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Optimal operation of a system of charging hubs and a fleet of shared autonomous electric vehicles

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  • Melendez, Kevin A.
  • Das, Tapas K.
  • Kwon, Changhyun

Abstract

Shared autonomous electric vehicles (SAEVs) are expected to serve a significant fraction of the passenger transportation needs in cities and surrounding urban areas. In this paper, we consider optimal operation of a cyber-physical system (CPS) comprising a large fleet of SAEVs and a set of charging hubs located across the transportation network and supported by the power grid. The hubs are considered to have a number of charging stations, a stand-alone battery bank for energy storage, and limited rooftop photo-voltaic (PV) generation capacity. We developed a robust mixed integer linear programming model. It considers a number of practical features of both the power and transportation systems, including day-ahead load commitment for electricity via an alternative current power flow model, real time price spikes of electricity, energy arbitrage, uncertainty in passenger demand, and balking of passengers while waiting for a ride. We demonstrated our methodology by implementing it on a sample CPS with 500 SAEVs and five hubs with fifty charging stations in each. Our methodology yields operational decisions for day ahead commitment of power and real time control of the SAEVs and the hubs. The sample CPS is used to examine impact of hub capacity and fleet size on various system performance measures. We discuss the computational challenges of our methodology and propose a simplified myopic approach that is capable of dealing with much larger fleet sizes and a variety of hub capacities. Reduction in computation time and the optimality gap for the myopic approach are examined.

Suggested Citation

  • Melendez, Kevin A. & Das, Tapas K. & Kwon, Changhyun, 2020. "Optimal operation of a system of charging hubs and a fleet of shared autonomous electric vehicles," Applied Energy, Elsevier, vol. 279(C).
    • Handle: RePEc:eee:appene:v:279:y:2020:i:c:s0306261920313349
    DOI: 10.1016/j.apenergy.2020.115861
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    1. Paudel, Diwas & Das, Tapas K., 2023. "A deep reinforcement learning approach for power management of battery-assisted fast-charging EV hubs participating in day-ahead and real-time electricity markets," Energy, Elsevier, vol. 283(C).
    2. Sevdari, Kristian & Calearo, Lisa & Andersen, Peter Bach & Marinelli, Mattia, 2022. "Ancillary services and electric vehicles: An overview from charging clusters and chargers technology perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. Sumitkumar, Rathor & Al-Sumaiti, Ameena Saad, 2024. "Shared autonomous electric vehicle: Towards social economy of energy and mobility from power-transportation nexus perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    4. Riccardo Iacobucci & Raffaele Bruno & Jan-Dirk Schmöcker, 2021. "An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles," Energies, MDPI, vol. 14(12), pages 1-22, June.
    5. Subramanian, Vignesh & Feijoo, Felipe & Sankaranarayanan, Sriram & Melendez, Kevin & Das, Tapas K., 2022. "A bilevel conic optimization model for routing and charging of EV fleets serving long distance delivery networks," Energy, Elsevier, vol. 251(C).
    6. Vilaça, Mariana & Santos, Gonçalo & Oliveira, Mónica S.A. & Coelho, Margarida C. & Correia, Gonçalo H.A., 2022. "Life cycle assessment of shared and private use of automated and electric vehicles on interurban mobility," Applied Energy, Elsevier, vol. 310(C).

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