Comparative MILP-Based Energy Cost Optimization for Smart Railway Hubs Integrating EV Charging, PV, and Energy Storage

In this paper, a Mixed-Integer Linear Programming (MILP)-based energy cost optimization framework is proposed for smart railway hub that integrate multiple energy technologies, including rooftop photovoltaic (PV) systems, energy storage systems (ESS), and electric vehicle (EV) charging infrastructure. The proposed model focuses on minimizing the total daily energy cost of a railway substation while ensuring operational reliability and system flexibility. The energy hub includes AC charging stations for private EVs and DC fast charging points dedicated to electric buses. Additionally, the model considers the regenerative braking energy (RBE) of trains as a recoverable energy source within the system. Different scenarios are analyzed to compare the economic performance and energy management efficiency under various scenarios, including grid-connected and semi-islanded modes. The MILP formulation accounts for time-of-use electricity tariffs, ESS charge/discharge cycles, and solar generation profiles, ensuring optimal energy flows between system components. San Donato station is chosen as a case study. Results highlight the benefits of coordinated energy management strategies in reducing dependency on grid energy and lowering operational costs. Moreover, the impact of RBE utilization and charging EVs from this is thoroughly evaluated. The proposed approach demonstrates significant potential for improving the sustainability and cost-effectiveness of next-generation railway energy hubs.