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Neural Computing and Applications

, Volume 30, Issue 11, pp 3545–3564 | Cite as

An effective cuckoo search algorithm for large-scale combined heat and power economic dispatch problem

  • Thang Trung NguyenEmail author
  • Thuan Thanh Nguyen
  • Dieu Ngoc Vo
Original Article

Abstract

This paper develops an effective cuckoo search algorithm (ECSA) for searching optimal solutions for the problem of combined heat and power economic dispatch. The main task of the problem is to determine the optimal value of power of the pure power generators, of the heat of the pure heat generators and of both power and heat of cogenerators so that fuel cost is minimized while exactly meeting power and heat demands and power and heat limits as well as the complicated feasible operating zone of cogenerators. The proposed ECSA is a newly improved version of conventional cuckoo search algorithm to improve the quality of solutions and reduce the maximum number of iterations based on two modified techniques. The first technique is based on the ratio of the difference between the fitness function value of each solution and the lowest fitness function value of the best current solution to the lowest one to determine an effective operation for producing the second new solution generation. The second technique aims to integrate both previous and current solutions into one group and sort them in the descending order of fitness value. The effectiveness of ECSA has been validated via six cases corresponding to six test systems where the scale of the systems is ranged from the smallest system with four units to the largest one with forty-eight units with valve point loading effects. The comparisons of obtained results with other existing methods have indicated that the proposed ECSA is very effective and robust for finding optimal solutions for the CHPED problem.

Keywords

Effective cuckoo search algorithm Combined heat and power Valve point loading effects Large-scale systems Fitness function 

List of symbols

Npp, Nph, Nc

Number of pure power units, pure heat units and cogeneration units

Fpi, Fcj, Fhk

Cost function of pure power unit i, cogeneration unit j, and pure heat unit k

api, bpi, cpi, epi, fpi

Cost function coefficients of pure power unit i

acj, bcj, ccj, kcj, lcj, mcj

Cost function coefficients of cogeneration unit j

ahk, bhk, chk

Cost function coefficients of pure heat unit k

Ppi,d, Pcj,d

Power generated by pure power unit i and cogeneration unit j associated with nest d

Hcj,d, Hhk,d

Heat generated by the jth cogeneration unit and the kth pure heat unit associated with nest d

Ppi,min, Ppi,max

Minimum and maximum power of pure power unit i

Pcj,min, Pcj,max

Minimum and maximum power of cogeneration unit j

Hhk,min, Hhk,max

Minimum and maximum heat of pure heat unit k

Hcj,min, Hcj,max

Minimum and maximum heat of cogeneration unit j

PD, HD

Power and heat load demand

PL

Transmission power loss

Bij, B0i, B00

Power loss coefficients

Notes

Compliance with ethical standards

Conflict of interest

The authors do declare that there is no conflict of interest between the study and others.

References

  1. 1.
    Vasebi A, Fesanghary M, Bathaee SMT (2007) Combined heat and power economic dispatch by harmony search algorithm. Electr Power Energy Syst 29:713–719. doi: 10.1016/j.ijepes.2007.06.006 CrossRefGoogle Scholar
  2. 2.
    Rooijers FJ, Van ARAM (1994) Static economic dispatch for co-generation systems. IEEE Trans Power Syst 3(9):1392–1398. doi: 10.1109/59.336125 CrossRefGoogle Scholar
  3. 3.
    Tao G, Henwood MI, Van OM (1996) An algorithm for heat and power dispatch. IEEE Trans Power Syst 11(4):1778–1784. doi: 10.1109/59.544642 CrossRefGoogle Scholar
  4. 4.
    Chapa G, Galaz V (2004) An economic dispatch algorithm for cogeneration systems. Proc IEEE Power Eng Soc General Meeting 1:989–994. doi: 10.1109/PES.2004.1372985 CrossRefGoogle Scholar
  5. 5.
    Dieu VN, Ongsakul W (2009) Augmented Lagrange hopfield network for economic load dispatch with combined heat and power. Electr Power Compon Syst 37(12):1289–1304. doi: 10.1080/15325000903054969 CrossRefGoogle Scholar
  6. 6.
    Chen CL, Lee TY, Jan RM, Lu CL (2012) A novel direct search approach for combined heat and power dispatch. Electr Power Energy Syst 43:766–773. doi: 10.1016/j.ijepes.2012.05.033 CrossRefGoogle Scholar
  7. 7.
    Sashirekha A, Pasupuleti J, Moin NH, Tan CS (2013) Combined heat and power economic dispatch solved using Lagrangian relaxation with surrogate subgradient multiplier updates. Electr Power Energy Syst 44:421–430. doi: 10.1016/j.ijepes.2012.07.038 CrossRefGoogle Scholar
  8. 8.
    Song YH, Xuan YQ (1998) Combined heat and power economic dispatch using genetic algorithm based penalty function method. Electric Mach Power Syst 26(4):363–372. doi: 10.1080/07313569808955828 CrossRefGoogle Scholar
  9. 9.
    Song YH, Chou CS, Stonham TJ (1999) Combined heat and power dispatch by improved ant colony search algorithm. Electr Power Syst Res 52:115–121. doi: 10.1016/S0378-7796(99)00011-5 CrossRefGoogle Scholar
  10. 10.
    Wong KP, Algie C (2002) Evolutionary programming approach for combined heat and power dispatch. Electr Power Syst Res 61:227–232. doi: 10.1016/S0378-7796(02)00028-7 CrossRefGoogle Scholar
  11. 11.
    Su CT, Chiang CL (2004) An incorporated algorithm for combined heat and power economic dispatch. Electr Power Syst Res 69:187–195. doi: 10.1016/j.epsr.2003.08.006 CrossRefGoogle Scholar
  12. 12.
    Ramesh V, Jayabarathi T, Shrivastava N, Baska A (2009) A novel selective particle swarm optimization approach for combined heat and power economic dispatch. Electr Power Compon Syst 37:1231–1240. doi: 10.1080/15325000902994348 CrossRefGoogle Scholar
  13. 13.
    Subbaraj P, Rengaraj R, Salivahanan S (2009) Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm. Appl Energy 86:915–921. doi: 10.1016/j.apenergy.2008.10.002 CrossRefGoogle Scholar
  14. 14.
    Basu M (2011) Bee colony optimization for combined heat and power economic dispatch. Expert Syst Appl 38:13527–13531. doi: 10.1016/j.eswa.2011.03.067 CrossRefGoogle Scholar
  15. 15.
    Esmaile K, Majid J (2011) Harmony search algorithm for solving combined heat and power economic dispatch problems. Energy Convers Manag 52:1550–1554. doi: 10.1016/j.enconman.2010.10.017 CrossRefGoogle Scholar
  16. 16.
    Javadi MS, Esmaeel NA, Sabramooz S (2012) Economic heat and power dispatch in modern power system harmony search algorithm versus analytical solution. Sci Iran D 19(6):1820–1828. doi: 10.1016/j.scient.2012.10.033 CrossRefGoogle Scholar
  17. 17.
    Basu M (2012) Artificial immune system for combined heat and power economic dispatch. Electr Power Energy Syst 43:1–5. doi: 10.1016/j.ijepes.2012.05.016 CrossRefGoogle Scholar
  18. 18.
    Behnam MI, Mohammad MD, Abbas R (2013) Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients. Electr Power Syst Res 95:9–18. doi: 10.1016/j.epsr.2012.08.005 CrossRefGoogle Scholar
  19. 19.
    Mehrdad TH, Saeed T, Manijeh A, Parinaz A (2014) Improved group search optimization method for solving CHPED in large scale power systems. Energy Convers Manag 80:446–456. doi: 10.1016/j.enconman.2014.01.051 CrossRefGoogle Scholar
  20. 20.
    Basu M (2016) Group search optimization for combined heat and power economic dispatch. Electr Power Energy Syst 78:138–147. doi: 10.1016/j.ijepes.2015.11.069 CrossRefGoogle Scholar
  21. 21.
    Provas KR, Chandan P, Sneha S (2014) Oppositional teaching learning based optimization approach for combined heat and power dispatch. Electr Power Energy Syst 57:392–403. doi: 10.1016/j.ijepes.2013.12.006 CrossRefGoogle Scholar
  22. 22.
    Basu M (2015) Combined heat and power economic dispatch using opposition-based group search optimization. Electr Power Energy Syst 73:819–829. doi: 10.1016/j.ijepes.2015.06.023 CrossRefGoogle Scholar
  23. 23.
    Nguyen TT, Vo DN (2016) Improved particle swarm optimization for combined heat and power economic dispatch. Sci Iran D 23(3):1318–1334Google Scholar
  24. 24.
    Jayakumar N, Subramanian S, Ganesan S, Elanchezhian EB (2016) Grey wolf optimization for combined heat and power dispatch with cogeneration systems. Electr Power Energy Syst 74:252–264. doi: 10.1016/j.ijepes.2015.07.031 CrossRefGoogle Scholar
  25. 25.
    Ghorbani N (2016) Combined heat and power economic dispatch using exchange market algorithm. Electr Power Energy Syst 82:58–66. doi: 10.1016/j.ijepes.2016.03.004 CrossRefGoogle Scholar
  26. 26.
    Nguyen TT, Vo DN, Dinh BH (2016) Cuckoo search algorithm for combined heat and power economic dispatch. Electr Power Energy Syst 81:204–214. doi: 10.1016/j.ijepes.2016.02.026 CrossRefGoogle Scholar
  27. 27.
    Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of world congress on nature and biologically inspired computing (NaBIC 2009), pp 210–214. doi: 10.1109/NABIC.2009.5393690
  28. 28.
    Walton S, Hassan O, Morgan K, Brown MR (2011) Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solut Fractals 44:710–718. doi: 10.1016/j.chaos.2011.06.004 CrossRefGoogle Scholar
  29. 29.
    Nguyen TT, Vo DN (2015) Modified cuckoo search algorithm for short-term hydrothermal scheduling. Electr Power Energy Syst 65:271–281. doi: 10.1016/j.ijepes.2014.10.004 CrossRefGoogle Scholar
  30. 30.
    Long W, Liang X, Huang Y, Chen Y (2014) An effective hybrid cuckoo search algorithm for constrained global optimization. Neural Comput Appl 25(3):911–926. doi: 10.1007/s00521-014-1577-1 CrossRefGoogle Scholar
  31. 31.
    Li X, Wang J, Yin M (2014) Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Comput Appl 24:1233–1256. doi: 10.1007/s00521-013-1354-6 CrossRefGoogle Scholar
  32. 32.
    Nguyen TT, Vo DN (2015) The application of one rank cuckoo search algorithm for solving economic load dispatch problems. Appl Soft Comput 37:763–773. doi: 10.1016/j.asoc.2015.09.010 CrossRefGoogle Scholar
  33. 33.
    Naik MK, Panda R (2015) A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Appl Soft Comput 38:661–675. doi: 10.1016/j.asoc.2015.10.039 CrossRefGoogle Scholar
  34. 34.
    Abd-Elazim SM, Al ES (2016) Optimal power system stabilizers design via cuckoo search algorithm. Electr Power Energy Syst 75:99–107. doi: 10.1016/j.ijepes.2015.08.018 CrossRefGoogle Scholar
  35. 35.
    Abd-Elazim SM, Ali ES (2016) Optimal location of Statcom in multimachine power system for increasing load ability by Cuckoo Search algorithm. Electr Power Energy Syst 80:240–251. doi: 10.1016/j.ijepes.2016.01.023 CrossRefGoogle Scholar
  36. 36.
    Sanajaoba S, Fernandez E (2016) Maiden application of cuckoo search algorithm for optimal sizing of a remote hybrid renewable energy system. Renewable Energy 96:1–10. doi: 10.1016/j.renene.2016.04.069 CrossRefGoogle Scholar
  37. 37.
    Mlakar U, Fister JR, Fister I (2016) Hybrid self-adaptive cuckoo search for global optimization. Swarm Evol Comput 29:47–72. doi: 10.1016/j.swevo.2016.03.001 CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Thang Trung Nguyen
    • 1
    Email author
  • Thuan Thanh Nguyen
    • 2
  • Dieu Ngoc Vo
    • 3
  1. 1.Power System Optimization Research Group, Faculty of Electrical and Electronics EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Dong An PolytechnicBinh Duong ProvinceVietnam
  3. 3.Department of Power SystemsHo Chi Minh City University of TechnologyHo Chi Minh CityVietnam

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