Integrated Taguchi Method with Immune Algorithm for Solving Profit-Base Unit Commitment

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 293)

Abstract

This paper presents a new approach to deal with the Price-Based Unit Commitment (PBUC) problem in day-ahead electricity markets. This new approach hybrids the Taguchi method (TM) and Immune Algorithm (IA), which provides a powerful global exploration capability. TM is embedded in the crossover operations to select the better gene for achieving crossover when the PBUC problem is solved by IA. TM has been widely used in experimental designs for problems with multiple parameters. The proposed approach is applied to a 15 units test system. Results show that the proposed method is feasible, robust, and efficiency.

Keywords

Immune algorithm Price-based unit commitment Taguchi method 

References

  1. 1.
    Hao, S. (2000). A study basic bidding strategy in clearing pricing auction. IEEE Transactions on Power Systems, 15(3), 975–980.CrossRefGoogle Scholar
  2. 2.
    Lin, W. M., & Chen, S. J. (2002). Bid-based dynamic economic dispatch with an efficient interior point algorithm. International Journal of Electrical Power and Energy Systems, 24(1), 51–57.CrossRefGoogle Scholar
  3. 3.
    Wood, A. J., & Wollenberg, B. F. (2003). Power Generation, Operation and Control. New York: John Wiley.Google Scholar
  4. 4.
    Granelli, G. P., Marannino, P., Montagna, M., & Zanellini, F. (2006). Monte Carlo based unit commitment procedures for the deregulated market environment. International Journal of Electrical Power and Energy Systems, 28, 712–722.Google Scholar
  5. 5.
    Li, T., & Shahidehpour, M. (2005). Price-based unit commitment; a case of Lagrangian relaxation versus mixed integer programming. IEEE Transactions on Power Systems, 20(4), 2015–2025.CrossRefGoogle Scholar
  6. 6.
    Sebastian, T., Arroyo, J. M., Conejo, A. J., & Contreras, J. (2002). Price maker self-scheduling in a pool-based electricity market: A mixed-integer LP approach. IEEE Transactions on Power Systems, 17(4), 1037–1042.CrossRefGoogle Scholar
  7. 7.
    Richter, C. W., & Shedble, G. B. (2005). A profit-based unit commitment GA for the competitive environment. IEEE Transactions on Power Systems, 20(4), 2015–2025.CrossRefGoogle Scholar
  8. 8.
    Yamin, H. Y., & Shahidehpour, S. M. (2004). Unit commitment using a hybrid model between Lagrangian relaxation and genetic algorithm in competitive markets. Electric Power Systems Research, 68, 83–92.CrossRefGoogle Scholar
  9. 9.
    Attaviriyanupap, P., Kita, H., Tanaka, E., & Hasegawa, J. (2003). A hybrid LR-EP for solving new profit-based UC problem under competitive environment. IEEE Transactions on Power Systems, 18(1), 229–236.CrossRefGoogle Scholar
  10. 10.
    Yamin, H. Y., El-Dwairi, Q., & Shahidehpour, S. M. (2007). A new approach for GenCos profit based unit commitment in day-ahead competitive electricity markets considering reserve uncertainty. International Journal of Electrical Power and Energy Systems, 29, 609–616.CrossRefGoogle Scholar
  11. 11.
    Ross, P. J. (1998). Taguchi techniques for quality engineering. New York: The McGraw-Hill Companies. Google Scholar
  12. 12.
    Mosca, E. (1995). Optimal predictive and adaptive control. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  13. 13.
    Astrom, K. J., & Wittenmark, B. (2009). Adptive control. Reading, MA: Addison-Wesley.Google Scholar
  14. 14.
    Hobbs, B. F., Rothkopf, M. H., O’neill, R. P., & Chao, H. P. (2001). The next generation of electric power units commitments models. Norwell, MA: Kluwer Academic Publishers (Appendix I).Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electrical EngineeringCheng-Shiu UniversityKaohsiungTaiwan, Republic of China

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