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, systems) and the electric power network. We propose a joint model that captures the coupling between the two systems stemming from the vehicles’ charging requirements, capturing time-varying customer demand, battery depreciation, and power transmission constraints. First, we show that the model is amenable to efficient optimization. Then, we prove that the socially optimal solution to the joint problem is a general equilibrium if locational marginal pricing is used for electricity. Finally, we show that the equilibrium can be computed by selfish transportation and generator operators (aided by a nonprofit independent system operator) without sharing private information. We assess the performance of the approach and its robustness to stochastic fluctuations in demand through case studies and agent-based simulations. Collectively, these results provide a first-of-a-kind characterization of the interaction between AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.

[1]  K. Cheung,et al.  Energy and ancillary service dispatch for the interim ISO New England electricity market , 1999, Proceedings of the 21st International Conference on Power Industry Computer Applications. Connecting Utilities. PICA 99. To the Millennium and Beyond (Cat. No.99CH36351).

[2]  David Sun,et al.  Energy and ancillary service dispatch for the interim ISO New England electricity market , 1999 .

[3]  Emilio Frazzoli,et al.  Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore , 2014 .

[4]  Ramteen Sioshansi,et al.  OR Forum - Modeling the Impacts of Electricity Tariffs on Plug-In Hybrid Electric Vehicle Charging, Costs, and Emissions , 2012, Oper. Res..

[5]  D. Kirschen,et al.  Fundamentals of power system economics , 1991 .

[6]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[7]  Emilio Frazzoli,et al.  On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment , 2017, Proceedings of the National Academy of Sciences.

[8]  J. G. Wardrop,et al.  Some Theoretical Aspects of Road Traffic Research , 1952 .

[9]  O. Alsaç,et al.  DC Power Flow Revisited , 2009, IEEE Transactions on Power Systems.

[10]  Michael Florian,et al.  Comparison of Assignment Methods for Simulation-Based Dynamic-Equilibrium Traffic Assignment , 2008 .

[11]  Javier Alonso-Mora,et al.  The Impact of Ridesharing in Mobility-on-Demand Systems: Simulation Case Study in Prague , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[12]  W. Hogan Markets in Real Electric Networks Require Reactive Prices , 1993 .

[13]  Zuyi Li,et al.  Electric Vehicle Mobility in Transmission-Constrained Hourly Power Generation Scheduling , 2013, IEEE Transactions on Smart Grid.

[14]  Stanton W. Hadley,et al.  Potential Impacts of Plug-in Hybrid Electric Vehicles on Regional Power Generation , 2009 .

[15]  Marco Pavone,et al.  Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms , 2016, Autonomous Robots.

[16]  Thomas J. Overbye,et al.  A comparison of the AC and DC power flow models for LMP calculations , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[17]  Dominik Goeke,et al.  Routing a mixed fleet of electric and conventional vehicles , 2015, Eur. J. Oper. Res..

[18]  Michael W. Levin,et al.  Congestion-aware system optimal route choice for shared autonomous vehicles , 2017 .

[19]  Willett Kempton,et al.  Vehicle-to-grid power fundamentals: Calculating capacity and net revenue , 2005 .

[20]  Marco Pavone,et al.  A BCMP network approach to modeling and controlling autonomous mobility-on-demand systems , 2016, Int. J. Robotics Res..

[21]  Lizhi Wang,et al.  Potential impact of recharging plug‐in hybrid electric vehicles on locational marginal prices , 2010 .

[22]  Tao Wang,et al.  Optimal routing and charging of energy‐limited vehicles in traffic networks , 2016 .

[23]  Susanne Ebersbach,et al.  Power System Analysis And Design , 2016 .

[24]  Sean P. Meyn,et al.  Dynamic Competitive Equilibria in Electricity Markets , 2012 .

[25]  Stephen D. Boyles,et al.  A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application , 2017, Comput. Environ. Urban Syst..

[26]  Paul Denholm,et al.  Overgeneration from Solar Energy in California - A Field Guide to the Duck Chart , 2015 .

[27]  Walid Saad,et al.  Economics of Electric Vehicle Charging: A Game Theoretic Approach , 2012, IEEE Transactions on Smart Grid.

[28]  Andrea J. Goldsmith,et al.  Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks , 2015, IEEE Transactions on Control of Network Systems.

[29]  M. Ilic,et al.  Optimal Charge Control of Plug-In Hybrid Electric Vehicles in Deregulated Electricity Markets , 2011, IEEE Transactions on Power Systems.

[30]  Andrea J. Goldsmith,et al.  Optimal Electricity Pricing for Societal Infrastructure Systems , 2017, HICSS.

[31]  Tsuyoshi Murata,et al.  {m , 1934, ACML.