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Multi-objective Optimization of Economic-Emission Load Dispatch Using Cluster Based Differential Evolution

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Abstract

A cluster based multi-objective differential evolution algorithm with non-domination based sorting is used to evaluate the environmental/economic dispatch (EED) problem containing the incommensurable objectives of best economic dispatch with minimum cost and least emission is presented in this paper. The environmental concerns arises due to fossil fuel fired electric generators and global warming that leads to the transformation of the classical single objective economic load dispatch problem into multi-objective environmental/economic dispatch problem. Also, an investigation of cluster based differential evolution algorithm is presented and applied on different test systems.

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References

  1. Le, K.D., Golden, J.L., Stansberry, C., Vice, R., Wood, J., Ballance, J., Brown, G., Kamya, J.Y., Nielsen, E.K., Nakajima, H., Ookubo, M., Iyoda, I., Cauley, G.: Potential impacts of clean air regulations on system operations. IEEE Trans. Power Syst. 10(2), 647–656 (1995)

    Article  Google Scholar 

  2. Zahavi, J., Eisenberg, L.: An application of the economic-environmental power dispatch. IEEE Trans. Syst. Man Cybern. 7(7), 523–530 (1977)

    Article  Google Scholar 

  3. Abido, M.: A novel multiobjective evolutionary algorithm for environmental/economic power dispatch. Int. J. Electr. Power Syst. Res. 65(1), 71–81 (2003)

    Article  Google Scholar 

  4. Abido, M.: A niched pareto genetic algorithm for multiobjective environmental/economic dispatch. Int. J. Electr. Power Energy Syst. 25(2), 97–105 (2003)

    Article  MATH  Google Scholar 

  5. Abido, M.: Environmental/economic power dispatch using multiobjective evolutionary algorithms. IEEE Trans. Power Syst. 18(4), 1529–1537 (2003)

    Article  Google Scholar 

  6. King, R.T.F.A., Rughooputh, H.C.S., Deb, K.: Evolutionary multi-objective environmental/economic dispatch: stochastic versus deterministic approaches. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 677–691. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  7. Storn, R., Price, K.: Differential evolution simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  8. Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: IEEE Congress Evolutionary Computation, vol. 2, pp. 1980–1987 (2004)

    Google Scholar 

  9. dos Santos Coelho, L., Mariani, V.: Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect. IEEE Trans. Power Syst. 21(2), 989–996 (2006)

    Article  Google Scholar 

  10. Perez-Guerrero, R., Cedeno-Maldonado, J.: Differential evolution based economic environmental power dispatch. In: Proceedings of the 37th Annual North American Power Symposium, pp. 191–197 (2005)

    Google Scholar 

  11. Sinha, N., Purkayastha, B., Purkayastha, B.: Optimal combined non-convex economic and emission load dispatch using NSDE. In: International Conference on Computational Intelligence and Multimedia Applications, vol.1, pp. 473–480 (2007)

    Google Scholar 

  12. Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 38(1), 218–237 (2008)

    Article  MATH  Google Scholar 

  13. Liu, G., Li, Y., Nie, X., Zheng, H.: A novel clustering-based differential evolution with 2 multi-parent crossovers for global optimization. Int. J. Appl. Soft Comput. 12(2), 663–681 (2012)

    Article  Google Scholar 

  14. Kwedlo, W.: A clustering method combining differential evolution with the k-means algorithm. Int. Assoc. Pattern Recognit. Lett. 32(12), 1613–1621 (2011)

    Article  MATH  Google Scholar 

  15. Balamurugan, R., Subramanian, S.: Self-adaptive differential evolution based power economic dispatch of generators with valve-point effects and multiple fuel options. Int. J. Comput. Sci. Eng. 1(1), 10–17 (2007)

    Google Scholar 

  16. Thitithamrongchai, C., Eua-Arporn, B.: Economic load dispatch for piecewise quadratic cost function using hybrid self-adaptive differential evolution with augmented lagrange multiplier method. In: International Conference on Power System Technology, pp. 1–8 (2006)

    Google Scholar 

  17. Van Veldhuizen, D., Lamont, G.: On measuring multiobjective evolutionary algorithm performance. In: Proceedings of the 2000 Congress on Evolution Computation, vol. 1, pp. 204–211 (2000)

    Google Scholar 

  18. Panigrahi, B., Pandi, V.R., Das, S., Das, S.: Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatch problem. Energy 35(12), 4761–4770 (2010)

    Article  Google Scholar 

  19. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  20. Lim, K.S., Buyamin, S., Ibrahim, Z.: Convergence and diversity measurement for vector evaluated particle swarm optimization based on ZDT test problems. In: Fifth Asia Modelling Symposium (AMS), pp. 32–36 (2011)

    Google Scholar 

  21. Kundur, P.: Power System Stability and Control. McGraw-Hill, Inc, New York (1994)

    Google Scholar 

  22. Abido, M., Bakhashwain, J.: Optimal var dispatch using a multiobjective evolutionary algorithm. Int. J. Electr. Power Energy Syst. 27(1), 13–20 (2005)

    Article  Google Scholar 

  23. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)

    Article  Google Scholar 

  24. Qin, A.K., Suganthan, P.: Self-adaptive differential evolution algorithm for numerical optimization. In: IEEE Congress Evolutionary Computation, vol. 2. pp. 1785–1791 (2005)

    Google Scholar 

  25. Alsac, O., Stott, B.: Optimal load flow with steady-state security. IEEE Trans. Power Apparatus Syst. 93(3) 745–751 (1974)

    Google Scholar 

  26. Guerrero, R.P.: Differential evolution based power dispatch algorithms. Master’s thesis, Electrical Engineering Department, University of Puerto Rico, Mayaguez Campus (2004)

    Google Scholar 

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

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India

    Deep Kiran, B. K. Panigrahi & A. R. Abhyankar

Authors
  1. Deep Kiran
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  2. B. K. Panigrahi
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  3. A. R. Abhyankar
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Correspondence to Deep Kiran .

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Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

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Kiran, D., Panigrahi, B.K., Abhyankar, A.R. (2015). Multi-objective Optimization of Economic-Emission Load Dispatch Using Cluster Based Differential Evolution. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_69

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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