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DC Field | Value | Language |
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dc.contributor.author | Feng, K | - |
dc.contributor.author | Zhong, Y | - |
dc.contributor.author | Hong, B | - |
dc.contributor.author | Wu, X | - |
dc.contributor.author | Lai, CS | - |
dc.contributor.author | Bai, C | - |
dc.date.accessioned | 2020-12-31T01:04:32Z | - |
dc.date.available | 2020-09-28 | - |
dc.date.available | 2020-12-31T01:04:32Z | - |
dc.date.issued | 2020-10-29 | - |
dc.identifier.citation | K. Feng, Y. Zhong, B. Hong, X. Wu, C. S. Lai and C. Bai, "The Impact of Plug-in Electric Vehicles on Distribution Network," 2020 IEEE International Smart Cities Conference (ISC2), Piscataway, NJ, USA, 2020, pp. 1-7 | en_US |
dc.identifier.isbn | 9781728182940 | - |
dc.identifier.issn | 2687-8860 | - |
dc.identifier.issn | 2687-8852 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/22054 | - |
dc.description.abstract | © 2020 IEEE. With concerned environmental problem, a large number of electric vehicles (EVs) has been adopted to replace the oil-fueled vehicles. If electric vehicles are charged simultaneously on a large-scale, it may cause peak load increase. Therefore, it is of great practical significance to study the influence of controlled charging behavior of electric vehicles on power grid. Firstly, Gaussian Mixture Model is used to modeling electric vehicles. Secondly, Monte Carlo method is studied to determine the charging load of electric vehicles, and the influence of uncontrolled charging of electric vehicles on the power grid is analyzed. Then the peak and valley hours are divided according to the membership function and the time-of-use pricing to minimize the difference between peak and valley load. Furthermore, the influence of controlled charging of EVs on power grid is analyzed. Finally, the model is applied to simulate and analyze the distribution network of Yangjiang, a coastal city in South China. The case study shows that the uncontrolled charging of EVs will increase the peak load of the power grid. The proposed controlled charging strategy can effectively transfer the charging load of EVs and lessen peak load demand. | en_US |
dc.description.sponsorship | Guangdong Power Grid Co., Ltd | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.rights | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | electric vehicles (EVs) | en_US |
dc.subject | Monte Carlo method | en_US |
dc.subject | controlled charging | en_US |
dc.subject | membership function | en_US |
dc.subject | time-of-use pricing | en_US |
dc.title | The Impact of Plug-in Electric Vehicles on Distribution Network | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/ISC251055.2020.9239073 | - |
dc.relation.isPartOf | 2020 IEEE International Smart Cities Conference, ISC2 2020 | - |
pubs.publication-status | Published | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | 886.68 kB | Adobe PDF | View/Open |
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