On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV, in prep. for journal submission. In V3, we add a proof that the socially-optimal solution can be enforced as a general equilibrium, a privacy-preserving distributed optimization algorithm, a description of the receding-horizon implementation and additional numerical results, and proofs of all theorem

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV and accepted by TCNS. In Version 4, the body of the paper is largely rewritten for clarity and consistency, and new numerical simulations are presented. All source code is available (MIT) at https://dx.doi.org/10.5281/zenodo.324165

A Review of Traffic Signal Control.

The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project

Strategic Infrastructure Planning for Autonomous Vehicles

Compared with conventional human-driven vehicles (HVs), AVs have various potential benefits, such as increasing road capacity and lowering vehicular fuel consumption and emissions. Road infrastructure management, adaptation, and upgrade plays a key role in promoting the adoption and benefit realization of AVs.This dissertation investigated several strategic infrastructure planning problems for AVs. First, it studied the potential impact of AVs on the congestion patterns of transportation networks. Second, it investigated the strategic planning problem for a new form of managed lanes for autonomous vehicles, designated as autonomous-vehicle/toll lanes, which are freely accessible to autonomous vehicles while allowing human-driven vehicles to utilize the lanes by paying a toll.This new type of managed lanes has the potential of increasing traffic capacity and fully utilizing the traffic capacity by selling redundant road capacity to HVs. Last, this dissertation studied the strategic infrastructure planning problem for an infrastructure-enabled autonomous driving system. The system combines vehicles and infrastructure in the realization of autonomous driving. Equipped with roadside sensor and control systems, a regular road can be upgraded into an automated road providing autonomous driving service to vehicles. Vehicles only need to carry minimum required on-board devices to enable their autonomous driving on an automated road. The costs of vehicles can thus be significantly reduced

Pricing transport networks with fixed residential location

We consider a congestible static traffic network which is used by different households and analyse the conditions for optimal congestion taxes on network links, when not all links in the network can be taxed (partial network pricing). This is done under two assumptions about the toll revenues. First, lump sum transfers are assumed to be available. It is shown that social welfare maximisation leads to unequal treatment of equal households, because of differences in transport costs, and that constraints on network pricing imply complex deviations from marginal social cost pricing, because of network interactions. The second assumption is that the congestion tax revenue is redistributed to households according to predetermined shares. In that case, the optimal link taxes consist of a Pigouvian component, a Ramsey-Mirrlees component and a network interaction component. The taxes will deviate from marginal external congestion costs, even in the absence of network pricing constraints. This result is qualitatively different from the partial equilibrium analysis. Stylised examples of two networks are used to illustrate (a) the impact of unequal treatment of equals and of tax redistribution rules on optimal link taxes and on their effectiveness in terms of social welfare, and (b) the effect of network pricing constraints. The results suggest that (1) the effectiveness of congestion taxes is strongly reduced when not all links in the network can be taxed, (2) assignment inefficiencies are of less importance than excess demand levels when no taxes are present, and (3) that optimal parking charges may outperform partial pricing schemes when the assignment inefficiencies are small.congestion; road transport; pricing; networks