Electric vehicle capacity forecasting model with application to load levelling

Bowen Zhou; Timothy Littler; Aoife Foley

doi:10.1109/PESGM.2015.7285829

Electric vehicle capacity forecasting model with application to load levelling

Bowen Zhou, Timothy Littler, Aoife Foley

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

11 Citations (Scopus)

261 Downloads (Pure)

Abstract

There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

Original language	English
Title of host publication	2015 IEEE Power & Energy Society General Meeting: Proceedings
DOIs	https://doi.org/10.1109/PESGM.2015.7285829
Publication status	Published - 05 Oct 2015
Event	IEEE Power & Energy Society General Meeting - Colorado, Denver, United States Duration: 27 Jul 2015 → 31 Jul 2015 http://www.pes-gm.org/2015/

Publication series

Name	2015 IEEE Power & Energy Society General Meeting: Proceedings
Publisher	IEEE
ISSN (Print)	1932-5517

Conference

Conference	IEEE Power & Energy Society General Meeting
Country/Territory	United States
City	Denver
Period	27/07/2015 → 31/07/2015
Internet address	http://www.pes-gm.org/2015/

Keywords

electric vehicle (EV), capacity forecasting, uncertainty analysis, load levelling

Access to Document

10.1109/PESGM.2015.7285829Licence: Unspecified

Electric Vehicle Capacity Forecasting Model with Application to Load Levelling
© 2015 IEEE. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 373 KB

11 Citations
2 Article

An Electric Vehicle Dispatch Module for Demand-Side Energy Participation
Zhou, B., Yao, F., Littler, T. & Zhang, H., 01 Sept 2016, In: Applied Energy. 177, p. 464-474 11 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
31 Citations (Scopus)

862 Downloads (Pure)
Local Storage Meets Local Demand: A Technical Solution to Future Power Distribution System
Zhou, B. & Littler, T., 18 Feb 2016, In: IET Generation, Transmission and Distribution. 10, 3, p. 704-711 7 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
19 Citations (Scopus)

345 Downloads (Pure)

Cite this

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title = "Electric vehicle capacity forecasting model with application to load levelling",

abstract = "There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.",

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author = "Bowen Zhou and Timothy Littler and Aoife Foley",

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language = "English",

series = "2015 IEEE Power & Energy Society General Meeting: Proceedings",

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AU - Littler, Timothy

AU - Foley, Aoife

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N2 - There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

AB - There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

KW - electric vehicle (EV), capacity forecasting, uncertainty analysis, load levelling

U2 - 10.1109/PESGM.2015.7285829

DO - 10.1109/PESGM.2015.7285829

M3 - Conference contribution

T3 - 2015 IEEE Power & Energy Society General Meeting: Proceedings

BT - 2015 IEEE Power & Energy Society General Meeting: Proceedings

T2 - IEEE Power & Energy Society General Meeting

Y2 - 27 July 2015 through 31 July 2015

ER -

Electric vehicle capacity forecasting model with application to load levelling

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Research output

An Electric Vehicle Dispatch Module for Demand-Side Energy Participation

Local Storage Meets Local Demand: A Technical Solution to Future Power Distribution System

Cite this