Data-Driven Economic Predictive Control for Coordinated Control System of Coal-fired Units

Coordinated control of coal-fired units is a key system to ensure the stability and economic operation of power supply. While the nonlinearity, strong coupling, and large inertia of coal-fired units bring about challenge for the model establishment resulting in unsatisfied control performance due to insufficient model accuracy. To address this issue, a novel data-driven economic predictive control method is proposed in this study. It utilizes a real-time updated dataset to affine the non-linear system as linear and predict the future trajectory of the system. In order to deal with the computational complexity caused by real-time updating of data sets and improve the computational efficiency, a two-stage algorithm is designed. The objective function of the first stage is replaced by an economic objective with real-time feedback instead of the original tracking objective, and the optimal solution is passed to the second stage, where it serves as a reference for tracking the economically optimal solution. Based on real data simulation, it is found that the proposed method efficiently responds to set point change as well as exhibits a small tracking error. It is also helpful for operating costs reduction, significantly improving the economic efficiency of the entire system.