• DocumentCode
    622151
  • Title

    Sensitivity analysis of GA parameters for ECED problem

  • Author

    Kamil, K. ; Razali, Noor Miza Muhammad ; Teh, Y.Y.

  • Author_Institution
    Jalan IKRAM-UNITEN, Univ. Tenaga Nasional, Kajang, Malaysia
  • fYear
    2013
  • fDate
    3-4 June 2013
  • Firstpage
    256
  • Lastpage
    260
  • Abstract
    To meet the requirement of the regulations and to reduce the pollution to the targeted level, minimization of emission level has been added into the dispatch strategies of generators by formulating emission constrained economic dispatch (ECED). Besides the conventional method by using the Lagrange Multiplier several evolutionary computation techniques such as Genetic Algorithm, Particle Swarm Optimisation, Ant Colony and Differential Evolution have been gaining popularity in solving general economic dispatch problems due to their desirable characteristics such as non-gradient dependent and ability to search for global optima. The effectiveness of these stochastic search techniques however is heavily dependent on the genetic operators and their parameters. The paper presents the study on sensitivity analysis of the parameters of Genetic Algorithm (GA) for the ECED problem. The results discuss the range of parameters suitable to be employed for the optimization and compare the difference between conventional economic dispatch and the ECED solutions.
  • Keywords
    genetic algorithms; power generation dispatch; power generation economics; search problems; sensitivity analysis; stochastic processes; ECED problem; ECED solutions; GA parameters; Lagrange multiplier several evolutionary computation techniques; ant colony; differential evolution; economic dispatch; emission constrained economic dispatch; emission level; general economic dispatch problems; generators strategies; genetic algorithm; global optima; nongradient dependent; particle swarm optimisation; sensitivity analysis; stochastic search techniques; Economics; Generators; Genetic algorithms; Linear programming; Optimization; Sociology; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering and Optimization Conference (PEOCO), 2013 IEEE 7th International
  • Conference_Location
    Langkawi
  • Print_ISBN
    978-1-4673-5072-3
  • Type

    conf

  • DOI
    10.1109/PEOCO.2013.6564553
  • Filename
    6564553