Integrated simulation-based calibration and sensitivity analysis of a compressed air energy storage system

Wind energy systems show tremendous potential toward the reduction of greenhouse gas (GHG) emissions; however, the rate of generation of this mode of clean energy remains predominantly intermittent, since it is produced by constantly changing natural drivers, such as wind availability and wind velocity. In this work, a novel framework is proposed which combines a modular process simulator, and a Python environment, to calibrate the operation, and perform a sensitivity analysis of a compressed air energy storage system (CAES) system. Six operational variables are identified via various Monte-Carlo simulations, and a SOBOL analysis of the results highlight three key variables that significantly influence the two primary outputs of a CAES system: the LCOE and the exergy destroyed. Our results successfully identify two novel design metrics that can inform D-CAES design and optimization, for future simulation and experimental works targeted toward wind energy capture and storage.