State Estimation and Linear Discrete Model Predictive Control for the Hydration Process in a Lime-Based Thermochemical Energy Storage System

Thermochemical energy storage based on the reversible reaction of CaO and H2O to Ca(OH)2 is a promising solution for sustainable storage of excessive renewable energy. For efficient and safe discharge of the system (hydration of CaO), a control strategy is necessary. Starting from a nonlinear dynamic model, the system is linearized and discretized to enable the design of a Kalman filter for state estimation and a Model Predictive Controller (MPC). Simulation results demonstrate that the Kalman filter provides accurate state re- construction and effectively filters measurement noise from the system outputs. Four different MPC objectives, targeting key temperatures and thermal power, are evaluated. All variants show good tracking performance and are suitable for real- time application. A comparison between the linear discrete- time MPC with state estimation and a nonlinear continuous- time MPC with full state information reveals no significant performance loss, while achieving a reduction in computation time.