Data reconciliation and exergy analysis: Application in a compressed carbon dioxide energy storage system simulation test rig

Amidst the escalating climate crisis, the instability of the grid is caused by the integration of a large amount of renewable energy. For this problem, the compressed carbon dioxide energy storage (CCES) System is thought to be a useful remedy. This paper establishes a CCES system, achieving an RTE (round-trip efficiency) of 74.32 % under design conditions. Building on this, a CCES system simulation test rig is designed, equipped with sensors for temperature, pressure, mass flow, and power. Six sets of operating condition data are generated through simulation, and by employing an iterative data reconciliation method, the uncertainty of the primary flow, pressure, and temperature sensors have respectively decreased by an average of 4.07 percentage points, 0.53 percentage points, and 0.8 °C. Data reconciliation analysis is conducted on the other six sets of gross error operating condition data, which successfully identifies the sensors with gross error. Exergy analysis is then performed on both random and gross error operating condition data, to ascertain each component's exergy efficiency and exergy destruction. The data reconciliation method effectively calibrates the indicators of exergy analysis, reducing the average uncertainty by 22.15 percentage points. This paper demonstrates that data reconciliation is equally applicable to energy storage systems.