Adaptive Intelligent Model Predictive Control for Microgrid Load Frequency

Dong Zhao (), Shuyan Sun, Ardashir Mohammadzadeh and Amir Mosavi ()
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Dong Zhao: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Shuyan Sun: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Ardashir Mohammadzadeh: Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang 110870, China
Amir Mosavi: Faculty of Civil Engineering, Technische Universität Dresden, 01067 Dresden, Germany

Sustainability, 2022, vol. 14, issue 18, 1-14

Abstract: 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.

Keywords: type-2 fuzzy; renewable energy; diesel; battery; frequency control; model predictive control; artificial intelligence; soft computing; predictive control; energy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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