压缩空气储能
总压比
高效能源利用
气体压缩机
储能
系统设计
可用能
压缩空气
工艺工程
火用
工艺设计
机械工程
工程类
汽车工程
功率(物理)
电气工程
过程集成
量子力学
物理
系统工程
作者
Lujing Huang,Heng Guo,Yujie Xu,Xuezhi Zhou,Haisheng Chen
标识
DOI:10.1016/j.est.2022.106181
摘要
Compressed air energy storage (CAES) systems often operate under off-design conditions on account of their own characteristics and application environment, and off-design conditions have a great impact on system performance. However, the effect of changing the design point on off-design performance and the effect of off-design performance on optimal system design point are not clear. Aiming at the above problems, the off-design model of CAES with thermal storage (TS-CAES) system under multiple design points is developed. The coupling characteristics of off-design operation and design point selection in the compression/expansion process and the overall system are respectively studied. The relationships between the power, process exergy efficiency and guide vane rotation angle with the mass flow rate ratio, the design pressure and the design/actual thermal storage temperature in key processes are revealed, and the energy storage efficiency of the whole system under different application scenarios and different design conditions is obtained. Results show that in the overall system, the closer the compressor design back pressure is to the final pressure of the air storage device, and the higher the expander inlet design pressure, the greater the system energy storage efficiency. The best case adopted in this paper can improve the system energy storage efficiency by 2.35–3.22 %. Meanwhile, in the compression process, with certain actual back pressure, the compressor working in the low mass flow rate ratio zone can obtain the highest exergy efficiency. In the expansion process, increasing the inlet design pressure and decreasing the inlet design temperature can increase the exergy efficiency, but as the inlet actual pressure increases, the effect of the temperature on the exergy efficiency is weakened.
科研通智能强力驱动
Strongly Powered by AbleSci AI