A Multi-Objective Optimization Schedule for the charging behavior of the EV user in a Smart Campus

Mohamed A. Hassan; Ahmed A. Oransa; Ahmed M. Elsallanty; Deema R. Fekry; Hatem H. Ibrahim; Bishoy E. Sedhom

doi:10.1109/ETFG55873.2023.10407166

A Multi-Objective Optimization Schedule for the charging behavior of the EV user in a Smart Campus

Mohamed A. Hassan^*
, Ahmed A. Oransa
, Ahmed M. Elsallanty
, Deema R. Fekry
, Hatem H. Ibrahim
, Bishoy E. Sedhom

^*Corresponding author for this work

Interdisciplinary Research Center for Sustainable Energy Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Recently, electric vehicles (EVs) demand has been intensely increased. Accordingly, more electrical vehicle charging stations (EVCSs) are essentially needed. The charging behavior of the EV user is affected by several issues. This paper proposes a multi-objective genetic algorithm (MOGA) optimization scheme for scheduling the charging behavior of the EV user in a smart campus. The problem is optimally formulated and designed to minimize the total traveling distance of the EV toward charging station (CS), needed fully charging time of the EV battery, total charging cost, total expected queuing time in the CS, and needed energy consumption of the EV to arrive the CS. The considered system involves 50 EVs and 3 EVCSs. Few solar cells are employed to recharge the EVs and reduce the energy loss and voltage drop. Different scenarios are provided to compare between the proposed and uncontrolled (unoptimized) schemes. The results prove the proposed technique effectiveness. The EV user's charging behavior is improved. Moreover, it is concluded that the charging cost depends on the state of charge (SoC) of the EV batteries and charging time of the EVs.

Original language	English
Title of host publication	2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665471640
DOIs	https://doi.org/10.1109/ETFG55873.2023.10407166
State	Published - 2023
Event	2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023 - Wollongong, Australia Duration: 3 Dec 2023 → 6 Dec 2023

Publication series

Name	2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

Conference

Conference	2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023
Country/Territory	Australia
City	Wollongong
Period	3/12/23 → 6/12/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Charging Behavior of the EV User
Charging Station
Electric Vehicle
Genetic Algorithm
Smart Campus

ASJC Scopus subject areas

Artificial Intelligence
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Control and Optimization
Safety, Risk, Reliability and Quality

Access to Document

10.1109/ETFG55873.2023.10407166

Cite this

Hassan, M. A., Oransa, A. A., Elsallanty, A. M., Fekry, D. R., Ibrahim, H. H., & Sedhom, B. E. (2023). A Multi-Objective Optimization Schedule for the charging behavior of the EV user in a Smart Campus. In 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023 (2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ETFG55873.2023.10407166

Hassan, Mohamed A. ; Oransa, Ahmed A. ; Elsallanty, Ahmed M. et al. / A Multi-Objective Optimization Schedule for the charging behavior of the EV user in a Smart Campus. 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023).

@inproceedings{713791c51ae2433b91e12cf8984f8241,

title = "A Multi-Objective Optimization Schedule for the charging behavior of the EV user in a Smart Campus",

abstract = "Recently, electric vehicles (EVs) demand has been intensely increased. Accordingly, more electrical vehicle charging stations (EVCSs) are essentially needed. The charging behavior of the EV user is affected by several issues. This paper proposes a multi-objective genetic algorithm (MOGA) optimization scheme for scheduling the charging behavior of the EV user in a smart campus. The problem is optimally formulated and designed to minimize the total traveling distance of the EV toward charging station (CS), needed fully charging time of the EV battery, total charging cost, total expected queuing time in the CS, and needed energy consumption of the EV to arrive the CS. The considered system involves 50 EVs and 3 EVCSs. Few solar cells are employed to recharge the EVs and reduce the energy loss and voltage drop. Different scenarios are provided to compare between the proposed and uncontrolled (unoptimized) schemes. The results prove the proposed technique effectiveness. The EV user's charging behavior is improved. Moreover, it is concluded that the charging cost depends on the state of charge (SoC) of the EV batteries and charging time of the EVs.",

keywords = "Charging Behavior of the EV User, Charging Station, Electric Vehicle, Genetic Algorithm, Smart Campus",

author = "Hassan, \{Mohamed A.\} and Oransa, \{Ahmed A.\} and Elsallanty, \{Ahmed M.\} and Fekry, \{Deema R.\} and Ibrahim, \{Hatem H.\} and Sedhom, \{Bishoy E.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023 ; Conference date: 03-12-2023 Through 06-12-2023",

year = "2023",

doi = "10.1109/ETFG55873.2023.10407166",

language = "English",

series = "2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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address = "United States",

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Hassan, MA, Oransa, AA, Elsallanty, AM, Fekry, DR, Ibrahim, HH & Sedhom, BE 2023, A Multi-Objective Optimization Schedule for the charging behavior of the EV user in a Smart Campus. in 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023. 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023, Wollongong, Australia, 3/12/23. https://doi.org/10.1109/ETFG55873.2023.10407166

A Multi-Objective Optimization Schedule for the charging behavior of the EV user in a Smart Campus. / Hassan, Mohamed A.; Oransa, Ahmed A.; Elsallanty, Ahmed M. et al.
2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Multi-Objective Optimization Schedule for the charging behavior of the EV user in a Smart Campus

AU - Hassan, Mohamed A.

AU - Oransa, Ahmed A.

AU - Elsallanty, Ahmed M.

AU - Fekry, Deema R.

AU - Ibrahim, Hatem H.

AU - Sedhom, Bishoy E.

PY - 2023

Y1 - 2023

N2 - Recently, electric vehicles (EVs) demand has been intensely increased. Accordingly, more electrical vehicle charging stations (EVCSs) are essentially needed. The charging behavior of the EV user is affected by several issues. This paper proposes a multi-objective genetic algorithm (MOGA) optimization scheme for scheduling the charging behavior of the EV user in a smart campus. The problem is optimally formulated and designed to minimize the total traveling distance of the EV toward charging station (CS), needed fully charging time of the EV battery, total charging cost, total expected queuing time in the CS, and needed energy consumption of the EV to arrive the CS. The considered system involves 50 EVs and 3 EVCSs. Few solar cells are employed to recharge the EVs and reduce the energy loss and voltage drop. Different scenarios are provided to compare between the proposed and uncontrolled (unoptimized) schemes. The results prove the proposed technique effectiveness. The EV user's charging behavior is improved. Moreover, it is concluded that the charging cost depends on the state of charge (SoC) of the EV batteries and charging time of the EVs.

AB - Recently, electric vehicles (EVs) demand has been intensely increased. Accordingly, more electrical vehicle charging stations (EVCSs) are essentially needed. The charging behavior of the EV user is affected by several issues. This paper proposes a multi-objective genetic algorithm (MOGA) optimization scheme for scheduling the charging behavior of the EV user in a smart campus. The problem is optimally formulated and designed to minimize the total traveling distance of the EV toward charging station (CS), needed fully charging time of the EV battery, total charging cost, total expected queuing time in the CS, and needed energy consumption of the EV to arrive the CS. The considered system involves 50 EVs and 3 EVCSs. Few solar cells are employed to recharge the EVs and reduce the energy loss and voltage drop. Different scenarios are provided to compare between the proposed and uncontrolled (unoptimized) schemes. The results prove the proposed technique effectiveness. The EV user's charging behavior is improved. Moreover, it is concluded that the charging cost depends on the state of charge (SoC) of the EV batteries and charging time of the EVs.

KW - Charging Behavior of the EV User

KW - Charging Station

KW - Electric Vehicle

KW - Genetic Algorithm

KW - Smart Campus

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DO - 10.1109/ETFG55873.2023.10407166

M3 - Conference contribution

AN - SCOPUS:85185815610

T3 - 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

BT - 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

Y2 - 3 December 2023 through 6 December 2023

ER -

Hassan MA, Oransa AA, Elsallanty AM, Fekry DR, Ibrahim HH, Sedhom BE. A Multi-Objective Optimization Schedule for the charging behavior of the EV user in a Smart Campus. In 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023). doi: 10.1109/ETFG55873.2023.10407166

A Multi-Objective Optimization Schedule for the charging behavior of the EV user in a Smart Campus

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this