Economic operation of EV charging hubs with DC fast chargers (DCFCs) requires advanced energy management to balance cost minimization, load control, and customer satisfaction. Existing EV management strategies mainly address low-power chargers and are unsuitable for DCFCs, where rapid charging is the priority. This reduces flexibility for demand response or peak shaving and introduces uncertainties absent in workplace or home charging, where vehicles typically remain connected longer than needed. Moreover, detailed EV data such as charging requirements and user preferences is often inaccessible due to privacy concerns. These challenges necessitate new approaches to optimize DCFC operations while ensuring sufficient energy to meet drivers’ mileage needs and enabling participation in electricity markets. This paper proposes a two-stage linear scheduling framework combining model predictive control and stochastic programming. The method applies a rolling-horizon strategy to capture uncertainties in EV owner behavior at DCFC hubs. A novel mileage-loss concept is introduced to differentiate between desired and actual states of charge at departure, enabling controlled flexibility in charging without compromising user expectations. The proposed approach aims to achieve economic operation of microgrid-based EV charging hubs while limiting mileage loss for drivers. The framework is validated through case studies implemented in Python/PYOMO using real-world data from the IKEA Adelaide retail store and aligned with the structure of the Australian electricity market. Results demonstrate that the method effectively balances cost efficiency, uncertainty management, and service quality for DCFC hubs, contributing to the reliable integration of fast charging into future energy systems.
Mousavizade et al. (Wed,) studied this question.