As the next generation of advanced adiabatic compressed air energy storage systems is being developed, designing a novel integrated system is essential for its successful adaptation in the various grid load demands. This study proposes a novel design framework for a hybrid energy system comprising a CAES system, gas turbine, and high-temperature solid oxide fuel cells, aiming for power generation and energy storage solutions. The overall model of the hybrid power generation system was constructed in Aspen Plus^TM, and the mass balance, energy balance, and thermodynamic properties of the thermal system were simulated and analyzed. The results demonstrate that the hybrid system utilizes the functional complementarity of CAES and solid oxide fuel cells (SOFCs), resulting in the cascade utilization of energy, a flexible operation mode, and increased efficiency. The overall round-trip efficiency of the system is 63%, and the overall exergy efficiency is 67%, with a design net power output of 12.5 MW. Additionally, thermodynamic analysis shows that it is advisable to operate the system under lower ambient temperatures of 25 °C, higher compressor and turbine isentropic efficiencies of 0.9, a higher fuel utilization of 0.62, and optimal SOFC/MGT split air flow rates of 1.1 kg/s. The results of this article provide guidance for designing innovative hybrid systems and system optimization.

Before carbon capture and storage technologies can truly be promoted and applied, and nuclear or renewable energy power generation can become predominant, it is important to further develop more efficient and ultra-low emission USC units on the basis of leveraging the strengths of CFB technology. In view of this complex system with strong nonlinearity such as the boiler-turbine unit of a thermal power unit, the establishment of a model that is suitable for control is indispensable for the operation and the economics of the process. In this study the form of the nonlinear model after linearization at the steady-state point has been fully considered and an improved subspace identification method, which is based on the steady-state point deviations data, was proposed in order to identify a piecewise affine model. In addition, the construction of the excitation signal in practical applications has been fully considered. The identification results demonstrate that this method has a better adaptability to strong nonlinear systems. The identification normalized root mean square errors of each working condition were almost all less than 10%. On this basis, a framework that is widely applicable to complex system control has been established by combining with the mixed logic dynamic (MLD) model. The canonical form realization was performed in order to transfer the local models into the same state basis. The predictive control was carried out on the boiler-turbine system of a 660-MW ultra-supercritical circulating fluidized bed unit that was based on the above framework. The results indicate that the predictive control performance is closely related to the setting value of the ramp rate and, therefore, prove the effectiveness of the framework.