Lee, R., Yazbeck, S. and Brown, S. orcid.org/0000-0001-8229-8004 (2020) Validation and application of agent-based electric vehicle charging model. In: Cruden, A., (ed.) Energy Reports. 4th Annual CDT Conference in Energy Storage and Its Applications, 09-10 Jul 2019, Southampton, UK. Elsevier , pp. 53-62.
Abstract
Agent-based models are a class of simulation in which many autonomous agents interact such that the mix of stochastic and deterministic actions each agent undertakes results in some emergent behaviour across the entire population. Such models have been used to explore the impacts of electric vehicles (EVs) on electricity grids. However, there has been little data available against which to validate this approach. In this study, we evaluate an agent based EV model against real data observed during the “My Electric Avenue” project; an Ofgem funded 3 year trial aiming to identify the impacts of EVs on local grids. We find that, within the constraints of the available trial data, the agent model is able to replicate dominant charging pattern features. The behaviour of owners will inevitably play a role in the actual charging patterns observed and we further explore how consumer adoption of time-of-use tariffs and vehicle range (battery capacity) preference would impact on demands at the local substation. We show that simplistic adoption of time-of-use tariffs would have undesirable consequences for network peak demands and that the expected increase in EV range and thus battery size, will both increase peaks and total energy supply to domestic properties.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | ⃝© 2020 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Electric vehicles; Agent-based modelling; Charging profiles; Distribution network demands |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Mar 2020 11:52 |
Last Modified: | 03 Jun 2020 15:17 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.egyr.2020.02.027 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158227 |