Gaber Magdy

Aswan University, Faculty of Energy Engineering, Electrical Engineering Department, Faculty Member

Aswan University, Electrical Engineering, Faculty Member

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Gaber Magdy was born in Qena, Egypt, in 1989. He received the B.Sc. and M.Sc. (Hons.) degrees in electrical engineering from the Faculty of Energy Engineering, Aswan University, Egypt, in 2011 and 2014, respectively, and the PhD degree in electrical engineering from the Faculty of Engineering, Minia University, El-Minia, Egypt in 2019. Since 2012, he joined the Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan, Egypt, first as a Demonstrator (Assistant Researcher), and then became an Assistant lecturer during the periods of 2012-2014, and 2014-2017, respectively. From 2017 to 2019, he was a researcher in the Department of Electrical and Electronic Engineering, Kyushu Institute of Technology, Japan. He is currently an assistant professor at the Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan, Egypt. His research interests include power system stability, dynamics and control, smart/micro-grid control, digital control techniques all applied to power systems, and renewable energy applications.
Phone: +81-7042898365
Address: 1-1 Sensui-cho, Tobata-ku, Kitakyushu-shi, Fukuoka, 804-8550, Japan

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Optimal Ultra-Local Model Control Integrated with Load Frequency Control of Renewable Energy Sources Based Microgrids

Energies, Dec 3, 2022

Since renewable energy sources (RESs) have an intermittent nature, conventional secondary frequen... more Since renewable energy sources (RESs) have an intermittent nature, conventional secondary frequency control, i.e., load frequency control (LFC), cannot mitigate the effects of variations in system frequency. Thus, this paper proposes incorporating ultralocal model (ULM) control into LFC to enhance microgrid (µG) frequency stability. ULM controllers are regarded as model-free controllers that yield high rejection rates for disturbances caused by load/RES uncertainties. Typically, ULM parameters are set using trial-and-error methods, which makes it difficult to determine the optimal values that will provide the best system performance and stability. To address this issue, the African vultures optimization algorithm (AVOA) was applied to fine-tune the ULM parameters, thereby stabilizing the system frequency despite different disturbances. The proposed LFC controller was compared with the traditional secondary controller based on an integral controller to prove its superior performance. For several contingencies, the simulation results demonstrated that the proposed controller based on the optimal ULM coupled with LFC could significantly promote RESs into the µG.

Gaber Magdy

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