Published October 19, 2016 | Version v1

MULTI-AREA ECONOMIC DISPATCH WITH PROHIBITED OPERATING ZONES USING IMPROVED BAT ALGORITHM

  • 1. Research Scholar, Department of Electrical and Electronics Engineering, Annamalai University, Chidambaram, Tamilnadu
  • 2. Professor, Department of Electrical and Electronics Engineering, Annamalai University, Chidambaram, Tamilnadu

Description

This paper presents application of Improved Bat algorithm for solving Multi-area economic load dispatch problem (MAED) considering tie line constraint, transmission loss, valve point loading effect and prohibited operating zones. Improved bat algorithm is an optimization algorithm motivated by the echolocation behavior of natural bats in finding their foods. Potency of the algorithm is tested on two area system with six generating units and it is compared with artificial bee colony optimization, differential evolution, evolutionary programming and real coded genetic algorithm. The promising results show the quick convergence and effectiveness of the projected technique.

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References

  • 1. Chowdhury BH, Rahman S. A review of recent advances in economic dispatch. IEEE Trans Power System 1990; 5(4):1248–59. 2. Shoults RR, Chang SK, Helmick S, Grady WM. A practical approach to unit commitment, economic dispatch and savings allocation for multiple-area pool operation with import/export constraints. IEEE Trans Power Apparatus System1980; 99(2):625–35. 3. Romano R, Quintana VH, Lopez R, Valadez V. Constrained economic dispatch of multi-area systems using the Dantzig–Wolfe decomposition principle. IEEE Trans Power Apparat Syst 1981; 100(4):2127–37. 4. Desell AL, McClelland EC, Tammar K, Van Horne PR. Transmission constrained production cost analysis in power system planning. IEEE Trans Power Apparat Syst 1984; 103(8):2192–8. 5. Ouyang Z, Shahidehpour SM. Heuristic multi-area unit commitment with economic dispatch. IEE Proc-C 1991; 138(3):242–52. 6. Wang C, Shahidehpour SM. A decomposition approach to non-linear multi area generation scheduling with tie-line constraints using expert systems. IEEE Trans Power Syst 1992; 7(4):1409–18. 7. Streiffert D.Multi-area economic dispatch with tie line constraints.IEEE Trans Power Syst 1995;10(4):1946–51. 8. Jayabarathi T, Sadasivam G, Ramachandran V. Evolutionary programming based multi-area economic dispatch with tie line constraints. Electr Mach Power Syst 2000; 28:1165–76. 9. Manoharan PS, Kannan PS, Baskar S, Willjuice Iruthayarajan M. Evolutionary algorithm solution and KKT based optimality verification to multi-area economic dispatch. Int J Electr Power Energy Syst 2009; 31(7–8):365–73. 10. Prasanna. T.S, and Somasundaram.P. multi-area security constrained economic dispatch by fuzzy- stochastic algorithms. Journal of theoretical and applied information technology. .2005 – 2009. 11. Sharma Manisha, Pandit Manjaree, Srivastava Laxmi. Reserve constrained multi-area economic dispatch employing differential evolution with timevarying mutation. Int J Electr Power Energy Syst 2011; 33(3):753–66. 12. Manoharan .P. S., Kannan p. S., Ramanathan. V. A Novel EP Approach for Multi-area Economic Dispatch with Multiple Fuel Options Turk J Elec Eng & Comp Sci, Vol.17, No.1, 2009, doi: 10.3906/elk-0705-10. 13. Zarei. M, Roozegar.A, Kazemzadeh.R., and Kauffmann J.M. Two Area Power Systems Economic Dispatch Problem Solving Considering Transmission Capacity Constraints. World Academy of Science, Engineering and Technology International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering Vol: 1, No:9, 2007. 14. Arthur K. Kordon. Applying Computational Intelligence. springer 2010. 15. James Kennedy and Russell C. Eberhart. Swarm intelligence. Academic Press 2001. 16. S. sumathi and surekha p. Computational Intelligence Paradigms Theory and Applications using MATLAB.CRC press 2010. 17. Andries P. Engelbrecht. Computational Intelligence an Introduction. John Wiley & Sons 2002. 18. Bijaya Ketan Panigrahi, Yuhui Shi, and Meng-Hiot Lim.Handbook of Swarm Intelligence Concepts, Principles and Applications. springer 2011. 19. X.-S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65-74 (2010). 20. Xin-She Yang, Bat algorithm: literature review and applications, Int. J. Bio-Inspired Computation, Vol. 5, No. 3, pp. 141–149 (2013). DOI: 10.1504/IJBIC. 2013.055093 21. Selim Yilmaz and Ecir U. Kucuksille. Improved Bat Algorithm on Continuous Optimization Problems. Lecture Notes on Software Engineering, Vol. 1, No. 3, August 2013. 22. Walter DC, Sheble GB. Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans Power Syst 1993; 8:1325–32. 23. Chiang C-L. Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels. IEEE Trans Power Syst 2005; 20(4):1690–9. 24. Basu.M .Artificial bee colony optimization for multi-area economic dispatch .Electricla power and energy systems 49(2013)181-187. 25. Gaing Z-L. Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans Power Syst 2003; 18(3):1187–95. 26. Sinha N, Chakrabarti R, Chattopadhyay PK. Evolutionary programming techniques for economic load dispatch. IEEE Trans Evol Comput 2003; 7(1):83–94.