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Dynamic economic dispatch using harmony search algorithm with modified differential mutation operator

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Abstract

Dynamic economic dispatch (DED), which is a complex non-linear constrained optimization problem, has a pivotal role in power system operation. It is one of the prime functions of power generation and control, where the aim is to operate an electrical power system most economically while the system operation is within its security limits. This problem possess non-convex characteristic when generation unit valve-point effects are considered. This paper proposes to solve DED problem with valve-point effects, using a modified form of recently developed differential harmony search algorithm. A five- and ten-unit system with non-smooth fuel cost function is used to establish the effectiveness of the proposed method over various other methods. It is shown that the proposed method is capable of providing better quality solutions.

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Abbreviations

F cost t :

Total power production cost

F it (P it ):

Fuel cost corresponding to ith generator for output power P i at time t

a i , b i , c i :

Cost coefficients of ith generator

P it :

Real power output (MW) of ith generator corresponding to time period t

e i , f i :

Cost coefficients to effectively model the valve point loading effect

B ij , B i0, B 00 :

Loss coefficients

P Dt :

Power demand at time t

P Lt :

Power loss at time t

P maxi :

Upper bound for power outputs of the ith generating unit

P min i :

Lower bound for power outputs of the ith generating unit

P i(t-1) :

Power generation of unit i at previous hour

UR i :

Upper ramp limit

DR i :

Lower ramp limit

μ 1μ 2μ 3 :

Penalty terms

HMS:

Harmony memory size

HMCR:

Harmony memory consideration rate

PAR:

Pitch adjusting rate

NI:

Number of improvisations

References

  1. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. 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 

  7. 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 

  8. 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 

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

    Article  Google Scholar 

  10. 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 

  11. Gaing Z (2003) Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans Power Syst 18(3): 1187–1195

    Article  Google Scholar 

  12. Park J, Lee K, Shin J, Lee KY (2005) A particle swarm optimization for economic dispatch with nonsmooth cost functions. IEEE Trans Power Syst 20(1): 34–42

    Article  Google Scholar 

  13. Selvakumar AI, Thanushkodi K (2007) A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans Power Syst 22(1): 42–51

    Article  Google Scholar 

  14. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. 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 Manag 49(12): 3447–3453

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. 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 

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

    Article  Google Scholar 

  22. Geem ZW, Kim JH, Loganathan GV (2002) A new heuristic optimization algorithm: harmony search. Simulation 76(2): 60–68

    Article  Google Scholar 

  23. Geem ZW, Kim JH, Loganathan GV (2002) Harmony search optimization: application to pipe network design. Int J Model Simul 22(2): 125–133

    Google Scholar 

  24. Geem ZW, Lee KS, Park Y (2005) Application of harmony search to vehicle routing. Am J Appl Sci 2: 1552–1557

    Article  Google Scholar 

  25. Vasebi A, Fesanghary M, Bathaeea SMT (2007) Combined heat and power economic dispatch by harmony search algorithm. Int J Electr Power Energy Syst 29: 713–719

    Article  Google Scholar 

  26. Chakraborty P, Roy GG, Das S, Abraham A (2009) An improved harmony search algorithm with differential mutation operator. Fundamenta Informaticae J. IOS Press, Netherlands

  27. Storn R, Price KV (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, ICSI. http://http.icsi.berkeley.edu/~storn/litera.html

  28. Storn R, Price KV, Lampinen J (2005) Differential evolution—a practical approach to global optimization. Springer, Berlin

    MATH  Google Scholar 

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Correspondence to R. C. Bansal.

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Chakraborty, P., Roy, G.G., Panigrahi, B.K. et al. Dynamic economic dispatch using harmony search algorithm with modified differential mutation operator. Electr Eng 94, 197–205 (2012). https://doi.org/10.1007/s00202-011-0230-6

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  • DOI: https://doi.org/10.1007/s00202-011-0230-6

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