IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v255y2022ics0360544222013536.html
   My bibliography  Save this article

A blockchain-enabled trust aware energy trading framework using games theory and multi-agent system in smat grid

Author

Listed:
  • Zulfiqar, M.
  • Kamran, M.
  • Rasheed, M.B.

Abstract

Recently, multi-agent systems (MASs) have received attention due to the consideration of distributed optimization and control in blockchain (BC) based energy trading applications. However, due to the dynamic behavior of uncertain and random variables, the coordination and control of MAS are still challenging to attain resilience and dynamicity. Traditional trust systems, which rely on access control and cryptography, cannot deal with dynamic behavior of agents. Furthermore, they are inefficient in addressing the computational overhead of cryptographic primitives. To overcome these limitations, this work proposes a BC-based trusted suite (TS) for MAS to handle privacy and anonymity issues during energy trading in a smart grid (SG). In this work, three objectives are simultaneously achieved: trust, cooperation, and confidentiality. Firstly, the proposed trust system is employed to perform trust credibility of agents based on trust deformation, coherence, and stability. The credibility evaluation is used to determine the dynamic behavior of agents and to detect dishonest agents in the system. Secondly, a tri-tit-for-tat (TTFT) repeated game approach is used to improve the cooperation among agents. The proposed strategy is more forgiving than the existing Di-TFT (DTFT) and TFT techniques. It motivates scammers and deceptive agents to regain their trust by cooperating in three consecutive rounds of a game. Furthermore a proof-of-cooperation (PoC) consensus mechanism is introduced to facilitate agent cooperation in block creation and validation. Thirdly, the publicly verifiable secret sharing (PVSS) technique is introduced to preserve the privacy of the agents. Unlike VSS, PVSS provides the immunity against different types of security threats. Where, the dealer agent maintained the trust, while the verification of the dealer and combiner is maintained within agent-to-agent cooperation. Simulation results show that the devised BTS model is superior to the existing benchmark model such as fuzzy logic trust (FLT) in terms of detecting the cheating and deceptive behavior of agents in the system. Besides, the devised TTFT allows cheating agents to effectively regain the trust if they cooperate thrice in a row as compared to the existing DTFT and TFT strategies. Furthermore, This study analyzes two trust-related attacks: bad-mouthing and on-off. Analysis shows that the proposed system is protected from trust related attacks.

Suggested Citation

  • Zulfiqar, M. & Kamran, M. & Rasheed, M.B., 2022. "A blockchain-enabled trust aware energy trading framework using games theory and multi-agent system in smat grid," Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:energy:v:255:y:2022:i:c:s0360544222013536
    DOI: 10.1016/j.energy.2022.124450
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222013536
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124450?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cason, Timothy N. & Mui, Vai-Lam, 2019. "Individual versus group choices of repeated game strategies: A strategy method approach," Games and Economic Behavior, Elsevier, vol. 114(C), pages 128-145.
    2. Li, Hui-Jia & Wang, Qian & Liu, Shenfeng & Hu, Jun, 2020. "Exploring the trust management mechanism in self-organizing complex network based on game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    3. Wang, Haiyang & Zhang, Chenghui & Li, Ke & Ma, Xin, 2021. "Game theory-based multi-agent capacity optimization for integrated energy systems with compressed air energy storage," Energy, Elsevier, vol. 221(C).
    4. Xiong, Linyun & Li, Penghan & Wang, Ziqiang & Wang, Jie, 2020. "Multi-agent based multi objective renewable energy management for diversified community power consumers," Applied Energy, Elsevier, vol. 259(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adam Stecyk & Ireneusz Miciuła, 2023. "Empowering Sustainable Energy Solutions through Real-Time Data, Visualization, and Fuzzy Logic," Energies, MDPI, vol. 16(21), pages 1-13, November.
    2. Jing Yu & Jicheng Liu & Jiakang Sun & Mengyu Shi, 2023. "Evolutionary Game of Digital-Driven Photovoltaic–Storage–Use Value Chain Collaboration: A Value Intelligence Creation Perspective," Sustainability, MDPI, vol. 15(4), pages 1-30, February.

    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. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(C).
    2. Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.
    3. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    4. Tan, Charmaine H.Y., 2021. "The effects of group decision-making on social preferences: An experimental study," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 134-153.
    5. Pfeifer, Antun & Feijoo, Felipe & Duić, Neven, 2023. "Fast energy transition as a best strategy for all? The nash equilibrium of long-term energy planning strategies in coupled power markets," Energy, Elsevier, vol. 284(C).
    6. Julian Romero & Yaroslav Rosokha, 2023. "Mixed Strategies in the Indefinitely Repeated Prisoner's Dilemma," Econometrica, Econometric Society, vol. 91(6), pages 2295-2331, November.
    7. Cullen, Andrew C. & Alpcan, Tansu & Kalloniatis, Alexander C., 2022. "Adversarial decisions on complex dynamical systems using game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    8. Pinto, Giuseppe & Kathirgamanathan, Anjukan & Mangina, Eleni & Finn, Donal P. & Capozzoli, Alfonso, 2022. "Enhancing energy management in grid-interactive buildings: A comparison among cooperative and coordinated architectures," Applied Energy, Elsevier, vol. 310(C).
    9. Jafari, Hamed & Safarzadeh, Soroush & Azad-Farsani, Ehsan, 2022. "Effects of governmental policies on energy-efficiency improvement of hydrogen fuel cell cars: A game-theoretic approach," Energy, Elsevier, vol. 254(PC).
    10. Peng, Feixiang & Hu, Shubo & Fan, Xuanxuan & Sun, Hui & Zhou, Wei & Guo, Furan & Song, Wenzhuo, 2021. "Sequential coalition formation for wind-thermal combined bidding," Energy, Elsevier, vol. 236(C).
    11. Guo, Huan & Xu, Yujie & Huang, Lujing & Sun, Jianting & Chen, Haisheng, 2023. "Optimization strategy using corresponding-point methodology (CPM) concerning finite time and heat conduction rate for CAES systems," Energy, Elsevier, vol. 266(C).
    12. Vansh Vyas & Hyun-woo Jeon & Chao Wang, 2021. "An Integrated Energy Simulation Model of a Compressed Air System for Sustainable Manufacturing: A Time-Discretized Approach," Sustainability, MDPI, vol. 13(18), pages 1-28, September.
    13. Mohammed, Nooriya A. & Al-Bazi, Ammar, 2021. "Management of renewable energy production and distribution planning using agent-based modelling," Renewable Energy, Elsevier, vol. 164(C), pages 509-520.
    14. Deng, Yawen & Ng Tsan Sheng, Adam & Xu, Jiuping, 2023. "Authority-enterprise equilibrium based mixed subsidy mechanism for the value-added treatment of food waste," Energy, Elsevier, vol. 282(C).
    15. Edoardo Gallo & Yohanes E. Riyanto & Nilanjan Roy & Tat-How Teh, 2022. "Cooperation and punishment mechanisms in uncertain and dynamic networks," Papers 2203.04001, arXiv.org.
    16. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    17. Fischer, Robert & Toffolo, Andrea, 2022. "Is total system cost minimization fair to all the actors of an energy system? Not according to game theory," Energy, Elsevier, vol. 239(PC).
    18. Cason, Timothy N. & Lau, Sau-Him Paul & Mui, Vai-Lam, 2019. "Prior interaction, identity, and cooperation in the Inter-group Prisoner's Dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 166(C), pages 613-629.
    19. Gallo, Edoardo & Riyanto, Yohanes E. & Roy, Nilanjan & Teh, Tat-How, 2022. "Cooperation and punishment mechanisms in uncertain and dynamic social networks," Games and Economic Behavior, Elsevier, vol. 134(C), pages 75-103.
    20. Duan, Pengfei & Zhao, Bingxu & Zhang, Xinghui & Fen, Mengdan, 2023. "A day-ahead optimal operation strategy for integrated energy systems in multi-public buildings based on cooperative game," Energy, Elsevier, vol. 275(C).

    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:eee:energy:v:255:y:2022:i:c:s0360544222013536. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.