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A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement

Author

Listed:
  • Divya R. Nair

    (Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri 690525, India
    These authors contributed equally to this work.)

  • Manjula G. Nair

    (Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri 690525, India
    These authors contributed equally to this work.)

  • Tripta Thakur

    (DG, National Power Training Institute (NPTI), Faridabad 121003, India
    These authors contributed equally to this work.)

Abstract

The widespread popularity of renewable and sustainable sources of energy such as solar and wind calls for the integration of renewable energy sources into electrical power grids for sustainable development. Microgrids minimize power quality issues in the main grid by linking with an active filter and furnishing reactive power compensation, harmonic mitigation, and load balancing at the point of common coupling. The reliability issues faced by standalone DC microgrids can be managed by interlinking microgrids with a power grid. An artificial intelligence-based Icos ϕ control algorithm for power sharing and power quality improvement in smart microgrid systems is proposed here to render grid-integrated power systems more intelligent. The proposed controller considers various uncertainties caused by load variations, state of charge of the battery of microgrids, and power tariff based on the availability of power in microgrids. This paper presents a detailed analysis of the integration of wind and solar microgrids with the grid for dynamic power flow management in order to improve the power quality and to reduce the burden, thereby strengthening the central grid. A smart grid system with multiple smart microgrids coupled with a renewable energy source with tariff control and judicious power flow management was simulated for power-sharing and power quality improvement. A hardware prototype of the artificial intelligence-based Icos ϕ control algorithm with nonlinear load was also implemented successfully. Furthermore, the economic viability was investigated to ensure the feasibility of the smart microgrid system with the proposed controller design for power flow management and power quality improvement.

Suggested Citation

  • Divya R. Nair & Manjula G. Nair & Tripta Thakur, 2022. "A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement," Energies, MDPI, vol. 15(15), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5409-:d:872615
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    References listed on IDEAS

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    1. Nuria Novas & Alfredo Alcayde & Isabel Robalo & Francisco Manzano-Agugliaro & Francisco G. Montoya, 2020. "Energies and Its Worldwide Research," Energies, MDPI, vol. 13(24), pages 1-41, December.
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    Cited by:

    1. Khalil Jouili & Mabrouk Jouili & Alsharef Mohammad & Abdulrahman J. Babqi & Walid Belhadj, 2024. "Neural Network Energy Management-Based Nonlinear Control of a DC Micro-Grid with Integrating Renewable Energies," Energies, MDPI, vol. 17(13), pages 1-23, July.
    2. Enas Taha Sayed & Abdul Ghani Olabi & Abdul Hai Alami & Ali Radwan & Ayman Mdallal & Ahmed Rezk & Mohammad Ali Abdelkareem, 2023. "Renewable Energy and Energy Storage Systems," Energies, MDPI, vol. 16(3), pages 1-26, February.
    3. Omar A. Beg & Asad Ali Khan & Waqas Ur Rehman & Ali Hassan, 2023. "A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids," Energies, MDPI, vol. 16(22), pages 1-23, November.
    4. Paul Arévalo & Francisco Jurado, 2024. "Impact of Artificial Intelligence on the Planning and Operation of Distributed Energy Systems in Smart Grids," Energies, MDPI, vol. 17(17), pages 1-22, September.
    5. Yang, Shengyao & Zhu, Meng Nan & Yu, Haiyan, 2024. "Are artificial intelligence and blockchain the key to unlocking the box of clean energy?," Energy Economics, Elsevier, vol. 134(C).
    6. 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.
    7. Ping Chen & Jiawei Gao & Zheng Ji & Han Liang & Yu Peng, 2022. "Do Artificial Intelligence Applications Affect Carbon Emission Performance?—Evidence from Panel Data Analysis of Chinese Cities," Energies, MDPI, vol. 15(15), pages 1-16, August.
    8. Yousef Asadi & Mohsen Eskandari & Milad Mansouri & Andrey V. Savkin & Erum Pathan, 2022. "Frequency and Voltage Control Techniques through Inverter-Interfaced Distributed Energy Resources in Microgrids: A Review," Energies, MDPI, vol. 15(22), pages 1-29, November.
    9. Anna Ostrowska & Łukasz Michalec & Marek Skarupski & Michał Jasiński & Tomasz Sikorski & Paweł Kostyła & Robert Lis & Grzegorz Mudrak & Tomasz Rodziewicz, 2022. "Power Quality Assessment in a Real Microgrid-Statistical Assessment of Different Long-Term Working Conditions," Energies, MDPI, vol. 15(21), pages 1-26, October.

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