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A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems

Author

Listed:
  • Mahmoud Kiasari

    (Smart Grid and Green Power Systems Research Laboratory, Electrical and Computer Engineering Department, Dalhousie University, Halifax, NS B3H 4R2, Canada)

  • Mahdi Ghaffari

    (Smart Grid and Green Power Systems Research Laboratory, Electrical and Computer Engineering Department, Dalhousie University, Halifax, NS B3H 4R2, Canada)

  • Hamed H. Aly

    (Smart Grid and Green Power Systems Research Laboratory, Electrical and Computer Engineering Department, Dalhousie University, Halifax, NS B3H 4R2, Canada)

Abstract

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel reliability, thereby contributing to environmental sustainability and empowering energy security. Moreover, key advancements in smart grid technologies, such as Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), and Supervisory Control and Data Acquisition (SCADA) systems, are explored to clarify the related topics to the smart grid. The usage of various technologies enhances grid reliability, efficiency, and resilience are introduced. This paper also investigates the application of Machine Learning (ML) techniques in energy management optimization within smart grids with the usage of various optimization techniques. The findings emphasize the transformative impact of integrating RES and advanced smart grid technologies alongside the need for continued innovation and supportive policy frameworks to achieve a sustainable energy future.

Suggested Citation

  • Mahmoud Kiasari & Mahdi Ghaffari & Hamed H. Aly, 2024. "A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems," Energies, MDPI, vol. 17(16), pages 1-38, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4128-:d:1459360
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    References listed on IDEAS

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    1. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    2. Zhang, Rufeng & Chen, Yan & Li, Zhengmao & Jiang, Tao & Li, Xue, 2024. "Two-stage robust operation of electricity-gas-heat integrated multi-energy microgrids considering heterogeneous uncertainties," Applied Energy, Elsevier, vol. 371(C).
    3. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    4. Faisal Mehmood & Shabir Ahmad & Taeg Keun Whangbo, 2023. "An Efficient Optimization Technique for Training Deep Neural Networks," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    5. Chassein, André & Goerigk, Marc & Kasperski, Adam & Zieliński, Paweł, 2020. "Approximating combinatorial optimization problems with the ordered weighted averaging criterion," European Journal of Operational Research, Elsevier, vol. 286(3), pages 828-838.
    6. Xu, Gaoyuan & Shi, Jian & Wu, Jiaman & Lu, Chenbei & Wu, Chenye & Wang, Dan & Han, Zhu, 2024. "An optimal solutions-guided deep reinforcement learning approach for online energy storage control," Applied Energy, Elsevier, vol. 361(C).
    7. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    8. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    9. Hui, Hongxun & Ding, Yi & Shi, Qingxin & Li, Fangxing & Song, Yonghua & Yan, Jinyue, 2020. "5G network-based Internet of Things for demand response in smart grid: A survey on application potential," Applied Energy, Elsevier, vol. 257(C).
    10. Fang Gao & Guojian Wu, 2023. "Application of Quantum Computing in Power Systems," Energies, MDPI, vol. 16(5), pages 1-3, February.
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