Enhancing Electric Vehicles Charging Service Through Mixed Charging: A Two-Stage Distributionally Robust Optimization Model

In response to the need for sustainable transportation, this study addresses the challenges of providing the charging service to fluctuating and geographically dispersed electric vehicles (EVs) charging demands. This work proposed a mixed EV charging system that incorporates mobile charging services to complement fixed charging stations. The overall coverage rate of charging services is enhanced under this charging system. To cope with the complexities of estimating the EV charging demands due to evolving adoption of EVs and uncertain external factors (e.g.,driver’s car use behaviors, fluctuations in electricity rates), a two-stage distributionally robust optimization (DRO) model is developed to determine the placement and capacity of fixed charging stations, the location of mobile depots, and the number of mobile trucks to deploy. Acase study on HongKong’s North District is conducted to demonstrate the effectiveness of the proposed model by solving it using a decomposition algorithm, through which the computational complexity is significantly reduced by iteratively solving the first andsecond stage problems. This study demonstrates that the mixed charging strategy significantly improves coverage, even with limited data, and highlights how investment levels affect performance. The mixed system improved coverage by 20% compared to fixed only infrastructure under the budget of 90million HKD. Additionally, the case study shows an increase of average coverage rate as budget level increases, which offers insights into the investment levels required to achieve a specific coverage rate. The contributions include a novel mixed charging strategy and a two-stage DRO model, providing a robust and scalable framework to support EV charging infrastructure planning under uncertainty.