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Enhancing renewable energy certificate transactions through reinforcement learning and smart contracts integration
Qingsu He1,2, Jinsong Wang3, Ruijie Shi4
1State Grid Digital Technology Holdings Co., Ltd. (State Grid Xiongan Financial Technology Group Co., Ltd.), Beijing, 100010, China. heqingsu@bupt.edu.cn.
Scientific Reports
|May 12, 2024
Summary
China's complex green certificate market needs better mechanisms. This study introduces a green power certificate trading (GC-TS) architecture using Q-learning and smart contracts to improve efficiency and collaboration, boosting trading success.
Area of Science:
- Energy Economics
- Market Design
- Computational Intelligence
Background:
- China's green power certificate market faces challenges in issuance, verification, and trading due to policy changes.
- Effective mechanisms and technical support are crucial for market functionality and stability.
Purpose of the Study:
- To develop an enhanced green power certificate trading (GC-TS) architecture.
- To improve quoting efficiency and multi-party collaboration in green certificate trading.
- To integrate green certificate trading with electricity and carbon asset markets.
Main Methods:
- Integration of Q-learning, smart contracts, and a multi-agent trading Nash strategy.
- Construction of pricing strategies for green certificate, carbon, and electricity markets.
- Development of a certificate-electricity-carbon efficiency model.
- Establishment of a multi-agent reinforcement learning game equilibrium model.
- Proposal of an integrated Nash Q-learning offer with a smart contract dynamic trading joint clearing mechanism.
Main Results:
- Trading prices increased by 20% and transaction success rate by 30 times.
- Analysis of trading performance across different numbers of agents (3, 5, 7, 9) showed high consistency and redundancy.
- The proposed model demonstrated higher convergence efficiency for trading quotes compared to models solely integrating smart contracts.
Conclusions:
- The proposed GC-TS architecture effectively addresses market complexities and policy challenges.
- The integration of advanced computational strategies enhances trading efficiency and collaboration.
- The system shows significant improvements in trading volume and success rates, offering a robust solution for green certificate markets.
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