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Backtracking search algorithm-based optimal power flow with valve point effect and prohibited zones

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Abstract

This paper presents a solution technique for optimal power flow (OPF) with valve-point effect and prohibited operating zones of power systems using backtracking search algorithm (BSA). BSA is a new population-based evolutionary algorithm. The most important property of the algorithm is not over precision to initial of value, unlike many other heuristic algorithms. The proposed algorithm having four different cases is tested on IEEE-30 bus test system. The results of BSA are compared to those reported in literature. Thus, its validity for so applications in this area is proved. In this paper, OPF problem of power systems is solved by BSA for the first time.

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References

  1. Habiabollahzadeh H, Luo GX, Semlyen A (1989) Hydrothermal optimal power flow based on a combined linear and nonlinear programming methodology. IEEE Trans Power Syst 4:530–537. doi:10.1109/59.193826

    Article  Google Scholar 

  2. Lee FN, Breipohl AM (1993) Reserve constrained economic dispatch with prohibited operating zones. IEEE Trans Power Syst 4:246–254. doi:10.1109/59.221233

    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. IEEE Proc General Trans Distrib 141:647–657. doi:10.1049/ip-gtd:19941479

    Article  Google Scholar 

  4. Mota-Palomino R, Quintana VH (1986) Sparse reactive power scheduling by a penalty-function linear programming technique. IEEE Trans Power Syst l:31–39. doi:10.1109/TPWRS.1986.4334951

    Article  Google Scholar 

  5. Osman MS, Abo-Sinna MA, Mousa AA (2004) A solution to the optimal power flow using genetic algorithm. Appl Math Comput 155:291–405. doi:10.1016/S0096-3003(03)00785-9

    Article  MathSciNet  Google Scholar 

  6. He S, Wen JY, Prempain E, Wu QH, Fitch J, Mann S (2004) An improved particle swarm optimization for optimal power flow. Int Conf Power Syst Technol 2:1633–1637. doi:10.1109/ICPST.2004.1460265

    Google Scholar 

  7. Soares J, Sousa T, Vale ZA, Morais H, Faria P (2011) Ant colony search algorithm for the optimal power flow problem. IEEE Power Energy Soc General Meet, 1–8: doi:10.1109/PES.2011.6039840

  8. Sayah S, Zehar K (2008) Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Conv Manag 49:3036–3042. doi:10.1016/j.enconman.2008.06.014

    Article  Google Scholar 

  9. Sood YR (2007) Evolutionary programming based optimal power flow and its validation for deregulated power system analysis. Int J Elect Power Energy Syst 29:65–75. doi:10.1016/j.ijepes.2006.03.024

    Article  Google Scholar 

  10. Sumpavakup C, Srikun I, Chusanapiputt S (2010) A solution to the optimal power flow using artificial bee colony algorithm. Power Syst Technol (POWERCON) 1–5. doi:10.1109/POWERCON.2010.5666516

  11. Abido MA (2002) Optimal power flow using tabu search algorithm. Elect Power Compon Syst 30:469–483. doi:10.1080/15325000252888425

    Article  Google Scholar 

  12. Roa-Sepulveda CA, Pavez-Lazo BJ (2003) A solution to the optimal power flow using simulated annealing. Int J Elect Power Energy Syst 25:47–57. doi:10.1016/S0142-0615(02)00020-0

    Article  Google Scholar 

  13. 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. doi:10.1016/j.energy.2011.09.027

    Article  Google Scholar 

  14. Srinivasa Rao B, Vaisakh K (2013) New variants/hybrid methods of memetic algorithm for solving optimal power flow problem with load uncertainty. Int J Hybrid Intell Syst 10:117–128. doi:10.3233/HIS-130170

    Google Scholar 

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

  16. Niknam T, Narimani MR, Abarghooee RA (2012) A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect. Energy Conv Manage 58:197–206. doi:10.1016/j.enconman.2012.01.017

    Article  Google Scholar 

  17. 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. doi:10.1016/j.asoc.2012.12.002

    Article  Google Scholar 

  18. Yaşar C, Özyön S (2011) A new hybrid approach for nonconvex economic dispatch problem with valve-point effect. Energy 36:5838–5845. doi:10.1016/j.energy.2011.08.041

    Article  Google Scholar 

  19. 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. Elect Power Syst Res 80:1128–1136. doi:10.1016/j.epsr.2010.03.004

    Article  Google Scholar 

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

  21. Özyön S, Yaşar C, Temurtas H (2011) Particle swarm optimization algorithm for the solution of nonconvex economic dispatch problem with valve point effect. 7th International Conference on Electrical and Electronics Engineering (ELECO’11) vol 1, pp 101–105

  22. Niknam T, Abarghooee RA, Narimani MR (2012) Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints. Energy 47:451–464. doi:10.1016/j.energy.2012.07.053

    Article  Google Scholar 

  23. Civivioğlu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219:8121–8144. doi:10.1016/j.amc.2013.02.017

    Article  MathSciNet  Google Scholar 

  24. Chen YY, Chung CY (2012) Multi-Constrained Optimal Power Flow by an opposition-based differential evolution. Power Energy Soc General Meet, 1–7. doi:10.1109/PESGM.2012.6343917

  25. http://www.mathworks.com/help/matlab/ref/randn.html. Accessed 02 December 2013

  26. http://www.pinarcivicioglu.com/bsa.html. Accessed 02 December 2013

  27. http://www.ee.washington.edu/research/pstca/pf30/pg_tca30bus.htm. Accessed 02 December 2013

  28. 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. doi:10.1016/S0142-0615(02)00006-6

    Article  Google Scholar 

  29. 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. doi:10.2316/Journal.203.2008.2.203-3501

    Google Scholar 

  30. Yuryevich J, Wong KP (1999) Evolutionary programming based optimal power flow algorithm. IEEE Trans Power Syst 14:1245–1250. doi:10.1109/PESS.1999.787503

    Article  Google Scholar 

  31. 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. doi:10.5019/j.ijcir.2007.99

    Google Scholar 

  32. Ongsakul W, Tantimaporn T (2006) Optimal power flow by improved evolutionary programming. Elect Power Compon Syst 34:79–95. doi:10.1080/15325000691001458

    Article  Google Scholar 

  33. Alsac O, Stott B (1974) Optimal load flow with steady-state security. IEEE Trans Power Appar Syst 93:745–751. doi:10.1109/ICIINFS.2008.4798414

  34. Bakistzis AG, Biskas PN, Zoumas CE, Petridis V (2002) Optimal power flow by enhanced genetic algorithm. IEEE Trans Power Syst 17:229–236. doi:10.1109/TPWRS.2002.1007886

    Article  Google Scholar 

  35. 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. doi:10.2202/1553-779X.1091

    Google Scholar 

  36. Basu M (2011) Multi-objective optimal power flow with FACTS devices. Energy Conv Manage 52:903–910. doi:10.1016/j.enconman.2010.08.017

    Article  Google Scholar 

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

    Google Scholar 

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Kılıç, U. Backtracking search algorithm-based optimal power flow with valve point effect and prohibited zones. Electr Eng 97, 101–110 (2015). https://doi.org/10.1007/s00202-014-0315-0

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