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This paper addresses electrical frequency management within a Microgrid (MG) comprising various renewable energy sources (RES) like photovoltaic (PV) and wind (WTG) energy, along with battery storage systems (a fuel cell (FC), two battery energy storage systems (BESS), a flywheel energy storage system (FESS), and an aqua electrolyze (AE) and a diesel engine generator (DEG) serving as a backup source in case of battery failure. The primary objective is to mitigate and improve the stability of the electrical frequency caused by unpredictable fluctuations in power production due to abrupt climate changes. To achieve this, we employed a proportional-integral-derivative (PID) controller. For efficient adjustment of the PID controller parameters, optimization algorithms are necessary. In this study, we utilized the Ziegler-Nichols method. Furthermore, to validate the efficacy of this method in addressing the issue, we conducted a comparative analysis between the results obtained from the Ziegler-Nichols method and the genetic algorithm. Our simulations in MATLAB/Simulink demonstrated that the genetic algorithm surpassed maximum deviation values.

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Frequency Control in Microgrid Isolated System Using PID Controller Using Ziegler Nichols Method and Genetic Algorithm

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  • 发表日期：

    2025年

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  • Benali Alouache,
  • M’hamed Helaimi,
  • Abdelkadir Belhadj Djilali,
  • Adil Yahdou

摘要

This paper addresses electrical frequency management within a Microgrid (MG) comprising various renewable energy sources (RES) like photovoltaic (PV) and wind (WTG) energy, along with battery storage systems (a fuel cell (FC), two battery energy storage systems (BESS), a flywheel energy storage system (FESS), and an aqua electrolyze (AE) and a diesel engine generator (DEG) serving as a backup source in case of battery failure. The primary objective is to mitigate and improve the stability of the electrical frequency caused by unpredictable fluctuations in power production due to abrupt climate changes. To achieve this, we employed a proportional-integral-derivative (PID) controller. For efficient adjustment of the PID controller parameters, optimization algorithms are necessary. In this study, we utilized the Ziegler-Nichols method. Furthermore, to validate the efficacy of this method in addressing the issue, we conducted a comparative analysis between the results obtained from the Ziegler-Nichols method and the genetic algorithm. Our simulations in MATLAB/Simulink demonstrated that the genetic algorithm surpassed maximum deviation values.