Advertisement

Application of Wind-Driven Optimization for Decision-Making in Economic Dispatch Problem

  • V. Udhay SankarEmail author
  • Bhanutej
  • C. H. Hussaian Basha
  • Derick Mathew
  • C. Rani
  • K. Busawon
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)

Abstract

A number of optimization techniques have been used by researchers to solve the non-convex Economic Dispatch (ED) problem. In this paper, we have applied Wind-Driven Optimization (WDO), a heuristic global optimization technique to solve the ED problem. The technique was applied to three different test systems and the results obtained were compared and analyzed with the results obtained from other techniques. MATLAB R2017a was used for the coding and execution of the algorithm.

Keywords

Economic dispatch Emission cost Wind-driven optimization Fuel cost 

Nomenclature

ED

Economic Dispatch

WDO

Wind-Driven Optimization

References

  1. 1.
    Kumar, A.I.S., et al.: Particle swarm optimization solution to emission and economic dispatch problem. In: Conference on Convergent Technologies for the Asia-Pacific Region, TENCON 2003, vol. 1. IEEE (2003)Google Scholar
  2. 2.
    Gopalakrishnan, R., Krishnan, A.: An efficient technique to solve combined economic and emission dispatch problem using modified Ant colony optimization. Sadhana 38(4), 545–556 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Abdelaziz, A.Y., Ali, E.S., Abd Elazim, S.M.: Combined economic and emission dispatch solution using flower pollination algorithm. Int. J. Electr. Power Energy Syst. 80, 264–274 (2016)CrossRefGoogle Scholar
  4. 4.
    Aydin, D., et al.: Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem. Int. J. Electr. Power Energy Syst. 54, 144–153 (2014)CrossRefGoogle Scholar
  5. 5.
    Venkatesh, P., Gnanadass, R., Padhy, N.P.: Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints. IEEE Trans. Power Syst. 18(2), 688–697 (2003)CrossRefGoogle Scholar
  6. 6.
    Jeyakumar, D.N., Jayabarathi, T., Raghunathan, T.: Particle swarm optimization for various types of economic dispatch problems. Int. J. Electr. Power Energy Syst. 28(1), 36–42 (2006)CrossRefGoogle Scholar
  7. 7.
    Basu, M.: Economic environmental dispatch using multi-objective differential evolution. Appl. Soft Comput. 11(2), 2845–2853 (2011)CrossRefGoogle Scholar
  8. 8.
    Güvenç, U., et al.: Combined economic and emission dispatch solution using gravitational search algorithm. Sci. Iran. 19(6), 1754–1762 (2012)CrossRefGoogle Scholar
  9. 9.
    Chatterjee, A., Ghoshal, S.P., Mukherjee, V.: Solution of combined economic and emission dispatch problems of power systems by an opposition-based harmony search algorithm. Int. J. Electr. Power Energy Syst. 39(1), 9–20 (2012)CrossRefGoogle Scholar
  10. 10.
    Bayraktar, Z., Komurcu, M., Bossard, J.A., Werner, D.H.: The wind driven optimization technique and its application in electromagnetics. IEEE Trans. Antennas Propag. 61(5), 2745–2757 (2013)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Mathew, D., Rani, C., Kumar, M.R., Wang, Y., Binns, R., Busawon, K.: Wind-driven optimization technique for estimation of solar photovoltaic parameters. IEEE J. Photovolt. 8(1), 248–256 (2018)CrossRefGoogle Scholar
  12. 12.
    Bhandari, A.K., Singh, V.K., Kumar, A., Singh, G.K.: Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst. Appl. 41(7), 3538–3560 (2014)CrossRefGoogle Scholar
  13. 13.
    Güvenç, U.: Combined economic emission dispatch solution using genetic algorithm based on similarity crossover. Sci. Res. Essays 5(17), 2451–2456 (2010)Google Scholar
  14. 14.
    dos Santos Coelho, L., Lee, C.-S.: Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches. Int. J. Electr. Power Energy Syst. 30(5), 297–307 (2008)CrossRefGoogle Scholar
  15. 15.
    Balamurugan, R., Subramanian, S.: A simplified recursive approach to combined economic emission dispatch. Electr. Power Comp. Syst. 36(1), 17–27 (2007)CrossRefGoogle Scholar
  16. 16.
    Dhanalakshmi, S., Kannan, S., Mahadevan, K., Baskar, S.: Application of modified NSGA-II algorithm to combined economic and emission dispatch problem. Int. J. Electr. Power Energy Syst. 33(4), 992–1002 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • V. Udhay Sankar
    • 1
    Email author
  • Bhanutej
    • 1
  • C. H. Hussaian Basha
    • 1
  • Derick Mathew
    • 1
  • C. Rani
    • 1
  • K. Busawon
    • 2
  1. 1.School of Electrical EngineeringVIT UniversityVelloreIndia
  2. 2.Faculty of Engineering and EnvironmentNorthumbria UniversityNewcastle upon TyneUK

Personalised recommendations