Several studies have presented electric vehicle smart charging schemes to increase the temporal matching between photovoltaic generation and electric vehicle charging, including a smart charging scheme with an objective to minimize the net-load variance. This method has proved, through simulations, that the self consumption could be increased, but the benefit of the approach has not been tested on a low voltage distribution system. To increase the quality of grid impact analyses of the smart charging scheme, probabilistic methods that include input and spatial allocation uncertainties are more appropriate. In this study, a probabilistic load flow analysis is performed by modelling the variability of electric vehicle mobility, household load, photovoltaic system generation, and the adoption of photovoltaic system and electric vehicle in society. The results show that the smart charging scheme improves the low voltage distribution system performance and increases the correlations between network nodes. It is also shown that concentrated allocation has more severe impacts, in particular at lower penetration levels. This paper can form the basis for the development of probabilistic impact analysis of smart charging to allow society to integrate more electric vehicles and photovoltaic systems for a more sustainable future.

多项研究提出了电动汽车智能充电方案，以增加光伏发电和电动汽车充电之间的时间匹配，包括以最小化净负载变化为目标的智能充电方案。这种方法已经通过模拟证明可以增加自耗，但该方法的好处尚未在低压配电系统上进行测试。为了提高智能充电方案的电网影响分析的质量，包括输入和空间分配不确定性的概率方法更合适。在本研究中，通过对电动汽车移动性、家庭负荷、光伏系统发电以及光伏系统和电动汽车在社会中的采用情况进行建模，进行概率潮流分析。结果表明，智能充电方案提高了低压配电系统性能，增加了网络节点之间的相关性。还表明，集中分配具有更严重的影响，尤其是在较低的渗透水平上。本文可以为智能充电的概率影响分析的发展奠定基础，使社会能够整合更多的电动汽车和光伏系统，以实现更可持续的未来。