• DocumentCode
    2394842
  • Title

    Generation scheduling with demand bids

  • Author

    Sheridan, W.P. ; Flynn, M.E. ; Malley, M. J O

  • Author_Institution
    Aer Lingus, Dublin, Ireland
  • Volume
    4
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    2109
  • Abstract
    The structure of a market for energy is a fundamental design problem in the move towards competition in the electricity supply industry. Many systems have moved away from the traditional methods of central optimisation towards auction mechanisms. However, there are problems associated with the incorporation of system and unit constraints, e.g., minimum up- and down-times and ramping limits, aspects that are dealt with more easily using central optimisation. This paper describes a centralised pool electricity market that optimises the system based on bids from the supply and the demand side. A simultaneous market for reserve that considers spinning reserve from generators and nonspinning reserve in the form of interruptible customer load is included. The problem is formulated as an augmented Lagrangian function and a recurrent neural network is used to solve it. The method is also applicable to a more general auction mechanism
  • Keywords
    electricity supply industry; power generation economics; power generation planning; power generation scheduling; power system analysis computing; recurrent neural nets; auction mechanisms; augmented Lagrangian function; centralised pool electricity market; competition; demand bids; electricity supply industry; generation scheduling; interruptible customer load; nonspinning reserve; ramping limits; recurrent neural network; Constraint optimization; Consumer electronics; Costs; Educational institutions; Electricity supply industry; Job shop scheduling; Lagrangian functions; Optimization methods; Power engineering and energy; Power generation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering Society Summer Meeting, 2000. IEEE
  • Conference_Location
    Seattle, WA
  • Print_ISBN
    0-7803-6420-1
  • Type

    conf

  • DOI
    10.1109/PESS.2000.866972
  • Filename
    866972