The impact of plug-in behavior on the spatial–temporal flexibility of electric vehicle charging load
Open accessAbstract
While electric vehicles (EVs) are expected to support decarbonizing transport, EVs can challenge the electricity system. Investigating the EV charging load and its flexibility, e.g., by shifting load, is therefore crucial to ensure a secure and sustainable energy system. We develop an agent-based model to investigate how different plug-in behaviors can affect (future) EV charging load profiles and their spatial–temporal flexibility. We contribute to extant literature by (1) revealing the effect of diverse plug-in behaviors on EV load profiles, particularly the flexibility potential resulting from different plug-in behaviors; (2) presenting the (future) charging load in different spatial structures, i.e. urban, rural, or suburban, and home, work, or public charging locations; and (3) demonstrating the effect of detailed driving profiles in high spatial and temporal resolution. We implement three future scenarios regarding EV and charging infrastructure diffusion and technology developments. We find that the impact of potential changes in plug-in behavior on EV charging load would be highest for urban areas and increases as charging infrastructure becomes more spatially diversified. Decision-makers in policy and industry can use these insights to evaluate the impact of EV charging on distribution grids and design incentives to leverage the flexibility potential of EVs.
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The simplified break-even calculation underscores the importance of the battery's carbon footprint but does not account for the multitude of other parameters that influence real-world lifetime CO2 emissions.
“Notably, this simplified calculation underscores the importance of the battery’s CF but does not account for the multitude of other parameters36–38 that influence the real-world lifetime CO2 emissions of thermal and EVs.”
From Carbon footprint distributions of lithium-ion batteries and their materials (2024) · cites this paper as [38]
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Desai, R., Steuteville, R., & Johnson, C. (2026) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2026.103480
Hua, G., Cheng, H., Li, P., Li, W., & Huang, X. (2026) · Electric Power Systems Research · doi:10.1016/j.epsr.2026.113944
Modeling electric vehicle uncertainty: Charging behavior & grid impact
Nobakht, S., & Ahmadi, A.-A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45298-7.00011-2
Habeeb, N., Shakhawat, N. S. B., Amjady, N., Shah, R., Dehghan, S., Terzija, V. V., & Štrbac, G. (2026) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2026.102364
- Authors
- Gschwendtner, C., Knoeri, C., & Stephan, A.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Gschwendtner, Christine
- Venue
- Sustainable Cities and Society
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 88
- Pages
- 104263
- ISSN
- 2210-6707 (ISSN-L)
- OpenAlex
- W4307002354
- Cited by
- 91
- References
- 130
- Citations by year
- 2026: 15 · 2025: 31 · 2024: 30 · 2023: 15
- FWCI
- 5.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Sustainable Cities and Society (publishedVersion, cc-by-nc-nd)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion, cc-by-nc-nd)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by-nc-nd)
- ETH Zürich Research Collection
- Funders
- Eidgenössische Technische Hochschule Zürich
- Bundesamt für Energie (SI/501904-01)
- 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 › Integrated Energy Systems Optimization
- Keywords
- electric vehicle charging, charging load profiles, load flexibility, charging infrastructure, driving profiles, urban-rural differences, distribution grid impact, agent-based modeling
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