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.

I. Introduction

Coal-fired unit is a complex system, which needs the coordination of the regulating mechanism of boiler and steam turbine to ensure the safety of steam pressure in front of steam turbine and respond to the load change of power grid efficiently. The coordinated control system (CCS) of coal-fired units integrates the independent control systems of steam turbines and boilers, forming an overall control system with comprehensive functions and remarkable effects, which plays a leading role in the automatic control system of coal-fired power plants. With the improvement of power grid automation, the requirements for CCS control quality are also increasing.