论文标题
定义用于逼真的电动汽车充电会话的合成数据生成器
Defining a synthetic data generator for realistic electric vehicle charging sessions
论文作者
论文摘要
在过去的几年中，电动汽车（EV）充电站在电网中变得突出。 EV充电会话的分析对于灵活性分析，负载平衡，向客户提供激励措施非常有用。但是，此类EV会话数据的可用性有限，这阻碍了这些领域的进一步发展。为了满足公开可用和现实数据的需求，我们开发了用于EV充电会话的合成数据生成器（SDG）。我们的可持续发展目标假设EV互及时间遵循指数分布。出发时间是通过定义连接时间的条件概率密度函数（PDF）来建模的。该PDF用于连接时间和所需的能量由高斯混合模型拟合。由于我们使用大型现实世界数据集训练可持续发展目标，因此其输出是现实的。
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, the 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.
