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

Chathurangi Edussuriya (), Umar Marikkar, Subash Wickramasinghe, Upul Jayasinghe and Janaka Alawatugoda
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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

Energies, 2023, vol. 16, issue 5, 1-17

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.

Keywords: peer-to-peer energy trading; smart grid; blockchain; Stackelberg game; swarm intelligence; energy market (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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