Model predictive power control of a heat pipe cooled reactor

Abstract

Heat pipe cooled reactor (HPR) has broad application prospects in deep space exploration, deep-sea submarine exploration, and other scenarios due to the small size, high inherent safety, and easy modularization and expansion. However, the HPR conducts thermal energy through evaporation and condensation of the working fluid inside the heat pipe. This feature makes the HPR a large time-delay system. If the power control system adopts the conventional PID algorithm, there will be a long settling time. Therefore, the model predictive control algorithm is proposed for the power control system to improve the control performance. The HPR linear model, which is developed by linearization of its nonlinear model, is chosen as the predictive model. The optimal control value is obtained by solving the optimization problem based on the predictive model and the electric power feedback value. The discrepancy between the predive model and the actual system response results in the presence of steady-state error. To solve this problem, an integral controller is added to eliminate the error. The appropriate control system parameters are tuned by the trial and error method. The proposed control system has satisfactory control performance, which can significantly shorten the settling time. The model predictive control can effectively overcome the influence of the large time-delay characteristic.

A decision framework for orderly power utilization based on a computationally enhanced algorithm

“…In order to obtain the OPU plan for the next day, the decision maker needs to forecast the load/generation information for an entire contract period (for example, 1 month or half a year) and finish OPU optimization. Take 1 month as an example, only the OPU plan of the second day will be adopted and other plans that are further away will be only used as a reference and will not be implemented, which is similar to the idea of the model predictive control method (Huang et al, 2023;Yin et al, 2023). Taking the entire contract cycle into account indicates dealing with the complexity of optimization and an increase in variables and the calculation cost, which also presents a challenge in OPU optimization.…”