Charging scheduling and energy management for mobile chargers in a grid-interactive transportation system

Abstract

Mobile charging refers to the mobility-on-demand battery packages that can provide flexible charging services and regulation capacities in a grid-interactive transportation system, especially for ridesharing fleets. In this study, we use real-world data to understand the impact of mobile charging in ridesharing operations, and propose a data-driven optimization framework to identify optimal integration scenarios for mobile chargers in urban transportation systems. A mixed-integer linear program is developed to determine how many mobile chargers are required to satisfy the charging demands of shared vehicles, and when and how much to store or discharge energy to the microgrid or to other shared vehicles vs. the time-of-use charging prices regimes during a day. The objective is to minimize the daily operating cost of mobile chargers. In the proposed optimization framework, we first analyze real-world datasets that include information on trips and charging demands of fleets sharing more than one thousand shared vehicles. We then investigate a microgrid power profile that consists of base load analysis and solar photovoltaic generation forecasting. Lastly, we implement our framework in a real-world case study to evaluate model performance. The results show that our approach can reduce the total costs by about 21 % compared to a non-scheduling greedy policy, and thus help stabilize the power profile by vehicle-to-grid implementation.