• DocumentCode
    1955552
  • Title

    Short term unit-commitment using genetic algorithms

  • Author

    Dasgupta, Dipankar ; McGregor, Douglas R.

  • Author_Institution
    Dept. of Comput. Sci., Stratchclyde Univ., Glasgow, UK
  • fYear
    1993
  • fDate
    8-11 Nov 1993
  • Firstpage
    240
  • Lastpage
    247
  • Abstract
    The authors present a genetic approach for determining the priority order in the commitment of thermal units in power generation. The objective of the problem is to properly schedule the on/off states of all thermal units in a system to meet the load demand and the reverse requirement at each time interval, such that the overall system generation cost is a minimum, while satisfying various operational constraints. The authors examine the feasiblity of using genetic algorithms and report some simulation results in near-optimal commitment of thermal units in a power system
  • Keywords
    economics; electric power generation; genetic algorithms; load distribution; thermal power stations; commitment; generation cost; genetic algorithms; load; on/off states; power generation; thermal units; Biological cells; Character generation; Costs; Dynamic programming; Genetic algorithms; Power generation; Power generation economics; Power system simulation; Processor scheduling; Thermal loading;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 1993. TAI '93. Proceedings., Fifth International Conference on
  • Conference_Location
    Boston, MA
  • ISSN
    1063-6730
  • Print_ISBN
    0-8186-4200-9
  • Type

    conf

  • DOI
    10.1109/TAI.1993.633963
  • Filename
    633963