The optimal scheduling of electric vehicle (EV) charging is a crucial problem for the efficient and reliable operation of the electricity grid. In this paper, a game-theoretic approach for optimal charging scheduling of EVs considering both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) prices is proposed while taking into account all the relevant uncertainty factors. The proposed approach aims to maximize the profits of both the EV owners and the charging station owners. A repeated and dynamic game framework is used to model the interactions between the EVs, charging station owners, and grid operator, where the EVs aim to minimize their charging costs and the charging station owners aim to maximize their profit. These are achieved additionally considering the V2G concept, which allows EVs to sell their excess electricity back to the grid, providing an additional source of revenue earning for the charging station owners. EV arrival times, charging requirement of EVs, and electricity prices are taken from a real time system. Stackelberg Game theoretic approach is employed to solve game-theoretic model. Based on the outcome of the results of Game Theory, a novel Nash bargaining-based optimization algorithm (NBOA), based on a hybrid of dynamic programming and reinforcement learning,) is used to determine the optimal charging schedules for the EVs. Proposed approach significantly reduces the charging costs for the EV owners and increase the profits for the charging station owners, while improving the overall efficiency of the electricity grid.


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    Title :

    Optimal Scheduling of Electric Vehicles: Leveraging Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Prices: A Game-Theoretic Approach


    Contributors:


    Publication date :

    2023-08-09


    Size :

    441658 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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