Call for Paper - September 2020 Edition
IJCA solicits original research papers for the September 2020 Edition. Last date of manuscript submission is August 20, 2020. Read More

Water Wave Optimization Algorithm for Solving Multi-Area Economic Dispatch Problem

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
L. Lakshminarasimman, M. Siva, R. Balamurugan
10.5120/ijca2017914247

L Lakshminarasimman, M Siva and R Balamurugan. Water Wave Optimization Algorithm for Solving Multi-Area Economic Dispatch Problem. International Journal of Computer Applications 167(5):19-27, June 2017. BibTeX

@article{10.5120/ijca2017914247,
	author = {L. Lakshminarasimman and M. Siva and R. Balamurugan},
	title = {Water Wave Optimization Algorithm for Solving Multi-Area Economic Dispatch Problem},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {5},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {19-27},
	numpages = {9},
	url = {http://www.ijcaonline.org/archives/volume167/number5/27768-2017914247},
	doi = {10.5120/ijca2017914247},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

This paper presents the Water Wave Optimization Algorithm (WWOA) for solving multi-area economic dispatch (MAED) problem with tie line constraints considering transmission losses, area demand constraints, multiple fuels options, valve-point loading effects and prohibited operating zones. Here, the amount of power that can be economically generated in one or more areas are exchanged with other areas with deficient generation through the interconnected tie-lines while meeting out the area wise and total power demand and other constraints is formulated as the MAED problem. WWOA is one of the nature inspired algorithm which mimics the phenomena of water waves for global optimization is implemented for the solution of multi-area economic dispatch problem. The effectiveness of the proposed algorithm has been verified on three different test systems and are compared with Teaching learning based optimization (TLBO), differential evolution (DE), evolutionary programming (EP) and real coded genetic algorithm (RCGA), considering the quality of the solution obtained, and the results shows a quick convergence of the proposed algorithm and are found to be superior than the other methods in the literature and seems to be a potential alternative advancement in practical power system for solving the MAED problems.

References

  1. Chowdhury B.H. and Rahman S. 1990 “A review of recent advances in economic dispatch”, IEEE Trans. Power Syst., 5 (4), 1248-1259.
  2. Shoults R.R., Chang S.K., Helmick S. and Grady W.M., 1980 “A practical approach to unit commitment, economic dispatch and savings allocation for multiple-area pool operation with import / export constraints”, IEEE Trans Power Apparat Syst., 99 (2), 625-635.
  3. Romano R., Quintana V. H., Lopez R and Valadez V., 1981 “Constrained economic dispatch of multi-area systems using the Dantzig–Wolfe decomposition principle”, IEEE Trans Power Apparat. Syst., 100 (4), 2127-2137.
  4. Lin C. E., and Chou C. Y., 1991 “Hierarchical economic dispatch for multi-area power systems”, Elect. Power Syst. Res., Vol. 10, 415-421.
  5. Tseng C. L., Guan, X., and Svoboda A. J., 1994 “Multi area unit commitment for large scale power systems”, IEE Proc. Generat. Transm. Distrib., Vol. 4, 415-421.
  6. Wang C., and Shahidehpour, S. M., 1992 “A decomposition approach to non-linear multi-area generation scheduling with tie line constrains using expert systems”, IEEE Trans. Power Syst., Vol. 7, 1409-1418.
  7. Gaing Z., and Huang H.-S., 2004 “A review of recent advances in economic dispatch”, IEEE TENCON Region 10 Conference, 3, 323-326.
  8. Paranjothi S. R., and Anburaja K., 2002 “Optimal power flow using refined genetic algorithm,”  Elect. Power Compon. Syst., Vol. 30, 1055–1063.
  9. Jeddi, B., Vahidinasab, V., 2014 “A modified harmony search method for environmental / economic load dispatch of real-world power systems”, Energy Convers. Manage, Vol. 78, 661-675
  10. Amjady N. and Sharifzadeh H., 2010 “Solution of non-convex economic dispatch problem considering valve loading effect by a new modified differential evolution algorithm”, Int. J. Electr. Power Energy Syst., 32 (8), 893-903.
  11. Basu M. and Chowdhury A., 2013 “Cuckoo search algorithm for economic dispatch”, Energy, Vol.  60, 99 - 108.
  12. Aragon V.S., Esquivel S.C. and Coello C.A., 2015 “An immune algorithm with power redistribution for solving economic dispatch problems”, Inf. Sci., Vol. 295, 609 -632.
  13. Sinha N., Chakrabarti R. and Chattopadhyay P.K., 2003 “Evolutionary Programming Techniques for Economic Load Dispatch”, IEEE Trans. on Evolutionary Computation, 7(1), 83-94.
  14. Secui D.C. 2015 “The chaotic global best artificial bee colony algorithm for the multi-area economic/emission dispatch”, Int. J. Energy, Vol. 93, 2518-2545.
  15. Zheng Y. J., 2015 “Water Wave Optimization: A New Nature-Inspired Metaheuristic”, Computers &Operations Research, Vol. 55, 1-11.
  16. Chiang C. L., 2005 “Improved Genetic Algorithm for Power Economic Dispatch of Units with Valve-Point Effects and Multiple Fuels”, IEEE Transactions On Power Systems, 20 (4), 1690-1699.
  17. Basu M., 2014 “Teaching-learning based optimization algorithm for multi-area economic dispatch”, Energy, Vol. 68, 21-28.
  18. Gaing Z-L., 2003 “Particle Swarm Optimization to Solving the Economic Dispatch Considering the  Generator Constraints”, IEEE Transactions On Power Systems, 18 (3), 1187-1195.
  19. Streiffert D., 1995 “Multi-area economic dispatch with tie line constraints”, IEEE Trans Power Syst., 10 (4), 1946-1951.

Keywords

Water wave optimization algorithm, multi-area economic dispatch, multiple fuel options, cost minimization, prohibited operating zones.