在碳定价下,为风能集成发电系统的低碳优化,采用碳捕获和能源储存
Qinglin Meng1, Ying He2, Sheharyar Hussain3
1Green Power Research Institute, Tianjin Renai College, Tianjin, 301636, China. qinglin.meng@ieee.org.
Scientific reports
|September 24, 2025
概括
整合风力发电增强了可持续性,但也面临着稳定性挑战. 这项研究使用了碳捕获和能源储存的低碳模型,表明增加风力发电可以降低成本并改善电网稳定性.
科学领域:
- 能源系统工程 能源系统工程
- 环境科学 环境科学
- 计算智能是一种计算智能.
背景情况:
- 向低碳能源系统的过渡在很大程度上依赖于整合风力发电等可再生能源.
- 风力发电的间歇性和有限的输电能力对电力系统稳定性构成重大挑战.
- 燃烧后碳捕获技术为减少传统发电产生的排放提供了一条途径.
研究的目的:
- 开发和评估一个低碳的最佳调度模型,用于风力发电透率高的电力系统.
- 分析碳捕获价格对储能分配和发电成本的影响.
- 评估改进的遗传算法 (GA) 在优化电力系统时间表方面的有效性.
主要方法:
- 开发一个低碳的最佳调度模型,包括燃烧后的碳捕获和能源储存.
- 实施了改进的遗传算法 (GA),以有效优化调度问题.
- 模拟和分析不同风电容量和碳捕获价格的场景.
主要成果:
- 增加的风力发电容量使单位电力供应成本从0.202元人民币/千瓦时降至0.164元人民币/千瓦时.
- 能源储存的部署对于在风力发电透率高的情况下提高系统稳定性至关重要.
- 较高的碳捕获价格增加了发电成本,但显著减少了碳排放,并提供了容量和产量的具体例子.
结论:
- 拟议的模型和改进的GA有效地平衡了经济,环境和系统稳定目标.
- 储能在管理风力发电间歇性和提高电网可靠性方面发挥着至关重要的作用.
- 该研究为可持续的电力系统运行提供了强有力的战略,特别是在高度集成可再生能源的情况下.
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