Model Predictive Control of a Solar Power System with Microturbine and Thermochemical Energy Storage

Abstract

This paper presents the model predictive control (MPC) application on the solar power system with microturbine and thermochemical energy storage (TCES). To investigate the potential of a solar-powered turbine, a solar receiver and a TCES are introduced to the Brayton cycle as the replacement of the combustor. MPC is applied to offer the constrained multi-variable real-time optimization control. To increase the practicability in the solar industry, a custom-made multi-modeling approach is proposed based on the close relationship between direct normal irradiance and system states. Feedback correction mechanisms are designed to improve the prediction and target tracking accuracies. During the regulatory control under both extreme and realistic conditions, the multi-MPC (MMPC) shows stronger adaptability and reliability than proportional–integration–differentiation (PID) and higher tracking accuracies than the single-linear-model-based MPC. Although the control performance could be further improved by employing nonlinear MPC (NMPC), the much longer optimization time of NMPC was unsuitable for real-time control. MMPC is further adapted to track the grid demand, which is technically unachievable by PID in the current system. While the output power precisely follows the demand, the performance parameters can still stay close to their design values, retaining a high system efficiency. Overall, the proposed MMPC enables power demand tracking operation of solar air turbine systems, and can ensure high stability and system efficiency.