考虑分布式光伏协调碳减排的多能源系统的能源优化模型
Shi Qiu1, Shuo Liu1, Guoqiang Lu1,2
1Shenyang University of Technology, Shenyang, 110870, China.
Scientific reports
|July 2, 2025
概括
本研究提出了一种用于多能源系统 (MES) 的能源优化方法,以减少碳排放. 深度强化学习方法有效地降低了运营成本,并最大限度地减少了热电厂的碳排放.
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:
- 热发电厂面临着复杂的能源优化挑战,原因是非线性,多变量相互作用和多能源系统 (MES) 内的强合.
- 减少碳排放的限制进一步增加了复杂性,特别是在各种设备和大型运营数据集的情况下.
- 整合像光伏这样的分布式能源需要复杂的协调策略.
研究的目的:
- 为多能源系统 (MES) 开发一种先进的能源优化方法,其中包括分布式光伏协调,以提高碳减排.
- 根据碳减排任务,解决MES的复杂特征,包括非线性,多变量相互作用和强联接.
- 尽量减少运营成本,同时最大限度地减少热电厂运营中的碳排放.
主要方法:
- 建立MES的能源特征模型,分析能源输入,输出和转换过程.
- 通过分析MES内的燃料,热量和功率平衡来开发碳排放强度模型.
- 应用深度强化学习算法来解决一个专注于降低成本和减轻碳排放的多目标优化模型.
主要成果:
- 基于来自中国东北部发电厂的真实运行数据,建立了MES的模拟模型.
- 提出的能源优化方法在协调分布式光伏以减少碳排放方面表现出有效性.
- 这种方法成功地平衡了降低运营成本和最大限度地减少碳排放的目标.
结论:
- 开发的能源优化方法对热电厂中复杂的多能源系统有效.
- 分布式光伏集成有助于协调碳减排战略.
- 深度强化学习为优化在环境约束下MES操作提供了可行的解决方案.
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