Leveraging the power of machine learning and data balancing techniques to evaluate stability in smart grids

Allal, Zaid; Noura, Hassan N.; Salman, Ola; Chahine, Khaled

doi:10.1016/j.engappai.2024.108304

Engineering Applications of Artificial Intelligence

2024

DOI: 10.1016/j.engappai.2024.108304

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Leveraging the power of machine learning and data balancing techniques to evaluate stability in smart grids

Zaid Allal,

Hassan N. Noura,

Ola Salman

et al.

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2024

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Cited by 2 publications

References 22 publications

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Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability

El Maghraoui,

El Hadraoui,

Ledmaoui

et al. 2024

Sustainable Energy, Grids and Networks

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Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability

El Maghraoui,

El Hadraoui,

Ledmaoui

et al. 2024

Sustainable Energy, Grids and Networks

View full text Add to dashboard Cite

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

Kiasari,

Ghaffari,

Aly

2024

Energies

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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.

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Leveraging the power of machine learning and data balancing techniques to evaluate stability in smart grids

Cited by 2 publications

References 22 publications

Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability

Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability

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

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