Customer-centric meso-level planning for electric vehicle charger distribution

This paper presents a framework for the meso-level planning of electric vehicle (EV) charger distribution, addressing customer-level plugin uncertainties and regional variations. To bridge the gap between nationwide macro-level targets and localized micro-level plans, a meso-level approach is essential, considering regional diversity, market dynamics, and customer-centric factors. To address these limitations in meso-level planning, this paper proposes a customer-segmentation approach coupled with a probabilistic Monte Carlo Simulation to identify charger requirements in the cities across the Australian capital. This paper employs a K-means clustering algorithm to classify EV customers, leveraging prioritized features derived from a Random Forest Classifier based on available models in regional markets. Regional uncertainties in EV plug-in behaviors are collected from Australian scenarios to capture the inherent variability in charging patterns and accurately reflect the local demographics influencing EV adoption and usage. The results indicate the need for installing 1239 regular and 235 fast-charging ports, with a projected 29.53 % increase in charging ports by 2040 due to population and vehicle sales growth. Additionally, comparing with existing reports reveals that neglecting customer plugin behaviors and vehicle types can lead to an inaccurate estimation of charger requirements, resulting in significant investment losses. The findings underscore the importance of incorporating customer segmentation and regional variations into EV infrastructure planning to optimize resource allocation without costly mismatches. • Customer-focused meso-scale electric vehicle charger distribution is proposed. • Electric Vehicle customers are segmented utilizing Australian contexts. • Energy needs and plugin intervals for each customer group are estimated. • Traffic volume and distances of distinct cities are used in charger distribution. • District-specific charger deployment plans in 2024 and 2040 are outlined.

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Publication Details

Journal
Applied Energy
Published
2025-03-30
DOI
https://doi.org/10.1016/j.apenergy.2025.125742
Citations
3
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
2.19

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article

Customer-centric meso-level planning for electric vehicle charger distribution

Ashish Kumar Karmaker, H. R. Pota, Bjorn C. P. Sturmberg, Sam Behrens
3 citations
Applied Energy
Electric Vehicles and Infrastructure
2.19
article

Customer-centric meso-level planning for electric vehicle charger distribution

Ashish Kumar Karmaker, H. R. Pota, Bjorn C. P. Sturmberg, Sam Behrens
article en
3 citations

Abstract

This paper presents a framework for the meso-level planning of electric vehicle (EV) charger distribution, addressing customer-level plugin uncertainties and regional variations. To bridge the gap between nationwide macro-level targets and localized micro-level plans, a meso-level approach is essential, considering regional diversity, market dynamics, and customer-centric factors. To address these limitations in meso-level planning, this paper proposes a customer-segmentation approach coupled with a probabilistic Monte Carlo Simulation to identify charger requirements in the cities across the Australian capital. This paper employs a K-means clustering algorithm to classify EV customers, leveraging prioritized features derived from a Random Forest Classifier based on available models in regional markets. Regional uncertainties in EV plug-in behaviors are collected from Australian scenarios to capture the inherent variability in charging patterns and accurately reflect the local demographics influencing EV adoption and usage. The results indicate the need for installing 1239 regular and 235 fast-charging ports, with a projected 29.53 % increase in charging ports by 2040 due to population and vehicle sales growth. Additionally, comparing with existing reports reveals that neglecting customer plugin behaviors and vehicle types can lead to an inaccurate estimation of charger requirements, resulting in significant investment losses. The findings underscore the importance of incorporating customer segmentation and regional variations into EV infrastructure planning to optimize resource allocation without costly mismatches. • Customer-focused meso-scale electric vehicle charger distribution is proposed. • Electric Vehicle customers are segmented utilizing Australian contexts. • Energy needs and plugin intervals for each customer group are estimated. • Traffic volume and distances of distinct cities are used in charger distribution. • District-specific charger deployment plans in 2024 and 2040 are outlined.

Applied EnergyVol. 389
Australian National University (AU), Commonwealth Scientific and Industrial Research Organisation (AU), UNSW Sydney (AU), Health Sciences and Nutrition (AU)
Commonwealth Scientific and Industrial Research Organisation, University of New South Wales
Affordable and clean energy
Openalex Percentile: Top 13%
Electric Vehicles and Infrastructure
2.19
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