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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.

The use of autonomous electric vehicles has gained significant attention due to the growing interest in sustainable transportation solutions. Shared autonomous vehicles also have the potential to minimize market investments, improve transit systems, and reduce local environmental impact. To address the challenges of scheduling and coordinating shared AEV fleets while considering their integration with power networks, a digital twin-based platform is proposed. The digital twin concept involves creating a virtual replica of a complex system, enabling real-time monitoring, analysis, and optimization. This paper introduces an adaptive digital twin model falling within the third phase of digital twin evolution, integrating transportation and power networks for efficient shared AEV scheduling. The platform utilizes real-time simulators for transportation and power networks, connected through communication protocols. The proposed system aims to enhance scheduling algorithms, consider power grid conditions, and optimize AEV charging infrastructure.

In the era of autonomous electric vehicles (AEVs), the emergence of Shared AEVs presents a unique set of challenges and opportunities. This paper introduces a new graph-based routing algorithm aimed at optimizing the allocation of AEVs and recharging them. The algorithm takes into account multiple factors, including energy requirements, charging infrastructure availability, time constraints, and trip distances, to efficiently respond to passenger requests. In addition, when AEVs face energy constraints, proposed algorithm selects the most cost-effective charging point to minimize charging costs, considering factors like electricity prices. By bridging the transportation network and the power grid, this algorithm can also address more complex constraints like weather situations, road conditions, and battery degradation. Our case studies demonstrate the algorithm's effectiveness in finding optimal routes and managing low state of charge situations. This research opens the door to further exploration in complex urban environments, dynamic request prioritization, and user involvement, offering a promising future for AEV routing and optimization.