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Evaluation of a Stochastic Vehicle Travel Pattern Generation Model with Real-World Travel Data

机译:利用现实世界旅行数据评估随机车辆旅行模式生成模型

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Higher electric vehicle penetration and associated grid charging will have significant reliability impacts on electric power systems. Therefore, distribution network planning studies need suitable models to capture spatial and temporal distribution of vehicle charging demand. When making expensive planning decisions, it is important to have high confidence in the models used. This paper proposes a systematic method and metrics to evaluate a stochastic model that generate random vehicle travel patterns in an urban area. The particular model considered consists of a collection of Hidden Markov Models, and its efficacy is evaluated using a database of actual vehicle travel data from Winnipeg, Manitoba, Canada. The probabilities associated with the model were extracted from the actual data. The evaluation approach involves comparison of actual and simulated values of model outputs representing the spatial-temporal distributions and use of Euclidean distances to measure the similarity of probability distributions produced by actual and simulated data.
机译:较高的电动车渗透和相关电网充电将对电力系统产生显着的可靠性影响。因此,分销网络规划研究需要适当的模型来捕获车辆充电需求的空间和时间分布。在进行昂贵的规划决策时,重要的是对所用模型具有高信心。本文提出了一种系统的方法和度量来评估在城市地区产生随机车辆旅行模式的随机模型。所考虑的特定模型包括一个隐藏的马尔可夫模型的集合,并且使用来自加拿大曼尼托巴的实际车辆旅行数据的数据库来评估其疗效。从实际数据中提取与模型相关的概率。评估方法涉及比较代表空间分布的模型输出的实际和模拟值,以及使用欧几里德距离来测量由实际和模拟数据产生的概率分布的相似性。

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