Agent-based modelling of EV energy storage systems considering human crowd behavior

Abstract

Large scale adoption of electric vehicles (EVs) would significantly increase the overall electricity demand of the 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 energy storage systems (ESS) used in EVs is an essential part of the infrastructure planning. Charging demand of ESS used in EVs is affected by several factors such as driver behavior, location of charging stations and electricity pricing. In order to implement the optimal charging infrastructure, it is important to consider all the crucial factors that affect the charging demand of ESS in EVs. Several studies have modelled and simulated the charging demand of individual as well as group of EVs. However, in many cases the models did not include factors that deal with the social characteristics of EV drivers, while the others did not emphasise on the economic elements. This paper aims to evaluate the effects of above factors on the EV charging demand using a simulation model. Agent-based approach using NetLogo is employed in this study to closely mimic the human crowd behaviour and its influence on the load demand due to charging of ESS used in EVs.