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Dynamic economic dispatch using Lbest-PSO with dynamically varying sub-swarms

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Abstract

Dynamic economic dispatch (DED) is one of the major planning problem in a power system. It is a non-linear optimization problem with various operational constraints, which includes the constraints of the generators operating characteristics and the system constraints. Its principal aim is to minimize the cost of power production of all the participating generators over a time horizon of 24 h, while satisfying the system constraints. This problem deals with non-convex characteristics if generation unit valve-point effects are taken into account. The paper intends to solve the DED problem with valve-point effects, using our modified form of Local-best variant of Particle Swarm Optimization (Lbest PSO) algorithm. We have tested our algorithm on 5-unit, 10-unit and 110-unit test system with non-smooth fuel cost functions to prove the effectiveness of the suggested method over different state of the art methods.

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References

  1. Hindi KS, Ab Ghani MR (1989) Multi-period economic dispatch for large scale power systems. lEE Proc Part C 136(3):130–136

    Google Scholar 

  2. Granelli GP, Marannino P, Montagna M, Silvestri A (1989) Fast and efficient gradient projection algorithm for dynamic generation dispatching. IEE Proc Gener Transm Distrib 136(5):295–302

    Article  Google Scholar 

  3. Somuah CB, Khunaizi N (1990) Application of linear programming re-dispatch technique to dynamic generation allocation. IEEE Trans Power Syst 5(1):20–26

    Article  Google Scholar 

  4. Travers DL, Kaye RJ (1998) Dynamic dispatch by constructive dynamic programming. IEEE Trans Power Syst 13(1):72–78

    Article  Google Scholar 

  5. Liang RH (1999) A neural-based re-dispatch approach to dynamic generation allocation. IEEE Trans Power Syst 14(4):1388–1393

    Article  Google Scholar 

  6. Yalcinoz T, Short MJ (1998) IEEE Trans Power Syst. Neural networks approach for solving economic dispatch problem with transmission capacity constraints 13(2):307–313

  7. Panigrahi BK, Chattopadhyay PK, Chakrabarti RN, Basu M (2006) Simulated annealing technique for dynamic economic dispatch. Electr Power Comp Syst 34(5):577–586

    Article  Google Scholar 

  8. Wong KP, Fung CC (1993) Simulated annealing based economic dispatch algorithm. IEE Proc Gener Transm Distrib 140(6):509–515

    Article  Google Scholar 

  9. Walters DC, Sheble GB (1993) Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans Power Syst 8(3):1325–1332

    Article  Google Scholar 

  10. Chiang C (2005) Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels. IEEE Trans Power Syst 20(4):1690–1699

    Article  Google Scholar 

  11. Yang H, Yang P, Huang C (1996) Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions. IEEE Trans Power Syst 11(1):112–118

    Article  Google Scholar 

  12. Ravi G, Chakrabarti R, Choudhuri S (2006) Nonconvex economic dispatch with heuristic load patterns using improved fast evolutionary program. Electr Power Comp Syst 34(1):37–45

    Article  Google Scholar 

  13. Lin W, Cheng F, Tsay M (2002) An improved tabu search for economic dispatch with multiple minima. IEEE Trans Power Syst 17(1):108–112

    Article  Google Scholar 

  14. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micromachine and human science, Nagoya

  15. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Piscataway, pp 1942–1948

  16. Park JB, Lee KS, Shin JR, Lee KY (2005) \(A\) particle swarm optimization for economic dispatch with non-smooth cost functions. IEEE Trans Power Syst 20(1):34–42 Feb.

    Article  Google Scholar 

  17. Ghosh A, Chowdhury A, Sinha S, Vasilakos AV, Das S (2012) A genetic Lbest particle swarm optimizer with dynamically varying subswarm topology. IEEE congress on evolutionary computation (CEC 2012), Brisbane, 978-1-4673-1508-1

  18. Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation (CEC 1999), Piscataway, pp 1931–1938

  19. Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2002), Honolulu

  20. Suganthan PN (1999) Particle swarm optimizer with neighborhood operator. In: Proceedings of the IEEE congress on evolutionary computation (CEC 1999), Piscataway, pp 1958–1962

  21. Hu X, Eberhart RC (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, Hawaii

  22. Mendes R, Kennedy J, Neves J (June 2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210

  23. Blackwell TM, Branke J (2004) Multi-swarm optimization in dynamic environments. LNCS No. 3005. In: Proceedings of applications of evolutionary computing: EvoWorkshops 2004: EvoBIO. EvoCOMNET.EvoHOT. EvoISAP. EvoMUSART and EvoSTOC, Coimbra, pp 489–500

  24. Løvbjerg M, Rasmussen TK, Krink T (2001) Hybrid particle swarm optimiser with breeding and subpopulations. In: Proceedings of the genetic and evolutionary computation conference, GECCO 2001

  25. Attaviriyanupap P, Kita H, Tanaka E, Hasegawa J (2002) A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Trans Power Syst 17(2):411–416

    Article  Google Scholar 

  26. Panigrahi BK, Pandi V Ravikumar, Das Sanjoy (2008) Adaptive particle swarm optimization approach for static and dynamic economic load dispatch. Energy Convers Manage 49:1407–1415

    Article  Google Scholar 

  27. Balamurugan R, Subramanian S (2008) Differential evolution-based dynamic economic dispatch of generating units with valve-point effects. Electr Power Comp Syst 36:828–843

    Article  Google Scholar 

  28. Yuan X, Wang L, Yuan Y, Zhang Y, Cao B, Yang B (2008) A modified differential evolution approach for dynamic economic dispatch with valve-point effects. Energy Convers Manage 49(12):3447–3453

  29. Yuan X, Wang L, Yuan Y, Zhang Y, Yuan Y (2009) A hybrid differential evolution method for dynamic economic dispatch with valve-point effects. Expert Syst Appl 36(2):4042–4048

    Article  Google Scholar 

  30. Victoire T, Jeyakumar A (2005) Deterministically guided PSO for dynamic dispatch considering valve-point effect. Electr Power Syst Res 73(3):313–322

    Article  Google Scholar 

  31. Victoire TAA, Jeyakumar AE (August 2005) Reserve constrained dynamic dispatch of units with valve-point effects. IEEE Trans Power Syst 20(3):1273–1282

  32. Basu M (2009) Hybridization of artificial immune systems and sequential quadratic programming for dynamic economic dispatch. Electr Power Comp Syst 37(9):1036–1045

    Article  Google Scholar 

  33. Hemamalini S, Simon SP (2011) Dynamic economic dispatch using artificial immune system for units with valve-point effect. Int J Electr Power Energy Syst 33:868–874

    Article  Google Scholar 

  34. Hemamalini S, Simon S (2011) Dynamic economic dispatch using artificial bee colony algorithm for units with valve-point effect. Eur Trans Electr Power 21:70–81

  35. Pandi VR (2011) Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm. Expert Syst Appl 38:8509e14

    Google Scholar 

  36. Dakuo H, Dong G, Wang F, Mao Z (2011) Optimization of dynamic economic dispatch with valve-point effect using chaotic sequence based differential evolution algorithms. Energy Convers Manage 52:1026e32

    Google Scholar 

  37. Yuan X, Su A, Yuan Y, Nie H, Wang L (2009) An improved pso for dynamic load dispatch of generators with valve-point effects. Energy 34:67e74

    Article  Google Scholar 

  38. Lu Y, Zhou J, Qin H, Wang Y, Zhang Y (2011) Chaotic differential evolution methods for dynamic economic dispatch with valve-point effects. Eng Appl Artif Intell 24:378e87

    Google Scholar 

  39. Lu Y, Zhou J, Qin H, Li Y, Zhang Y (2010) An adaptive hybrid differential evolution algorithm for dynamic economic dispatch with valve-point effects. Expert Syst Appl 37:4842e9

    Google Scholar 

  40. Orero SO, Irving MR (1997) Large scale unit commitment using a hybrid genetic algorithm. Electr Power Energy Syst 19(1):45–55

    Article  Google Scholar 

Download references

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Correspondence to B. K. Panigrahi.

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Chowdhury, A., Zafar, H., Panigrahi, B.K. et al. Dynamic economic dispatch using Lbest-PSO with dynamically varying sub-swarms. Memetic Comp. 6, 85–95 (2014). https://doi.org/10.1007/s12293-013-0127-1

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  • DOI: https://doi.org/10.1007/s12293-013-0127-1

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