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

Jake Robbennolt, Meiyi Li, Javad Mohammadi, Stephen D. Boyles

TL;DR

The paper tackles the challenge of balancing on-demand passenger service with grid-support capabilities of a large fleet of shared autonomous electric vehicles, particularly in post-disaster contexts. It develops a model predictive control framework that jointly optimizes transportation and power flows as a mixed-integer linear program, incorporating four constraint families and a social-welfare objective that includes revenues, costs, and battery degradation. To scale to real-world sizes and preserve privacy, it introduces a hierarchical ADMM-based distributed solution with upper-level coordination between the grid operator and vehicle dispatcher and lower-level vehicle-level optimizations, enabling parallel computation. Numerical demonstrations across toy and large-scale networks show that the distributed approach achieves near-optimal performance, maintains queue stability, and provides substantial grid-support benefits, highlighting the value of integrating transportation and electric-system objectives in dispatch decisions.

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.

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

TL;DR

The paper tackles the challenge of balancing on-demand passenger service with grid-support capabilities of a large fleet of shared autonomous electric vehicles, particularly in post-disaster contexts. It develops a model predictive control framework that jointly optimizes transportation and power flows as a mixed-integer linear program, incorporating four constraint families and a social-welfare objective that includes revenues, costs, and battery degradation. To scale to real-world sizes and preserve privacy, it introduces a hierarchical ADMM-based distributed solution with upper-level coordination between the grid operator and vehicle dispatcher and lower-level vehicle-level optimizations, enabling parallel computation. Numerical demonstrations across toy and large-scale networks show that the distributed approach achieves near-optimal performance, maintains queue stability, and provides substantial grid-support benefits, highlighting the value of integrating transportation and electric-system objectives in dispatch decisions.

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.

Paper Structure

This paper contains 12 sections, 38 equations, 11 figures.

Table of Contents

  1. Introduction
  2. Dispatch Policy
  3. Passenger Queuing Constraints
  4. Vehicle Charging Constraints
  5. Grid Topology Constraints
  6. Power Flow Constraints
  7. Objective Function
  8. Distributed Dispatch
  9. Vehicle Dispatch Subproblem
  10. Convergence
  11. Numerical Demonstrations
  12. Conclusions

Figures (11)

  • Figure 1: Joint optimization between the fleet dispatcher and the electric grid operator.
  • Figure 2: Communications between power distribution system operator and SAEV dispatcher.
  • Figure 3: Communications between vehicles and central controller.
  • Figure 4: Cumulative unserved passengers for different vehicle fleets. Queues are only stable for fleets of SAEVs and SAVs.
  • Figure 5: Cumulative unserved energy (MWh) for different vehicle fleets. SAEVs and 50/50 fleets are able to serve almost all energy demand that cannot be served by the grid.
  • ...and 6 more figures