Emission Constrained Economic Dispatch Using Logistic Map Adaptive Differential Evolution

  • Kamal K. Mandal
  • Bidisna Bhattacharya
  • Bhimsen Tudu
  • N. Chakravorty
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 132)


A novel adaptive differential evolution based algorithm for solving emission constrained economic dispatch (ECED) problem is presented in this paper. The key factor for successful operation DE is the proper selection of user defined parameters. Choosing suitable values of parameters are difficult for DE, which is usually a problem-dependent task. Unfortunately, there is no fix rule for selection of parameters. The trial-and-error method adopted generally for tuning the parameters in DE requires multiple optimization runs. Even this method can not guarantee optimal results every time and sometimes it may lead to premature convergence. The proposed method combines differential evolution with chaos theory for self adaptation of DE parameters. The performance of the proposed method is demonstrated on a sample test system. The results of the proposed method are compared with other methods. It is found that the results obtained by the proposed method are superior in terms of fuel cost, emission output and losses.


Differential Evolution Fuel Cost Chaos Theory Chaotic Sequence Trial Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Talaq, J.H., El-Hawary, F., El-Hawary, M.E.: A summary of environmental/economic dispatch algorithms. IEEE Transaction on Power Systems 9, 1508–1516 (1994)CrossRefGoogle Scholar
  2. 2.
    Gent, M.R., Lamont, J.W.: Minimum Emission Dispatch. IEEE Transaction on Power Systems PAS-90, 2650–2660 (1972)Google Scholar
  3. 3.
    Kulkarni, P.S., Kothari, A.G., Kothari, D.P.: Combined economic and emission dispatch using improved backpropagation neural network. Electric Power Components and System 28, 31–44 (2000)Google Scholar
  4. 4.
    Abido, M.A.: Environmental/Economic Power Dispatch Using Multiobjective Evolutionary Algorithms. IEEE Transactions on Power Systems 18(4), 1529–1537 (2003)CrossRefGoogle Scholar
  5. 5.
    Muralidharan, S., Subramanian, S., Srikrishna, K.: Emission constrained economic dispatch – A new recursive approach. Electric Power Components and Systems 34, 343–353 (2006)CrossRefGoogle Scholar
  6. 6.
    Roy, P.K., Ghoshal, S.P., Thakur, S.S.: Combined Economic Emission dispatch bio-geography based optimization. Electrical Engineering 92, 173–184Google Scholar
  7. 7.
    Storn, R., Price, K.: Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012. International Computer Science Institute, Berkeley (1995)Google Scholar
  8. 8.
    Yuan, X., Cao, B., Yang, B., Yaun, Y.: Hydrothermal scheduling using chaotic hybrid differential evolution. Energy Conversion and Management 49, 3627–3633 (2008)CrossRefGoogle Scholar
  9. 9.
    May, R.: Simple mathematical models with very complicated dynamics. Nature 261, 459–467 (1976)CrossRefGoogle Scholar
  10. 10.
    Song, Y.H., Wang, G.S., Wang, P.Y., Johns, A.T.: Environmental /Economic Dispatch using Fuzzy Logic Controlled Genetic Algorithms. IEE Proc. on Generation, Transmission, Distribution 144(4), 377 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kamal K. Mandal
    • 1
  • Bidisna Bhattacharya
    • 2
  • Bhimsen Tudu
    • 1
  • N. Chakravorty
    • 1
  1. 1.Department of Power EngineeringJadavpur UniversityKolkataIndia
  2. 2.Dept. of Electrical EngineeringTechno IndiaKolkataIndia

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