Solid-oxide fuel cell (SOFC) power plant plays a vital role in a hybrid alternative energy based microgrid due to its reliability and flexibility in power supply. However, the control of SOFC is challenging in terms of providing a fast load tracking while maintaining the fuel utilization rate within a safe range. To this end, this paper builds two basic coordinated control strategies (CCS) for power management of SOFC-based microgrid. The first is “Fuel Cell follows Inverter” scheme, where fast tracking is preferred while SOFC may operate on the edge of the safety range. The second is “Inverter follows Fuel Cell” scheme, where high security is guaranteed by sacrificing performance in load tracking. To obtain a robust and simple scheme, energy balance principle is used in CCS such that PI controller is sufficient to fulfill the basic duties. Moreover, a simple supervisory control strategy is proposed for microgrid to provide a reasonable power reference for SOFC. The control system is designed and tuned based on an SOFC plant with inverter's average model. The efficiency of the proposed strategies is validated via a grid-connected SOFC/photovoltaic microgrid.

This paper develops a novel data-driven modeling strategy and predictive controller for boiler-turbine unit using subspace identification and multimodel method. To deal with the nonlinear behavior of boiler-turbine unit, the system is divided into a number of local regions following the analysis of the nonlinearity distribution along the operation range, and then the corresponding measurement data are organized to identify the local models through the subspace method. By transforming local models into the same basis, the resulting multimodel system (MMS) is shown to represent the boiler-turbine unit very closely, and thus, used in designing a multimodel-based model predictive control (MMPC). As an alternative approach, a data-driven direct predictive controller (DDPC) is developed by utilizing the intermediate subspace matrices as local predictors. Online update of the predictor is also implemented on the multimodel structure to make the controller responsive to plant behavior variations. Simulation results demonstrate the feasibility and effectiveness of the proposed approach.