Agent-Based Aggregated Behavior Modeling for Electric Vehicle Charging Load

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.