Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Hybrid Artificial Immune System Approach for Profit Based Unit Commitment Problem

  • Lakshmi, K. (Dept. of Electrical and Electronics Engineering, K. S. Rangasamy College of Technology) ;
  • Vasantharathna, S. (Dept. of Electrical and Electronic Engineering, Coimbatore Institute of Technology)
  • Received : 2012.12.21
  • Accepted : 2013.03.27
  • Published : 2013.09.01
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Abstract

This paper presents a new approach with artificial immune system algorithm to solve the profit based unit commitment problem. The objective of this work is to find the optimal generation scheduling and to maximize the profit of generation companies (Gencos) when subjected to various constraints such as power balance, spinning reserve, minimum up/down time and ramp rate limits. The proposed hybrid method is developed through adaptive search which is inspired from artificial immune system and genetic algorithm to carry out profit maximization of generation companies. The effectiveness of the proposed approach has been tested for different Gencos consists of 3, 10 and 36 generating units and the results are compared with the existing methods.

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References

  1. K. Bhattacharya, M. H. J. Bollen and J. E. Daalder, Operation of Restructured Power Systems, Kluwer Academic Publishers, 2001.
  2. L. L. Lai, Power System Restructuring and Deregulation, John Wiley & Sons, Ltd., 2001.
  3. S.M. Shahidehpour, H.Y.Yamin and Z. Li, Market Operations in Electric Power Systems, John Wiley and Sons, 2002.
  4. Conejo Antonio J, Carrion Miguel, Morales Juan M. "Decision making under uncertainty in electricity markets", International series in operations research & management science, Vol. 153, 2010.
  5. Bavafa M, Navidi N, Monsef H. "A new approach for profit based unit commitment using Lagrangian relaxation combined with ant colony search algorithm", In Proceedings of IEEE UPEC 2008, pp. 1-6, 2008.
  6. N. P. Padhy, "Unit commitment - a bibliographical survey", IEEE Trans. Power Syst. 19 (2), pp. 1196-1205, 2004. https://doi.org/10.1109/TPWRS.2003.821611
  7. Narayana Prasad Padhy, "Unit commitment problem under deregulated environment - a review" Power Engineering Society General Meeting, Vol. 2, pp. 1088-1094, 2003.
  8. C. W. Richter, Gerald B. Sheble, "profit based unit commitment GA for competitive environment", IEEE Trans. Power Syst. 15 (2), pp.715-721, 2000. https://doi.org/10.1109/59.867164
  9. M. Madrigal and Victor H. Quintana, "Existence And Determination Of Competitive Equilibrium In Unit Commitment Power Pool Auctions: Price Setting And Scheduling Alternatives". IEEE Transactions on Power Systems. Vol. 16. pp. 380-388, 2001 https://doi.org/10.1109/59.932272
  10. J. Valenzuela and M. Mazumdar, "Making Unit Commitment Decisions When Electricity is Traded at Spat Market Prices", Proceedings of IEEE Winter Meeting, 2001.
  11. Pathom Attaviriyanupap, Hiroyuki Kita, Eiichi Tanka, Jun Hasegawa, "A hybrid LR-EP for solving new profit-based UC problem under competitive environment", IEEE Trans. Power Syst. 18 (1) pp. 229-237, 2003. https://doi.org/10.1109/TPWRS.2002.807080
  12. H.Y. Yamin, S.M. Shahidehpour, "Unit commitment using a hybrid model between Lagrangian relaxation and genetic algorithm in competitive electricity markets", Electr. Power Syst. Res. 6, pp. 883-92, 2004.
  13. K. Chandram, N. Subrahmanyam, M. Sydulu, "Improved Pre-Prepared Power Demand Table And Muller's Method to Solve The Profit Based Unit Commitment Problem", Journal of Electrical Engineering & Technology, Vol. 4, No. 2, pp. 159-167, 2009. https://doi.org/10.5370/JEET.2009.4.2.159
  14. Delarue Erik, Van den Bosch Pieterjan, D'haeseleer William, "Effect of the accuracy of price forecasting on profit in a price based unit commitment", Electr. Power Syst. Res; 80(10), 1306-13, 2010. https://doi.org/10.1016/j.epsr.2010.05.001
  15. Jacob Reglend C, Raghuveer G, Rakesh Avinash, Padhy N.P, Kothari D.P., " Solution to profit based unit commitment problem using particle swarm optimization", Appl Soft Comput, pp. 247-56, 2010.
  16. Dimitroulas Dionisios K, Georgilakis Pavlos S, "A new memetic algorithm approach for the price based unit commitment problem", Appl Energy, 88(12), pp. 4687-99, 2011. https://doi.org/10.1016/j.apenergy.2011.06.009
  17. Columbus Christopher C, Chandrasekaran K, Simon Sishaj P., "Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs", Appl Soft Comput, 12(1), pp. 145-60, 2012. https://doi.org/10.1016/j.asoc.2011.08.057
  18. Huang, S. J. "Enhancement of thermal unit commitment using immune algorithms based optimization approaches", Int. J. Electr. Power Energy Syst., 21(4), pp. 245-252, 1999. https://doi.org/10.1016/S0142-0615(98)00050-7
  19. Sun, Y.Z., Wei, W., "Solution of optimal power flow problem based on artificial immune algorithm" Autom. Electr. Power Syst., 26, pp. 30-34, 2002.
  20. Li, W., Sheng, D. R., Chen, J. H., Yuan, Z. F., Cen, K.F.,.A "New Method Based on Immune Algorithm to Solve the Unit Commitment Problem" Intelligent Strategies in Product Design, Manufacturing, and Management. Springer, 207, pp. 840-846, 2006. https://doi.org/10.1007/0-387-34403-9_117
  21. Liao, G.C. "Short-term thermal generation scheduling using improved immune algorithm" Electr. Power Syst. Res., 76(5), pp. 360-373, 2006. https://doi.org/10.1016/j.epsr.2005.06.009
  22. Ruochen Liu, Xiangrong Zhang, Neng Yang, Qifeng Lei and Licheng Jiao, "Immunodomaince based Clonal Selection Clustering Algorithm", Appl. Soft Comput., Vol. 12, pp. 302-312, 2012.. https://doi.org/10.1016/j.asoc.2011.08.042
  23. H. Y Yamin, Q. El-Dwairi, S. M Shahidehpour, "A new approach for GENCOs profit based unit commitment in day-ahead competitive electricity markets considering reserve uncertainty", Int J Electr Power Energy Syst, 29(8), pp. 609-16, 2007. https://doi.org/10.1016/j.ijepes.2006.09.004
  24. L. N. de Castro and J. Timmis, "Artificial Immune Systems : A Novel paradigm to Pattern Recognition", In Artificial Neural Networks in Pattern Recognition, SOCO-2002, University of Paisley, UK, pp. 67-84, 2002.
  25. X. Wang, X. Z. Gao, and S. J. Ovaska," Artificial Immune Optimization Methods and Applications - A Survey", IEEE International Conference on Systems, Man and Cybernetics, 2004.
  26. de Castro, L. N. and Von Zuben, F. J., "The Clonal Selection Algorithm with Engineering Applications", in Proceedings of the Workshop on Artificial Immune Systems and Their Applications (GECCO'2000), pages 36-37, Las Vegas, Nevada, 2000.
  27. T.K.A. Rahman, Saiful Izwan Suliman, and Ismail Musirin," Artificial Immune-Based Optimization Technique for Solving Economic Dispatch in Power System", Springer-Verlag Berlin, pp. 338-345, 2006.
  28. A. C. Coello and N. C. C. es. "Hybridizing a genetic algorithm with an artificial immune system for global optimization", Engineering Optimization, Vol. 5, pp. 607-634, 2004.
  29. Morteza Eslamian, Seyed Hossein Hosseinian, and Behrooz Vahidi, "Bacterial Foraging-Based Solution to the Unit-Commitment Problem", IEEE Transactions on power systems, Vol. 24, No. 3, 2009.
  30. Wei LI, Hao-yu Peng, Wei-hang Zhu, De-ren Sheng, Jian-hong Chen, "An immune-tabu hybrid algorithm for thermal unit commitment of electric power systems", Journal of Zhejiang University Science A, pp. 877-889, 2009.

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