Artificial Immune System Applied to the Multi-stage Transmission Expansion Planning

  • Leandro S. Rezende
  • Armando M. Leite da Silva
  • Leonardo de Mello Honório
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5666)


Transmission expansion planning (TEP) is a complex optimization task to ensure that the power system will meet the forecasted demand and the reliability criterion, along the planning horizon, while minimizing investment, operational, and interruption costs. Metaheuristic methods have demonstrated the potential to find good feasible solutions, but not necessarily optimal. These methods can provide high quality solutions, within an acceptable CPU time, even for large-scale problems. This paper presents an optimization tool based on the Artificial Immune System used to solve the TEP problem. The proposed methodology includes the search for the least cost solution, bearing in mind investments and ohmic transmission losses. The multi-stage nature of the TEP will be also taken into consideration. Case studies on a small test system and on a real sub-transmission network are presented and discussed.


Transmission Expansion Planning Artificial Immune System Power System Metaheuristics 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Billinton, R., Salvaderi, L., McCalley, J.D., Chao, H., Seitz, T., Allan, R.N., Odom, J., Fallon, C.: Reliability Issues in Today’s Electric Power Utility Environment. IEEE Trans. on Power Syst. 12, 1708–1714 (1997)CrossRefGoogle Scholar
  2. 2.
    Chowdhury, A.A., Koval, D.O.: Value-based System Facility Planning. IEEE Power and Energy Magazine 2, 58–67 (2004)CrossRefGoogle Scholar
  3. 3.
    Latorre, G., Cruz, R.D., Areiza, J.M., Villegas, A.: Classification of Publications and Models on Transmission Expansion Planning. IEEE Trans. on Power Syst. 18, 938–946 (2003)CrossRefGoogle Scholar
  4. 4.
    Xu, Z., Dong, Z.Y., Wong, K.P.: Transmission Planning in a Deregulated Environment. IEE Proceedings-Generation, Transmission and Distribution 153, 326–334 (2006)CrossRefGoogle Scholar
  5. 5.
    Gallego, R.A., Alves, A.B., Monticelli, A., Romero, R.: Parallel Simulated Annealing Applied to Long Term Transmission Network Expansion Planning. IEEE Trans. on Power Syst. 12, 181–187 (1997)CrossRefGoogle Scholar
  6. 6.
    Gallego, R.A., Romero, R., Monticelli, A.: Tabu Search Algorithm for Network Synthesis. IEEE Trans. on Power Syst. 15, 490–495 (2000)CrossRefGoogle Scholar
  7. 7.
    Leite da Silva, A.M., Manso, L.A.F., Resende, L.C., Rezende, L.S.: Tabu Search Applied to Transmission Expansion Planning Considering Losses and Interruption Costs. In: 10th Probabilistic Methods Applied to Power Systems – PMAPS, Puerto Rico (2008)Google Scholar
  8. 8.
    Gil, H.A., da Silva, E.L.: A Reliable Approach for Solving the Transmission Network Expansion Planning Problem Using Genetic Algorithms. Electric Power System Research 58, 45–51 (2001)CrossRefGoogle Scholar
  9. 9.
    Escobar, A.H., Gallego, R.A., Romero, R.: Multistage and Coordinated Planning of the Expansion of Transmission Systems. IEEE Trans. on Power Syst. 19, 735–744 (2004)CrossRefGoogle Scholar
  10. 10.
    Binato, S., Oliveira, G.C., Araújo, J.J.: A Greedy Randomized Search Procedure for Transmission Expansion Planning. IEEE Trans. on Power Syst. 16, 247–253 (2001)CrossRefGoogle Scholar
  11. 11.
    Faria Jr., H., Binato, S., Resende, M.G.C., Falcão, D.M.: Power Transmission Network Design by Greedy Randomized Adaptive Path Relinking. IEEE Trans. on Power Syst. 20, 43–49 (2005)CrossRefGoogle Scholar
  12. 12.
    Leite da Silva, A.M., Sales, W.S., Resende, L.C., Manso, L.A.F., Sacramento, C.E., Rezende, L.S.: Evolution Strategies to Transmission Expansion Planning Considering Unreliability Costs. In: 9th Probabilistic Methods Applied to Power Systems – PMAPS, Stockholm (2006)Google Scholar
  13. 13.
    Dong, Z.Y., Lu, M., Lu, Z., Wong, K.P.: A Differential Evolution Based Method for Power System Planning. In: IEEE Congress on Evolutionary Computation, Vancouver, pp. 2699–2706 (2006)Google Scholar
  14. 14.
    Jin, Y.X., Cheng, H.Z., Yan, J.Y., Zhang, L.: New Discrete Method for Particle Swarm Optimization and Its Application in Transmission Network Expansion Planning. Electric Power Systems Research 77, 227–233 (2007)CrossRefGoogle Scholar
  15. 15.
    Leite da Silva, A.M., Sacramento, C.E., Manso, L.A.F., Rezende, L.S., Resende, L.C., Sales, W.S.: Metaheuristic-Based Optimization Methods for Transmission Expansion Planning Considering Unreliability Costs. In: Castronuovo, E.D. (ed.) Optimization Advances in Electric Power Systems. Nova Publishers, USA (2008)Google Scholar
  16. 16.
    Lee, K.Y., El-Sharkawi, M.A.: Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems. Wiley - IEEE Press Series on Power Engineering (2008)Google Scholar
  17. 17.
    Hunt, J.E., Cooke, D.E.: Learning Using an Artificial Immune System. Journal of Network and Computer Applications 19, 189–212 (1996)CrossRefGoogle Scholar
  18. 18.
    Castro, L.N., Zubben, F.J.V.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation 6, 239–251 (2002)CrossRefGoogle Scholar
  19. 19.
    Honorio, L.M., Leite da Silva, A.M., Barbosa, D.A.: A Gradient-based Artificial Immune System Applied to Optimal Power Flow Problems. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 1–12. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Andrews, P.S., Timmis, J.: On Diversity and Artificial Immune Systems: Incorporating a Diversity Operator into aiNet. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds.) WIRN 2005 and NAIS 2005. LNCS, vol. 3931, pp. 293–306. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Rau, N.S.: Optimization Principles – Practical Applications to the Operation and Markets of the Electric Power industry. Wiley - IEEE Press Series on Power Engineering (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Leandro S. Rezende
    • 1
  • Armando M. Leite da Silva
    • 1
  • Leonardo de Mello Honório
    • 1
  1. 1.UNIFEI – Federal University of ItajubáMinas GeraisBrazil

Personalised recommendations