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Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones

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Abstract

In this study, symbiotic organisms search (SOS) stochastic method is proposed to solve the optimal power flow (OPF) problem with valve-point effect and prohibited zones, which is one of the most important problems of the modern power system. The SOS approach is defined as the symbiotic relationships observed between two organisms in the ecosystem, which do not need the control parameters unlike other meta-heuristic algorithms in the literature. The effectiveness of the proposed SOS method is tested on modified IEEE 30-bus test system. The OPF problem is considered with four different test cases, such as (1) without valve-point effect and prohibited zones, (2) with valve-point effect, (3) with prohibited zones and (4) with valve-point effect and prohibited zones. The obtained results from the SOS algorithm are compared with the other optimization techniques in the literature. The obtained comparison results indicate that proposed approach is effective to reach optimal solution for the OPF problem.

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References

  1. Habiabollahzadeh H, Luo GX, Semlyen A (1989) Hydrothermal optimal power flow based on combined linear and nonlinear programming methodology. IEEE Trans Power Apparent Syst PWRS 4(2):530–537

    Article  Google Scholar 

  2. Yan X, Quantana VH (1999) Improving an interior point based OPF by dynamic adjustments of step sizes and tolerances. IEEE Trans Power Syst 14(2):709–717

    Article  Google Scholar 

  3. Zhang S, Irving MR (1994) Enhanced Newton-Raphson algorithm for normal, controlled and optimal power flow solutions using column exchange techniques. IEE Proc Gener Transm Distrib 141(6):647–657

    Article  Google Scholar 

  4. Burchet RC, Happ HH, Vierath DR (1984) Quadratically convergent optimal power flow. IEEE Trans Power Apparent Syst PAS 103:3267–3276

    Article  Google Scholar 

  5. Huneault M, Galina FD (1991) A survey of the optimal power flow literature. IEEE Trans Power Syst 6(2):762–770

    Article  Google Scholar 

  6. Osman MS, Abo-Sinna MA, Mousa AA (2004) A solution to the optimal power flow using genetic algorithm. Appl Math Comput 155:391–405

    MathSciNet  MATH  Google Scholar 

  7. Abido MA (2002) Optimal power flow using particle swarm optimization. Int J Electr Power Energy Syst 24(7):563–571

    Article  Google Scholar 

  8. El Ela AAA, Abido MA, Spea SR (2010) Optimal power flow using differential evolution algorithm. Electric Power Syst Res 80(7):878–885

    Article  Google Scholar 

  9. Niknam T, Narimani MR, Abarghooee RA (2012) A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect. Energy Convers Manag 58:197–206

    Article  Google Scholar 

  10. Duman S, Güvenç U, Sönmez Y, Yörükeren N (2012) Optimal power flow using gravitational search algorithm. Energy Convers Manag 59:86–95

    Article  Google Scholar 

  11. Bhattacharya A, Roy PK (2012) Solution of multi-objective optimal power flow using gravitational search algorithm. IET Gener Transm Distrib 6(8):751–763

    Article  Google Scholar 

  12. Bhattacharya A, Chattopadhyay PK (2011) Application of biogeography-based optimization to solve different optimal power flow problems. IET Gener Transm Distrib 5(1):70–80

    Article  Google Scholar 

  13. Sivasubramani S, Swarup KS (2011) Multi-objective harmony search algorithm for optimal power flow problem. Int J Electr Power Energy Syst 33:745–752

    Article  Google Scholar 

  14. Niknam T, Narimani MR, Jabbari M (2013) Dynamic optimal power flow using hybrid particle swarm optimization and simulated annealing. Int Trans Electr Energy Syst 23:975–1001

    Article  Google Scholar 

  15. Rao BS, Vaisakh K (2013) Multi-objective adaptive clonal selection algorithm for solving environmental/economic dispatch and OPF problems with load uncertainty. Int J Electr Power Energy Syst 53:390–408

    Article  Google Scholar 

  16. Bouchekara HREH (2014) Optimal power flow using black-hole-based optimization approach. Appl Soft Comput 24:879–888

    Article  Google Scholar 

  17. Manoranjitham GE, Shunmugalatha A (2015) Application of firefly algorithm on optimal power flow control incorporating simplified impedance UPFC model. Int J Electr Power Energy Syst 71:358–363

    Article  Google Scholar 

  18. Ghasemi M, Ghavidel S, Ghanbarian MM, Gharibzadeh M, Vahed AA (2014) Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm. Energy 78:276–289

    Article  Google Scholar 

  19. Mahdad B, Srairi K (2015) Security optimal power flow considering loading margin stability using hybrid FFA–PS assisted with brainstorming rules. Appl Soft Comput 35:291–309

    Article  Google Scholar 

  20. Mahdad B, Srairi K (2015) Blackout risk prevention in a smart grid based flexible optimal strategy using Grey Wolf-pattern search algorithms. Energy Convers Manag 98:411–429

    Article  Google Scholar 

  21. Edward JB, Rajasekar N, Sathiyasekar K, Senthilnathan N, Sarjila R (2013) An enhanced bacterial foraging algorithm for optimal power flow problem including FACTS devices considering system loadability. ISA Trans 52:622–628

    Article  Google Scholar 

  22. Adaryani MR, Karami A (2013) Artificial bee colony algorithm for solving multi-objective optimal power low problem. Int J Electr Power Energy Syst 53:219–230

    Article  Google Scholar 

  23. Bouchekara HREH, Abido MA, Boucherma M (2014) Optimal power flow using teaching-learning-based optimization technique. Electr Power Syst Res 114:49–59

    Article  Google Scholar 

  24. Mukherjee A, Mukherjee V (2015) Solution of optimal power flow using chaotic krill herd algorithm. Chaos, Solitons Fractals 78:10–21

    Article  MathSciNet  Google Scholar 

  25. Tan Y, Li C, Cao Y, Lee KY, Li L, Tang S, Zhou L (2015) Improved group search optimization method for optimal power flow problem considering valve-point loading effects. Neurocomputing 148:229–239

    Article  Google Scholar 

  26. Yuan X, Wang P, Yuan Y, Huang Y, Zhang X (2015) A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem. Energy Convers Manag 100:1–9

    Article  Google Scholar 

  27. Xia SW, Zhou B, Chan KW, Guo ZZ (2015) An improved GSO method for discontinuous non-convex transient stability constrained optimal power flow with complex system model. Int J Electr Power Energy Syst 64:483–492

    Article  Google Scholar 

  28. Singh RP, Mukherjee V, Ghoshal SP (2015) Particle swarm optimization with an aging leader and challengers algorithm for optimal power flow problem with FACTS devices. Int J Electr Power Energy Syst 64:1185–1196

    Article  Google Scholar 

  29. Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    Article  Google Scholar 

  30. Lee FN, Breipohi AM (1993) Reserve constrained economic dispatch with prohibited operating zones. IEEE Trans Power Syst 8(1):246–254

    Article  Google Scholar 

  31. Kılıç U (2015) Backtracking search algorithm based optimal power flow with valve point effect and prohibited zones. Electr Eng 97:101–110

    Article  Google Scholar 

  32. IEEE 30-bus test system data. http://www.ee.washington.edu/research/pstca/pf30/pg_tca30bus.htm

  33. Ongsakul W, Bhasaputra P (2002) Optimal power flow with FACTS devices by hybrid TS/SA approach. Int J Electr Power Energy Syst 24:851–857

    Article  Google Scholar 

  34. Sood YR (2007) Evolutionary programming based optimal power flow and its validation for deregulated power system analysis. Int J Electr Power Energy Syst 29:65–75

    Article  Google Scholar 

  35. Bouktir T, Slimani L, Mahdad B (2008) Optimal power dispatch for large scale power system using stochastic search algorithms. Int J Power Energy Syst 28:1–10

    Google Scholar 

  36. Yuryevich J, Wong KP (1999) Evolutionary programming based optimal power flow algorithm. IEEE Trans Power Syst 14(4):1245–1250

    Article  Google Scholar 

  37. Slimani L, Bouktir T (2007) Economic power dispatch of power system with pollution control using multi objective ant colony optimization. Int J Comput Intel Res 3:145–153

    Google Scholar 

  38. Ongsakul W, Tantimaporn T (2006) Optimal power flow by improved evolutionary programming. Electric Power Compon Syst 34(1):79–95

    Article  Google Scholar 

  39. Alsac O, Stott B (1974) Optimal load flow with steady-state security. IEEE Trans Power Appar Syst 93:745–751

    Article  Google Scholar 

  40. Sayah S, Zehar K (2008) Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers Manag 49:3036–3042

    Article  Google Scholar 

  41. Abido MA (2002) Optimal power flow using tabu search algorithm. Electric Power Compon Syst 30:469–483

    Article  Google Scholar 

  42. Bakistzis AG, Biskas PN, Zoumas CE, Petridis V (2002) Optimal power flow by enhanced genetic algorithm. IEEE Trans Power Syst 17(2):229–236

    Article  Google Scholar 

  43. Saini A, Chaturvedi DK, Saxena AK (2006) Optimal power flow solution: a GA-fuzzy system approach. Int J Emerg Electr Power Syst 5:1–21

    Google Scholar 

  44. Niknam T, Narimani MR, Jabbari M, Malekpour AR (2011) A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy 36:6420–6432

    Article  Google Scholar 

  45. Niknam T, Narimani MR, Aghaei J, Nayeripour M (2011) Modified honey bee mating optimisation to solve dynamic optimal power flow considering generator constraints. IET Gener Transm Distrib 5(10):989–1002

    Article  Google Scholar 

  46. Narimani MR, Azizipanah-Abarghooee R, Zoghdar-Moghadam-Shahrekohne B, Gholami K (2013) A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type. Energy 49:119–136

    Article  Google Scholar 

  47. Basu M (2010) Multi-objective optimal power flow with FACTS devices. Energy Convers Manag 52:903–910

    Article  Google Scholar 

  48. Malik TN, Asar A, Wyne MF, Akhtar S (2010) A new hybrid approach for the solution of nonconvex economic dispatch problem with valve-point effects. Electr Power Syst Res 80:1128–1136

    Article  Google Scholar 

  49. Özyön S, Yasar C, Temurtas H (2011) Differential evolution algorithm approach to nonconvex economic power dispatch problems with valve point effect. In: 6th International Advanced Technologies Symposium (IATS’11), pp 181–186

  50. Thitithamrongchai C, Eua-arporn B (2007) Self-adaptive differential evolution based optimal power flow for units with non-smooth fuel cost functions. J Electr Syst 3:88–99

    Google Scholar 

  51. Özyön S, Yasar C, Özcan G, Temurtas H (2011) An artificial bee colony algorithm (ABC) approach to nonconvex economic power dispatch problems with valve point effect. In: National Conference on Electrical, Electronics and Computer (FEEB’11), pp 294–299

  52. Yasar C, Özyön S (2011) A new hybrid approach for nonconvex economic dispatch problem with valve-point effect. Energy 36:5838–5845

    Article  Google Scholar 

  53. Özyön S, Yasar C, Temurtas H (2011) Particle swarm optimization algorithm for the solution of nonconvex economic dispatch problem with valve point effect. In: 7th International Conference on Electrical and Electronics Engineering (ELECO’11), pp 101–105

  54. Aydın D, Özyön S (2013) Solution to non-convex economic dispatch problem with valve point effects by incremental artificial bee colony with local search. Appl Soft Comput 13:2456–2466

    Article  Google Scholar 

  55. García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15:617–644

    Article  MATH  Google Scholar 

  56. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1:3–18

    Article  Google Scholar 

  57. Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evolut Comput 9:1–14

    Article  Google Scholar 

  58. Ayyıldız M, Çetinkaya K (2015) Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator. Neural Comput Appl. doi:10.1007/s00521-015-1898-8

    Google Scholar 

  59. Mirjalili S, Lewis A (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput Appl 25:1569–1584

    Article  Google Scholar 

  60. Mirjalili S, Mirjalili SM, Yang XS (2014) Binary bat algorithm. Neural Comput Appl 25:663–681

    Article  Google Scholar 

  61. Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimization with chaos. Neural Comput Appl 25:1077–1097

    Article  Google Scholar 

  62. Mirjalili S, Mirjalili SM, Hatamlou A (2015) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl. doi:10.1007/s00521-015-1870-7

    Google Scholar 

  63. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Technology Faculty, Duzce University, Duzce, Turkey

    Serhat Duman

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Correspondence to Serhat Duman.

Appendix

Appendix

See Table 11.

Table 11 Setting parameters of the SOS algorithm for the OPF problem
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Duman, S. Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones. Neural Comput & Applic 28, 3571–3585 (2017). https://doi.org/10.1007/s00521-016-2265-0

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