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Application of Biogeography-based Optimization for Economic Dispatch Problems

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 47 - Number 13
Year of Publication: 2012
Authors:
Bhuvnesh Khokhar
K. P. Singh Parmar
Surender Dahiya
10.5120/7249-0309

Bhuvnesh Khokhar, K.P.Singh Parmar and Surender Dahiya. Article: Application of Biogeography-based Optimization for Economic Dispatch Problems. International Journal of Computer Applications 47(13):25-30, June 2012. Full text available. BibTeX

@article{key:article,
	author = {Bhuvnesh Khokhar and K.P.Singh Parmar and Surender Dahiya},
	title = {Article: Application of Biogeography-based Optimization for Economic Dispatch Problems},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {47},
	number = {13},
	pages = {25-30},
	month = {June},
	note = {Full text available}
}

Abstract

In this paper, Biogeography-based optimization (BBO) algorithm has been presented for solving the economic dispatch (ED) problems. An optimal short-term thermal generation schedule for 24 time intervals has been presented for the same purpose. The BBO algorithm has been applied to two different test systems, one consisting of three generators and the other of six generators. The results obtained are compared with the conventional Lagrange multiplier method and the particle swarm optimization (PSO) method. The results show that the presented BBOalgorithm provides comparatively better solutions in terms of total fuel cost as compared to other methods. Also, the global search capability is enhanced and premature convergence is avoided.

References

  • Kothari, D. P. andDhillon, J. S. 2010. 'Power System Optimization', 2nd edition, PHI, New Delhi
  • Mohamed-Nor, K. and Rashid, A. H. A. 1991. 'Efficient economic dispatch algorithm for thermal unit commitment', IEE Proceedings C, vol. 138 (3), pp. 213-217
  • Chung-Lung, Chen and ChenNanming 2001. 'Direct search method for solving economic dispatch problem considering transmission capacity constraints', IEEE Trans. on Power Systems, vol. PWRS-16 (4), pp. 764-769
  • Brar, Y. S. , Dhillon J. S. and Kothari D. P. 2002. 'Multi-objective load dispatch by fuzzy logic searching weightage pattern', Electric Power Systems Research, vol. 63 (2), pp. 149-160
  • Lee, K. Y. , Sode-Yome A. and Park J. H. 1998. 'Adaptive Hopfield neural networks for economic load dispatch', IEEE Trans. on Power Systems, vol. 13 (2), pp. 519-526
  • Yalcinoz, T. and Short M. J. 1998. 'Neural networks approach for solving economic dispatch problem with transmission capacity constraints', IEEE Trans. on Power Systems, vol. 13, pp. 307-313
  • Walter, D. C. andSheble G. B. 1993. 'Genetic algorithm solution of economic dispatch with valve-point loading', IEEE Trans. on Power Systems, vol. 8 (3), pp. 1325-1332
  • Sheble, G. B. and Brittig K. 1995. 'Redefined genetic algorithm – economic dispatch example', IEEE Trans. on Power Systems, vol. 10, pp. 117-124
  • Fogel, D. B. 2000. 'Evolutionary computation: Towards a new philosophy of machine intelligence', 2nd edition, Piscataway, NJ: IEEE Press
  • Eberhart, R. C. and Shi Y. 1998. 'Comparison between genetic algorithms and particle swarm optimization', Proceedings of IEEE Int. Conf. on Evol. Comput. , pp. 611-616
  • Gaing, Z. L. 2003. 'Particle swarm optimization to solve the economic dispatch considering the generator constraints', IEEE Trans. on Power Systems, vol. 18 (3), pp. 1187-1195
  • Ratnaweera, A. , Halgamuge S. K. and Watson H. C. 2004. 'Self-organizing hierarchical particle swarm optimizer with time varying acceleration coefficients, IEEE Trans. on Evol. Comput. , vol. 8 (3), pp. 240-255
  • Selvakumar, A. I. and Thanushkodi K. 2007. 'A new particle swarm optimization solution to nonconvexeconomic dispatch problems, IEEE Trans. on Power Systems, vol. 22 (1), pp. 42-51
  • Panigrahi, B. K. and Pandi V. R. 2008 'Bacterial foraging optimization: Nelder-Mead algorithm for economic load dispatch', Generation, Transmission and Distribution, IET, vol. 2 (4), pp. 556-565
  • NomanNasimul, and Iba Hitoshi 2008. 'Differential evolution for economic load dispatch problems', Electric Power Systems Research, vol. 78, pp. 1322-1331
  • Mezura-Montes, E. , Velazquez-Reyes, J. and Coello C. A. C. 2006. 'Modified differential evolution for constrained optimization', Proceedings of the 2006 IEEE Congress on Evolutionary Computation, pp. 332– 339
  • Simon D. 2008. 'Biogeography-based optimization', IEEE Trans. on Evol. Comput. , vol. 12 (6), pp. 702-713
  • Bhattacharya A. and Chattopadhyay P. K. 2010. 'Biogeography-based optimization for different economic load dispatch problems', IEEE Trans. on Power Systems, vol. 25(2), pp. 1064-1077
  • Roy, P. K. , Ghoshal S. P. and Thakur S. S. 2009 'Biogeography based optimization technique applied to multi-constraints economic load dispatch problems', IEEE T & D Asia
  • Kothari, D. P. and Parmar K. P. Singh 2006. 'A novel approach for eco-friendly and economic power dispatch using MATLAB', International Conference on Power Electronics, Drives and Energy Systems, PEDES '06, pp. 1-6
  • Khokhar,Bhuvnesh and Parmar K. P. Singh 2012. 'A novel weight-improved particle swarm optimization for combined economic and emission dispatch problems', IJEST, vol. 4(5), pp. 2008-2014.