含水层
地温梯度
压缩空气储能
石油工程
井口
地热能
地热能
环境科学
压缩空气
储能
环境工程
地质学
岩土工程
工程类
地下水
地球物理学
功率(物理)
机械工程
量子力学
物理
作者
Yang Li,Ruikang Sun,Yang Li,Bin Hu,Jiawei Dong
标识
DOI:10.1016/j.est.2021.103483
摘要
Compressed air energy storage in aquifers (CAESA) can be a widespread low-cost application in large-scale energy storage technology that balances the power system generated by wind and solar energy. In the underground part of CAESA, a favorable deep saline aquifer with suitable permeability and porosity is utilized as the compressed air storage space, and the wellbore acts as a channel for cyclic injection and production. However, the geothermal energy corresponding to geothermal temperature distribution is commonly non-negligible in deep underground engineering. Aiming at enhancing the role understanding of geothermal energy on CAESA, the wellbore-aquifer coupled models for the entire underground processes substantiated with evidence of different geothermal gradients are developed and simulated. The air trends accumulate upwards on the top of the aquifer and move further away from the wellbore under higher geothermal temperature conditions due to the air expanding and a lighter density. And the better cushion effect and pressure support result in the small pressure fluctuation upon the air injection and production in the daily cycle. Comparative roles of geothermal energy on pressure and air distribution, the rising air production temperature heating by a high-temperature aquifer is more pronounced. In addition, the energy performance results show that the geothermal energy supplement is remarkable, even result in energy recovery from wellhead larger than the injection energy. With different geothermal temperature distributions, the energy efficiency difference in underground processes can reach 15%. The results enhance the understanding of underground processes impacted by geothermal temperature distribution. It can also help to correct the geothermal energy resources position in the site selection and provide a coupling system.
科研通智能强力驱动
Strongly Powered by AbleSci AI