Solution to economic dispatch problem with valve-point loading effect by using catfish PSO algorithm

doi:10.1007/s11708-014-0305-y

Front. Energy

2014, Vol. 8

Issue (3) : 290-296 https://doi.org/10.1007/s11708-014-0305-y

RESEARCH ARTICLE

Solution to economic dispatch problem with valve-point loading effect by using catfish PSO algorithm

K. MURALI,T. JAYABARATHI(

)

School of Electrical Engineering, VIT University, Vellore 632014, India

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Abstract

This paper proposes application of a catfish particle swarm optimization (PSO) algorithm to economic dispatch (ED) problems. The ED problems considered in this paper include valve-point loading effect, power balance constraints, and generator limits. The conventional PSO and catfish PSO algorithms are applied to three different test systems and the solutions obtained are compared with each other and with those reported in literature. The comparison of solutions shows that catfish PSO outperforms the conventional PSO and other methods in terms of solution quality though there is a slight increase in computational time.

Keywords economic dispatch (ED) valve point loading catfish particle swarm optimization (PSO) optimization

Corresponding Author(s): T. JAYABARATHI

Issue Date: 09 September 2014

Cite this article:

K. MURALI,T. JAYABARATHI. Solution to economic dispatch problem with valve-point loading effect by using catfish PSO algorithm[J]. Front. Energy, 2014, 8(3): 290-296.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-014-0305-y
https://academic.hep.com.cn/fie/EN/Y2014/V8/I3/290

Tab.1 Comparison of best and mean results for 3-unit test system

Tab.2 Comparison of best results between catfish PSO and PSO for the 3-unit test system

Tab.3 Relative frequency of convergence for 50 trials for test case 1

Fig.1 Convergence characteristics of catfish PSO and PSO for the 3-unit test system

Tab.4 Comparison of best and mean results for the 13-unit test system

Tab.5 Comparison of best results between catfish PSO and PSO for the 13-unit test system

Tab.6 Relative frequency of convergence for 50 trials for case 2

Fig.2 Convergence characteristics of Catfish PSO and PSO for the 13-unit test system

Tab.7 Comparison of best and mean results for the 40-unit test system

Tab.8 Comparison of best results between catfish PSO and PSO for the 40-unit test system

Tab.9 Relative frequency of convergence for 50 trials for case 3

Fig.3 Convergence characteristics of catfish PSO and PSO for the 40-unit test system

1	Wood A J, Wollenberg B F. Power generation Operation, and Control. 2nd. Wiley, 1996, 96
2	Dodu J C, Martin P, Merlin A, Pouget J. An optimal formulation and solution of short-range operating problems for a power system with flow constraints. Proceedings of the IEEE, 1972, 60(1): 54–63 doi: 10.1109/PROC.1972.8557
3	Chen C L, Wang S C. Branch-and-bound scheduling for thermal generating units. IEEE Transactions on Energy Conversion, 1993, 8(2): 184–189 doi: 10.1109/60.222703
4	Parikh J, Chattopadhyay D. A multi-area linear programming approach for analysis of economic operation of the Indian power system. IEEE Transactions on Power Systems, 1996, 11(1): 52– 58 doi: 10.1109/59.485985
5	Nabona N, Freris L L. Optimisation of economic dispatch through quadratic and linear programming. Proceedings of the Institution of Electrical Engineers, 1973, 120(5): 574–580 doi: 10.1049/piee.1973.0122
6	Liang Z X, Glover J D. A zoom feature for a dynamic programming solution to economic dispatch including transmission losses. IEEE Transactions on Power Systems, 1992, 7(2): 544–550 doi: 10.1109/59.141757
7	Kumar S, Naresh R. Nonconvex economic load dispatch using an efficient real-coded genetic algorithm. Applied Soft Computing, 2009, 9(1): 321–329 doi: 10.1016/j.asoc.2008.04.009
8	Sa-ngiamvibool W, Pothiya S, Ngamroo I. Multiple tabu search algorithm for economic dispatch problem considering valve-point effects. International Journal of Electrical Power & Energy Systems, 2011, 33(4): 846–854 doi: 10.1016/j.ijepes.2010.11.011
9	Su C T, Lin C T. New approach with a Hopfield modeling framework to economic dispatch. IEEE Transactions on Power Systems, 2000, 15(2): 541–545 doi: 10.1109/59.867138
10	Pothiya S, Ngamroo I, Kongprawechnon W. Ant colony optimisation for economic dispatch problem with non-smooth cost functions. International Journal of Electrical Power & Energy Systems, 2010, 32(5): 478–487 doi: 10.1016/j.ijepes.2009.09.016
11	Sinha N, Chakrabarti R, Chattopadhyay P. Evolutionary programming techniques for economic load dispatch. IEEE Transactions on Evolutionary Computation, 2003, 7(1): 83–94 doi: 10.1109/TEVC.2002.806788
12	Jayabarathi T, Bahl P, Ohri H, Yazdani A, Ramesh V. A hybrid BFA-PSO algorithm for economic dispatch with valve-point effects. Frontiers in Energy, 2012, 6(2): 155–163 doi: 10.1007/s11708-012-0189-7
13	Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of 1995 IEEE International Conference on Neural Networks. Perth, Australia, 1995, 1942–1948
14	Chuanwen J, Bompard E. A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment. Energy Conversion and Management, 2005, 46(17): 2689–2696 doi: 10.1016/j.enconman.2005.01.002
15	Selvakumar A I, Thanushkodi K. A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Transactions on Power Systems, 2007, 22(1): 42–51 doi: 10.1109/TPWRS.2006.889132
16	Yang X S, Sadat Hosseini S S, Gandomi A H. Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect. Applied Soft Computing, 2012, 12(3): 1180–1186 doi: 10.1016/j.asoc.2011.09.017
17	Wang L, Li L P. An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems. International Journal of Electrical Power & Energy Systems, 2013, 44(1): 832–843 doi: 10.1016/j.ijepes.2012.08.021
18	Zare K, Haque M T, Davoodi E. Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method. Electric Power Systems Research, 2012, 84(1): 83–89 doi: 10.1016/j.epsr.2011.10.004
19	Victoire T A A, Jeyakumar A E. Hybrid PSO–SQP for economic dispatch with valve-point effect. Electric Power Systems Research, 2004, 71(1): 51–59 doi: 10.1016/j.epsr.2003.12.017
20	Meng K, Wang H G, Dong Z, Wong K P. Quantum-inspired particle swarm optimization for valve-point economic load dispatch. IEEE Transactions on Power Systems, 2010, 25(1): 215–222 doi: 10.1109/TPWRS.2009.2030359
21	Chuang L Y, Tsai S W, Yang C H. Catfish particle swarm optimization. In: 2008 IEEE Swarm Intelligence Symposium. Missouri, USA, 2008, 1–5

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