Abstract With the rapid development of electric vehicles (EVs), EV charging load simulation is of significance to tackle the challenges for planning and operating a highly-penetrated power system. However, the lack of historical charging data, as well as consideration on the temperature and traffic, pose obstacles to establish an accurate model. This paper presents a spatial-temporal EV charging load profile simulation method considering weather and traffics. First, the impacts of temperature on battery capacity and air-conditioning power are formulated. Second, the energy consumed by air conditioning and car-driving under various traffic conditions is formulated after defining two traffic-related indices. Third, the refined probabilistic models regarding the spatial-temporal vehicle travel pattern are established to improve accuracy. Daily charging load profiles at multiple regions are generated with inputs of refined models and formulations based on Monte Carlo. The real-world data are used to validate the proposed model under various scenarios. The results show that the magnitude, profile shape and peak time of the charging loads have significant differences in different seasons, traffics, day type and regions. Optimal planning of the distributed wind and solar capacities is made to improve the renewable power supply to the EV charging based on the simulated regional profiles.

中文翻译：

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考虑交通拥堵和天气的电动汽车充电负载模拟和预测，以支持可再生能源和电动汽车的融合

摘要 随着电动汽车（EV）的快速发展，电动汽车充电负载仿真对于解决高渗透电力系统规划和运行的挑战具有重要意义。然而，缺乏历史充电数据，以及对温度和交通的考虑，对建立准确的模型构成了障碍。本文提出了一种考虑天气和交通的时空电动汽车充电负荷曲线仿真方法。首先，制定温度对电池容量和空调功率的影响。其次，在定义了两个交通相关的指标后，制定了各种交通条件下空调和汽车行驶所消耗的能量。第三，建立关于时空车辆行驶模式的精细概率模型以提高准确性。多个地区的每日充电负荷曲线是通过输入基于蒙特卡罗的精细模型和公式生成的。真实世界的数据用于在各种场景下验证所提出的模型。结果表明，充电负荷的大小、剖面形状和高峰时间在不同季节、交通量、白天类型和地区都有显着差异。基于模拟的区域分布，对分布式风能和太阳能容量进行优化规划，以改善电动汽车充电的可再生能源供应。充电负荷的剖面形状和高峰时间在不同的季节、交通、白天类型和地区有显着差异。基于模拟的区域分布，对分布式风能和太阳能容量进行优化规划，以改善电动汽车充电的可再生能源供应。充电负荷的剖面形状和高峰时间在不同的季节、交通、白天类型和地区有显着差异。基于模拟的区域分布，对分布式风能和太阳能容量进行优化规划，以改善电动汽车充电的可再生能源供应。