• DocumentCode
    2039705
  • Title

    The uncertainties of probabilistic LV network analysis

  • Author

    Frame, D.F. ; Ault, G.W. ; Huang, S.

  • Author_Institution
    Inst. for Energy & Environ., Univ. of Strathclyde, Glasgow, UK
  • fYear
    2012
  • fDate
    22-26 July 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    The anticipated impact of low-carbon technology and the advent of the Smart Grid has provoked increased interest in the low voltage (LV) power distribution networks. Probabilistic and long period time-series analysis of LV networks is becoming increasingly common, as is the use of unbalanced, three-phase network modeling. This paper reviews some recent approaches to probabilistic analysis of LV networks and considers the uncertainty introduced by the underlying assumptions. A specific case study analysis of electric vehicle (EV) penetration on a generic UK distribution network is used to investigate the effect of key assumptions on the results of a probabilistic analysis. The paper concludes that probabilistic LV network analysis is a powerful tool for distribution network planning, however the trade-offs between imperfect modeling data and the reliability of results need to be well understood and incorporated into the interpretation of results.
  • Keywords
    electric vehicles; power distribution planning; smart power grids; time series; EV penetration; distribution network planning; electric vehicle penetration; generic UK distribution network; low-carbon technology; low-voltage power distribution networks; probabilistic LV network analysis; probabilistic analysis; smart grid; three-phase network modeling; time-series analysis; Impedance; Load flow; Load modeling; Probabilistic logic; Stochastic processes; Thermal loading; Threshold voltage; Electric Vehicles; LV networks; probabilistic load flow; stochastic simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Society General Meeting, 2012 IEEE
  • Conference_Location
    San Diego, CA
  • ISSN
    1944-9925
  • Print_ISBN
    978-1-4673-2727-5
  • Electronic_ISBN
    1944-9925
  • Type

    conf

  • DOI
    10.1109/PESGM.2012.6344587
  • Filename
    6344587