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Time-Series Modeling of Aggregated Electric Vehicle Charging Station Load

机译：电动汽车充电站总负荷的时间序列建模

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摘要

The widespread proliferation of Electric Vehicles (EVs) can have a transformative effect on the electric power system. The power and energy consumed by EVs when charging is substantial, which has consequences on power system operation and planning. This paper identifies, evaluates, and proposes time-series seasonal autoregressive integrated moving average (ARIMA) models of aggregated EV charging station load. The modeling is based on 2 years of time-stamped aggregate power consumption from over 2400 charging stations in Washington State and San Diego, California. The different data sets allow the influence of time-of-use pricing on the time-series models to be explored. Weekday, weekend, and near-term and long-term models are developed and analyzed. The best performing near-term weekday models are (2, 0, 0) × (0,1,1 )_(24) × (1, 0,0)_(120) for Washington State and (2, 0, 0) × (1, 1, 0)_(24) × (0, 0, 1 )_(48) for San Diego. Applications of the seasonal ARIMA models to aggregate EV charging station load forecasting and creation of synthetic time-series at different penetration levels are discussed.
机译：电动汽车（EV）的广泛普及可以对电力系统产生变革性的影响。电动汽车充电时消耗的功率和能量非常大，这对电力系统的运行和规划产生影响。本文确定，评估并提出了电动汽车充电站总负荷的时间序列季节性自回归综合移动平均（ARIMA）模型。该模型基于两年时间的加盖华盛顿州和圣地亚哥的2400个充电站的总电量消耗。不同的数据集允许使用时间定价对时间序列模型的影响。开发和分析工作日，周末以及近期和长期模型。华盛顿州近期表现最好的短期工作日模型是（2，0，0）×（0,1,1）_（24）×（1，0,0）_（120）和（2，0，0 ）×（1，1，0）_（24）×（0，0，1）_（48）用于圣地亚哥。讨论了季节性ARIMA模型在电动汽车充电站总负荷预测和不同渗透水平下合成时间序列的创建中的应用。

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