• DocumentCode
    1761028
  • Title

    Solving the Power Economic Dispatch Problem With Generator Constraints by Random Drift Particle Swarm Optimization

  • Author

    Jun Sun ; Palade, Vasile ; Xiao-Jun Wu ; Wei Fang ; Zhenyu Wang

  • Author_Institution
    Dept. of Comput. Sci. & Technol., Jiangnan Univ., Wuxi, China
  • Volume
    10
  • Issue
    1
  • fYear
    2014
  • fDate
    Feb. 2014
  • Firstpage
    222
  • Lastpage
    232
  • Abstract
    This paper proposes the random drift particle swarm optimization (RDPSO) algorithm to solve economic dispatch (ED) problems from power systems area. The RDPSO is inspired by the free electron model in metal conductors placed in an external electric field, and it employs a novel set of evolution equations that can enhance the global search ability of the algorithm. Many nonlinear characteristics of a power generator, such as the ramp rate limits, prohibited operating zones and nonsmooth cost functions are considered when the proposed method is used in practice for optimizing the generators´ operation. The performance of the RDPSO method is evaluated on three different power systems, and compared with that of other optimization methods in terms of the solution quality, robustness, and convergence performance. The experimental results show that the RDPSO method performs better in solving the ED problems than any other tested optimization techniques.
  • Keywords
    AC generators; particle swarm optimisation; power generation dispatch; power generation economics; ED problems; RDPSO algorithm; convergence performance; external electric field; free electron model; generator constraints; generator operation optimization; global search ability; metal conductors; nonlinear characteristics; nonsmooth cost functions; operating zones; power economic dispatch problem; power generator; power systems; ramp rate limits; random drift particle swarm optimization algorithm; solution quality; Constrained nonlinear optimization; computational intelligence; economic dispatch problem; particle swarm optimization;
  • fLanguage
    English
  • Journal_Title
    Industrial Informatics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1551-3203
  • Type

    jour

  • DOI
    10.1109/TII.2013.2267392
  • Filename
    6527973