Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand–supply mismatch. Accurately predicting the realistic charging demand of EVs is an essential part of the infrastructure planning. Charging demand of EVs is influenced by several factors, such as driver behavior, location of charging stations, electricity pricing, etc. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors that influence the charging demand of EVs. Several studies have modeled and simulated the charging demands of individual and groups of EVs. However, in many cases, the models do not consider factors related to the social characteristics of EV drivers. Other studies do not emphasize on economic elements. This paper aims at evaluating the effects of the above factors on EV charging demand using a simulation model. An agent-based approach using NetLogo is employed in this paper to closely mimic the human aggregate behavior and its influence on the load demand due to charging of EVs.

中文翻译：

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基于Agent的电动汽车充电负荷综合行为建模。

电动汽车（EV）的广泛采用将大大增加配电网络中的总体电力负载需求。因此，需要对充电基础设施进行全面规划，以防止出现电源故障或供需不匹配的情况。准确预测电动汽车的实际充电需求是基础设施规划的重要组成部分。电动汽车的充电需求受驾驶员行为，充电站位置，电价等多个因素的影响。为了实现最佳的充电基础设施，重要的是要考虑所有影响电动汽车充电需求的相关因素。 。多项研究已经建模并模拟了单个电动汽车和电动汽车的充电需求。但是，在很多情况下，这些模型未考虑与电动汽车驾驶员的社会特征相关的因素。其他研究没有强调经济因素。本文旨在使用仿真模型评估上述因素对电动汽车充电需求的影响。本文使用基于代理的方法使用NetLogo来模仿人类的聚集行为及其对电动汽车充电引起的负荷需求的影响。