A scenario-based approach to predict energy demand and carbon emission of electric vehicles on the electric grid

Cheung, Wai Ming (2022) A scenario-based approach to predict energy demand and carbon emission of electric vehicles on the electric grid. Environmental Science and Pollution Research. ISSN 0944-1344 (In Press)

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Official URL: https://doi.org/10.1007/s11356-022-21214-w

Abstract

UK plans to ban the sale of new diesel and petrol cars by 2030 to be replaced by electric vehicles (EVs). The question is, will the UK’s electrical grid infrastructure ready for this change? This comparative study investigates the effect of UK green vehicles on the electrical grid and presents a new insight into improving their energy demand and carbon dioxide (CO2) emissions to the electrical grid. The results show that even when there is a very high level of market penetration of EVs, the overall effect on annual energy consumption may seem minimal. On the contrary, the effect that EVs may have on the electrical grid is dependent on the time-of-day EVs are being charged. Therefore, this study concludes that measures need to be put in place to control charging times of EVs and this would help restrict the total daily electricity and electrical energy demands. The introduction of EVs reduces the overall CO2 emissions mainly because a proportion of petrol and diesel cars are replaced by EVs. However, CO2 emissions can only reduce up to a certain level and this reduction of CO2 will have less effect due to an increasing number of EVs in the electrical grid. To reduce CO2 emissions further, the electricity that relies on high-carbon fossil fuels in the electrical grid should be set at the minimum level.

Item Type: Article
Uncontrolled Keywords: Electric vehicles, Electrical grid, CO2 emissions, Energy demand, Green vehicles
Subjects: H300 Mechanical Engineering
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Rachel Branson
Date Deposited: 07 Jun 2022 09:14
Last Modified: 09 Jun 2022 09:45
URI: http://nrl.northumbria.ac.uk/id/eprint/49261

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