Advertisement

A new chaos and global competitive ranking-based symbiotic organisms search algorithm for solving reactive power dispatch problem with discrete and continuous control variable

  • Enes YalçınEmail author
  • Ertuğrul Çam
  • Müslüm C. Taplamacıoğlu
Original Paper
  • 23 Downloads

Abstract

In this paper, optimal reactive power dispatch problem (ORPD) is solved by using a new chaos and global competitive ranking-based symbiotic organisms search algorithm (A-CSOS). SOS is an effective meta-heuristic algorithm, especially for optimization problems with continuous variable, with important features such as the absence of any user-defined algorithmic parameters and the easily applicable. However, some essential features of SOS such as trap into local optima and slow convergence problems need to be improved in order to find better solutions for more complex, nonlinear, multi-modal optimization problems such as ORPD. In this study, to solve ORPD and enhance the capability of the standard SOS even further, A-CSOS algorithm is developed. To test the performance of the developed algorithm in ORPD, the both SOS and the proposed A-CSOS are applied to the two different objective functions including power loss minimization and total voltage deviation minimization in IEEE 57-, 118-, 300-bus power systems. According to the results of ten different test cases, the proposed method gives better solutions up to 15.3% and 40.52% than the state-of-art algorithms and SOS, respectively. Moreover, the convergence performance of A-CSOS is considerably better than all tried algorithms. The effectiveness of A-CSOS for solving ORPD and other complex constrained optimization problems is proofed by this study.

Keywords

Optimal reactive power dispatch Symbiotic organisms search Adaptive chaotic symbiotic organisms search Power loss minimization Voltage profile improvement 

Notes

References

  1. 1.
    Terra LDB, Short MJ (1991) Security-constrained reactive power dispatch. IEEE Trans Power Syst 6:109–117CrossRefGoogle Scholar
  2. 2.
    Lee KY, Park YM, Ortiz JL (1985) A united approach to optimal real and reactive power dispatch. IEEE Trans Power Appar Syst 104:1147–1153CrossRefGoogle Scholar
  3. 3.
    Quintana VH, Santos-Nieto M (1989) Reactive power dispatch by successive quadratic programming. IEEE Trans Energy Convers 4:425–435CrossRefGoogle Scholar
  4. 4.
    Granville S (1994) Optimal reactive dispatch through interior point methods. IEEE Trans Power Syst 9:136–146CrossRefGoogle Scholar
  5. 5.
    Chettih S, Khiat M, Chaker A (2011) Voltage control and reactive power optimisation using the meta heuristics method: application in the western Algerian transmission system. J Artif Intell 4:12–20CrossRefGoogle Scholar
  6. 6.
    Wu QH, Cao YJ, Wen JY (1998) Optimal reactive power dispatch using an adaptive genetic algorithm. Int J Electr Power Energy Syst 20:563–569CrossRefGoogle Scholar
  7. 7.
    Abou El Ela AA, Abido MA, Spea SR (2011) Differential evolution algorithm for optimal reactive power dispatch. Electr Power Syst Res 81:458–464CrossRefGoogle Scholar
  8. 8.
    Roy PK, Ghoshal SP, Thakur SS (2012) Optimal VAR control for improvements in voltage profiles and for real power loss minimization using biogeography based optimization. Int J Electr Power Energy Syst 43:830–838CrossRefGoogle Scholar
  9. 9.
    Dai C, Chen W, Zhu Y, Zhang X (2009) Seeker optimization algorithm for optimal reactive power dispatch. IEEE Trans Power Syst 24:1218–1231Google Scholar
  10. 10.
    Duman S, Sonmez Y, Guvenc U, Yorukeren N (2012) Optimal reactive power dispatch using a gravitational search algorithm. IET Gener Transm Dis 6:563–576CrossRefGoogle Scholar
  11. 11.
    Shaw B, Mukherjee V, Ghoshal SP (2014) Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm. Electr Power Energy Syst 55:29–40CrossRefGoogle Scholar
  12. 12.
    Singh RP, Mukherjee V, Ghoshal SP (2015) Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers. Appl Softw Comput 29:298–309CrossRefGoogle Scholar
  13. 13.
    Mehdinejad M, Mohammadi-Ivatloo B, Dadashzadeh-Bonab R, Zare K (2016) Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms. Electr Power Energy Syst 83:104–116CrossRefGoogle Scholar
  14. 14.
    Ghasemi M, Ghavidel S, Ghanbarian MM, Habibi A (2014) A new hybrid algorithm for optimal reactive power dispatch problem with discrete and continuous control variables. Appl Soft Comput 22:126–140CrossRefGoogle Scholar
  15. 15.
    Radosavljevic J, Jevtic M, Milovanovic M (2016) A solution to the ORPD problem and critical analysis of the results. Electr Eng.  https://doi.org/10.1007/s00202-016-0503-1 CrossRefGoogle Scholar
  16. 16.
    Chen G, Liu L, Song P, Du Y (2014) Chaotic improved PSO-based multi-objective optimization for minimization of power losses and L index in power systems. Energy Convers Manag 86:548–560CrossRefGoogle Scholar
  17. 17.
    Rajan A, Malakar T (2015) Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm. Electr Power Energy Syst 66:9–24CrossRefGoogle Scholar
  18. 18.
    Sulaiman MH, Mustaffa Z, Mohamed MR et al (2015) Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Appl Soft Comput 32:286–292CrossRefGoogle Scholar
  19. 19.
    Mei RNS, Sulaiman MH, Mustaffa Z et al (2017) Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Appl Soft Comput 59:210–222CrossRefGoogle Scholar
  20. 20.
    Heidari AA, Abbaspour RA, Jordehi AR (2017) Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems. Appl Soft Comput 57:657–671CrossRefGoogle Scholar
  21. 21.
    Ghasemi A, Valipour K, Tohidi A (2014) Multi objective optimal reactive power dispatch using a new multi objective strategy. Int J Electr Power Energy Syst 57:318–334CrossRefGoogle Scholar
  22. 22.
    Mouassa S, Bouktir T, Salhi A (2017) Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Eng Sci Technol 20:885–895Google Scholar
  23. 23.
    Rajan A, Jeevan K, Malakar T (2017) Weighted elitism based ant lion optimizer to solve optimum VAr planning problem. Appl Soft Comput 55:352–370CrossRefGoogle Scholar
  24. 24.
    Rajan A, Malakar T (2016) Exchange market algorithm based optimum reactive power dispatch. Appl Soft Comput 43:320–336CrossRefGoogle Scholar
  25. 25.
    Saha S, Mukherjee V (2017) A novel chaos-integrated symbiotic organisms search algorithm for global optimization. Soft Comput 22:1–20Google Scholar
  26. 26.
    Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112CrossRefGoogle Scholar
  27. 27.
    Guha D, Roy P, Banerjee S (2017) Quasi-oppositional symbiotic organism search algorithm applied to load frequency control. Swarm Evol Comput 33:46–67CrossRefGoogle Scholar
  28. 28.
    Secui DC (2017) Large-scale multi-area economic/emission dispatch based on a new symbiotic organisms search algorithm. Energy Convers Manag 154:203–223CrossRefGoogle Scholar
  29. 29.
    Tejani GG, Savsani VJ, Patel VK (2016) Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization. J Comput Des Eng 3:226–249Google Scholar
  30. 30.
    Runarsson T, Yao X (2003) Constrained evolutionary optimization. Evol Optim Int Ser Oper Res Manag Sci 48:87–113Google Scholar
  31. 31.
    Munakata T, Sinha S, Ditto WL (2002) Chaos computing: implementation of fundamental logical gates by chaotic elements. IEEE Trans Circuits Syst I Fundam Theory Appl 49:1629–1633MathSciNetCrossRefGoogle Scholar
  32. 32.
    Runarsson TP, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4:284–294CrossRefGoogle Scholar
  33. 33.
    Zimmerman RD, Murillo-Sánchez CE, Thomas RJ (2011) MATPOWER: steady-state operations, planning and analysis tools for power systems research and education. IEEE Trans Power Sys 26:12–19CrossRefGoogle Scholar
  34. 34.
    Erlich I, Lee K, Rueda J, Wildenhues S (2014) Competition on application of modern heuristic optimization algorithms for solving optimal power flow problems. IEEE PES general meeting, Washington, DC, USA. Retrieved from https://www.uni-due.de/ieee-wgmho/competition2014. Accessed 4 Oct 2017

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Inspection BoardTurkish Electricity Transmission Co.AnkaraTurkey
  2. 2.Department of Electrical and Electronics EngineeringKırıkkale UniversityKirikkaleTurkey
  3. 3.Department of Electrical and Electronics EngineeringGazi UniversityAnkaraTurkey

Personalised recommendations