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Monte Carlo modelling for domestic car use patterns in united kingdom

机译:蒙特卡罗建模为英国国内汽车使用模式

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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.
机译:为量化广泛电动汽车对英国配电系统充电的潜在影响,必须获得关于国内车辆使用的相关统计数据。由于电动车辆所有权目前非常有限,因此这些数据将不可避免地用于传统的内燃机车辆,并且特别是私人拥有的车辆。这不应该是一个问题,因为在这项工作中处理的有限旅程距离可以容易地通过电动车作为传统车辆进行。特别注意联合王国2000年使用调查,因为它包含有关家用汽车使用的详细和有价值的统计信息。该数据库已经分析以获得详细的汽车使用统计数据,例如出发和到达时间,个人旅程时间等。然后,这种统计信息将用于建立两个蒙特卡罗模拟模型,以基于这些来重现平日汽车驾驶模式概率分布。蒙特卡罗方法是一种解决不确定性问题的知名技术。在本文中,国内汽车使用的关键统计数据与两种不同的蒙特卡罗模拟一起呈现,已经分析的模拟结果验证结果与从TUS数据中提取的统计数据一致。

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