Leasing business model for trunk mobile charging stations: From the view of operators

He, Kecheng, Jia, Hongjie, Mu, Yunfei, Yu, Xiaodan, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and Dong, Xiaohong 2025. Leasing business model for trunk mobile charging stations: From the view of operators. IEEE Transactions on Transportation Electrification 10.1109/tte.2025.3557026

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Abstract

Truck mobile charging stations (TMCS) are emerging as an effective solution to bridge the gap between supply and demand for electric vehicle (EV) charging. However, traditional business models face barriers due to high initial costs and low utilization rates, hindering operator participation. To this end, this paper introduces a bi-level optimization model for TMCS leasing to balance the TMCS operator (TMCO) and charging facility operators (CFOs). The upper-level objective maximizes the profit of TMCO, focusing on TMCS fleet size, differentiated pricing for long-term and short-term rentals, and scheduling grid energy arbitrage during idle periods. The lower level aims to maximize the profit of CFOs by determining rental quantities and durations based on leasing offers. A distributionally robust optimization (DRO) approach is employed to address the uncertainties in EV charging demand, using chance constraints with the Wasserstein distance to capture forecast errors. The probabilistic constraints are transformed into tractable linear constraints through conditional value-at-risk (CVaR) approximation. The model is solved by the genetic algorithm (GA) at the upper layer and the nested column-and-constraint generation (NC&CG) algorithm at the lower layer. Case studies show that the model effectively balances the objectives of TMCO and CFOs. With adaptive pricing and TMCS allocation strategies, the model ensures the TMCO’s profitability while improving CFOs’ economics.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html, Start Date: 2025-01-01
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2332-7782
Last Modified:	14 Apr 2025 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/177636

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