• DocumentCode
    184352
  • Title

    Connecting automatic generation control and economic dispatch from an optimization view

  • Author

    Na Li ; Lijun Chen ; Changhong Zhao ; Low, S.H.

  • Author_Institution
    Lab. of Inf. & Decision Syst., Massachusetts Inst. of Technol., Cambridge, MA, USA
  • fYear
    2014
  • fDate
    4-6 June 2014
  • Firstpage
    735
  • Lastpage
    740
  • Abstract
    Automatic generation control (AGC) regulates mechanical power generation in response to load changes through local measurements. Its main objective is to maintain system frequency and keep energy balanced within each control area so as to maintain the scheduled net interchanges between control areas. The scheduled interchanges as well as some other factors of AGC are determined at a slower time scale by considering a centralized economic dispatch (ED) problem among different generators. However, how to make AGC more economically efficient is less studied. In this paper, we study the connections between AGC and ED by reverse engineering AGC from an optimization view, and then we propose a distributed approach to slightly modify the conventional AGC to improve its economic efficiency by incorporating ED into the AGC automatically and dynamically.
  • Keywords
    distributed control; power generation control; power generation dispatch; power generation economics; power generation scheduling; reverse engineering; ED problem; automatic generation control; centralized economic dispatch problem; energy balancing; generators; mechanical power generation regulation; optimization; reverse engineering; scheduled net interchange maintenance; system frequency maintenance; Algorithm design and analysis; Automatic generation control; Economics; Generators; Heuristic algorithms; Optimization; Power system dynamics; Decentralized control; Optimization; Power systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2014
  • Conference_Location
    Portland, OR
  • ISSN
    0743-1619
  • Print_ISBN
    978-1-4799-3272-6
  • Type

    conf

  • DOI
    10.1109/ACC.2014.6859060
  • Filename
    6859060