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Peer-to-Peer Energy Trading through Swarm Intelligent Stackelberg Game

Author

Listed:
  • Chathurangi Edussuriya

    (Department of Computer Science, Aalto University, 02150 Espoo, Finland)

  • Umar Marikkar

    (Department of Electrical and Electronic Engineering, Surrey University, Surrey GU2 7XH, UK)

  • Subash Wickramasinghe

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka)

  • Upul Jayasinghe

    (Department of Computer Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka)

  • Janaka Alawatugoda

    (Research & Innovation Centers Division, Faculty of Resilience, Rabdan Academy, Abu Dhabi P.O. Box 114646, United Arab Emirates
    Institute for Integrated and Intelligent Systems, Griffith University, Nathan, QLD 4111, Australia)

Abstract

The development of smart grids has paved the way for sustainable energy infrastructure to transition towards decentralized energy trading. As intelligent agents, energy sources engage in energy trading based on their energy surplus or deficit. Buyers and sellers (participants) should achieve maximum payoffs in which buyers cut costs and sellers improve their utilities, and the security of sensitive information of smart agents must be guaranteed. This paper provides a blockchain-based energy trading network where intelligent agents can exchange energy in a secure manner, without the intervention of third parties. We model energy trading as a Stackelberg game, ensuring that the platform maximizes social welfare while participants increase their payoffs. Using the inherited characteristics of blockchain technology, a novel decentralized swarm intelligence technique is applied to solve the game while ensuring the privacy of the smart agents’ sensitive information. The numerical analysis demonstrates that the suggested method outperforms the present methods (Constant Utility Optimization, average method...) for optimizing the objectives of each agent by maximizing the sellers’ utilities and reducing the buyers’ costs. In addition, the experimental results demonstrate that it significantly reduces carbon footprint (15%) by enhancing energy exchange between intelligent agents.

Suggested Citation

  • Chathurangi Edussuriya & Umar Marikkar & Subash Wickramasinghe & Upul Jayasinghe & Janaka Alawatugoda, 2023. "Peer-to-Peer Energy Trading through Swarm Intelligent Stackelberg Game," Energies, MDPI, vol. 16(5), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2434-:d:1087081
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    References listed on IDEAS

    as
    1. Esmat, Ayman & de Vos, Martijn & Ghiassi-Farrokhfal, Yashar & Palensky, Peter & Epema, Dick, 2021. "A novel decentralized platform for peer-to-peer energy trading market with blockchain technology," Applied Energy, Elsevier, vol. 282(PA).
    2. Ye-Byoul Son & Jong-Hyuk Im & Hee-Yong Kwon & Seong-Yun Jeon & Mun-Kyu Lee, 2020. "Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption," Energies, MDPI, vol. 13(6), pages 1-22, March.
    3. Soto, Esteban A. & Bosman, Lisa B. & Wollega, Ebisa & Leon-Salas, Walter D., 2021. "Peer-to-peer energy trading: A review of the literature," Applied Energy, Elsevier, vol. 283(C).
    4. Yatchew, Adonis & Baziliauskas, Andy, 2011. "Ontario feed-in-tariff programs," Energy Policy, Elsevier, vol. 39(7), pages 3885-3893, July.
    5. Colak, Ilhami & Fulli, Gianluca & Sagiroglu, Seref & Yesilbudak, Mehmet & Covrig, Catalin-Felix, 2015. "Smart grid projects in Europe: Current status, maturity and future scenarios," Applied Energy, Elsevier, vol. 152(C), pages 58-70.
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