Xiaolin Mou
Achieving Low Carbon Emission for Dynamically Charging Electric Vehicles through Renewable Energy Integration
Mou, Xiaolin; Zhang, Yingji; Jiang, Jing; Sun, Hongjian
Abstract
Dynamic wireless charging for Electric Vehicles (EVs) can promote the take-up of EVs due to its potential of extending the driving range and reducing the size and cost of batteries of EVs. However, its dynamic charging demand and rigorous operation requirements may stress the power grid and increase carbon emissions. A novel adaptive dynamic wireless charging system is proposed that enables mobile EVs to be powered by renewable wind energy by taking advantages of our proposed traffic flow-based charging demand prediction programme. The aim is to cut down the system cost and carbon emissions at the same time, whilst realising fast demand prediction and supply response as well as relieving the peak demand on the power grid. Simulation results show that the proposed system can adaptively adjust the demand side energy response according to customers’ welfare analysis and charging price, thereby to determine the power supply method. Moreover, due to the prioritised use of renewable energy, EV charging system requires less electricity from the power grid and thus the overall carbon emissions are reduced by 63.7%.
Citation
Mou, X., Zhang, Y., Jiang, J., & Sun, H. (2019). Achieving Low Carbon Emission for Dynamically Charging Electric Vehicles through Renewable Energy Integration. IEEE Access, 7, 118876-118888. https://doi.org/10.1109/access.2019.2936935
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 16, 2019 |
Online Publication Date | Aug 22, 2019 |
Publication Date | 2019 |
Deposit Date | Aug 20, 2019 |
Publicly Available Date | Aug 20, 2019 |
Journal | IEEE Access |
Electronic ISSN | 2169-3536 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 7 |
Pages | 118876-118888 |
DOI | https://doi.org/10.1109/access.2019.2936935 |
Public URL | https://durham-repository.worktribe.com/output/1294667 |
Files
Published Journal Article
(8.9 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Published Journal Article (Advance online version)
(2.2 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
Advance online version This article has been published under a CC BY 4.0 license.
You might also like
Energy-based Predictive Root Cause Analysis for Real-Time Anomaly Detection in Big Data Systems
(2025)
Presentation / Conference Contribution
Integrated Sensing and Communications With Mixed Fields Using Transmit Beamforming
(2024)
Journal Article
Decarbonising Heating with Power-Hydrogen Optimisation
(2024)
Presentation / Conference Contribution
Communication-Centric Integrated Sensing and Communications With Mixed Fields
(2024)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search