吸附式液态二氧化碳储能系统热经济性分析

翟璇; 张文挺; 王松; 周嘉; 尹素真; 刘展

doi:10.16516/j.ceec.2025-155

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吸附式液态二氧化碳储能系统热经济性分析

新能源与储能系统 | 更新时间：2026-10-14

- 吸附式液态二氧化碳储能系统热经济性分析
- Thermo-Economic Analysis of Adsorption-Based Liquid Carbon Dioxide Energy Storage System
- 南方能源建设 2026年13卷第4期页码：105-115
- 作者机构：
  
  1. 东方电气集团东方汽轮机有限公司,四川,德阳,618000
  2. 青岛科技大学机电工程学院,山东,青岛,266061
- 作者简介：
  
  翟璇，1988-，女，硕士，高级工程师，主要从事压缩气体储能技术研究工作（e-mail）zhaixuan@dongfang.com
  刘展，（e-mail）zhanliu168@qust.edu.cn
- 基金信息：
  
  山东省海洋工程装备及材料创新创业共同体技术攻关项目“船舶和海工装备超临界CO₂装备研制及示范应用”（20240734）
- DOI：10.16516/j.ceec.2025-155
  中图分类号： TK02;TK-9
- 纸质出版：2026
- 稿件说明：
移动端阅览
翟璇, 张文挺, 王松, 等. 吸附式液态二氧化碳储能系统热经济性分析[J]. 南方能源建设, 2026, 13(4): 105-115.

.
翟璇, 张文挺, 王松, 等. 吸附式液态二氧化碳储能系统热经济性分析[J]. 南方能源建设, 2026, 13(4): 105-115. DOI： 10.16516/j.ceec.2025-155.

. DOI： 10.16516/j.ceec.2025-155.

摘要

目的

可再生能源的间歇性对电网稳定运行构成严峻挑战，对大规模储能技术提出了更高要求。压缩二氧化碳储能技术是满足电网调节的有效解决方案之一。然而，低压侧二氧化碳的储存问题是制约该技术发展的关键瓶颈。

方法

文章提出了一种吸附式液态二氧化碳储能系统，通过构建热力学与经济性模型，系统分析了关键运行参数的优化路径。

结果

对于100 MW/600 MWh储能系统，储液罐温度与高压冷却器出口温度分别为26 ℃和46 ℃时，系统的度电成本达到最低值。系统的循环效率为70.06%，能量密度7.7 kWh/m

，储能度电成本为

0.8081

元/kWh，动态回收期为8.46年。

结论

敏感性分析表明，优化关键部件参数可显著提升系统综合性能，为大规模储能系统的灵活部署提供了理论支撑与技术依据。

Abstract

Objective

The intermittency of renewable energy severely challenges grid stability

increasing the demand for large-scale energy storage technologies. Compressed CO

energy storage emerges as a potential solution for grid peak shaving. However

storing low-pressure CO

is a critical bottleneck limiting the CCES development.

Method

This paper proposes an adsorption-based liquid CO

energy storage system. With the developed thermo-economic models

we systematically analyze the optimization of k

ey operating parameters.

Result

Results for an 100 MW/600 MWh system show that the system levelized cost of storage was minimized when the liquid tank and high-pressure cooler outlet temperatures were 26 ℃ and 46 ℃

respectively. The system can achieve a cycle efficiency of 70.06%

energy density of 7.7 kWh/m

levelized cost of storage of

0.8081

CNY/kWh and dynamic payback period of 8.46 years.

Conclusion

Sensitivity analysis indicates optimizing key component parameters can significantly improve system performance

providing essential theoretical and technical support for the flexible deployment of large-scale energy storage systems.

关键词

Keywords

references

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