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Cybersecurity in smart microgrids using blockchain-federated learning and quantum-safe approaches: A comprehensive review

Author

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  • Ahmad, Jameel
  • Rizwan, Muhammad
  • Ali, Syed Farooq
  • Inayat, Usman
  • Abdul Muqeet, Hafiz
  • Imran, Muhammad
  • Awotwe, Tabbi

Abstract

Smart microgrids are decentralized energy systems that effectively integrate renewable energy sources, energy storage technologies, and advanced communication and control frameworks, thereby facilitating efficient and sustainable energy distribution while enhancing grid resilience. These systems empower active participation from consumers and prosumers in energy trading, which significantly transforms traditional energy management practices. However, the increased connectivity and dependence on digital infrastructure inherent in smart microgrids introduce substantial cybersecurity vulnerabilities, underscoring the necessity for robust security protocols. This article provides a comprehensive review of cybersecurity threats directed at distributed generation in both AC and DC microgrids, energy trading platforms, and transactive energy management frameworks within the broader context of the smart grid. We systematically analyze both conventional and sophisticated stealth cyberattacks, identifying critical countermeasures essential for safeguarding modern smart grids. Furthermore, we explore the integration of emerging technologies, including machine learning, federated learning, blockchain security, and quantum-safe cryptographic mechanisms, as synergistic strategies to enhance cyber resilience in smart microgrids. Ultimately, this study identifies existing research gaps, barriers to adopting emerging technologies and proposes future research directions, with the goal of advancing the cybersecurity of these complex and evolving energy systems.

Suggested Citation

  • Ahmad, Jameel & Rizwan, Muhammad & Ali, Syed Farooq & Inayat, Usman & Abdul Muqeet, Hafiz & Imran, Muhammad & Awotwe, Tabbi, 2025. "Cybersecurity in smart microgrids using blockchain-federated learning and quantum-safe approaches: A comprehensive review," Applied Energy, Elsevier, vol. 393(C).
    • Handle: RePEc:eee:appene:v:393:y:2025:i:c:s0306261925008487
    DOI: 10.1016/j.apenergy.2025.126118
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    References listed on IDEAS

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