Adaptive Intelligent Model Predictive Control for Microgrid Load Frequency

被引：3

作者：

Zhao, Dong ^{[1
]}

Sun, Shuyan ^{[1
]}

Mohammadzadeh, Ardashir ^{[2
]}

Mosavi, Amir ^{[3
,4
,5
]}

机构：

[1] North China Elect Power Univ, Sch Elect & Elect Engn, Beijing 102206, Peoples R China

[2] Shenyang Univ Technol, Multidisciplinary Ctr Infrastruct Engn, Shenyang 110870, Peoples R China

[3] Tech Univ Dresden, Fac Civil Engn, D-01067 Dresden, Germany

[4] Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary

[5] Slovak Univ Technol Bratislava, Inst Informat Engn Automat & Math, Bratislava 81237, Slovakia

来源：

SUSTAINABILITY | 2022年 / 14卷 / 18期

关键词：

type-2; fuzzy; renewable energy; diesel; battery; frequency control; model predictive control; artificial intelligence; soft computing; predictive control; energy; FUZZY-LOGIC; SYSTEM; PV;

D O I：

10.3390/su141811772

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In this paper, self-tuning model predictive control (MPC) based on a type-2 fuzzy system for microgrid frequency is presented. The type-2 fuzzy system calculates the parameters and coefficients of the control system online. In the microgrid examined, there are sources of photovoltaic power generation, wind, diesel, fuel cells (with a hydrogen electrolyzer), batteries and flywheels. In simulating the load changes, changes in the production capacity of solar and wind resources as well as changes (uncertainty) in all parameters of the microgrid are considered. The performances of three control systems including traditional MPC, self-tuning MPC based on a type-1 fuzzy system and self-tuning MPC based on a type-2 fuzzy system are compared. The results show that type-2 fuzzy MPC has the best performance, followed by type-1 fuzzy MPC, with a slight difference between the two results.

引用

页数：14

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