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  Accurate and scalable representation of electric vehicles in energy system models: A virtual storage-based aggregation approach

Müßel, J., Ruhnau, O., Madlener, R. (2023): Accurate and scalable representation of electric vehicles in energy system models: A virtual storage-based aggregation approach. - iScience, 26, 10, 107816.
https://doi.org/10.1016/j.isci.2023.107816

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 Creators:
Müßel, Jarusch1, Author              
Ruhnau, Oliver2, Author
Madlener, Reinhard2, Author
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: The growing number of electric vehicles (EVs) will challenge the power system, but EVs may also support system balancing via smart charging. Modeling EVs’ system-level impact while respecting computational constraints requires the aggregation of individual profiles. We show that studies typically rely on too few profiles to accurately model EVs’ system-level impact and that a naïve aggregation of individual profiles leads to an overestimation of the fleet’s flexibility potential. To overcome this problem, we introduce a scalable and accurate aggregation approach based on the idea of modeling deviations from an uncontrolled charging strategy as virtual energy storage. We apply this to a German case study and estimate an average flexibility potential of 6.2 kWh/EV, only 10% of the result of a naïve aggregation. We conclude that our approach allows for a more realistic representation of EVs in energy system models and suggest applying it to other flexible assets.

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Language(s): eng - English
 Dates: 2023-06-082023-08-302023-08-312023-10-20
 Publication Status: Finally published
 Pages: 14
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.isci.2023.107816
Organisational keyword: RD3 - Transformation Pathways
PIKDOMAIN: RD3 - Transformation Pathways
Working Group: Energy Systems
MDB-ID: No data to archive
Regional keyword: Germany
Research topic keyword: Economics
Model / method: Quantitative Methods
OATYPE: Gold Open Access
 Degree: -

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Project name : ARIADNE
Grant ID : 03SFK5A
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Title: iScience
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 26 (10) Sequence Number: 107816 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/2589-0042
Publisher: Cell Press
Publisher: Elsevier