swarm optimization: A review," in Renewable and Sustainable Energy Reviews , India, 2009.
13. [13]  J. S. &. A. Mohan, "Particle Swarm Optimizatiom Approach for Economic Load Dispatch," International Journal of Engineering Research & Appplication, vol. 3, no. 1, pp. 013-022, 2013.
14. [14]  "Solution of Economic Load Dispatch Problems by a Novel," International Journal on Electrical Engineering and Informatics ,, vol. Volume 3, no. 1, pp. 26-36, 2011.
15. [15]  International Journal on Computational Sciences Applications (IJCSA) Vol.3, No.3, June 2013.