Flexibility potential quantification of electric vehicle charging clusters
Open accessAbstract
A significant obstacle to providing flexibility services with electric vehicles (EVs) is the uncertainty surrounding the profitability and flexibility potential of charging clusters when utilized as a flexible load. Currently, there is a lack of comprehensive and easily applicable methods for quantifying flexibility in the literature. This paper introduces an evaluation tool and a set of flexibility indexes to assess the capability of charging clusters to deliver flexibility services. The method is designed to evaluate and quantify the flexibility potential of charging clusters in terms of short-term and long-term power adjustments and charge scheduling. Through sensitivity analysis, we examine how connection capacity, EV battery capacities, power capabilities, and the number of daily charging sessions influence the flexibility potential of charging clusters. Our findings highlight a direct relationship between the grid connection capacity of clusters and their ability to perform short-term power adjustments. Moreover, while larger batteries tend to reduce energy and time flexibility, their increased storage capability facilitates managing and scheduling a larger energy volume. Furthermore, for the days analysed, the flexibility potential showed minimal sensitivity to the number of daily charging sessions. Instead, the amount of energy requested and connection patterns emerge as key determinants of overall flexibility. In summary, this research provides valuable insights that…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The proposed model quantifies the adjustable flexibility of integrated resources, including PV, ESS, and EVs within FCS, providing a basis for real-time flexibility supply-demand balance evaluation.
“Unlike previous approaches that focus primarily on individual resource optimization [9], [17], the proposed model quantifies the adjustable flexibility of integrated resources, including PV, ESS, and EVs within FCS, providing a basis for real-time flexibility supply-demand balance evaluation.”
From Flexibility Region Quantification and Optimal Scheduling of Fast Charging Stations Considering EV User Discharge Willingness (2025) · cites this paper as [17]
Cited by
Sorted by citations
Zhang, X., Zhao, Q., Guo, L., & Wang, S. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3594615 · Cited by 1 · see citing works
- Authors
- Striani, S., Unterluggauer, T., Andersen, P. B., & Marinelli, M.
- Affiliations
- Technical University of Denmark (DK)
- Corresponding author
- Striani, Simone
- Venue
- Sustainable Energy, Grids and Networks
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 40
- Pages
- 101547
- ISSN
- 2352-4677 (ISSN-L)
- OpenAlex
- W4403460008
- Cited by
- 16
- References
- 0
- Citations by year
- 2026: 7 · 2025: 9
- FWCI
- 1.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Sustainable Energy Grids and Networks (publishedVersion, cc-by)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by)
- Funders
- The Energy Technology Development and Demonstration Programme
- 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
- flexibility potential, flexible loads, flexibility index, charging scheduling, grid connection capacity, charging sessions, energy flexibility, temporal flexibility
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review