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Optimal Voltage–Frequency Regulation in Distributed Sustainable Energy-Based Hybrid Microgrids with Integrated Resource Planning

Author

Listed:
  • Amar Kumar Barik

    (Department of Electrical Engineering, National Institute of Technology Silchar, Assam 788010, India)

  • Dulal Chandra Das

    (Department of Electrical Engineering, National Institute of Technology Silchar, Assam 788010, India)

  • Abdul Latif

    (Department of Electrical Engineering, National Institute of Technology Silchar, Assam 788010, India)

  • S. M. Suhail Hussain

    (Fukushima Renewable Energy Institute, AIST (FREA), National Institute of Advanced Industrial Science and Technology (AIST), Koriyama 963-0298, Japan)

  • Taha Selim Ustun

    (Fukushima Renewable Energy Institute, AIST (FREA), National Institute of Advanced Industrial Science and Technology (AIST), Koriyama 963-0298, Japan)

Abstract

This work is the earliest attempt to propose an integrated resource planning for distributed hybrid microgrids considering virtual-inertia support (VIS) and demand-response support (DRS) systems. Initially, three-distributed sustainable energy-based unequal hybrid microgrids are envisioned with the availability of solar/wind/bioenergy resources. In order to overcome the effects of intermittency in renewable resources and low inertia, each microgrid is incorporated with DRS and VIS units for demand- and supply-side management, respectively. The proposed system is simulated in MATLAB considering real-time recorded solar/wind data with realistic loading for 12 months. A novel quasi-oppositional chaotic selfish-herd optimization (QCSHO) algorithm is proposed by hybridizing quasi-opposition-based learning and chaotic linear search techniques into the selfish-herd optimization, for optimal regulation of voltage and frequency in microgrids. Then, the system responses are compared with 7 algorithms and 5 error functions to tune PID controllers’ gains, which confirmed the superiority of QCSHO over others. Then, the study proceeds to investigate the voltage, frequency, and tie-line power coordination in 5 extreme scenarios of source and load variations in the proposed system without retuning the controllers. Finally, the system responses are analyzed for 10 different possible allocation of VIS and DRS units in different microgrids to find the most suitable combinations, and the results are recorded.

Suggested Citation

  • Amar Kumar Barik & Dulal Chandra Das & Abdul Latif & S. M. Suhail Hussain & Taha Selim Ustun, 2021. "Optimal Voltage–Frequency Regulation in Distributed Sustainable Energy-Based Hybrid Microgrids with Integrated Resource Planning," Energies, MDPI, vol. 14(10), pages 1-26, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2735-:d:551994
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    References listed on IDEAS

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    1. Thongchart Kerdphol & Masayuki Watanabe & Yasunori Mitani & Veena Phunpeng, 2019. "Applying Virtual Inertia Control Topology to SMES System for Frequency Stability Improvement of Low-Inertia Microgrids Driven by High Renewables," Energies, MDPI, vol. 12(20), pages 1-16, October.
    2. Abdul Latif & S. M. Suhail Hussain & Dulal Chandra Das & Taha Selim Ustun, 2020. "Optimum Synthesis of a BOA Optimized Novel Dual-Stage PI − (1 + ID) Controller for Frequency Response of a Microgrid," Energies, MDPI, vol. 13(13), pages 1-12, July.
    3. Andrew Ly & Saeid Bashash, 2020. "Fast Transactive Control for Frequency Regulation in Smart Grids with Demand Response and Energy Storage," Energies, MDPI, vol. 13(18), pages 1-23, September.
    4. Zakariazadeh, Alireza & Homaee, Omid & Jadid, Shahram & Siano, Pierluigi, 2014. "A new approach for real time voltage control using demand response in an automated distribution system," Applied Energy, Elsevier, vol. 117(C), pages 157-166.
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    Cited by:

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    3. Hao Liu & Bo Yang & Song Xu & Mingjian Du & Shuai Lu, 2023. "Universal Virtual Synchronous Generator Based on Extended Virtual Inertia to Enhance Power and Frequency Response," Energies, MDPI, vol. 16(7), pages 1-20, March.
    4. Taha Selim Ustun, 2022. "Power Systems Imitate Nature for Improved Performance Use of Nature-Inspired Optimization Techniques," Energies, MDPI, vol. 15(17), pages 1-2, August.
    5. Fahad M. Almasoudi, 2023. "Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    6. Taha Selim Ustun, 2023. "Microgrids Imitate Nature for Improved Performance— Use of Nature-Inspired Optimization Techniques in Future Power Systems," Energies, MDPI, vol. 16(3), pages 1-15, February.
    7. Sudhanshu Ranjan & Smriti Jaiswal & Abdul Latif & Dulal Chandra Das & Nidul Sinha & S. M. Suhail Hussain & Taha Selim Ustun, 2021. "Isolated and Interconnected Multi-Area Hybrid Power Systems: A Review on Control Strategies," Energies, MDPI, vol. 14(24), pages 1-20, December.

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