A clustering-based approach to scenario-driven planning for EV charging with autonomous mobile chargers

Khalil Gorgani Firouzjah and Jamal Ghasemi

Applied Energy, 2025, vol. 379, issue C, No S0306261924023080

Abstract: The main goal of this paper is long-term planning for electric vehicle (EV) charging infrastructure using autonomous mobile chargers (AMCs). The proposed method employs a clustering-based strategy to group EVs based on similar charging patterns, thereby reducing the number of scenarios and simplifying the planning problem. This reduces the number of possible scenarios and simplifies the planning problem. Each cluster then undergoes a short-term scheduling process to determine the optimal allocation of AMCs among its EVs. The program evaluates the probability of each scenario as well as the corresponding time results. Eventually, it formulates an ideal long-term strategy for the deployment and operation of AMC. This plan incorporates the concept of confidence level to address uncertainty in forecasting vehicle behavior and charging requirements. It ensures that the number and capacity of chargers are sufficient to meet system requirements at various confidence levels. The concept of confidence level strikes a balance between the cost of deploying mobile chargers and the risk of failing to satisfy the charging demand. This approach leads to optimal and reliable planning for EV charging infrastructure.

Keywords: Electric vehicle; Autonomous mobile charger; K-means cluttering; Planning; Scheduling (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924023080
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:379:y:2025:i:c:s0306261924023080

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.124925

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().