Process system modelling of gas injection characteristics for underground CAES

• Published in: Deep Resources Engineering Vol. 2; no. 2; p. 100174
• Main Authors: Wang, LiGe, Lu, Cunzhuang, Zhang, Shishu, Peng, Di, Xiong, Qingrong, Chen, Xizhong, Ma, Hongling, Sun, Zizheng, Li, Liping
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2025
• Subjects: Compressed air energy storage (CAES); Process system modelling; Renewable energy; System integration; Underground cavern; Compressed air energy storage (CAES); Renewable energy; Underground cavern; Process system modelling; System integration
• ISSN: 2949-9305; 2949-9305
• DOI: 10.1016/j.deepre.2025.100174

Renewable energies including solar and wind are intermittent, causing difficulty in connection to conventional power grids due to instability of output duty. Compressed air energy storage (CAES) in underground caverns has been considered a potential large-scale energy storage technology. In order to explore the gas injection characteristic of underground cavern, a detailed thermodynamic model of the system is established in the process modelling software gPROMS. The four subsystem models, i.e. the compressor, heat exchanger, underground cavern storage and expander, are connected with inlet-outlet equilibrium of flow rate/pressure/temperature to form an integrated CAES system model in gPROMS. The maximum air pressure and temperature in the cavern are focused to interrogate the critical condition of the cavern during the injection process. When analyzing the mass flow rate-pressure ratio relationship, it's found that under specified operating conditions, an increase in mass flow rate can lead to a higher pressure ratio. Compression power demand also escalates significantly with increasing mass flow rates, underscoring the system’s energy-intensive nature. Additionally, the cooler outlet energy rate progressively decreases, becoming increasingly negative as the mass flow rate increases. These insights offer critical theoretical foundations for optimizing practical efficiency of CAES.