Distributed Reputation-Distance iterative auction system for Peer-To-Peer power trading

Juan Wang, Junjun Zheng, Liukai Yu, Mark Goh, Yunying Tang and Yongchao Huang

Applied Energy, 2023, vol. 345, issue C, No S0306261923006700

Abstract: Peer-to-peer energy trading is a promising approach for decentralized electricity markets to achieve the decarbonization of power. In the trading process, the misbehavior of contracted users and power loss in network delivery can lead to transaction failure and higher delivery costs. This paper presents a distributed reputation-distance-driven iterative auction mechanism to promote energy trading among users who commit contracts well and among electrically closer peers. A reputation-distance index is built based on the historical trading performance and the electricity distance between users. The index is introduced into the auction matching process. Then, a self-adaption algorithm based on game theory is used to model the iterative auction to arrive at a Nash equilibrium. This paper also extends the combined factors framework to improve the flexibility and extensibility of the mechanism. A case study validates that the proposed decentralized mechanism can reduce network loss and peak loads, ensure power reliability, achieve high market efficiency, social welfare. Specifically, compared with the traditional model without considering reputation and distance, the proposed decentralized mechanism can reduce network loss by 50%, increase 7.1% the market share of users with good reputations, improve market efficiency by 10%, and increase social welfare by 7.69%.

Keywords: P2P energy trading; Reputation-distance index; Game theory; Iterative auction (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2023.121306

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