A spatial–temporal model for grid impact analysis of plug-in electric vehicles
Open accessAbstract
A Spatial–Temporal model (STM) was developed to evaluate the impact of large scale deployment of plug-in electric vehicles (EVs) on urban distribution networks. The STM runs based on the integration of power system analysis and transportation analysis. Origin–Destination (OD) analysis from intelligent transportation research was used to model the EV mobility. Based on the EV technical and market information provided by the EU MERGE project and the output of OD analysis, a Monte Carlo simulation method was developed within the STM to obtain the EV charging load of each load busbar over time. The STM aims to facilitate power system evaluation and planning, and is able to provide both average values and probabilities of nodal bus voltages and branch loadings. The STM is able to identify the critical network components that will require to be upgraded. A high customer density urban network from the United Kingdom Generic Distribution System combined with geographic information was used as a test system. Two EV charging strategies, “dumb” charging and “smart” charging, were simulated and compared under different EV penetration levels (0%, 25% and 50%) to verify the effectiveness of STM.
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Uncoordinated EV charging can cause considerable congestion in electrical networks, especially in residential areas.
“Since drivers tend to change in times when electrical grids are already highly loaded, for example after going back home from work, EVs can cause considerable congestion in electrical networks [4,5], especially in residential areas.”
From Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids (2019) · cites this paper as [4]
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- Authors
- Mu, Y., Wu, J., Jenkins, N., Jia, H., & Wang, C.
- Affiliations
- Tianjin University (CN)
- Cardiff University (GB)
- Corresponding author
- Wu, Jianzhong
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 114
- Pages
- 456-465
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2089574748
- Cited by
- 424
- References
- 1
- Citations by year
- 2026: 22 · 2025: 30 · 2024: 45 · 2023: 46 · 2022: 39 · 2021: 37 · 2020: 61 · 2019: 32 · 2018: 35 · 2017: 25 · 2016: 19 · 2015: 21 · 2014: 12
- FWCI
- 10.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Applied Energy (publishedVersion, cc-by)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Research Councils UK (EP/I031707/1)
- National Natural Science Foundation of China (51361130152)
- Engineering and Physical Sciences Research Council (EP/L001039/1, EP/E036503/1, EP/I031707/1, EP/K006274/1)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- plug-in electric vehicles, spatio-temporal modeling, urban distribution networks, electric vehicle charging load, origin-destination analysis, Monte Carlo simulation, EV penetration, smart charging, distribution network planning
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