Neural Nets pp 338-345 | Cite as

Artificial Immune-Based Optimization Technique for Solving Economic Dispatch in Power System

  • Titik Khawa Abdul Rahman
  • Saiful Izwan Suliman
  • Ismail Musirin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3931)


This paper presents an Artificial Immune-based optimization technique for solving the economic dispatch problem in a power system. The main role of electrical power utility is to ensure that electrical energy requirement from the customer is served. However in doing so, the power utility has also to ensure that the electrical power is generated with minimum cost. Hence, for economic operation of the system, the total demand must be appropriately shared among the generating units with an objective to minimize the total generation cost for the system. Economic Dispatch is a procedure to determine the electrical power to be generated by the committed generating units in a power system so that the total generation cost of the system is minimized, while satisfying the load demand simultaneously. The proposed technique implemented Clonal Selection algorithm with several cloning, mutation and selection approaches. These approaches were tested and compared in order to determine the best strategy for solving the economic dispatch problem. The feasibility of the proposed techniques was demonstrated on a system with 18 generating units at various loading conditions. The results show that Artificial Immune System optimization technique that employed adaptive cloning, selective mutation and pair-wise tournament selection has provided the best result in terms of cost minimization and least execution time. A comparative study with λ-iteration optimization method and Genetic Algorithm was also presented.


Power System Artificial Immune System Load Demand Optimal Power Flow Selective Mutation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Tippayachai, J., Ongsakul, W., Ngamroo, I.: Parallel Micro Genetic Algorithm for Constrained Economic Dispatch. IEEE Trans. Power Syst. 17, 790–797 (2002)CrossRefGoogle Scholar
  2. 2.
    Lee, F.N., Breipohl, A.M.: Reserved Constrained Economic Dispatch With Prohibitive Operating Zones. IEEE Trans. Power Syst. 8, 246–254 (1993)CrossRefGoogle Scholar
  3. 3.
    Wong, K.P., Wong, Y.W.: Genetic and Genetic/Simulated – Annealing Approaches to Economic Dispatch. Proc. IEE Gen. Trans. Dist. 141(5), 507–513 (1994)CrossRefGoogle Scholar
  4. 4.
    Attavriyanupp, P., Kita, H., Tanaka, T., Hasegawa, J.: A Hybrid EP and SQP for Dynamic Economic Dispatch With Nonsmooth Fuel Cost Function. IEEE Trans. Power Syst. 17, 411–416 (2002)CrossRefGoogle Scholar
  5. 5.
    Bakirtzis, A.G., Petridis, V., Kazarlis, S.: Genetic Algorithm Solution to The Economic Dispatch Problem. Proc. IEE Gen. Trans. Dist. 141(4), 377–382 (1994)CrossRefGoogle Scholar
  6. 6.
    Damousis, J.G., Bakirtzis, A.G., Dokopoulos, P.S.: Network-Constrained Economic Dispatch Using Real-Coded Genetic Algorithm. IEEE Trans. Power Syst. 18, 198–205 (2003)CrossRefGoogle Scholar
  7. 7.
    Rahimullah, B.N.S., Ramlan, E.I., Rahman, T.K.A.: Evolutionary Approach for Solving Economic Dispatch in Power System. In: Proceedings of the IEEE/PES National Power Engineering Conference, vol. 1, pp. 32–36 (2003)Google Scholar
  8. 8.
    de Castro, L.N., Timmis, J.: 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)Google Scholar
  9. 9.
    Dasgupta, D., Okine, N.A.: Immunity-Based Systems: A Survey. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 369–374 (1997)Google Scholar
  10. 10.
    de Castro, L.N., Von Zuben, F.J.: Learning and Optimisation Using the Clonal Selection Principle. IEEE Trans. Evolutionary Computation 6, 239–251 (2002)CrossRefGoogle Scholar
  11. 11.
    de Castro, L.N., Von Zuben, F.J.: Artificial Immune System: Part 1 – Basic Theory and Applications. Technical report, TR-DCA 01/99 (December 1999)Google Scholar
  12. 12.
    Matsumura, Y., Okhura, K., Ueda, K.: Evolutionary Programming with Non-Coding Segments for Real Valued Function Optimization. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernatics 1999, vol. 4, pp. 242–247 (1999)Google Scholar
  13. 13.
    Yao, X., Liu, Y.: Fast Evolutionary Programming. In: Proc. Of the 5th Annual conference on Evolutionary Programming, pp. 451–460. MIT Press, Cambridge (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Titik Khawa Abdul Rahman
    • 1
  • Saiful Izwan Suliman
    • 1
  • Ismail Musirin
    • 1
  1. 1.Faculty of Electrical EngineeringUniversiti Teknologi MaraShah Alam, SelangorMalaysia

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