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A Blockchain and PKI-Based Secure Vehicle-to-Vehicle Energy-Trading Protocol

Author

Listed:
  • Md Sahabul Hossain

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA)

  • Craig Rodine

    (Sandia National Laboratories, Albuquerque, NM 87185, USA)

  • Eirini Eleni Tsiropoulou

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA)

Abstract

With the increasing awareness for sustainable future and green energy, the demand for electric vehicles (EVs) is growing rapidly, thus placing immense pressure on the energy grid. To alleviate this, local trading between EVs should be encouraged. In this paper, we propose a blockchain and public key infrastructure (PKI)-based secure vehicle-to-vehicle (V2V) energy-trading protocol. A permissioned blockchain utilizing the proof of authority (PoA) consensus and smart contracts is used to securely store data. Encrypted communication is ensured through transport layer security (TLS), with PKI managing the necessary digital certificates and keys. A multi-leader, multi-follower Stackelberg game-based trade algorithm is formulated to determine the optimal energy demands, supplies, and prices. Finally, we propose a detailed communication protocol that ties all the components together, enabling smooth interaction between them. Key findings, such as system behavior and performance, scalability of the trade algorithm and the blockchain, smart contract execution costs, etc., are presented through numerical results by implementing and simulating the protocol in various scenarios. This work not only enhances local energy trading among EVs, encouraging efficient energy usage and reducing burden on the power grid, but also paves a way for future research in sustainable energy management.

Suggested Citation

  • Md Sahabul Hossain & Craig Rodine & Eirini Eleni Tsiropoulou, 2024. "A Blockchain and PKI-Based Secure Vehicle-to-Vehicle Energy-Trading Protocol," Energies, MDPI, vol. 17(17), pages 1-52, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4245-:d:1463771
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    References listed on IDEAS

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    1. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Ko, Hee-Un & Yoon, Seong-Min & Kang, Sang Hoon, 2020. "Why cryptocurrency markets are inefficient: The impact of liquidity and volatility," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Kim, Myeonghyun & Lee, Joonyoung & Oh, Jihyeon & Park, Kisung & Park, Youngho & Park, Kilhoum, 2022. "Blockchain based energy trading scheme for vehicle-to-vehicle using decentralized identifiers," Applied Energy, Elsevier, vol. 322(C).
    3. Grzegorz Mentel & Anna Lewandowska & Justyna Berniak-Woźny & Waldemar Tarczyński, 2023. "Green and Renewable Energy Innovations: A Comprehensive Bibliometric Analysis," Energies, MDPI, vol. 16(3), pages 1-21, February.
    4. Lee, Sangyoon & Choi, Dae-Hyun, 2021. "Dynamic pricing and energy management for profit maximization in multiple smart electric vehicle charging stations: A privacy-preserving deep reinforcement learning approach," Applied Energy, Elsevier, vol. 304(C).
    5. Zhang, Chenxi & Yang, Yi & Wang, Yunqi & Qiu, Jing & Zhao, Junhua, 2024. "Auction-based peer-to-peer energy trading considering echelon utilization of retired electric vehicle second-life batteries," Applied Energy, Elsevier, vol. 358(C).
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