Highly accurate and fast power tracking for integrated SOFC-MGT CCHP system using wide-dynamic-range models and MPC framework

Solid Oxide Fuel Cell (SOFC) based cogeneration systems are highly promising nowadays. However, challenges like fluctuations in renewable energy, uncertainties in user demand, and differences in the dynamic characteristics of system components often lead to failure of scheduling plans. To address this issue, this paper constructs a novel high-order dynamic scheduling model for a SOFC-based combined cooling, heating, and power (CCHP) system. Using parameter identification, high-order transfer functions are developed to precisely capture dynamic characteristics with large delays and inertia, and new wide-dynamic-range models are developed to resolve the diversity of system dynamic characteristics. Building on this, a dynamic scheduling and control optimization strategy based on Model Predictive Control (MPC) framework is proposed. By employing two-stage scheduling optimization and multivariable predictive controller, the approach effectively mitigates external disturbances caused by uncertainties in renewable energy and load demand, reduces system output response time, and ensures accuracy and efficiency of scheduling plan execution. Simulation results show the proposed method significantly reduces the risk of scheduling strategy failure to zero compared to traditional steady-state and low-order dynamic scheduling methods. Controller application realizes fast load power tracking, reducing output deviations of electric power by 87.29 %, heating power by 98.51 %, and cooling power by 75.78 %.