GIS-based modelling of electric-vehicle–grid integration in a 100% renewable electricity grid

Mengyu Li, Manfred Lenzen, Dai Wang and Keisuke Nansai

Applied Energy, 2020, vol. 262, issue C, No S0306261920300891

Abstract: We examine the spatio-temporal interactions of widespread electric vehicle (EV) charging with a future, 100% renewable electricity system in Australia. More specifically, we use a GIS-based electricity supply-demand model simulating an hourly competitive-bidding process over an entire year. We obtain least-cost grid configurations that include both renewable energy (RE) generators and EVs, the latter under both uncontrolled and controlled charging, and adoption rates between 0 and 100%. We characterise the vehicle-to-grid interaction in terms of overall installed capacity, hourly generation and spillage, levelized cost of electricity (LCOE), as well as transmission network expansion topology. We show that supplying 100% renewable electricity to cover current electricity needs in Australia, as well as powering all Australian passenger vehicles as controlled-charged EVs, requires 205 GW of installed capacity at an LCOE of 14.7 AUD¢/kWh. This 100% RE supply with EV charging leads to an additional electricity cost of 1710 AUD/capita annually, comparing to the current annual expenditure for electricity and conventional vehicle fuel.

Keywords: Low-carbon electricity supply; Electric vehicles; Vehicle-to-grid integration; GIS (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920300891
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:262:y:2020:i:c:s0306261920300891

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.114577

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().