Transient Analysis and Execution-Level Power Tracking Control of the Concentrating Solar Thermal Power Plant

Abstract

Concentrating solar power (CSP) is a promising technology for exploiting solar energy. A major advantage of CSP plants lies in their capability of integrating with thermal energy storage; hence, they can have a similar operability to that of fossil-fired power plants, i.e., their power output can be adjusted as required. For this reason, the power output of such CSP plants is generally scheduled to maximize the operating revenue by participating in electric markets, which can result in frequent changes in the power reference signal and introduces challenges to real-time power tracking. To address this issue, this paper systematically studies the execution-level power tracking control strategy of an CSP plant, primarily aiming at coordinating the control of the sluggish steam generator (including the economizer, the boiler, and the superheater) and the fast steam turbine. The governing equations of the key energy conversion processes in the CSP plant are first presented and used as the simulation platform. Then, the transient behavior of the CSP plant is analyzed to gain an insight into the system dynamic characteristics and control difficulties. Then, based on the step-response data, the transfer functions of the CSP plant are identified, which form the prediction model of the model predictive controller. Finally, two control strategies are studied through simulation experiments: (1) the heuristic PI control with two operation modes, which can be conveniently implemented but cannot coordinate the control of the power tracking speed and the main steam parameters, and (2) advanced model predictive control (MPC), which overcomes the shortcoming of PI (Proportional-Integral) control and can significantly improve the control performance.

Design of an energy management system for microgrids applied to an isolated system incorporating solar thermal energies

“…10. and generate a set of 27 rules to be considered in the "Fuzzy Logic Smart Controller (FLSC)" [23].…”

Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control

“…Once the turbine has been correctly started, the management of the variability of the inlet steam conditions and the tracking of the power setpoint are often performed through PI control techniques, fuzzy PID [24], robust control [25], and in some cases through MPC techniques [26].…”