• DocumentCode
    756300
  • Title

    Refined genetic algorithm-economic dispatch example

  • Author

    Sheble, Gerald B. ; Brittig, Kristin

  • Author_Institution
    Iowa State Univ., Ames, IA, USA
  • Volume
    10
  • Issue
    1
  • fYear
    1995
  • fDate
    2/1/1995 12:00:00 AM
  • Firstpage
    117
  • Lastpage
    124
  • Abstract
    A genetic-based algorithm is used to solve a power system economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique
  • Keywords
    approximation theory; digital simulation; economics; genetic algorithms; load dispatching; power system analysis computing; accuracy; computer simulation; elitism; interval approximation; mutation prediction; optimality; payoff information; penalty factors; perspective solutions; power system economic dispatch; program efficiency; refined genetic algorithm; Biological cells; Economic forecasting; Encoding; Genetic algorithms; Genetic mutations; Power generation economics; Power system economics; Power systems; Senior members; Student members;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/59.373934
  • Filename
    373934