• DocumentCode
    2553116
  • Title

    The research of united optimal operation aid decision system for cascade hydropower plants in local power network

  • Author

    Zhong Wei ; Song Yang

  • Author_Institution
    Sch. of Manage., Tianjin Univ. of Technol., Tianjin, China
  • fYear
    2009
  • fDate
    21-23 Oct. 2009
  • Firstpage
    316
  • Lastpage
    320
  • Abstract
    The decision-making support system for optimal operation of cascaded hydropower plants in local electric power network, which includes system structure build up, and module design methods, is studied and designed in order to satisfy the demands of cascade hydropower plants optimal management in local electric power network. The system function include data management, hydrological forecast, long-time optimal operation planning, short-term optimal operation planning, and daily operation scheming. The system can provide decision support for optimal operation of small cascade hydropower plants in local power network. The system is applied in one local power network, and the results show that it is efficient.
  • Keywords
    hydroelectric power; hydroelectric power stations; power distribution planning; cascade hydropower plants; daily operation scheming; data management; decision-making support system; hydrological forecast; local power network; long-time optimal operation planning; short-term optimal operation planning; united optimal operation aid decision system; Decision making; Economic forecasting; Energy management; Engineering management; Hydroelectric power generation; Power generation economics; Power system management; Power system planning; Rain; Technology management; aid decision system; cascaded hydropower plants; optimal operation; system integration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Engineering and Engineering Management, 2009. IE&EM '09. 16th International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-3671-2
  • Electronic_ISBN
    978-1-4244-3672-9
  • Type

    conf

  • DOI
    10.1109/ICIEEM.2009.5344582
  • Filename
    5344582