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Application of WDO for Decision-Making in Combined Economic and Emission Dispatch Problem

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Soft Computing for Problem Solving

Abstract

A number of optimization techniques have been used by researchers to solve the combined economic and emission dispatch problem. In this paper, we have applied Wind Driven Optimization (WDO), a heuristic global optimization technique to solve the CEED 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.

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Abbreviations

CEED:

Combined Economic and Emission Dispatch

WDO:

Wind Driven Optimization

References

  1. Kumar, A.I.S., et al.: Particle swarm optimization solution to emission and economic dispatch problem. In: TENCON 2003, Conference on Convergent Technologies for the Asia-Pacific Region, vol. 1. IEEE (2003)

    Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  7. Basu, M.: Economic environmental dispatch using multi-objective differential evolution. Appl. Soft Comput. 11(2), 2845–2853 (2011)

    Article  Google Scholar 

  8. Güvenç, U., et al.: Combined economic and emission dispatch solution using gravitational search algorithm. Sci. Iranica 19(6), 1754–1762 (2012)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  13. Guuml, U.: Combined economic emission dispatch solution using genetic algorithm based on similarity crossover. Sci. Res. Essays 5(17), 2451–2456 (2010)

    Google Scholar 

  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)

    Article  Google Scholar 

  15. Balamurugan, R., Subramanian, S.: A simplified recursive approach to combined economic emission dispatch. Electr. Power Compon. Syst, 36(1), 17–27 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Correspondence to V. Udhay Sankar .

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Udhay Sankar, V., Bhanutej, Hussaian Basha, C.H., Mathew, D., Rani, C., Busawon, K. (2020). Application of WDO for Decision-Making in Combined Economic and Emission Dispatch Problem. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_73

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