Optimal Sizing of Electric Vehicle Charging Stacks Considering a Multiscenario Strategy and User Satisfaction

The rapid growth of EVs relies on the development of supporting infrastructure, e.g., charging stations (CSs). The sizing problem of a CS typically involves minimizing the investment costs. Therefore, a flexible and precise sizing strategy is crucial. However, the existing methods suffer from the following issues: (1) they do not consider charging station deployments based on the charging stack; (2) existing sizing strategies based on smart charging technology consider a single scenario and fail to meet the demand for flexible operation under multiple scenarios in real-life situations. This paper proposes a novel CS sizing framework specific for charging stacks to overcome these challenges. Specifically, it first addresses the charging-stack-based CS sizing problem, and then it proposes the corresponding multiscenario constraints, i.e., exclusive and shared, for capacity-setting optimization. In addition, a novel quality of service (QoS) formulation is also proposed to better relate the user QoS levels to the CS sizing problem. Finally, it also explores the relationship between the investment costs and the total power of the needed charging stack under three business models. Extensive experiments show that the proposed framework can offer valuable guidance to CS operators in competitive environments.

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Publication Details

Journal
Electronics
Published
2024-08-11
DOI
https://doi.org/10.3390/electronics13163176
Primary Topic
Artificial Intelligence and Decision Support Systems
Type
article
Field-Weighted Citation Impact
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article

Optimal Sizing of Electric Vehicle Charging Stacks Considering a Multiscenario Strategy and User Satisfaction

Weihao Yang, Yinghong Zhou, Zhijing Yang, Ruihan Chen
Electronics
Artificial Intelligence and Decision Support Systems
article

Optimal Sizing of Electric Vehicle Charging Stacks Considering a Multiscenario Strategy and User Satisfaction

Weihao Yang, Yinghong Zhou, Zhijing Yang, Ruihan Chen
article en

Abstract

The rapid growth of EVs relies on the development of supporting infrastructure, e.g., charging stations (CSs). The sizing problem of a CS typically involves minimizing the investment costs. Therefore, a flexible and precise sizing strategy is crucial. However, the existing methods suffer from the following issues: (1) they do not consider charging station deployments based on the charging stack; (2) existing sizing strategies based on smart charging technology consider a single scenario and fail to meet the demand for flexible operation under multiple scenarios in real-life situations. This paper proposes a novel CS sizing framework specific for charging stacks to overcome these challenges. Specifically, it first addresses the charging-stack-based CS sizing problem, and then it proposes the corresponding multiscenario constraints, i.e., exclusive and shared, for capacity-setting optimization. In addition, a novel quality of service (QoS) formulation is also proposed to better relate the user QoS levels to the CS sizing problem. Finally, it also explores the relationship between the investment costs and the total power of the needed charging stack under three business models. Extensive experiments show that the proposed framework can offer valuable guidance to CS operators in competitive environments.

ElectronicsVol. 13(16)
Guangdong University of Technology (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 81%
Artificial Intelligence and Decision Support Systems
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Optimal Sizing of Electric Vehicle Charging Stacks Considering a Multiscenario Strategy and User Satisfaction — Weihao Yang, Yinghong Zhou, et al. · Electronics (2024) | TGRS Research Map | TGRS