IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i17p4245-d1463771.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/17/4245/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/17/4245/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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).
    4. 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).
    5. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhong, Meirui & Zhang, Rui & Ren, Xiaohang, 2023. "The time-varying effects of liquidity and market efficiency of the European Union carbon market: Evidence from the TVP-SVAR-SV approach," Energy Economics, Elsevier, vol. 123(C).
    2. Sabina Kordana-Obuch & Michał Wojtoń & Mariusz Starzec & Beata Piotrowska, 2023. "Opportunities and Challenges for Research on Heat Recovery from Wastewater: Bibliometric and Strategic Analyses," Energies, MDPI, vol. 16(17), pages 1-36, September.
    3. dos Santos Maciel, Leandro, 2023. "Brazilian stock-market efficiency before and after COVID-19: The roles of fractality and predictability," Global Finance Journal, Elsevier, vol. 58(C).
    4. Mensi, Walid & Al-Yahyaee, Khamis Hamed & Al-Jarrah, Idries Mohammad Wanas & Vo, Xuan Vinh & Kang, Sang Hoon, 2020. "Dynamic volatility transmission and portfolio management across major cryptocurrencies: Evidence from hourly data," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    5. Zeyi Fu & Hongli Niu & Weiqing Wang, 2023. "Market Efficiency and Cross-Correlations of Chinese New Energy Market with Other Assets: Evidence from Multifractality Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1287-1311, October.
    6. Ibrahim Mutambik, 2024. "Culturally Informed Technology: Assessing Its Importance in the Transition to Smart Sustainable Cities," Sustainability, MDPI, vol. 16(10), pages 1-21, May.
    7. Alan Chernoff & Julapa Jagtiani, 2024. "Beneath the Crypto Currents: The Hidden Effect of Crypto “Whales”," Working Papers 24-14, Federal Reserve Bank of Philadelphia.
    8. Alessio Brini & Jimmie Lenz, 2024. "A comparison of cryptocurrency volatility-benchmarking new and mature asset classes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-38, December.
    9. Maciel, Leandro, 2021. "A new approach to portfolio management in the Brazilian equity market: Does assets efficiency level improve performance?," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 38-56.
    10. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
    11. Naeem, Muhammad Abubakr & Bouri, Elie & Peng, Zhe & Shahzad, Syed Jawad Hussain & Vo, Xuan Vinh, 2021. "Asymmetric efficiency of cryptocurrencies during COVID19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    12. Zhang, Shulei & Jia, Runda & Pan, Hengxin & Cao, Yankai, 2023. "A safe reinforcement learning-based charging strategy for electric vehicles in residential microgrid," Applied Energy, Elsevier, vol. 348(C).
    13. Qin, Meng & Zhang, Xiaojing & Li, Yameng & Badarcea, Roxana Maria, 2023. "Blockchain market and green finance: The enablers of carbon neutrality in China," Energy Economics, Elsevier, vol. 118(C).
    14. Efstathios Polyzos & Ghulame Rubbaniy & Mieszko Mazur, 2024. "Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency," The Financial Review, Eastern Finance Association, vol. 59(3), pages 807-829, August.
    15. Cao, Guangxi & Xie, Wenhao, 2021. "The impact of the shutdown policy on the asymmetric interdependence structure and risk transmission of cryptocurrency and China’s financial market," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    16. George-Williams, H. & Wade, N. & Carpenter, R.N., 2022. "A probabilistic framework for the techno-economic assessment of smart energy hubs for electric vehicle charging," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    17. Chuming Pang & Yongkui Shi & Yang Liu, 2023. "Research on Sustainable Development of Mining Goaf Management Based on Economic Models," Sustainability, MDPI, vol. 15(20), pages 1-21, October.
    18. Chen, Minghao & Sun, Yi & Xie, Zhiyuan & Lin, Nvgui & Wu, Peng, 2023. "An efficient and privacy-preserving algorithm for multiple energy hubs scheduling with federated and matching deep reinforcement learning," Energy, Elsevier, vol. 284(C).
    19. Yao, Can-Zhong & Mo, Yi-Na & Zhang, Ze-Kun, 2021. "A study of the efficiency of the Chinese clean energy stock market and its correlation with the crude oil market based on an asymmetric multifractal scaling behavior analysis," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    20. Oliveira, Alexandre Silva de & Ceretta, Paulo Sergio & Albrecht, Peter, 2023. "Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios," Finance Research Letters, Elsevier, vol. 55(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4245-:d:1463771. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.