Transmission Expansion Planning: A Methodology to Include Security Criteria and Uncertainties Using Optimization Techniques

  • Armando M. Leite da Silva
  • Leandro S. Rezende
  • Luiz Antônio F. Manso
Part of the Springer Series in Reliability Engineering book series (RELIABILITY)


Transmission expansion planning (TEP) is a complex optimization task to ensure that the power system will meet the forecasted demand and the security criteria along the planning horizon, while minimizing investment, operational, and interruption costs. Optimization techniques based on metaheuristics have demonstrated the potential to find high-quality solutions. Numerous advantages can be linked to these tools: the software complexity is acceptable; they are able to mix integer and non-integer variables; and also present relatively faster computational times. Their success is related to the ability to avoid local optima by exploring the basic structure of each problem. However, owing to today’s power network dimensions, random behavior of transmission and generation equipments, load growth uncertainties, etc., the TEP problem has become combinatorial, stochastic, and highly complex. When uncertainties and chronological aspects are added to these problems, the optimal solution becomes almost inaccessible, even when using metaheuristics. This chapter proposes a methodology to solve the multi-stage TEP problem considering security criteria and the treatment of external uncertainties, as load/generation growth. In addition, a discussion about how to include security criteria using deterministic and probabilistic approaches is presented through a case study on a small test system. A real transmission network is used as an illustration of the application of the proposed methodology.


Particle Swarm Optimization Reliability Index Planning Horizon Greedy Randomize Adaptive Search Procedure Artificial Immune System 
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.



The authors would like to thank Dr. Cleber E. Sacramento from CEMIG (Compania Energética de Minas Gerais), Brazil, for providing data and discussions on planning strategies. The authors would like to extend their thanks to Mr. Larry Lee and Dr. Gomaa Hamoud from Hydro One, Canada, to Dr. George Anders from Kinectrics, Canada, to Prof. Leonardo M. Honório from UNIFEI, Brazil, and also to Prof. Leonidas Chaves de Resende from UFSJ, Brazil, for discussions on transmission expansion planning and optimization issues.


  1. 1.
    Billinton R, Salvaderi L, McCalley JD, Chao H, Seitz Th, Allan RN, Odom J, Fallon C (1997) Reliability issues in today’s electric power utility environment. IEEE Trans Power Syst 12:1708–1714CrossRefGoogle Scholar
  2. 2.
    Chowdhury AA, Koval DO (2004) Value-based system facility planning. IEEE Power Energy Mag 2:58–67CrossRefGoogle Scholar
  3. 3.
    Latorre G, Cruz RD, Areiza JM, Villegas A (2003) Classification of publications and models on transmission expansion planning. IEEE Trans Power Syst 18:938–946CrossRefGoogle Scholar
  4. 4.
    Xu Z, Dong ZY, Wong KP (2006) Transmission planning in a deregulated environment. IEE Proc Gener Transm Distrib 153:326–334CrossRefGoogle Scholar
  5. 5.
    Gallego RA, Alves AB, Monticelli A, Romero R (1997) Parallel simulated annealing applied to long term transmission network expansion planning. IEEE Trans Power Syst 12:181–187CrossRefGoogle Scholar
  6. 6.
    Gallego RA, Romero R, Monticelli A (2000) Tabu search algorithm for network synthesis. IEEE Trans Power Syst 15:490–495CrossRefGoogle Scholar
  7. 7.
    Leite da Silva AM, Manso LAF, Resende LC, Rezende LS (2008) Tabu search applied to transmission expansion planning considering losses and interruption costs. In: Proceedings of 10th PMAPS, Puerto RicoGoogle Scholar
  8. 8.
    Gil HA, da Silva EL (2001) A reliable approach for solving the transmission network expansion planning problem using genetic algorithms. Electr Power Syst Res 58:45–51CrossRefGoogle Scholar
  9. 9.
    Escobar AH, Gallego RA, Romero R (2004) Multistage and coordinated planning of the expansion of transmission systems. IEEE Trans Power Syst 19:735–744CrossRefGoogle Scholar
  10. 10.
    Binato S, Oliveira GC, Araújo JJ (2001) A greedy randomized search procedure for transmission expansion planning. IEEE Trans Power Syst 16:247–253CrossRefGoogle Scholar
  11. 11.
    Faria H Jr, Binato S, Resende MGC, Falcão DM (2005) Power transmission network design by greedy randomized adaptive path relinking. IEEE Trans Power Syst 20:43–49CrossRefGoogle Scholar
  12. 12.
    Leite da Silva AM, Sales WS, Resende LC, Manso LAF, Sacramento CE, Rezende LS (2006) Evolution strategies to transmission expansion planning considering unreliability costs. In: Proceedings of 9th PMAPS, StockholmGoogle Scholar
  13. 13.
    Dong ZY, Lu M, Lu Z, Wong KP (2006) A differential evolution based method for power system planning. In: IEEE congress on evolutionary computation 2699-2706, VancouverGoogle Scholar
  14. 14.
    Jin YX, Cheng HZ, Yan JY, Zhang L (2007) New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electr Power Syst Res 77:227–233CrossRefGoogle Scholar
  15. 15.
    Leite da Silva AM, Sacramento CE, Manso LAF, Rezende LS, Resende LC, Sales WS (2008) Metaheuristic-based optimization methods for transmission expansion planning considering unreliability costs. In: Castronuovo ED (ed) Optimization advances in electric power systems, 1st edn. Nova Publishers, USAGoogle Scholar
  16. 16.
    Rezende LS, Leite da Silva AM, Honorio LM (2009) Artificial immune system applied to the multi-stage transmission expansion planning. In: Proceedings of 8th ICARIS. LNCS (To be published)Google Scholar
  17. 17.
    Lee KY, El-Sharkawi MA (2008) Modern heuristic optimization techniques: theory and applications to power systems. IEEE Press Series on Power Engineering, WileyGoogle Scholar
  18. 18.
    Silva IJ, Rider MJ, Romero R, Garcia AV, Murari CA (2005) Transmission network expansion planning with security constraints. IEEE Proc Gener Transm Distrib 152:828–836CrossRefGoogle Scholar
  19. 19.
    Tor OB, Guven AN, Shahidehpour M (2008) Congestion-driven transmission planning considering the impact of generator expansion. IEEE Trans Power Syst 23:781–789CrossRefGoogle Scholar
  20. 20.
    Manso LAF, Leite da Silva AM (2004) Probabilistic criteria for power system expansion planning. Electr Power Syst Res 69:51–58CrossRefGoogle Scholar
  21. 21.
    Leite da Silva AM, Manso LAF, Mello JCO, Billinton R (2000) Pseudo-chronological simulation for composite reliability analysis with time varying loads. IEEE Trans Power Syst 15:73–80CrossRefGoogle Scholar
  22. 22.
    CIGRE W G 37.10 (1993) Dealing with uncertainty in system planning—has flexibility proved to be an adequate answer? ELECTRA 151:53–65Google Scholar
  23. 23.
    CIGRE W G 37.10 (1995) Methods for planning under uncertainty—towards flexibility in power system development. ELECTRA 161:143–164Google Scholar
  24. 24.
    Buygi MO, Shanechi HM, Balzer G, Shahidehpour M, Pariz N (2006) Network planning in unbundled power systems. IEEE Trans Power Syst 21:1379–1387CrossRefGoogle Scholar
  25. 25.
    Garver LL (1970) Transmission network estimation using linear programming. IEEE Trans PAS 89:1688–1697Google Scholar
  26. 26.
    Castro LN, Zubben FJV (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6:239–251CrossRefGoogle Scholar
  27. 27.
    IEEE APM Subcommittee (1979) IEEE reliability test system. IEEE Trans PAS 99:2047–2054Google Scholar
  28. 28.
    Ward JB (1949) Equivalent circuits for power flow studies. AIEE Trans 98:498–508Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Institute of Electric Systems and EnergyFederal University of Itajubá – UNIFEIItajubáBrazil
  2. 2.Department of Electrical EngineeringFederal University of São João Del Rei – UFSJSão João Del ReiBrazil

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