• DocumentCode
    2439521
  • Title

    Multi objective evolutionary programming to solve environmental economic dispatch problem

  • Author

    Qu, Bo-Yang ; Suganthan, P.N. ; Pandi, V.R. ; Panigrahi, B.K.

  • Author_Institution
    Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
  • fYear
    2010
  • fDate
    7-10 Dec. 2010
  • Firstpage
    673
  • Lastpage
    1679
  • Abstract
    In this paper, the nonlinear constrained multi-objective environmental economic dispatch (EED) problem is solved using fast multi-objective evolutionary programming (FMOEP). Due to the global warming by fossil fuel, environmental concern becomes more and more important in recent years. The purpose of multi-objective optimization algorithm is minimizing all the different objectives simultaneously and finds the best tradeoff solution for this environmental/economic dispatch problem. In order to evaluate the performance of FMOEP on EED problems, the standard IEEE 30-bus six-generator test system is studied. The performance is compared against NSGAH and a number of results reported in literature. The results show that the FMOEP is effective in solving EED problems.
  • Keywords
    environmental economics; evolutionary computation; fossil fuels; global warming; load dispatching; minimisation; power engineering computing; power system economics; EED problem; FMOEP; IEEE 30-bus six-generator test system; environmental economic dispatch problem; fast multiobjective evolutionary programming; fossil fuel; global warming; multiobjective optimization algorithm; nonlinear constrained multiobjective problem; Biological system modeling; Economics; Generators; Optimization; Particle swarm optimization; Programming; Sorting; Environmental/Economic dispatch; Multi objective optimization; constraint handling method; evolutionary programming;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4244-7814-9
  • Type

    conf

  • DOI
    10.1109/ICARCV.2010.5707926
  • Filename
    5707926