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

Bexley report: a report to MCCH on a suitable transport policy for its Bexley services

This report presents the findings of and recommendations from the study commissioned by MCCH to advise on a comprehensive transport policy for MCCH to use in providing services in both its residential homes and day-care centres in Bexley. It describes the current positions of transport supply for, and of transport demand by the community of people with learning difficulties in the London Borough of Bexley. It also considers the extent to which the transport supply is meeting or not meeting the transport demands and the expressed needs of the people and/or their representatives. The report considers the implications for improvement in transport provision of certain proposed actions by MCCH. Finally, the report presents some recommendations based on a user-centred strategy to help MCCH incorporate their concept of empowering their service users through suitable transport provision. This study has been conducted with the ethos and operational objectives of the MCCH group firmly in mind. MCCH has an objective to enhance quality of life for their service users and is very concerned with ensuring that its service users are enabled to exercise the rights and opportunities of citizenship with particular reference to freedom of choice in time and mode of travel. MCCH holds that real improvement in services to learning disability people must include increased range and choice of people-centred opportunities that address the total needs and aspirations of service users and their carers, underpinned by values and principles of good practice. Thus MCCH desires to put back in the control of users, the lever of decision making as regards services provided to 4 them and intends to do this by actively eliciting user/stakeholders involvement in decision-making. Contrary to the standard social service transport provision style, MCCH desires to create choice for service-users, feeling that people should be able to decide whether, e.g. to go by bus or train and be supported in their decision and not be constrained by the schedule of the provided transport. The specific terms of reference for this study are 1. To examine the current demand for, and provision of, transport within MCCH’s Bexley services. To assess how best these services might be reconfigured and managed, having regard to: · Desire to increase empowerment and choice for service users · Optimizing the integration of the transport management in Bexley within MCCH’s organization, in the light of most efficient use of resources and practice elsewhere in MCCH · Desire to better integrate residential services with day services in Bexley · MCCH’s intention to reconfigure Bexley day services · The move of service users towards ‘supported living’ as opposed to registered care · The objectives and concerns of all parties involved, including Bexley Social Services, Bexley Transport Services, the parents/relatives/carers of the service users and the service users themselves · The way vehicles are currently owned and funded · Efficiency and cost 2. To produce outline proposals, plans and specifications of how a reconfigured transport service would look and operate, including details of resource requirements in enough detail to allow reasonably accurate costing to be derived

A hierarchical distributed control model for coordinating intelligent systems

A hierarchical distributed control (HDC) model for coordinating cooperative problem-solving among intelligent systems is described. The model was implemented using SOCIAL, an innovative object-oriented tool for integrating heterogeneous, distributed software systems. SOCIAL embeds applications in 'wrapper' objects called Agents, which supply predefined capabilities for distributed communication, control, data specification, and translation. The HDC model is realized in SOCIAL as a 'Manager'Agent that coordinates interactions among application Agents. The HDC Manager: indexes the capabilities of application Agents; routes request messages to suitable server Agents; and stores results in a commonly accessible 'Bulletin-Board'. This centralized control model is illustrated in a fault diagnosis application for launch operations support of the Space Shuttle fleet at NASA, Kennedy Space Center

An Intelligent Fleet Condition-Based Maintenance Decision Making Method Based on Multi-Agent

According to the demand for condition-based maintenance online decision making among a mission oriented fleet, an intelligent maintenance decision making method based on Multi-agent and heuristic rules is proposed. The process of condition-based maintenance within an aircraft fleet (each containing one or more Line Replaceable Modules) based on multiple maintenance thresholds is analyzed. Then the process is abstracted into a Multi-Agent Model, a 2-layer model structure containing host negotiation and independent negotiation is established, and the heuristic rules applied to global and local maintenance decision making is proposed. Based on Contract Net Protocol and the heuristic rules, the maintenance decision making algorithm is put forward. Finally, a fleet consisting of 10 aircrafts on a 3-wave continuous mission is illustrated to verify this method. Simulation results indicate that this method can improve the availability of the fleet, meet mission demands, rationalize the utilization of support resources and provide support for online maintenance decision making among a mission oriented fleet

Coordinating complex problem-solving among distributed intelligent agents

A process-oriented control model is described for distributed problem solving. The model coordinates the transfer and manipulation of information across independent networked applications, both intelligent and conventional. The model was implemented using SOCIAL, a set of object-oriented tools for distributing computing. Complex sequences of distributed tasks are specified in terms of high level scripts. Scripts are executed by SOCIAL objects called Manager Agents, which realize an intelligent coordination model that routes individual tasks to suitable server applications across the network. These tools are illustrated in a prototype distributed system for decision support of ground operations for NASA's Space Shuttle fleet