压缩空气储能
热能储存
熔盐
绝热过程
储能
工艺工程
可再生能源
材料科学
计算机数据存储
核工程
环境科学
功率(物理)
能量回收
能量(信号处理)
绝热壁
高效能源利用
计算机科学
可用能
平滑的
热能
机械工程
热的
节能
按来源划分的电力成本
热交换器
分类
投资回收期
汽车工程
压缩空气
热效率
工作(物理)
发电站
网格
能量转换
系统设计
遗传算法
作者
Yongkang Fan,Yue Cao,Tianyu He,Fengqi Si
标识
DOI:10.1016/j.csite.2025.107486
摘要
The integration of renewable energy sources poses challenges to the stability of the power grid. Compressed air energy storage (CAES) is one of the effective technologies for smoothing grid fluctuations, as it can store energy during low-power periods and release it when needed. This study proposes a large-scale high-temperature adiabatic CAES (HTA-CAES) system integrated with a cascaded molten salt heat recovery thermal energy storage (TES) system, and analyzes the thermodynamic and techno-economic performance of the proposed system. The feasibility of the system and its superiority over traditional systems were evaluated through thermo-economic analysis, and the effects of design conditions such as compression ratio distribution and expansion ratio distribution on system performance were investigated. A multi-objective optimization was performed using the non-dominated sorting genetic algorithm III (NSGA-III) to obtain the optimal solution balancing system round-trip efficiency (RTE) and levelized cost of energy (LCOE). Results show that the HTA-CAES system exhibits better performance than traditional systems, with an RTE of 71.56 % under design conditions. The multi-objective optimization considering both RTE and LCOE highlights significant improvements in system performance: RTE, LCOE, energy storage density, and dynamic payback period are enhanced to 72.21 %, 6.79 × 10 −2 $/kWh, 4.97 kWh/m 3 , and 7.55 years, respectively. • A high-temperature CAES system coupled with cascaded molten salt TES was proposed. • Selecting Molten salt and pressurized water to expand the system temperature range. • Heat exchanger effectiveness exceeding 78.6 % enabled adiabatic operation. • Unequal compression/expansion ratios could improve energy and economic efficiency. • Multi-objective optimization enhanced RTE and LCOE to 72.21 % and 6.79 × 10 −2 $/kWh.
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