Adaptive Optimal Management of EV Battery Distributed Energy for Concurrent Services to Transportation and Power Grid in a Fleet System under Dynamic Service Pricing

Abdullah Azhar Al-Obaidi, Hany E.Z. Farag

    Research output: Contribution to journalArticlepeer-review

    4 Scopus citations

    Abstract

    Deployment of electric vehicles (EVs) in a fleet system to deal with environmental issues has been at the center of attention over the past several years. While the battery of each EV offers small storage, hundreds of EVs collectively can offer large energy storage to serve a power grid. This article develops a model for a central controller in a fleet system that allows adaptive utilization of EV batteries distributed energy for concurrent services to the transportation and power grid. The optimization model integrates various slack variables and control parameters for managing real-time fare prices, adaptive energy, and reserve margin allocation, interaction with the grid operator, and meeting the fleet target revenue. The proposed model incorporates EV driver's input into the scheduling process to allow the driver to flexibly manage their battery capacities based on their availability and assessment of the transportation services demand. A dynamic pricing mechanism is developed for real-time calculation of fare rates to allow the EV fleet optimization problem to achieve a daily revenue target while limiting fare prices in a competitive market. Numerical results indicate that the model can manage several EVs for various services while enhancing the fleet financial metrics.

    Original languageBritish English
    Pages (from-to)1618-1628
    Number of pages11
    JournalIEEE Transactions on Industrial Informatics
    Volume18
    Issue number3
    DOIs
    StatePublished - 1 Mar 2022

    Keywords

    • Adaptive management
    • ancillary services
    • dynamic pricing
    • electric vehicle (EV) fleet
    • optimization
    • transportation sector

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