针对分布式级岛屿集成能源系统的智能调度,考虑多种能源利用和激励-惩罚阶段碳交易机制
J H Zheng1,2, N L Hu1, S C Yao1,2
1School of Electric Power Engineering, South China University of Technology, Guangzhou, 510641, China.
Scientific reports
|October 29, 2025
概括
本研究介绍了多能源利用模块 (MEUM) 和岛屿综合能源系统 (IES) 的激励-罚款级碳交易机制 (IPSCTM). 无模型的深度强化学习 (DRL) 方法可显著降低运营成本和碳排放.
科学领域:
- 能源系统工程 能源系统工程
- 环境科学 环境科学
- 人工智能的人工智能
背景情况:
- 由于地理限制,岛屿地区面临资源短缺和高碳排放.
- 分布式的集成能源系统 (IES) 需要高效的资源管理和减排战略.
- 传统的优化方法与IES调度的复杂性和不确定性作斗争.
研究的目的:
- 提出一个多能源利用模块 (MEUM),以提高岛屿IES的资源效率.
- 为减少碳排放引入激励-罚款阶段化碳交易机制 (IPSCTM).
- 开发一种无模型的深度强化学习 (DRL) 方法,以优化岛屿IES调度.
主要方法:
- 实施多种能源利用模块 (MEUM) 用于二次资源回收.
- 整合激励-罚款阶段碳交易机制 (IPSCTM),以鼓励参与碳交易.
- 应用一个无模型的深度强化学习 (DRL) 算法来解决复杂的调度问题.
主要成果:
- 拟议的MEUM有效地回收和利用二次资源,产生经济效益.
- IPSCTM成功地鼓励减少碳排放,并加强参与碳交易.
- 在中国的一个岛屿上进行的模拟显示了运营成本和碳排放的大幅降低.
结论:
- 开发的岛屿IES模型与MEUM和IPSCTM在改善资源利用和减少环境影响方面是有效的.
- 无模型的DRL方法为复杂的IES调度问题提供了强大而高效的解决方案.
- 拟议的战略为偏远地区和岛屿地区的可持续能源管理提供了可行的途径.
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