Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89511
Title: Agent based aggregated behavior modelling for electric vehicle charging load
Authors: Chaudhari, Kalpesh Subhash
Kandasamy, Nandha Kumar
Krishnan, Ashok
Ukil, Abhisek
Gooi, Hoay Beng
Keywords: Agent Based Model
Electric Vehicles
Issue Date: 2018
Source: Chaudhari, K. S., Kandasamy, N. K., Krishnan, A., Ukil, A., & Gooi, H. B. Agent Based Aggregated Behavior Modelling For Electric Vehicle Charging Load. IEEE Transactions on Industrial Informatics, in press.
Series/Report no.: IEEE Transactions on Industrial Informatics
Abstract: 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 which influence the charging demand of EVs. Several studies have modelled 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 behaviour and its influence on the load demand due to charging of EVs.
URI: https://hdl.handle.net/10356/89511
http://hdl.handle.net/10220/44931
ISSN: 1551-3203
DOI: http://dx.doi.org/10.1109/TII.2018.2823321
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TII.2018.2823321].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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