A framework to investigate charger capacity utilization and network voltage profile through residential EV charging data clustering
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A significant increase in the adoption of electric vehicles (EVs) is expected over the next decade. Hence, an investigation of the potential impact of EVs on the electricity grid is critical. This paper presents a framework for grid impact analysis of residential EVs using time series clustering (to perform sequential simulation) and discrete clustering (to estimate peak EV power consumption). This paper employs four clustering techniques, which are (i) K-means, (ii) hierarchical, (iii) DBSCAN, and (iv) fuzzy c-means, by analyzing 348 EV customers’ charging data. Clustering techniques have been implemented in Python, and power flow simulations have been performed using MATLAB/MATPOWER software. The results demonstrated daily and weekly EV profile clusters and EV charger utilization factors. The clustered EV profiles have been passed through to the power flow simulation to identify the network voltage violations. In the daily and weekly clusters, both K-means and hierarchical methods have two dominant clusters having 30 to 40% customers and two minor clusters with 10 to 20% customers. On the other hand, DBSCAN has one dominant cluster (in daily profile) with around 70% customers — as this method is used for anomaly detection. The fuzzy c-means has four almost similar clusters with around 25% customers. The abovementioned trend is evident in the network voltage violation heatmaps having more voltage violations in the weekdays evening (5:00 to 8:00 PM). • Demonstration of the un…
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Most previous studies using clustering techniques such as k-means, decision tree-based clustering, self-organising maps, and hierarchical clustering do not explore how such clusters can be directly used by DSOs, nor address the challenge of scalability for future large-scale EV deployment.
“While techniques such as k-means, decision tree-based clustering, self-organising maps, and hierarchical clustering have been used in previous studies [2], [3], most do not explore how such clusters can be directly used by DSOs, nor address the challenge of scalability for future large-scale EV deployment.”
From Optimal Coordination of Local Flexibility from Electric Vehicles with Social Impact Consideration (2025) · cites this paper as [3]
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Impact of uncoordinated plug-in electric vehicle charging on residential power demand
Muratori, M. (2018) · Nature Energy · doi:10.1038/s41560-017-0074-z · Cited by 695 · see citing works
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Optimal Coordination of Local Flexibility from Electric Vehicles with Social Impact Consideration
Chen, S., Couraud, B., Norbu, S., Andoni, M., Iqbal, Z., Djokic, S., Kirli, D., Kanugrahan, S. P., Cherubini, P., Krumdieck, S., et al. (2025) · 2025 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe) · doi:10.1109/isgteurope64741.2025.11305513
“…such as k-means, decision tree-based clustering, self-organising maps, and hierarchical clustering have been used in previous studies [2], [3], most do not explore how such clusters can be directly used by grid operators, nor address the challenge of scalability for future large-scale EV…”
- Authors
- Hasan, K. N., Rahman, M. T., Terrill, C., McLean, R., Rodricks, R., Sharma, A., & Islam, A.
- Affiliations
- RMIT University (AU)
- King Fahd University of Petroleum and Minerals (SA)
- Corresponding author
- Hasan, Kazi N.
- Venue
- Sustainable Energy Technologies and Assessments
- Publisher
- Elsevier BV
- Published
- 2025-02
- Volume
- 74
- Pages
- 104141
- ISSN
- 2213-1388 (ISSN-L)
- OpenAlex
- W4406140292
- Cited by
- 10
- References
- 1
- Citations by year
- 2026: 1 · 2025: 9
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Sustainable Energy Technologies and Assessments (publishedVersion, cc-by)
- 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 › Optimal Power Flow Distribution
- Keywords
- voltage violation, time series clustering, K-means clustering, hierarchical clustering, DBSCAN, fuzzy c-means clustering, distribution network impact, power flow simulation
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