Balancing Passenger Transport and Power Distribution: A Distributed Dispatch Policy for Shared Autonomous Electric Vehicles

TL;DR

This paper proposes a distributed dispatch policy for shared autonomous electric vehicles that balances passenger service and energy transfer, enhancing resilience and efficiency in transportation and power systems.

Contribution

It introduces a novel model predictive control approach with a distributed solution for balancing transportation and energy objectives in autonomous EV fleets.

Findings

The heuristic achieves near-optimal solutions quickly.

Vehicles effectively balance passenger service and energy transfer.

Including operational constraints improves system performance.

Abstract

Shared autonomous electric vehicles can provide on-demand transportation for passengers while also interacting extensively with the electric distribution system. This interaction is especially beneficial after a disaster when the large battery capacity of the fleet can be used to restore critical electric loads. We develop a dispatch policy that balances the need to continue serving passengers (especially critical workers) and the ability to transfer energy across the network. The model predictive control policy tracks both passenger and energy flows and provides maximum passenger throughput if any policy can. The resulting mixed integer linear programming problem is difficult to solve for large-scale problems, so a distributed solution approach is developed to improve scalability, privacy, and resilience. We demonstrate that the proposed heuristic, based on the alternating direction method of multipliers, is effective in achieving near-optimal solutions quickly. The dispatch policy is examined in simulation to demonstrate the ability of vehicles to balance these competing objectives with benefits to both systems. Finally, we compare several dispatch behaviors, demonstrating the importance of including operational constraints and objectives from both the transportation and electric systems in the model.