Economic Dispatch with Multiple Fuel Options Using CCF
R. Anandhakumar, S. Subramanian
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DOI: 10.4236/epe.2011.32015   PDF    HTML     7,414 Downloads   12,541 Views   Citations

Abstract

This paper presents an efficient analytical approach using Composite Cost Function (CCF) for solving the Economic Dispatch problem with Multiple Fuel Options (EDMFO). The solution methodology comprises two stages. Firstly, the CCF of the plant is developed and the most economical fuel of each set can be easily identified for any load demand. In the next stage, for the selected fuels, CCF is evaluated and the optimal scheduling is obtained. The Proposed Method (PM) has been tested on the standard ten-generation set system; each set consists of two or three fuel options. The total fuel cost obtained by the PM is compared with earlier reports in order to validate its effectiveness. The comparison clears that this approach is a promising alterna-tive for solving EDMFO problems in practical power system.

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R. Anandhakumar and S. Subramanian, "Economic Dispatch with Multiple Fuel Options Using CCF," Energy and Power Engineering, Vol. 3 No. 2, 2011, pp. 113-119. doi: 10.4236/epe.2011.32015.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] A. J. Wood and B. F. Wollenberg, “Power Generation Operation and Control,” 2nd edition, Wiley, New York, 1996.
[2] A. Chakrabarti and S. Halder, “Power System Analysis Operation and Control,” 3rd edition, PHI, New Delhi, 2010.
[3] K. P. Wong and Y. W. Wong, “Genetic and Genetic/ Simulated-Annealing Approaches to Economic Dispatch,” IEE Proceedings of Generation, Transmission and Distribution, Vol. 141, No. 5, 1994, pp. 507-513. doi: 10.1049/ip-gtd:19941354
[4] Z. L. Gaing, “Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constraints,” IEEE Transactions on Power Systems, Vol. 18, No.3, 2003, pp. 1187-1195. doi: 10.1109/TPWRS.2003.814889
[5] B. K. Panigrahi, S. R. Yadav, Shubham Agrawal and M. K. Tiwari, “A Clonal Algorithm to Solve Economic Load Dispatch,” Electric Power Systems Research, Vol. 77, No. 10, 2007, pp. 1381-1389. doi:10.1016/j.epsr.2006.10.007
[6] Cheng-Chien Kuo, “A Novel Coding Scheme for Practical Economic Dispatch by Modified Particle Swarm Approach,” IEEE Transactions on Power Systems, Vol. 23, No.4, 2008, pp. 1825-1835. doi: 10.1109/TPWRS.2008.2002297
[7] N. Noman and H. Iba, “Differential Evolution for Economic Dispatch Problems,” Electric Power Systems Research, Vol. 78, No. 8, 2008, pp. 1322-1331. doi:10.1016/j.epsr.2007.11.007
[8] B. K. Panigrahi and V. R. Pandi, “Bacterial Foraging Optimization: Nelder-Mead Hybrid Algorithm for Economic Load Dispatch,” IET Generation Transmission Distribution, Vol. 2, No.4, 2008, pp. 556-565. doi:10.1049/iet-gtd:20070422
[9] C. E. Lin and G. L. Viviani, “Hierarchical Economic Dispatch for Piecewise Quadratic Cost Functions,” IEEE Transaction on Power Application Systems, PAS-103, No. 6, 1984, pp. 1170-1175. doi:10.1109/TPAS.1984.318445
[10] J. H. Park, Y. S. Kim, I. K. Eom and K. Y. Lee, “Economic Load Dispatch for Piecewise Quadratic Cost Function using Hopfiled Neural Network,” IEEE Transactions on Power Systems, Vol. 8, No. 3, 1993, pp. 1030-1038. doi:10.1109/59.260897
[11] K. Y. Lee, A. Sode-Yome and J. H. Park, “Adaptive Hopfiled Neural Network for Economic Load Dispatch,” IEEE Transactions on Power Systems, Vol. 13, No. 13, 1998, pp. 519-526. doi:10.1109/59.667377
[12] S. Baskar, P. Subbaraj and M. V. C. Rao, “Hybrid Real Coded Genetic Algorithm Solution to Economic Dispatch Problem,” Computers Electrical Engineering, Vol. 29, No. 3, 2003, pp. 407-419. doi:10.1016/S0045-7906(01)00039-8
[13] S. C. Lee and Y. H. Kim, “An Enhanced Lagrangian Neural Network for the ELD Problems with Piecewise Quadratic Cost Functions and Nonlinear Constraints,” Electric Power Systems Research, Vol. 60, No. 3, 2002, pp. 167-177. doi:10.1016/S0378-7796(01)00181-X
[14] J. Ryul won and Y. Moon park, “Economic Dispatch Solutions with Piecewise Quadratic Cost Functions Using Improved Genetic Algorithm,” Electric Power Energy Systems, Vol. 25, No. 5, 2003, pp. 355-361. doi:10.1016/S0142-0615(02)00098-4
[15] Jong-Bae Park, Ki-Song Lee, Joong-Rin Shin and K. Y. Lee, “A Particle Swarm Optimization for Economic Dispatch with Nonsmooth Cost Functions,” IEEE Transactions on Power Systems, Vol. 20, No. 1, 2005, pp. 34-42. doi: 10.1109/TPWRS.2004.831275
[16] Derong Liu and Ying cai, “Taguchi Method for Solving the Economic Dispatch Problem with Nonsmooth Cost Functions,” IEEE Transactions on Power Systems, Vol. 20, No. 4, 2005, pp. 2006-2014. doi:10.1109/TPWRS.2005.857939
[17] T. Jayabarathi, K. Jayaprakash, D. N. Jayakumar and T. Raghunathan, “Evolutionary Programming Techniques for Different Kinds of Economic Dispatch Problems,” Electric Power Systems Research, Vol. 73, No. 2, 2005, pp. 169-176. doi:10.1016/j.epsr.2004.08.001
[18] D. N. Jeyakumar, T. Jayabharathi and T. Raghunathan, “Particle Swarm optimization for Various Types of Economic Dispatch Problems,” Electric Power Energy Systems, Vol. 28, No. 1, 2006, pp. 36-42. doi:10.1016/j.ijepes.2005.09.004
[19] Y. M. Park, J. R. Won and J. B. Park, “A New Approach to Economic Load Dispatch Based on Improved Evolutionary Programming,” Engineering Intelligent Systems Electrical Engineering Communication, Vol. 6, 1998, pp. 103-110.

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