EV charging scheduling for cost and greenhouse gases emissions minimization

Rentao Wu; George Tsagarakis; A. J. Collin; A. E. Kiprakis

doi:10.1109/EVER.2017.7935877

EV charging scheduling for cost and greenhouse gases emissions minimization

Rentao Wu^*, George Tsagarakis, A. J. Collin, A. E. Kiprakis

^*Corresponding author for this work

School of Engineering

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

Abstract

This paper investigates the potential impact of a fleet of electric vehicles charging on the cost of electricity generation, greenhouse gas emissions (GHG) and power system demand through low voltage residential demand-side management (DSM). The proposed optimisation algorithm is used to shift electric vehicles charging loads to minimize the combined impact of three key parameters: Financial, environmental, and demand variability. The results show that it is effective to reshape the power demand and reduce electricity cost and GHG emissions without affecting people's driving patterns.

Original language	English
Title of host publication	2017 12th International Conference on Ecological Vehicles and Renewable Energies, EVER 2017
Publisher	Institute of Electrical and Electronics Engineers
ISBN (Electronic)	9781538616925
DOIs	https://doi.org/10.1109/EVER.2017.7935877
Publication status	Published - 30 May 2017
Event	12th International Conference on Ecological Vehicles and Renewable Energies, EVER 2017 - Monte-Carlo, Monaco Duration: 11 Apr 2017 → 13 Apr 2017

Conference

Conference	12th International Conference on Ecological Vehicles and Renewable Energies, EVER 2017
Country/Territory	Monaco
City	Monte-Carlo
Period	11/04/17 → 13/04/17

Keywords / Materials (for Non-textual outputs)

demand side management
electric vehicles
low voltage
optimisation algorithm
residential load

Access to Document

10.1109/EVER.2017.7935877

Cite this

@inproceedings{cbfc9f0209cd4dae8c7a4896b549cda9,

title = "EV charging scheduling for cost and greenhouse gases emissions minimization",

abstract = "This paper investigates the potential impact of a fleet of electric vehicles charging on the cost of electricity generation, greenhouse gas emissions (GHG) and power system demand through low voltage residential demand-side management (DSM). The proposed optimisation algorithm is used to shift electric vehicles charging loads to minimize the combined impact of three key parameters: Financial, environmental, and demand variability. The results show that it is effective to reshape the power demand and reduce electricity cost and GHG emissions without affecting people's driving patterns.",

keywords = "demand side management, electric vehicles, low voltage, optimisation algorithm, residential load",

author = "Rentao Wu and George Tsagarakis and Collin, {A. J.} and Kiprakis, {A. E.}",

year = "2017",

month = may,

day = "30",

doi = "10.1109/EVER.2017.7935877",

language = "English",

booktitle = "2017 12th International Conference on Ecological Vehicles and Renewable Energies, EVER 2017",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "12th International Conference on Ecological Vehicles and Renewable Energies, EVER 2017 ; Conference date: 11-04-2017 Through 13-04-2017",

}

Wu, R, Tsagarakis, G, Collin, AJ & Kiprakis, AE 2017, EV charging scheduling for cost and greenhouse gases emissions minimization. in 2017 12th International Conference on Ecological Vehicles and Renewable Energies, EVER 2017., 7935877, Institute of Electrical and Electronics Engineers, 12th International Conference on Ecological Vehicles and Renewable Energies, EVER 2017, Monte-Carlo, Monaco, 11/04/17. https://doi.org/10.1109/EVER.2017.7935877

EV charging scheduling for cost and greenhouse gases emissions minimization. / Wu, Rentao; Tsagarakis, George; Collin, A. J. et al.
2017 12th International Conference on Ecological Vehicles and Renewable Energies, EVER 2017. Institute of Electrical and Electronics Engineers, 2017. 7935877.

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

TY - GEN

T1 - EV charging scheduling for cost and greenhouse gases emissions minimization

AU - Wu, Rentao

AU - Tsagarakis, George

AU - Collin, A. J.

AU - Kiprakis, A. E.

PY - 2017/5/30

Y1 - 2017/5/30

N2 - This paper investigates the potential impact of a fleet of electric vehicles charging on the cost of electricity generation, greenhouse gas emissions (GHG) and power system demand through low voltage residential demand-side management (DSM). The proposed optimisation algorithm is used to shift electric vehicles charging loads to minimize the combined impact of three key parameters: Financial, environmental, and demand variability. The results show that it is effective to reshape the power demand and reduce electricity cost and GHG emissions without affecting people's driving patterns.

AB - This paper investigates the potential impact of a fleet of electric vehicles charging on the cost of electricity generation, greenhouse gas emissions (GHG) and power system demand through low voltage residential demand-side management (DSM). The proposed optimisation algorithm is used to shift electric vehicles charging loads to minimize the combined impact of three key parameters: Financial, environmental, and demand variability. The results show that it is effective to reshape the power demand and reduce electricity cost and GHG emissions without affecting people's driving patterns.

KW - demand side management

KW - electric vehicles

KW - low voltage

KW - optimisation algorithm

KW - residential load

UR - http://www.scopus.com/inward/record.url?scp=85021386742&partnerID=8YFLogxK

U2 - 10.1109/EVER.2017.7935877

DO - 10.1109/EVER.2017.7935877

M3 - Conference contribution

AN - SCOPUS:85021386742

BT - 2017 12th International Conference on Ecological Vehicles and Renewable Energies, EVER 2017

PB - Institute of Electrical and Electronics Engineers

T2 - 12th International Conference on Ecological Vehicles and Renewable Energies, EVER 2017

Y2 - 11 April 2017 through 13 April 2017

ER -

EV charging scheduling for cost and greenhouse gases emissions minimization

Abstract

Conference

Keywords / Materials (for Non-textual outputs)

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