Enhancing renewable energy certificate transactions through reinforcement learning and smart contracts integration

• Published in: Scientific reports Vol. 14; no. 1; p. 10838
• Main Authors: He, Qingsu, Wang, Jingsong, Shi, Ruijie, He, Yifan, Wu, Muqing
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.05.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/4077; 639/4077/909; Carbon; Carbon asset; Efficiency; Electricity; Green certificate; Green energy; Humanities and Social Sciences; Learning; multidisciplinary; Nash Q-learning; Reinforcement; Renewable energy; Science; Science (multidisciplinary); Smartcontract; China; Green certificate; Carbon asset; Smartcontract; Nash Q-learning; Renewable energy
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-60527-3

Given the complexity of issuing, verifying, and trading green power certificates in China, along with the challenges posed by policy changes, ensuring that China’s green certificate market trading system receives proper mechanisms and technical support is crucial. This study presents a green power certificate trading (GC-TS) architecture based on an equilibrium strategy, which enhances the quoting efficiency and multi-party collaboration capability of green certificate trading by introducing Q-learning, smart contracts, and effectively integrating a multi-agent trading Nash strategy. Firstly, we integrate green certificate trading with electricity and carbon asset trading, constructing pricing strategies for the green certificate, carbon, and electricity trading markets; secondly, we design a certificate-electricity-carbon efficiency model based on ensuring the consistency of green certificates, green electricity, and carbon markets; then, to achieve diversified green certificate trading, we establish a multi-agent reinforcement learning game equilibrium model. Additionally, we propose an integrated Nash Q-learning offer with a smart contract dynamic trading joint clearing mechanism. Experiments show that trading prices have increased by 20%, and the transaction success rate by 30 times, with an analysis of trading performance from groups of 3, 5, 7, and 9 trading agents exhibiting high consistency and redundancy. Compared with models integrating smart contracts, it possesses a higher convergence efficiency of trading quotes.