eprintid: 10082062
rev_number: 20
eprint_status: archive
userid: 608
dir: disk0/10/08/20/62
datestamp: 2019-09-23 11:50:28
lastmod: 2021-09-26 23:12:19
status_changed: 2019-09-23 11:50:28
type: article
metadata_visibility: show
creators_name: Lin, H
creators_name: Fu, K
creators_name: Wang, Y
creators_name: Sun, Q
creators_name: Li, H
creators_name: Hu, Y
creators_name: Sun, B
creators_name: Wennersten, R
title: Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F44
keywords: Electric vehicle; Agent-based trip chain model; Vehicle to grid; Fast charging; Charging flexibility
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: This paper developed an agent-based trip chain model (ABTCM) to study the distribution of electric vehicles (EVs) charging demand and its dynamic characteristics, including flexibility and uncertainty, at different types of location. Key parameters affecting charging demand include charging strategies, i.e. uncontrolled charging (UC) and off-peak charging (OPC), and EV supply equipment, including three levels of charging equipment.

The results indicate that the distributions of charging demand are similar as the travel patterns, featured by traffic flow at each location. A discrete peak effect was found in revealing the relation between traffic flow and charging demand, and it results in the smallest equivalent daily charging demand and peak load at public locations. EV charging and vehicle-to-grid (V2G) flexibility were examined by instantaneous adjustable power and accumulative adjustable amount of electricity. The EVs at home locations have the largest charging and V2G flexibility under the UC strategy, except for a period of regular working time. The V2G flexibility at work and public locations is generally larger than charging flexibility. Due to the fast charging application, the uncertainties of charging demand at public locations are the highest in all locations. In addition, the OPC strategy mitigates the uncertainty of charging demand.
date: 2019-12-01
date_type: published
publisher: Elsevier BV
official_url: https://doi.org/10.1016/j.energy.2019.116122
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1696111
doi: 10.1016/j.energy.2019.116122
lyricists_name: Hu, Yukun
lyricists_id: YHUDX81
actors_name: Austen, Jennifer
actors_id: JAUST66
actors_role: owner
full_text_status: public
publication: Energy
volume: 188
article_number: 116122
issn: 0360-5442
citation:        Lin, H;    Fu, K;    Wang, Y;    Sun, Q;    Li, H;    Hu, Y;    Sun, B;           Lin, H;  Fu, K;  Wang, Y;  Sun, Q;  Li, H;  Hu, Y;  Sun, B;  Wennersten, R;   - view fewer <#>    (2019)    Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model.                   Energy , 188     , Article 116122.  10.1016/j.energy.2019.116122 <https://doi.org/10.1016/j.energy.2019.116122>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10082062/1/JA-EV-Energy.pdf