• DocumentCode
    3084470
  • Title

    Application of double matrix hybrid coded genetic algorithm in unit commitment

  • Author

    Chang Wen-ping ; Luo Xian-Jue

  • Author_Institution
    Sch. of Electr. Eng., Xi´an Jiaotong Univ., Xi´an, China
  • fYear
    2008
  • fDate
    10-13 Dec. 2008
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    A double matrix hybrid coded genetic algorithm (DMHGA) is presented to solve the unit commitment (UC). The DMHGA chromosome consists of three-dimensional matrix representing the UC schedule, it effectively uses the binary-coded genetic algorithm to solve the unit commitment up/down problem and the real-coded genetic algorithm based on the equal incremental-cost criterion to solve the power economic dispatch problem, this modification helps to explore the search space very effectively, It generates better solutions than the other methods. Problem formulation, representation and the simulation results with systems of up to 10 units and 24-h scheduling horizon are presented. The results show that the DMHGA has strong ability in global search and efficient.
  • Keywords
    genetic algorithms; power generation dispatch; power generation economics; power generation scheduling; binary-coded genetic algorithm; double matrix hybrid coded genetic algorithm; equal incremental-cost criterion; power economic dispatch; three-dimensional matrix; unit commitment; Artificial neural networks; Costs; Expert systems; Genetic algorithms; Power generation; Power generation economics; Power markets; Power system economics; Power systems; Space exploration; Unit commitment; double matrix hybrid coded; equal incremental-cost criterion; genetic algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electricity Distribution, 2008. CICED 2008. China International Conference on
  • Conference_Location
    Guangzhou
  • Print_ISBN
    978-1-4244-3373-5
  • Electronic_ISBN
    978-1-4244-3372-8
  • Type

    conf

  • DOI
    10.1109/CICED.2008.5211826
  • Filename
    5211826