Skip to main content
SpringerLink
Log in

Application and Comparison of PSO, its Variants and HDE Techniques to Emission/Economic Dispatch

  • Research Article - Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The conventional particle swarm optimization (PSO) algorithm known for its global searching capabilities has been successfully applied to emission/economic dispatch (EED) problems. In this paper, the applicability of PSO and its four variants, namely, self-adaptive PSO, dispersed PSO, chaotic PSO and new PSO to emission/economic dispatch problems has been presented. Furthermore, the employability of hybrid differential evolution (HDE) technique to EED problems is also proved here. Two numerical examples are tested—one having 9 units, and limits on SO2 and NO x emissions and the other having 6 units with NO x emission and B-loss co-efficients. Results obtained are compared with those reported in literature. The comparison of the results shows that the conventional PSO, its variants and HDE algorithm converge to optimal or near optimal solutions in the two examples tested in this paper. But the convergence time in HDE is far greater when compared with PSO and its variants.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Lamont J.W., Obessis E.V.: Emission dispatch models and algorithms for the 1990’s. IEEE Trans. Power Syst. 10(2), 941–947 (1995)

    Article  Google Scholar 

  2. Wong, K.P.; Yuryevich, J.: Evolutionary programming based algorithm for environmentally constrained economic dispatch. IEEE Trans. Power Syst. 13(2), 301–306 (1998)

    Google Scholar 

  3. Chang, C.; Wong, K.; Fan, B.: Security-constrained multi-objective generation dispatch using bicriterion global optimization. Proc. Inst. Elect. Eng. Gen. Trans. Distrib. 142(4), 406–414 (1995)

    Google Scholar 

  4. Dhillon J.S., Parti S.C., Kothari D.P.: Stochastic economic emission load dispatch. Electr. Power Syst. Res. 26, 186–197 (1993)

    Article  Google Scholar 

  5. Sudhakaran M., Slochanal S.M.R.: Integrating genetic algorithms and tabu search for emission and economic dispatch problems. J. Indian Inst. Eng. (India) 86, 22–27 (2005)

    Google Scholar 

  6. Venkatesh, P.; Gnanadass, R.; Padhy, N.P.: Comparison and application of evolutionary techniques to combined economic emission dispatch with line flow constraints. IEEE Trans. Power Syst. 18(2), 688–697 (2003)

    Google Scholar 

  7. Bhattacharya A., Chattopadhyay P.K.: Hybrid differential evolution with biogeography-based optimization algorithm for solution of economic emission load dispatch problems. Expert Syst. Appl. 38, 14001–14010 (2011)

    Google Scholar 

  8. Bhattacharya A., Chattopadhyay P.K.: Solving economic emission load dispatch problems using hybrid differential evolution. Appl. Soft Comput. 11, 2526–2537 (2011)

    Article  Google Scholar 

  9. Zhang, Y.; et al.: A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch. Inf. Sci. (2011). doi:10.1016/j.ins.2011.06.004

  10. Hooshmand, R.-A.; Parastegari, M.; Morshed, M.J.: Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using the hybrid bacterial foraging-Nelder–Mead algorithm. Appl. Energy 89, 443–453 (2012)

    Google Scholar 

  11. Panigrahi, B.K.; Ravikumar Pandi, V.; Sharma, R.; Das, S.; Das, S.: Multiobjective bacteria foraging algorithm for electrical load dispatch problem. Energy Convers. Manag. 52, 1334–1342 (2011)

    Google Scholar 

  12. Sivasubramani S., Swarup K.S.: Environmental/economic dispatch using multi-objective harmony search algorithm. Electr. Power Syst. Res. 81, 1778–1785 (2011)

    Article  Google Scholar 

  13. Lu, Y.; Zhou, J.; Qin, H.; Wang, Y.; Zhang, Y.: Environmental/economic dispatch problem of power system by using an enhanced multi-objective differential evolution algorithm. Energy Convers. Manag. 52, 1175–1183 (2011)

    Google Scholar 

  14. Dhanalakshmi, S.; Kannan, S.; Mahadevan, K.; Baskar, S.: Application of modified NSGA-II algorithm to combined economic and emission dispatch problem. Electr. Power Energy Syst. 33, 992–1002 (2011)

    Google Scholar 

  15. Wu, Z.; Zhou, J.: A self adaptive particle swarm optimization algorithm with individual coefficients adjustment. Int. Conf. Comput. Intell. Secur., pp. 133–136 (2007)

  16. Cai X., Cui Z., Zeng J., Tan Y.: Dispersed particle swarm optimization. Inf. Process. Lett. 105, 231–235 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  17. Chuanwen J., Bompard E.: A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization. Math. Comput. Simul. 68, 57–65 (2005)

    Article  MathSciNet  Google Scholar 

  18. Yang, C.; Simon, D.: A new particle swarm optimization technique. In: Proceedings of the 18th International Conference on Systems Engineering (ISCEng’05), pp. 164–169 (2005)

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

    Google Scholar 

  20. Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)

    Google Scholar 

  21. Storn, R.; Price, K.: Minimizing the real functions of the ICEC’96 contest by Differential Evolution. IEEE Int. Conf. Evol. Comput., pp. 842–844 (1996)

  22. Lin, Y.; Hwang, K.; Wang, F.: Co-evolutionary hybrid differential evolution for mixed-integer optimization problems. Eng. Optim. 33(6), 1–20 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, VIT University, Vellore, 632014, TN, India

    T. Jayabarathi, Ravi Tej Kolipakula, M. Vamsi Krishna & Afshin Yazdani

Authors
  1. T. Jayabarathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ravi Tej Kolipakula
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Vamsi Krishna
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Afshin Yazdani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Jayabarathi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jayabarathi, T., Kolipakula, R.T., Krishna, M.V. et al. Application and Comparison of PSO, its Variants and HDE Techniques to Emission/Economic Dispatch. Arab J Sci Eng 39, 967–976 (2014). https://doi.org/10.1007/s13369-013-0635-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-013-0635-9

Keywords

Search

Navigation