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Sustainable Energy Management and Control for Variable Load Conditions Using Improved Mayfly Optimization

Author

Listed:
  • Prabu Subramani

    (Department of ECE, Mahendra Institute of Technology, Namakkal 637503, India)

  • Sugadev Mani

    (Department of ECE, Sathyabama Institute of Science and Technology, Chennai 600119, India)

  • Wen-Cheng Lai

    (Bachelor Program in Industrial Projects, National Yunlin University of Science and Technology, Douliu 640301, Taiwan
    Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu 640301, Taiwan)

  • Dineshkumar Ramamurthy

    (Department of ECE, Kings Engineering College, Irungattukottai, Sriperumbudur 602117, India)

Abstract

In recent trends, renewable energies are infinite, safe, and are becoming a reliable source for electricity requirements. However, they have certain variations in their results because of climate change, which is its major issue. To solve this challenge, a hybrid renewable energy system was created by combining various energy sources. Energy management strategies must be employed to determine the best possible performance of renewable energy-based hybrid systems, as well as to fulfil demand and improve system efficiency. This work describes an Energy Management System (EMS) for a Hybrid Renewable Energy System (HRES) called Improved Mayfly Optimization-based Modified Perturb and Observe (IMO-MP&O). The developed EMS is based on basic conceptual constraints and has the goal of meeting the energy demand of connected load, ensuring energy flow stabilization, and optimizing battery utilization. In addition, the suggested IMO-MP&O can identify the condition and operating state of every HRES sub-system and assure the network stability of frequency and voltage changes. Numerical simulations in the MATLAB/Simulink environment were used to evaluate the proposed EMS. The simulated results show that the proposed IMO-MP&O achieves the harmonic error of 0.77%, which is less than the existing Maximum Power Point Tracking (MPPT) control and Artificial Neural Network (ANN)-based Z-Source Converter methods.

Suggested Citation

  • Prabu Subramani & Sugadev Mani & Wen-Cheng Lai & Dineshkumar Ramamurthy, 2022. "Sustainable Energy Management and Control for Variable Load Conditions Using Improved Mayfly Optimization," Sustainability, MDPI, vol. 14(11), pages 1-22, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6478-:d:823926
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    References listed on IDEAS

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    2. Younes Sahri & Youcef Belkhier & Salah Tamalouzt & Nasim Ullah & Rabindra Nath Shaw & Md. Shahariar Chowdhury & Kuaanan Techato, 2021. "Energy Management System for Hybrid PV/Wind/Battery/Fuel Cell in Microgrid-Based Hydrogen and Economical Hybrid Battery/Super Capacitor Energy Storage," Energies, MDPI, vol. 14(18), pages 1-32, September.
    3. Ayman Al-Quraan & Muhannad Al-Qaisi, 2021. "Modelling, Design and Control of a Standalone Hybrid PV-Wind Micro-Grid System," Energies, MDPI, vol. 14(16), pages 1-23, August.
    4. Abdelshafy, Alaaeldin M. & Jurasz, Jakub & Hassan, Hamdy & Mohamed, Abdelfatah M., 2020. "Optimized energy management strategy for grid connected double storage (pumped storage-battery) system powered by renewable energy resources," Energy, Elsevier, vol. 192(C).
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    1. M. N. Sudha & Velan Balamurugan & Wen-Cheng Lai & Parameshachari Bidare Divakarachari, 2022. "Sustainable Multipath Routing for Improving Cross-Layer Performance in MANET Using an Energy Centric Tunicate Swarm Algorithm," Sustainability, MDPI, vol. 14(21), pages 1-20, October.

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