Sustainable suburban mobility: Shared autonomous electric vehicles day-ahead transit and charging optimization using TOU rates and renewable energy

Abstract

Shared Autonomous Electric Vehicles (SAEVs) offer a transformative solution to bridge the mobility gap in suburban regions where public transportation means are scarce. Integration of SAEVs into the current electrical grid system poses operational challenges due to the anticipated surge in electricity demand for their charging. This paper proposes a strategy based on the Vehicle Scheduling Problem (VSP) for SAEVs to fulfill passenger travel demand and provide optimal charge scheduling using location based charging prices derived from Time of Use (TOU) rates. A significant portion of this study also investigates the fiscal benefits of utilization of local renewable energy for charging SAEVs. A multi-objective function to minimize charging costs, mobility costs and waiting time for passengers is formulated using mixed-integer linear programming (MILP). The proposed strategy is simulated and analyzed on a coupled traffic and low voltage suburban power grid of the French region considering coordinated charging strategy in the presence and absence of renewable energy. The comparison of results shows that the algorithm optimally schedules charging to maximize the utilization of renewable energy while serving passenger requests.