Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87327
Title: Agent-based modelling of EV energy storage systems considering human crowd behavior
Authors: Chaudhari, Kalpesh
Su, Piao Sen Fabian
Kandasamy, Nandha Kumar
Ukil, Abhisek
Gooi, Hoay Beng
Keywords: Electric Vehicles
Energy Storage Systems
Issue Date: 2017
Source: Chaudhari, K., Su, P. S. F., Kandasamy, N. K., Ukil, A., & Gooi, H. B. (2017). Agent-based modelling of EV energy storage systems considering human crowd behavior. 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), 5247-5253.
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.
URI: https://hdl.handle.net/10356/87327
http://hdl.handle.net/10220/44392
DOI: http://dx.doi.org/10.1109/IECON.2017.8216909
Rights: © 2017 IEEE.
metadata.item.grantfulltext: none
metadata.item.fulltext: No Fulltext
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