• DocumentCode
    892931
  • Title

    Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion

  • Author

    Zhang, Pei ; Lee, Stephen T.

  • Author_Institution
    Electr. Power Res. Inst., Palo Alto, CA, USA
  • Volume
    19
  • Issue
    1
  • fYear
    2004
  • Firstpage
    676
  • Lastpage
    682
  • Abstract
    Open access transmission has created a deregulated power market and brought new challenges to system planning. This paper proposes a new method to compute a probabilistic load flow in extensive power systems for the purpose of using it as a quick screening tool to determine the major investment on improving transmission system inadequacy. This innovative method combines the concept of Cumulants and Gram-Charlier expansion theory to obtain probabilistic distribution functions of transmission line flows. It has significantly reduced the computational time while maintaining a high degree of accuracy. This enables probabilistic analysis of power flow problems to be treated objectively and allows quantitative assessment of system reliability.
  • Keywords
    higher order statistics; investment; load flow; power markets; power transmission planning; probability; DC load flow; Gram-Charlier expansion theory; combined cumulants method; deregulated power market; extensive power systems; investment; open access transmission; power flow problems; probabilistic distribution function; probabilistic load flow computation; quantitative assessment; screening tool; system planning; system reliability; transmission line flow; transmission system inadequacy improvement; Distribution functions; Investments; Load flow; Maintenance; Power markets; Power system analysis computing; Power system planning; Power transmission lines; Reliability; Transmission line theory;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/TPWRS.2003.818743
  • Filename
    1266628