Multi-objective Charging Scheduling Model for Electric Vehicles Considering Urgent Demand Under Different Charging Modes

Lu Liu; Kaile Zhou; Xinhui Lu; Junqing Yu; Shanlin Yang

doi:10.1109/EI252483.2021.9713165

Multi-objective Charging Scheduling Model for Electric Vehicles Considering Urgent Demand Under Different Charging Modes

Lu Liu
, Kaile Zhou
, Xinhui Lu
, Junqing Yu
, Shanlin Yang

Hefei University of Technology

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

4 Scopus citations

Abstract

This study proposes a multi-objective electric vehicle (EV) charging scheduling model aiming at minimizing both the peak-valley load difference of power grid and the total charging costs of EV users. Two scenarios with different charging power and tariff schemes are investigated for EVs with urgent and unurgent charging demands. A case study is carried out with 100, 200, 300 and 500 EVs under home and public charging mode to demonstrate the effectiveness of the proposed model. In terms of peak-valley load difference and total charging costs, the proposed method can achieve a greater reduction rate in home charging mode than in public charging mode, except when the scale of EV is 100. Difference of coordinated charging method and uncoordinated charging method in total charging costs appears to be much lower than it in peak-valley load difference. The proposed multi-objective charging scheduling model can benefit both power grid and EV users, since it can not only reduce the impact of EVs on the stable and safe operation of power grid but also reduce the charging costs of EV users.

Original language	English
Title of host publication	5th IEEE Conference on Energy Internet and Energy System Integration
Subtitle of host publication	Energy Internet for Carbon Neutrality, EI2 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3936-3940
Number of pages	5
ISBN (Electronic)	9781665434256
DOIs	https://doi.org/10.1109/EI252483.2021.9713165
State	Published - 2021
Externally published	Yes
Event	5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021 - Taiyuan, China Duration: 22 Oct 2021 → 25 Oct 2021

Publication series

Name	5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021

Conference

Conference	5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021
Country/Territory	China
City	Taiyuan
Period	22/10/21 → 25/10/21

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

charging mode
electrical vehicles charging
multi-objective scheduling
urgent charging demand

Access to Document

10.1109/EI252483.2021.9713165

Cite this

Liu, L., Zhou, K., Lu, X., Yu, J., & Yang, S. (2021). Multi-objective Charging Scheduling Model for Electric Vehicles Considering Urgent Demand Under Different Charging Modes. In 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021 (pp. 3936-3940). (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EI252483.2021.9713165

Liu, Lu ; Zhou, Kaile ; Lu, Xinhui et al. / Multi-objective Charging Scheduling Model for Electric Vehicles Considering Urgent Demand Under Different Charging Modes. 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 3936-3940 (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021).

@inproceedings{8dbe212920cd423baa72265eb1851978,

title = "Multi-objective Charging Scheduling Model for Electric Vehicles Considering Urgent Demand Under Different Charging Modes",

abstract = "This study proposes a multi-objective electric vehicle (EV) charging scheduling model aiming at minimizing both the peak-valley load difference of power grid and the total charging costs of EV users. Two scenarios with different charging power and tariff schemes are investigated for EVs with urgent and unurgent charging demands. A case study is carried out with 100, 200, 300 and 500 EVs under home and public charging mode to demonstrate the effectiveness of the proposed model. In terms of peak-valley load difference and total charging costs, the proposed method can achieve a greater reduction rate in home charging mode than in public charging mode, except when the scale of EV is 100. Difference of coordinated charging method and uncoordinated charging method in total charging costs appears to be much lower than it in peak-valley load difference. The proposed multi-objective charging scheduling model can benefit both power grid and EV users, since it can not only reduce the impact of EVs on the stable and safe operation of power grid but also reduce the charging costs of EV users.",

keywords = "charging mode, electrical vehicles charging, multi-objective scheduling, urgent charging demand",

author = "Lu Liu and Kaile Zhou and Xinhui Lu and Junqing Yu and Shanlin Yang",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021 ; Conference date: 22-10-2021 Through 25-10-2021",

year = "2021",

doi = "10.1109/EI252483.2021.9713165",

language = "英语",

series = "5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021",

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

pages = "3936--3940",

booktitle = "5th IEEE Conference on Energy Internet and Energy System Integration",

}

Liu, L, Zhou, K, Lu, X, Yu, J & Yang, S 2021, Multi-objective Charging Scheduling Model for Electric Vehicles Considering Urgent Demand Under Different Charging Modes. in 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021, Institute of Electrical and Electronics Engineers Inc., pp. 3936-3940, 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021, Taiyuan, China, 22/10/21. https://doi.org/10.1109/EI252483.2021.9713165

Multi-objective Charging Scheduling Model for Electric Vehicles Considering Urgent Demand Under Different Charging Modes. / Liu, Lu; Zhou, Kaile; Lu, Xinhui et al.
5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 3936-3940 (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021).

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

TY - GEN

T1 - Multi-objective Charging Scheduling Model for Electric Vehicles Considering Urgent Demand Under Different Charging Modes

AU - Liu, Lu

AU - Zhou, Kaile

AU - Lu, Xinhui

AU - Yu, Junqing

AU - Yang, Shanlin

PY - 2021

Y1 - 2021

N2 - This study proposes a multi-objective electric vehicle (EV) charging scheduling model aiming at minimizing both the peak-valley load difference of power grid and the total charging costs of EV users. Two scenarios with different charging power and tariff schemes are investigated for EVs with urgent and unurgent charging demands. A case study is carried out with 100, 200, 300 and 500 EVs under home and public charging mode to demonstrate the effectiveness of the proposed model. In terms of peak-valley load difference and total charging costs, the proposed method can achieve a greater reduction rate in home charging mode than in public charging mode, except when the scale of EV is 100. Difference of coordinated charging method and uncoordinated charging method in total charging costs appears to be much lower than it in peak-valley load difference. The proposed multi-objective charging scheduling model can benefit both power grid and EV users, since it can not only reduce the impact of EVs on the stable and safe operation of power grid but also reduce the charging costs of EV users.

AB - This study proposes a multi-objective electric vehicle (EV) charging scheduling model aiming at minimizing both the peak-valley load difference of power grid and the total charging costs of EV users. Two scenarios with different charging power and tariff schemes are investigated for EVs with urgent and unurgent charging demands. A case study is carried out with 100, 200, 300 and 500 EVs under home and public charging mode to demonstrate the effectiveness of the proposed model. In terms of peak-valley load difference and total charging costs, the proposed method can achieve a greater reduction rate in home charging mode than in public charging mode, except when the scale of EV is 100. Difference of coordinated charging method and uncoordinated charging method in total charging costs appears to be much lower than it in peak-valley load difference. The proposed multi-objective charging scheduling model can benefit both power grid and EV users, since it can not only reduce the impact of EVs on the stable and safe operation of power grid but also reduce the charging costs of EV users.

KW - charging mode

KW - electrical vehicles charging

KW - multi-objective scheduling

KW - urgent charging demand

UR - https://www.scopus.com/pages/publications/85128214642

U2 - 10.1109/EI252483.2021.9713165

DO - 10.1109/EI252483.2021.9713165

M3 - 会议稿件

AN - SCOPUS:85128214642

T3 - 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021

SP - 3936

EP - 3940

BT - 5th IEEE Conference on Energy Internet and Energy System Integration

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

Y2 - 22 October 2021 through 25 October 2021

ER -

Liu L, Zhou K, Lu X, Yu J, Yang S. Multi-objective Charging Scheduling Model for Electric Vehicles Considering Urgent Demand Under Different Charging Modes. In 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 3936-3940. (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021). doi: 10.1109/EI252483.2021.9713165

Multi-objective Charging Scheduling Model for Electric Vehicles Considering Urgent Demand Under Different Charging Modes

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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