Defining a synthetic data generator for realistic electric vehicle charging sessions

Manu Lahariya, Dries F. Benoit, Chris Develder
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引用次数: 5

Abstract

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past years. Analysis of EV charging sessions is useful for flexibility analysis, load balancing, offering incentives to customers, etc. Yet, limited availability of such EV sessions' data hinders further development in these fields. Addressing this need for publicly available and realistic data, we develop a synthetic data generator (SDG) for EV charging sessions. Our SDG assumes the EV inter-arrival time to follow an exponential distribution. Departure times are modeled by defining a conditional probability density function (pdf) for connection times. This pdf for connection time and required energy is fitted by Gaussian mixture models. Since we train our SDG using a large real-world dataset, its output is realistic.
为现实的电动汽车充电过程定义一个合成数据生成器
近年来,电动汽车充电站在电网中占有重要地位。电动汽车充电时段的分析有助于灵活性分析、负载平衡、向客户提供激励等。然而,这些会议数据的有限可用性阻碍了这些领域的进一步发展。为了满足公众对真实数据的需求,我们为电动汽车充电过程开发了一种合成数据发生器(SDG)。我们的可持续发展目标假设电动汽车到达间隔时间遵循指数分布。出发时间通过定义连接时间的条件概率密度函数(pdf)来建模。连接时间和所需能量的pdf由高斯混合模型拟合。由于我们使用大型真实数据集来训练我们的可持续发展目标,因此其输出是真实的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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