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Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers

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  • Khaizaran Abdulhussein Al Sumarmad

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia)

  • Nasri Sulaiman

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia)

  • Noor Izzri Abdul Wahab

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia)

  • Hashim Hizam

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia)

Abstract

Microgrids, comprising distributed generation, energy storage systems, and loads, have recently piqued users’ interest as a potentially viable renewable energy solution for combating climate change. According to the upstream electricity grid conditions, microgrid can operate in grid-connected and islanded modes. Energy storage systems play a critical role in maintaining the frequency and voltage stability of an islanded microgrid. As a result, several energy management systems techniques have been proposed. This paper introduces a microgrid system, an overview of local control in a microgrid, and an efficient EMS for effective microgrid operations using three smart controllers for optimal microgrid stability. We designed a microgrid consisting of renewable sources, Li-ion batteries, the main grid as a backup system, and AC/DC loads. The proposed system control was based on supplying loads as efficiently as possible using renewable energy sources and monitoring the battery’s state of charge. The simulation results using MATLAB Simulink demonstrate the performance of the three proposed microgrid stability strategies (PID, artificial neural network, and fuzzy logic). The comparison results confirmed the viability and effectiveness of the proposed technique for energy management in a microgrid which is based on fuzzy logic controllers.

Suggested Citation

  • Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers," Energies, MDPI, vol. 15(1), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:1:p:303-:d:716580
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    References listed on IDEAS

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    1. Palizban, Omid & Kauhaniemi, Kimmo & Guerrero, Josep M., 2014. "Microgrids in active network management – part II: System operation, power quality and protection," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 440-451.
    2. Justo, Jackson John & Mwasilu, Francis & Lee, Ju & Jung, Jin-Woo, 2013. "AC-microgrids versus DC-microgrids with distributed energy resources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 387-405.
    3. Roslan, M.F. & Hannan, M.A. & Ker, Pin Jern & Uddin, M.N., 2019. "Microgrid control methods toward achieving sustainable energy management," Applied Energy, Elsevier, vol. 240(C), pages 583-607.
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    Cited by:

    1. Dominic Savio Abraham & Balaji Chandrasekar & Narayanamoorthi Rajamanickam & Pradeep Vishnuram & Venkatesan Ramakrishnan & Mohit Bajaj & Marian Piecha & Vojtech Blazek & Lukas Prokop, 2023. "Fuzzy-Based Efficient Control of DC Microgrid Configuration for PV-Energized EV Charging Station," Energies, MDPI, vol. 16(6), pages 1-17, March.
    2. J. Dionísio Barros & Luis Rocha & J. Fernando Silva, 2023. "Backstepping Control of NPC Multilevel Converter Interfacing AC and DC Microgrids," Energies, MDPI, vol. 16(14), pages 1-18, July.
    3. Saqib Yousuf & Viqar Yousuf & Neeraj Gupta & Talal Alharbi & Omar Alrumayh, 2023. "Enhanced Control Designs to Abate Frequency Oscillations in Compensated Power System," Energies, MDPI, vol. 16(5), pages 1-20, February.
    4. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis," Energies, MDPI, vol. 15(11), pages 1-19, June.
    5. Yan Yang & Yeqin Wang & Weixing Zhang & Zhenghao Li & Rui Liang, 2022. "Design of Adaptive Fuzzy Sliding-Mode Control for High-Performance Islanded Inverter in Micro-Grid," Energies, MDPI, vol. 15(23), pages 1-25, December.
    6. Hamid Saeed Khan & Attaullah Y. Memon, 2022. "Robust Output Feedback Control of the Voltage Source Inverter in an AC Microgrid," Energies, MDPI, vol. 15(15), pages 1-21, August.
    7. Erdal Irmak & Ersan Kabalci & Yasin Kabalci, 2023. "Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity," Energies, MDPI, vol. 16(12), pages 1-58, June.
    8. Dong Zhao & Shuyan Sun & Ardashir Mohammadzadeh & Amir Mosavi, 2022. "Adaptive Intelligent Model Predictive Control for Microgrid Load Frequency," Sustainability, MDPI, vol. 14(18), pages 1-14, September.

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