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

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...

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Autor principal: Cheung, Wai Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581848/
https://www.ncbi.nlm.nih.gov/pubmed/35676573
http://dx.doi.org/10.1007/s11356-022-21214-w
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author Cheung, Wai Ming
author_facet Cheung, Wai Ming
author_sort Cheung, Wai Ming
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description 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 (CO(2)) 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 CO(2) emissions mainly because a proportion of petrol and diesel cars are replaced by EVs. However, CO(2) emissions can only reduce up to a certain level and this reduction of CO(2) will have less effect due to an increasing number of EVs in the electrical grid. To reduce CO(2) emissions further, the electricity that relies on high-carbon fossil fuels in the electrical grid should be set at the minimum level.
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spelling pubmed-95818482022-10-21 A scenario-based approach to predict energy demand and carbon emission of electric vehicles on the electric grid Cheung, Wai Ming Environ Sci Pollut Res Int Research Article 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 (CO(2)) 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 CO(2) emissions mainly because a proportion of petrol and diesel cars are replaced by EVs. However, CO(2) emissions can only reduce up to a certain level and this reduction of CO(2) will have less effect due to an increasing number of EVs in the electrical grid. To reduce CO(2) emissions further, the electricity that relies on high-carbon fossil fuels in the electrical grid should be set at the minimum level. Springer Berlin Heidelberg 2022-06-08 2022 /pmc/articles/PMC9581848/ /pubmed/35676573 http://dx.doi.org/10.1007/s11356-022-21214-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Cheung, Wai Ming
A scenario-based approach to predict energy demand and carbon emission of electric vehicles on the electric grid
title A scenario-based approach to predict energy demand and carbon emission of electric vehicles on the electric grid
title_full A scenario-based approach to predict energy demand and carbon emission of electric vehicles on the electric grid
title_fullStr A scenario-based approach to predict energy demand and carbon emission of electric vehicles on the electric grid
title_full_unstemmed A scenario-based approach to predict energy demand and carbon emission of electric vehicles on the electric grid
title_short A scenario-based approach to predict energy demand and carbon emission of electric vehicles on the electric grid
title_sort scenario-based approach to predict energy demand and carbon emission of electric vehicles on the electric grid
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581848/
https://www.ncbi.nlm.nih.gov/pubmed/35676573
http://dx.doi.org/10.1007/s11356-022-21214-w
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