• DocumentCode
    2580408
  • Title

    Solving economic dispatch problem using hybrid GA-PS-SQP method

  • Author

    Alsumait, Jamal S. ; Sykulski, Jan K.

  • Author_Institution
    Electr. Power Eng. Res. Group, Southampton Univ., Southampton, UK
  • fYear
    2009
  • fDate
    18-23 May 2009
  • Firstpage
    333
  • Lastpage
    338
  • Abstract
    This study presents a new approach to solve the well-known power system economic load dispatch problem (ED) using a hybrid algorithm consisting of genetic algorithm (GA), pattern search (PS) and sequential quadratic programming (SQP). GA is the main optimizer of this algorithm, whereas PS and SQP are used to fine-tune the results obtained from the GA, thereby increasing solution confidence. To test the effectiveness of this approach it was applied to various test systems. Furthermore, the convergence characteristics and robustness of the proposed method have been explored through comparisons with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA-PS-SQP algorithm is very effective in solving the power system economic load dispatch problem.
  • Keywords
    genetic algorithms; load dispatching; power generation dispatch; power system economics; quadratic programming; economic dispatch problem; genetic algorithm; pattern search programming; power system economic load dispatch problem; sequential quadratic programming; Cost function; Environmental economics; Fuel economy; Genetic algorithms; Particle swarm optimization; Power generation economics; Power system economics; Quadratic programming; Search methods; System testing; Economic Dispatch; Genetic Algorithms (GA); Pattern Search method (PS); Sequential Quadratic Programming (SQP); Valve-Point effect;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    EUROCON 2009, EUROCON '09. IEEE
  • Conference_Location
    St.-Petersburg
  • Print_ISBN
    978-1-4244-3860-0
  • Electronic_ISBN
    978-1-4244-3861-7
  • Type

    conf

  • DOI
    10.1109/EURCON.2009.5167652
  • Filename
    5167652