Bio-inspired Optimization Algorithms for Solving the Optimal Power Flow Problem in Power Systems

  • Erik CuevasEmail author
  • Emilio Barocio Espejo
  • Arturo Conde Enríquez
Part of the Studies in Computational Intelligence book series (SCI, volume 822)


In this chapter the Optimal Power Flow problem solution based in Bio-inspired optimization algorithms with one single function and with multiple and competing objective functions is presented. As a first approach the Modified Flower Pollination Algorithm (MFPA) to show its potential application to solve the OPF problem, then Normal Boundary Intersection (NBI) method are used in a complementary way to determine the Pareto front solution of the Multi-Objective OPF problem.


  1. 1.
    M.R. AlRashidi, M.E. El-Hawary, Applications of computational intelligence techniques for solving the revived optimal power flow problem. Electr. Power Syst. Res. 79(4), 694–702 (2009)CrossRefGoogle Scholar
  2. 2.
    Y.L. Chen, Weighted-norm approach for multi-objective VAR planning. IEE Proc. Gener. Transm. Distrib. 145(4), 369–374 (1998)CrossRefGoogle Scholar
  3. 3.
    J.S. Dhillon, S.C. Parti, D.P. Kothari, Stochastic economic emission load dispatch. Electr. Power Syst. Res. 2(3), 179–186 (1993)CrossRefGoogle Scholar
  4. 4.
    R. Yokoyama, S.H. Bae, T. Morita et al., Multi-objective generation dispatch based on probability security criteria. IEEE Trans. Power Syst. 3(1), 317–324 (1998)CrossRefGoogle Scholar
  5. 5.
    W.D. Rosehart, C.A. Cañizares, V.H. Quintana, Multi-objective optimal power flows to evaluate voltage security cost in power networks. IEEE Trans. Power Syst. 18(2), 578–587 (2003)CrossRefGoogle Scholar
  6. 6.
    F. Milano, C.A. Cañizares, M. Invernizzi, Multi-objective optimization for pricing system security in electricity markets. IEEE Trans. Power Syst. 18(2), 596–604 (2003)CrossRefGoogle Scholar
  7. 7.
    I. Das, J.E. Dennis, Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multi-criteria optimization problems. SIAM J. Optim. 8(3), 631–657 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    C. Roman, W. Rosehart, Evenly distributed Pareto points in multi-objective optimal power flow. IEEE Trans. Power Syst. 21(2), 1011–1012 (2006)CrossRefGoogle Scholar
  9. 9.
    M. Varadarajan, K.S. Swarup, Solving multi-objective optimal power flow using differential evolution. IET Gener. Transm. Distrib. 2(5), 720–730 (2008)CrossRefGoogle Scholar
  10. 10.
    S. Tan, S. Lin, L. Yang et al., Multi-objective optimal power flow model for power system operation dispatching, in Power and Energy Engineering Conference (APPEEC), IEEE PES Asia-Pacific, 2013Google Scholar
  11. 11.
    C.A.C. Coeello, A comprehensive survey of evolutionary based multi-objective optimization techniques. Knowl. Inf. Syst. 1(3), 269–308 (1999)CrossRefGoogle Scholar
  12. 12.
    M. Abido, J. Bakhaswain, Optimal VAR dispatch using a multi-objective evolutionary algorithm. Int. J. Electr. Power Energy Syst. 27(1), 13–20 (2005)CrossRefGoogle Scholar
  13. 13.
    M. Sailaja, S. Maheswarapu, Enhanced genetic algorithm based computation technique for multi-objective OPF Solution. Electr. Power Energy Syst. 32(6), 736–742 (2010)CrossRefGoogle Scholar
  14. 14.
    T. Niknam, M.R. Narimani, J. Aghaei et al., Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Gener. Transm. Distrib. 6(6), 515–527 (2012)CrossRefGoogle Scholar
  15. 15.
    M.A. Abido, Optimal power flow using particle swarm optimization. Int. J. Electr. Power Energy Syst. 24(7), 563–571 (2002)CrossRefGoogle Scholar
  16. 16.
    S. Kahourzade, A. Mahmoudi, H.B. Mokhlis, A comparative study of multi-objective optimal power flow base on particle swarm, evolutionary programming and genetic algorithm. Electr. Eng. 97, 1–12 (2015)CrossRefGoogle Scholar
  17. 17.
    A.K. Kar, Bio-inspired computing—a review of algorithms and scope of applications. Expert Syst. Appl. 59, 20–32 (2016)CrossRefGoogle Scholar
  18. 18.
    Adaryani M. Rezaei, A. Karami, Artificial bee colony algorithm for solving multi-objective optimal power flow problem. Electr. Power Energy Syst. 53, 219–230 (2013)CrossRefGoogle Scholar
  19. 19.
    A. Panda, M. Tripathy, Optimal power flow solution of wind integrated power system using modified bacteria foraging algorithm. Electr. Power Energy Syst. 54, 306–314 (2014)CrossRefGoogle Scholar
  20. 20.
    S. Chandrasekaran, P. Simon P, Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evol. Comput. 5, 1–16 (2012)CrossRefGoogle Scholar
  21. 21.
    X.S. Yang, M. Karamanoglu, X. He, Multi-objective flower algorithm for optimization. Int. Conf. Comput. Sci. 18, 861–868 (2013)Google Scholar
  22. 22.
    X.S. Yang, Flower pollination algorithm for global optimization, in: Unconventional Computation and Natural Computation 2012. Lecture Notes in Computer Science. vol. 7445 (2012), pp. 240–249CrossRefGoogle Scholar
  23. 23.
    A.M. Reynolds, M.A. Frye, Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search. Plos One 2, e354 (2007)CrossRefGoogle Scholar
  24. 24.
    R.N. Mantegna, Fast, accurate algorithm for numerical simulation of Lévy stable stochastic process. Phys. Rev. E 49(5), 4677–4683 (1994)CrossRefGoogle Scholar
  25. 25.
    H.R. Tizhoosh, Opposition-based learning, a new scheme for machine intelligence, in, International Conference on Computational Intelligence for Modelling, Control and Automation 2005, vol. 1 (2005), pp. 695–701Google Scholar
  26. 26.
    J.A. Regalado, B. Emilio, E. Cuevas, Optimal power flow solution using modified flower pollination algorithm, in, IEEE, International Autumn Meeting on Power, Electronics and Computing ROPEC (Guerrero, Mexico, 2015), pp. 1–6Google Scholar
  27. 27.
    Z. Jia, M.G. Ierapetritou, Generate Pareto optimal solutions of scheduling problems using normal boundary intersection technique. Comput. Chem. Eng. 31(4), 268–280 (2007)CrossRefGoogle Scholar
  28. 28.
    A.A. Abou El Ela, M.A. Abido, Optimal power flow using differential evolution algorithm. Electr. Power Syst. Res. 80(7), 878–885 (2010)CrossRefGoogle Scholar
  29. 29.
    H.R.E.H. Bouchekara, Optimal power flow using black-hole-based optimization approach, in Applied Soft Computing, vol. 24 (Elsevier, 2014), pp. 879–888Google Scholar
  30. 30.
    H.R.E.H. Bouchekara, M.A. Abido, Optimal power flow using electromagnetism-like mechanism. Electr. Energy 114, 4959 (2014)Google Scholar
  31. 31.
    M.R. Adaryani, A. Karami, Artificial bee colony algorithm for solving multi-objective optimal power flow problem, in Electrical Power and Energy Systems. vol. 53 (Elsevier, 2013), pp. 219–230Google Scholar
  32. 32.
    S. Duman, U. Güvenç, Y. Sönmez, N. Yörükeren, Optimal power flow using gravitational search algorithm, in Energy Conversion and Management, vol. 59 (Elsevier, 2012), pp. 86–95Google Scholar
  33. 33.
    E. Barocio, J. Regalado1, E. Cuevas, F. Uribe, P. Zúñiga, P.J.R. Torres, Modified bio-inspired optimization algorithm with a centroid decision making approach for solving a multi-objective optimal power flow problem. IET Gener. Transm. Distrib. 11(4), 1012–1022 (2017)CrossRefGoogle Scholar
  34. 34.
    J.B. Park, K.S. Lee, J.R. Shin et al., A particle swarm optimization for economic dispatch with non-smooth cost functions. IEEE Trans. Power Syst. 20(1), 34–42 (2005)CrossRefGoogle Scholar
  35. 35.
    M. Ghasemi, S. Ghavidel, M. Ghanbarian et al., Multi-objective optimal power flow considering the cost, emission, voltage deviation, and power losses using multi-objective modified imperialist competitive algorithm. Energy 78, 276–299 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Erik Cuevas
    • 1
    Email author
  • Emilio Barocio Espejo
    • 2
  • Arturo Conde Enríquez
    • 3
  1. 1.Departamento de Electrónica, CUCEIUniversidad de GuadalajaraGuadalajaraMexico
  2. 2.CUCEIUniversidad de GuadalajaraGuadalajaraMexico
  3. 3.Universidad Autónoma de Nuevo LeónSan Nicolás de los GarzaMexico

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