Study on Parameter Correction and Estimation for a Supercritical Carbon Dioxide Energy Storage System through Data Reconciliation Method - You, Jiarui; Liu, Yikang; Xie, Yonghui; Zhang, Di (Journal of Environmental Accounting and Management, 2026)
As one of the most promising energy storage strategies, S-CO2 energy storage (SCES) system has gained rising concern of the researchers. However, there are no studies about the potential problems of the SCES system in practice, including correction of the measurements and estimation of unknown parameters. In this paper, a data reconciliation framework applicable for SCES systems is proposed, and evaluation of the effect on a SCES system model for gas turbine exhaust recovery is carried out. A preconditioning method for unknown parameters is developed to overcome the fallbacks of the existing QR decomposition algorithm. Computation is carried out on 2000 random conditions and 2000 gross error conditions, and almost all parameters have seen notable declines in overall errors comparing with the design values. After data reconciliation, the mean of root mean square error (RMSE) over all sensors decreases from 2.74% to 1.42% under random conditions, and the mean RMSE over the sensors with gross errors drops from 4.28% down to 1.88%. The estimations of unknown parameters are also extremely improved, where the maximum RMSE can even declines from 64.13% to only 2.89%. The results clearly indicated the great potential of applying data reconciliation method into the SCES test rig and demonstration project in the future.