Leasing Business Model for Truck Mobile Charging Stations: From the View of Operators

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.