基于居民出行模拟的电动汽车负荷时空分布预测

针对电动汽车充电负荷时空分布预测中的随机性、不确定性问题，本文在已有出行链理论研究的基础上，提出了一种融合出行链理论与实际地理信息的电动汽车负荷预测方法。论文分析了基于出行链理论的EV时空分布模型，对影响电动汽车充电需求的因素进行了建模分析，用以模拟用户的出行行为特性。同时，通过对目标区域的路网进行建模，按功能区进行划分，将出行链理论的用户行为特性与目标地理信息相结合，通过Floyd算法对电动汽车用户的出行路径进行了规划设计，以预测电动汽车充电需求负荷。算例结果表明，所提出的模型能能够基于实际地理信息，预测电动汽车充电负荷的时空分布，分析不同功能区域、不同类型城市下的电动汽车充电需求负荷特性。

Analysis of the spatiotemporal distribution characteristics of electric vehicle load based on resident travel simulation

In view of the randomness and uncertainty in the spatial-temporal distribution prediction of electric vehicle charging load, this paper proposes a method of electric vehicle load prediction based on the existing travel chain theory and the actual geographic information. This paper analyzes the space-time distribution model of EV based on the travel chain theory, and models and analyzes the factors that affect the charging demand of electric vehicles, so as to simulate the travel behavior characteristics of users. At the same time, by modeling the road network of the target area, dividing it by functional area, combining the user behavior characteristics of the travel chain theory with the target geographic information, and planning and designing the travel path of the electric vehicle users through Floyd algorithm to predict the electric vehicle charging demand load. The results show that the proposed model can predict the space-time distribution of electric vehicle charging load based on the actual geographic information, and analyze the charging demand and load characteristics of electric vehicles in different functional areas and different types of cities.