Strategy for the Design and Operation Regulation of Compressed Gas Energy Storage System Based on a Comprehensive Comparison between Four Different Systems: Thermodynamic Analysis and Machine Learning

Abstract

This paper optimized the flow distribution of heat-exchanging CO2 in adsorption based compressed CO2 energy storage system (A-CCES). A comparative analysis among compressed air energy storage system (CAES), liquid compressed CO2 energy storage system (L-CCES) and A-CCES under similar design conditions was carried out. The round-trip efficiency (RTE) of A-CCES is 17.20 % higher than that of CAES reaching 87.99 %, and energy storage density (ESD) is 2.4 times that of CAES reaching 6.28 kW·h/m3. Based on the results of sensitive analysis, a machine learning study was conducted to quantify the impact weights of each parameter on system performance and to analyze the coupling effects of dual-parameter variations on system performance. The guiding suggestions for design and operation regulation of compressed gas energy storage system is provided. Increasing temperature of heat storage tank (HT) is the best way to improve RTE, and raising pressure in high-pressure tank (HPT) is the best way to enhance ESD. To balance both RTE and ESD, both HPT pressure and HT temperature should be increased to meet the high ESD requirements, additionally, more efficient turbine machinery will be needed to mitigate the negative impact of HPT pressure on RTE.

Keywords: Compressed gas energy storage, Thermodynamic simulation, Machine learning, Quantitative analysis