Distributed electric energy trading model and strategy analysis based on prospect theory

• Published in: International journal of electrical power & energy systems 2021-10, Vol.131, p.106865, Article 106865
• Main Authors: Yao, Yunting, Gao, Ciwei, Chen, Tao, Yang, Jianlin, Chen, Songsong
• Format: Article
• Language: English
• Subjects: Adaptive learning; Distributed trading; Prospect theory; Prosumer; Smart contract
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2021.106865

•Prosumers’ subjective preferences are precisely reflected based on prospect theory.•Optimal trading volume decision model with continuous double auction is constructed.•The adaptive learning strategy which considers prosumers’ subjectivity is adopted.•The social welfare and market efficiency are promoted by the proposed trading model. With the continuous increase of the penetration rate of renewable energy, the traditional centralized clearing mechanism is no longer suitable for the distributed energy trading market. Meanwhile, prosumers’ subjective preferences cannot be precisely reflected in the existing researches, which causes deviations from optimal multi-featured real-life human choices, thereby reducing social welfare and market efficiency considering subjectivity. To address these problems, this paper proposes a distributed trading strategy to determine optimal trading decisions for multi-featured prosumers based on prospect theory and adaptive learning process, and promote successful transactions between competitive market participants. Moreover, under the framework of the blockchain, a matching and settlement mechanism based on smart contract is proposed to achieve decentralized and efficient transactions in the distributed power market. Finally, the advantages of our proposed model are discussed by comparing with the zero intelligence method, and the feasibility and rationality of the proposed transaction model are verified on the Remix Ethereum platform.