通过强化学习和智能合约集成,加强可再生能源证书交易
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
概括
中国复杂的绿色证书市场需要更好的机制. 本研究介绍了一种使用Q学习和智能合约的绿色电力证书交易 (GC-TS) 架构,以提高效率和协作,促进交易成功.
科学领域:
- 能源经济学 能源经济学
- 市场设计市场设计
- 计算智能是一种计算智能.
背景情况:
- 由于政策变化,中国的绿色电力证书市场在发行,核查和交易方面面临挑战.
- 有效的机制和技术支持对于市场的功能和稳定至关重要.
研究的目的:
- 开发一个增强的绿色电力证书交易 (GC-TS) 架构.
- 提高绿色证书交易的报价效率和多方合作.
- 将绿色证书交易与电力和碳资产市场整合起来.
主要方法:
- 整合Q学习,智能合约和多代理交易纳什策略.
- 建设绿色证书,碳和电力市场的定价策略.
- 开发一个证书-电力-碳效率模型.
- 建立一个多代理强化学习游戏平衡模型.
- 关于一个集成的Nash Q学习产品的建议,其中包括一个智能合约动态交易联合清算机制.
主要成果:
- 交易价格上了20%,交易成功率增加了30倍.
- 对不同数量的代理商 (3,5,7,9) 的交易绩效分析显示出高的一致性和冗余性.
- 与仅集成智能合约的模型相比,拟议的模型在交易报价方面表现出更高的趋同效率.
结论:
- 拟议的GC-TS架构有效地解决了市场复杂性和政策挑战.
- 集成先进的计算策略可以提高交易效率和协作.
- 该系统在交易量和成功率上显著改善,为绿色证书市场提供了强大的解决方案.
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