• DocumentCode
    3671695
  • Title

    Monte Carlo modelling for domestic car use patterns in united kingdom

  • Author

    Sikai Huang;David Infield

  • Author_Institution
    Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, United Kingdom
  • fYear
    2014
  • Firstpage
    68
  • Lastpage
    73
  • Abstract
    For the purposes of quantifying the potential impact of widespread electric vehicles charging on the UK´s power distribution system, it is essential to obtain relevant statistical data on domestic vehicle usage. Since electric vehicle ownership is presently very limited, these data will inevitably be for conventional internal combustion engine vehicles, and in particular privately owned vehicles. This should not be an issue since the limited journey distances that will dealt with in this work could as easily be undertaken by an electric vehicle as a conventional vehicle. Particular attention is paid to the United Kingdom 2000 Time Use Survey as it contains detailed and valuable statistical information about household car use. This database has been analyzed to obtain detailed car use statistics, such as departure and arrival time, individual journey time, etc. This statistical information is then used to build up two Monte Carlo simulation models in order to reproduce weekday car driving patterns based on these probability distributions. The Monte Carlo methodology is a well-known technique for solving uncertainty problems. In this paper, key statistics of domestic car use are presented together with two different Monte Carlo simulation approaches the simulation results that have been analyzed to verify the results being consistent with the statistics extracted from the TUS data.
  • Keywords
    "Monte Carlo methods","Data models","Probability distribution","Simulation","Convergence","Automobiles"
  • Publisher
    ieee
  • Conference_Titel
    Connected Vehicles and Expo (ICCVE), 2014 International Conference on
  • Type

    conf

  • DOI
    10.1109/ICCVE.2014.7297635
  • Filename
    7297635