• DocumentCode
    3029183
  • Title

    Reinforcement Learning Solution for Unit Commitment Problem through Pursuit Method

  • Author

    Jasmin, E.A. ; Ahamed, T. P. Imthias ; Raj, V. P. Jagathy

  • Author_Institution
    Dept. of Electr. & Electron., Gov. Eng. Coll. Thrissur, Thrissur, India
  • fYear
    2009
  • fDate
    28-29 Dec. 2009
  • Firstpage
    324
  • Lastpage
    327
  • Abstract
    Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems.
  • Keywords
    control engineering computing; learning (artificial intelligence); power engineering computing; power generation control; power generation dispatch; power generation scheduling; electric power generation control sector; pursuit method; reinforcement learning solution; unit commitment problem; Constraint optimization; Cost function; Learning; Medical control systems; Power generation; Power system dynamics; Power system planning; Power systems; Scheduling; Telecommunication control; Q learning; Unit Commitment; reinforcement learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT '09. International Conference on
  • Conference_Location
    Trivandrum, Kerala
  • Print_ISBN
    978-1-4244-5321-4
  • Electronic_ISBN
    978-0-7695-3915-7
  • Type

    conf

  • DOI
    10.1109/ACT.2009.87
  • Filename
    5376660