Journal
RENEWABLE ENERGY
Volume 161, Issue -, Pages 1072-1091Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2020.07.017
Keywords
Electric vehicles; Renewable energy; Carbon intensity; Optimisation; Scheduling
Funding
- EPSRC AGILE project [EP/S003088/1]
- Realising Energy Storage Technologies in Low-carbon Energy Systems (RESTLESS) project [EP/N001893/1]
- [EP/L015471/1]
- EPSRC [EP/S003088/1, EP/S029575/1, EP/R035288/1] Funding Source: UKRI
Ask authors/readers for more resources
This paper presents an investigation of the potential for coordinated charging of electric vehicles to i) reduce the CO2 emissions associated with their charging by selectively charging when grid carbon intensity (gCO2/kWh) is low and ii) absorb excess wind generation in times when it would otherwise be curtailed. A method of scheduling charge events that seeks the minimum carbon intensity of charging while respecting EV and network constraints is presented via a time-coupled linearised optimal power flow formulation, based on plugging-in periods derived from a large travel dataset. Schedules are derived using real half-hourly grid intensity data; if charging in a particular event can be done entirely through use of renewable energy that would otherwise have been curtailed, its carbon intensity is zero. It was found that if 'dumb' charged from the current UK mainland (GB) grid, average emissions related to electric vehicle (EV) charging are in the range 35-56 gCO2/km; this can be reduced to 28-40 gCO2/km by controlled charging - approximately 20-30% of the tailpipe emissions of an average new petrol or diesel car sold in Europe. There is potential for EVs to absorb excess wind generation; based on the modelled charging behaviour, 500,000 EVs (20% of Scotland's current car fleet) could absorb around three quarters of curtailment at Scotland's largest onshore wind farm. (C) 2020 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available