File:Impact of electric vehicle charging demand on power distribution grid congestion.pdf
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DescriptionImpact of electric vehicle charging demand on power distribution grid congestion.pdf |
English: California, a pioneer in EV adoption, has enacted ambitious electric vehicle (EV) policies that will generate a large burden on the state’s electric distribution system. We investigate the statewide impact of uncontrolled EV charging on the electric distribution networks at a large scale and high granularity, by employing an EV charging profile projection that combines travel demand model, EV adoption model, and real-world EV charging data. We find a substantial need for infrastructure upgrades in 50% of feeders by 2035, and 67% of feeders by 2045. The distribution system across California must upgrade its capacity by 25 GW by 2045, corresponding to a cost between $6 and $20 billion. While the additional infrastructure cost drives the electricity price up, it is offset by the downward pressure from the growth of total electricity consumption and leads to a reduction in electricity rate between $0.01 and $0.06/kWh by 2045. We also find that overloading conditions are highly diverse spatially, with feeders in residential areas requiring twice as much upgrade compared to commercial areas. Our study provides a framework for evaluating EVs’ impact on the distribution grid and indicates the potential to reduce infrastructure upgrade costs by shifting home-charging demand. The imminent challenges confronting California serve as a microcosm of the forthcoming obstacles anticipated worldwide due to the prevailing global trend of EV adoption. |
Date | |
Source | doi:10.1073/pnas.2317599121 |
Author | Yanning Li and Alan Jenn |
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current | 07:47, 25 April 2024 | 1,216 × 1,631, 10 pages (2.6 MB) | Koavf (talk | contribs) | Uploaded a work by Yanning Li and Alan Jenn from https://www.pnas.org/doi/abs/10.1073/pnas.2317599121 with UploadWizard |
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Image title | Proc. Natl. Acad. Sci. U.S.A. 2024.121:e2317599121 |
Author | |
Date and time of digitizing | 18:21, 18 April 2024 |
Date metadata was last modified | 17:11, 24 April 2024 |
File change date and time | 17:11, 24 April 2024 |
Software used | Adobe InDesign 15.1 (Windows) |
Unique ID of original document | xmp.did:FC7F117407206811822A8C10FEE461B6 |
Conversion program | Adobe PDF Library 15.0; modified using iText 4.2.0 by 1T3XT |
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Page size | 584.784 x 783 pts |
Version of PDF format | 1.4 |