Min-max Operation Optimization of Renewable Energy Combined Cooling, Heating, and Power Systems Based on Model Predictive Control

被引：2

作者：

Dong, Xing ^{[1
]}

Lu, Jianbo ^{[1
]}

Sun, Bo ^{[1
]}

机构：

[1] Shangdong Univ, Sch Control Sci & Engn, Jinan, Peoples R China

来源：

IFAC PAPERSONLINE | 2020年 / 53卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Renewable energy CCHP systems; Prediction error; Min-max optimization; Model predictive control; MICRO-CHP;

D O I：

10.1016/j.ifacol.2020.12.1956

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The renewable energy combined cooling, heating, and power (CCHP) systems can effectively utilize the residual heat generated by the system to provide thermal energy or cooling energy for users, which can highly improve the utilization efficiency of primary energy. However, the prediction error of renewable energy sources (RES) and load can affect the optimal operation of the system. This paper considers the prediction error of RES and load. According to the dynamic characteristics of energy storage unit, an energy optimization model with prediction error is proposed. On this basis, a min-max optimization operation strategy based on model predictive control (MPC) is proposed. The operating cost of the system is taken as the objective function. By optimizing the output of each device in the system, the operating cost of the system is minimized under the maximum prediction error. Copyright (C) 2020 The Authors.

引用

页码：12809 / 12814

页数：6

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