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Commitment-driven penalty mechanism with dynamic pricing for V2V energy trading: A multi-armed bandit reinforcement learning and game theoretic approach

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  • Arumugam, Rajapandiyan
  • Subbaiyan, Thangavel

Abstract

Electric vehicles (EVs) are rapidly emerging as the optimal alternative to internal combustion engine vehicles, offering a greener and more eco-friendly mode of transportation. While the rapid adoption of EVs poses significant challenges to the electrical grid, vehicle-to-vehicle (V2V) charging presents a feasible solution to alleviate these issues. A significant challenge in the widespread adoption of V2V energy trading is developing an effective V2V energy management framework that maximizes participant benefits. This paper introduces a penalty based holistic energy management framework for V2V energy trading, incorporating a multi-armed bandit reinforcement learning based dynamic pricing strategy to enhance financial benefits for both energy demanding and energy supplying EVs. The penalty mechanism addresses the issue of EVs refusing to charge or discharge based on the outcome of the V2V matching protocol. A real-world data is used in the simulation to offer a practical evaluation of the proposed approach. Simulation results validate the effectiveness of the proposed V2V energy management framework in enhancing EV utility and matching optimality. Comparisons between the approach with and without penalties show that achieving efficient, practical, and beneficial V2V matches is possible, which supports the potential adoption of this framework.

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  • Arumugam, Rajapandiyan & Subbaiyan, Thangavel, 2025. "Commitment-driven penalty mechanism with dynamic pricing for V2V energy trading: A multi-armed bandit reinforcement learning and game theoretic approach," Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225013842
    DOI: 10.1016/j.energy.2025.135742
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    References listed on IDEAS

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    1. Soares, João & Ghazvini, Mohammad Ali Fotouhi & Borges, Nuno & Vale, Zita, 2017. "Dynamic electricity pricing for electric vehicles using stochastic programming," Energy, Elsevier, vol. 122(C), pages 111-127.
    2. Chao Luo & Yih-Fang Huang & Vijay Gupta, 2018. "Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage," Papers 1801.02128, arXiv.org.
    3. Jiannan Ke & Dan Zhang & Huan Zheng, 2019. "An Approximate Dynamic Programming Approach to Dynamic Pricing for Network Revenue Management," Production and Operations Management, Production and Operations Management Society, vol. 28(11), pages 2719-2737, November.
    4. Liu, Shuohan & Cao, Yue & Ni, Qiang & Xu, Lexi & Zhu, Yongdong & Zhang, Xin, 2023. "Towards reservation-based E-mobility service via hybrid of V2V and G2V charging modes," Energy, Elsevier, vol. 268(C).
    5. Nguyen, Su & Peng, Wei & Sokolowski, Peter & Alahakoon, Damminda & Yu, Xinghuo, 2018. "Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading," Applied Energy, Elsevier, vol. 228(C), pages 2567-2580.
    6. Richard, René & Cao, Hung & Wachowicz, Monica, 2022. "EVStationSIM: An end-to-end platform to identify and interpret similar clustering patterns of EV charging stations across multiple time slices," Applied Energy, Elsevier, vol. 322(C).
    7. Hoang Huu Viet & Nguyen Thi Uyen & SeungGwan Lee & TaeChoong Chung & Le Hong Trang, 2021. "A max-conflicts based heuristic search for the stable marriage problem with ties and incomplete lists," Journal of Heuristics, Springer, vol. 27(3), pages 439-458, June.
    8. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    9. Tan, Bifei & Chen, Simin & Liang, Zipeng & Zheng, Xiaodong & Zhu, Yanjin & Chen, Haoyong, 2024. "An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles," Applied Energy, Elsevier, vol. 356(C).
    10. He, Hongwen & Wang, Yunlong & Han, Ruoyan & Han, Mo & Bai, Yunfei & Liu, Qingwu, 2021. "An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications," Energy, Elsevier, vol. 225(C).
    11. Hashemipour, Naser & Crespo del Granado, Pedro & Aghaei, Jamshid, 2021. "Dynamic allocation of peer-to-peer clusters in virtual local electricity markets: A marketplace for EV flexibility," Energy, Elsevier, vol. 236(C).
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