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Impact of uncoordinated plug-in electric vehicle charging on residential power demand
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“In comparison, deep learning models like Long Short-Term Memory networks (LSTMs) and convolutional neural networks (CNNs) can better capture nonlinear, time-dependent usage patterns [96],[104].”
From Economic Impacts and Mitigation for the Grid Connected Electric Vehicle Charging: A Technical and Operational Overview (2026) · cites this paper as [96]
In the citing paper
“If the charging occurs where there is a capacity deficit in the grid, a large-scale diffusion of EVs can cause grid issues [ [9][10][11]].”
From A large-scale high-resolution geographic analysis of impacts of electric vehicle charging on low-voltage grids (2022) · cites this paper as [9]
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Metadata
- Authors
- Muratori, M.
- Affiliations
- National Laboratory of the Rockies (US)
- Corresponding author
- Muratori, Matteo
- ORCID
- Muratori, Matteo
- Venue
- Nature Energy
- Published
- 2018-01-22
- Volume
- 3
- Issue
- 3
- Pages
- 193-201
- ISSN
- 2058-7546 (ISSN-L)
- OpenAlex
- W2789027126
- Cited by
- 695
- References
- 36
- Citations by year
- 2026: 104 · 2025: 120 · 2024: 86 · 2023: 105 · 2022: 71 · 2021: 99 · 2020: 62 · 2019: 35 · 2018: 12
- FWCI
- 19.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- plug-in electric vehicles, uncoordinated charging, peak load, electric mobility
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