Applied Intelligence

, Volume 48, Issue 10, pp 3672–3690 | Cite as

Solving security constrained optimal power flow problems: a hybrid evolutionary approach

  • Carolina G. MarcelinoEmail author
  • Paulo E. M. Almeida
  • Elizabeth F. Wanner
  • Manuel Baumann
  • Marcel Weil
  • Leonel M. Carvalho
  • Vladimiro Miranda


A hybrid population-based metaheuristic, Hybrid Canonical Differential Evolutionary Particle Swarm Optimization (hC-DEEPSO), is applied to solve Security Constrained Optimal Power Flow (SCOPF) problems. Despite the inherent difficulties of tackling these real-world problems, they must be solved several times a day taking into account operation and security conditions. A combination of the C-DEEPSO metaheuristic coupled with a multipoint search operator is proposed to better exploit the search space in the vicinity of the best solution found so far by the current population in the first stages of the search process. A simple diversity mechanism is also applied to avoid premature convergence and to escape from local optima. A experimental design is devised to fine-tune the parameters of the proposed algorithm for each instance of the SCOPF problem. The effectiveness of the proposed hC-DEEPSO is tested on the IEEE 57-bus, IEEE 118-bus and IEEE 300-bus standard systems. The numerical results obtained by hC-DEEPSO are compared with other evolutionary methods reported in the literature to prove the potential and capability of the proposed hC-DEEPSO for solving the SCOPF at acceptable economical and technical levels.


Evolutionary optimization methods Optimal power flow Hybrid algorithms hC-DEEPSO algorithm Fining tunining parameters Statistical inference 



The authors would like to thank CEFET-MG, ITAS and INESC TEC for the infrastructure used by this project and also CAPES, CNPq and FAPEMIG for the financial support. This work is financed by the BE MUNDUS Project, the Helmholtz-Project Energy System 2050.


  1. 1.
    Frank S, Steponavice I, Rebennack S (2012) Optimal power flow: a bibliographic survey (I) - formulations and deterministic methods. Energy Syst Springer 3:221–258CrossRefGoogle Scholar
  2. 2.
    Frank S, Steponavice I, Rebennack S (2012) Optimal power flow: a bibliographic survey II: non-deterministic an hybrid methods. Energy Syst Springer 3:259–289CrossRefGoogle Scholar
  3. 3.
    Bhaskar M, Muthyala S, Maheswarapu S (2010) Security Constraint Optimal Power Flow (SCOPF) - a compreensive Suvery. Int J Comput Appl 2(6):42–52Google Scholar
  4. 4.
    Carpertier J (1962) Contribution to the economic dispatch problem. Bull Soc Fr Electri 8(3):431–447Google Scholar
  5. 5.
    Phan D, Kalagnanam J (2014) Some Efficient Optimization Methods for Solving the Security-Constrained Optimal Power Flow Problem, vol 29Google Scholar
  6. 6.
    Ela A, Abido M, Spea A (2010) Optimal power flow using differential evolution algorithm. Electric Power Syst Res 80:878–885CrossRefGoogle Scholar
  7. 7.
    Suharto MN, Hassan MY, Majid MS, Abdullah MP, Hussin F (2011) Optimal Power Flow Solution Using Evolutionary Computation Techniques. In: Proceedings of the IEEE Region 10 Conference TENCON, vol 1, pp 1–8Google Scholar
  8. 8.
    Abido M, Ali N (2012) Multi-objective Optimal Power Flow Using Differential Evolution. Arab J Sci Eng 37(4):991–1005CrossRefzbMATHGoogle Scholar
  9. 9.
    Liang JJ, Mao XB, Qu BY, Niu B, Chen TJ (2012) Elite Multi-Group differential evolution. WCCI 2012 IEEE world congress on computational intelligenceGoogle Scholar
  10. 10.
    Kang Q, Zhou M, An J, Wu Q (2013) Swarm intelligence approaches to optimal power flow problem with distributed generator failures in power networks. IEEE Trans Autom Sci Eng 10:343–353CrossRefGoogle Scholar
  11. 11.
    Dixit S, Srivastava L, Agnihotri G (2014) Minimization of power loss and voltage deviation by SVC placement using GA. Int J Control Autom 7(6):95–108CrossRefGoogle Scholar
  12. 12.
    Radosavljevic J, Klimenta D, Jevtic M, Arsic N (2015) Optimal Power Flow Using a Hybrid Optimization Algorithm of Particle Swarm Optimization and Gravitational Search Algorithm. Electric Power Components Syst 00(00):1–13Google Scholar
  13. 13.
    Shaheen A, El-Sehiemy R, Farrag S (2016) Solving multi-objective optimal power flow problem via forced initialised differential evolution algorithm. Transm Distrib 10(7):1634–1647CrossRefGoogle Scholar
  14. 14.
    Nakawiro W, Erlich I (2009) A Combined GA-ANN Strategy for Solving Optimal Power FLow with Voltage Security Constraint. In: Proceedings of the Asia-Pacific Power and Energy Engineering ConferenceGoogle Scholar
  15. 15.
    Phan D, Kalagmanam J (2014) Some efficient optimization methods for solving the Security-Contrained optimal power flow problem. IEEE Trans Power Syst 29(2):863–872CrossRefGoogle Scholar
  16. 16.
    Zhang R, Dong ZY, Xu Y, Wong KP, Lai M (2014) Hybrid computation of corrective security-constrained optimal power flow problems. IET Gener Transm Distrib 8(6):995–1006CrossRefGoogle Scholar
  17. 17.
    Carvalho L, Loureiro F, Sumali J, Keko H, Miranda V, Marcelino C, Wanner E (2015) Statistical tuning of DEEPSO soft constraints in the security constrained optimal power flow problem. In: Proceedings of the 18th International Conference on Intelligent System Application to Power Systems, vol 1, pp 1–7Google Scholar
  18. 18.
    Marcelino C, Almeida P, Wanner E, Carvalho L, Miranda V (2016) Fundamentals of the c-DEEPSO Algorithm and its Aplication to the Reactive Power Optimization of Wind Farms. In: Proceedings of the IEEE Congress on Evolutionary Computation. vol 1, pp:Google Scholar
  19. 19.
    Pham H, Rueda J, Erlich I (2014) Online Optimal control or Reactive sources in Wind Power Plants. IEEE Trans Sustaina Energy 5(2):608–616CrossRefGoogle Scholar
  20. 20.
    Teeparthi K, Kumar DM (2017) Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators. Engineering Science and Technology, an International Journal, acepted March, pp 2–16Google Scholar
  21. 21.
    Raju CP, Vaisakh K, Raju SS (2009) An IPM-EPSO based hybrid method for security enhancement unsing SSSC. Int J Recent Trends Eng 2(5):208–2012Google Scholar
  22. 22.
    Kumari M, Maheswarapu S (2010) Enhaced genetic algotithm based computation technique for multi-objective optimal power flow solution. Int J Electr Power Energy Syst 32(6):736–742CrossRefGoogle Scholar
  23. 23.
    Chung CY, Liang CH, Wong KP, Duan XZ (2010) Hybrid algorithmm of Differentialial evolution and evolutionary programming for optimal reactive power flow. IET Gener Transm Distrib 4(1):84–93CrossRefGoogle Scholar
  24. 24.
    Radosavljevic J, Arsic N, Jevitic M (2014) Optimal Power Flow Using Hybrid PSOGSA Algorithm. In: Proceedings of the 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)Google Scholar
  25. 25.
    Zeng Y, Sun Y (2012) Comparasion of multiobjecive particle swarm optimization and evolutionary algorithms for optimal reactive power dispatch problem. In: Proceedings of the IEEE Congress on Evolutionary Computation. vol 1, pp 258–265Google Scholar
  26. 26.
    Shi L, Wnag C, Yao L, Yixin N, Bazargan M (2012) Optimal power flow solution incorporating wind power. IEEE Syst J 6(2):233–241CrossRefGoogle Scholar
  27. 27.
    Srivastava L, Singh H (2015) Hybrid multi-swarm particle swarm opitimisation based multi-objective reactive power. IET Gener Transm Distrib 9(8):727–739CrossRefGoogle Scholar
  28. 28.
    Bai W, Eke I, Lee K (2015) Heuristic optimization for wind energy integrated optimal power flow. In: Proceedings of the IEEE Power & Energy Society General Meeting. vol 1, 1–5Google Scholar
  29. 29.
    Baumann M, Marcelino C, Peter J, Wanner E, Weil M, Almeida P (2017) Environmental impacts of different battery technologies in renewable hybrid micro grid systems. In: IEEE PES Conference europe (ISGT-europe) innovative smart grid technologiesGoogle Scholar
  30. 30.
    Xiang Y, Peng Y, Zhong Y, Chen Z, Lu X, Zhong X (2014) A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization. Comput Optim Appl 57:493–516MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Zellagui M, Abdelaziz A (2015) Optimal Coordination of Directional Overcurrent Relays using Hybrid PSO-DE Algorithm. Int Electr Eng J (IEEJ) 6(4):1841–1849Google Scholar
  32. 32.
    Vaisakh K, Praveena P, Rao R, Meah K (2012) Solving dynamic economic dispatch problem with security constraints using bacterial foraging PSO-DE algorithm. Electr Power Energy Syst 39:56–67CrossRefGoogle Scholar
  33. 33.
    Xin B, Chen J, Zhang J, Fang H, Peng Z-H (2012) Hybridizing differential evolution and particle swarm optimization to design powerful optimizers: A review and taxonomy. IEEE Transactions on Systems Man and Cybernetics: applications and reviews 42(5):1–24Google Scholar
  34. 34.
    Miranda V, Alves R (2013) Differential evolutionary particle swarm optimization (DEEPSO): a successful hybrid. In Proceedings of the 11th Brazilian Congress on Computational Intelligence (BRICS-CCI), pp 368–374Google Scholar
  35. 35.
    Price K, Storn R, Lampinen J (2005) Differential evolution- a pratical aprooach to global Optimization. Springer, BerlinzbMATHGoogle Scholar
  36. 36.
    Zhang J, Sanderson A (2009) JADE: Adaptative Differential Evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958CrossRefGoogle Scholar
  37. 37.
    Tamura K, Yasuda K (2011) Primary study of spiral ddynamic inspired optimization. IEEJ Trans Electr Eletron Eng 6(S1):S98–S100CrossRefGoogle Scholar
  38. 38.
    Benasla L, Belmadani A, Rahli M (2014) Spiral Optimization Algorihm for solving Combined Economic and Emission Dispatch. Electr Power Energy Syst 62:163–174CrossRefGoogle Scholar
  39. 39.
    Tamura K, Yasuda K (2011) Spiral dynamics inspired optimmization. J Adv Comput Intell Inform 15(8):1116–1122CrossRefGoogle Scholar
  40. 40.
    Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms Model, taxonomy and design issues. IEEE Trans Evol Comput 9(5):474–488CrossRefGoogle Scholar
  41. 41.
    Pant M, Thangaraj R, Grosan C, Abraham A (2011) Hybrid Differential Evolution - Particle Swarm Optimization algorithm for Solving Global Optimization Problems. In: Proceedings of the 3Th International Conference on Digital Information Management, pp 18–24Google Scholar
  42. 42.
    Erlich I, Lee K, Rueda J, Wildenhues S (2014) Competition on application of modern heuristic optimization algorithms for solving optimal power flow problems. In: Technical report, working group on modern heuristic optimization, intelligent systems subcommittee power system analysis, Computing, and Economic CommitteeGoogle Scholar
  43. 43.
    Montgomery D (2012) Design and analysis of Experiments. 8th editionGoogle Scholar
  44. 44.
    Niu M, Jia Y, Xu Z, Wong KP (2014) Differential Evolution Algorithm with a Modified Archiving-based Adaptive Tradeoff Model for Optimal Power Flow. Technical report, avaible:,

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Federal Center of Technological Education of Minas Gerais (CEFET-MG)Minas GeraisBrazil
  2. 2.Aston UniversityBirminghamUK
  3. 3.Institute for Technology Assessment and Systems Analysis (ITAS)KarlsruheGermany
  4. 4.Systems and Computer Engineering, Technology and Science (INESC TEC)PortoPortugal

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