3️⃣3️⃣3️⃣Cyber-Resilient Energy Management System for Smart Microgrids Under False Data Injection Attacks
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🔴Abstract
As smart grids become increasingly dependent on communication networks and digital infrastructures, cybersecurity has emerged as a critical challenge for energy management systems. False Data Injection (FDI) attacks can manipulate measurement data, resulting in incorrect dispatch decisions, economic losses, and potential system instability. This paper proposes a cyber-resilient energy management system capable of maintaining secure and reliable operation under malicious cyberattacks. The proposed framework combines machine learning-based anomaly detection with adaptive optimization to identify compromised measurements and reconstruct trustworthy operational data before executing energy scheduling decisions. The optimization framework simultaneously minimizes operating costs while preserving voltage stability, power balance, and battery lifetime under both normal and cyberattack scenarios. Different attack strategies, including coordinated FDI attacks targeting renewable generation, load measurements, and battery state-of-charge, will be investigated. Comparative analyses against conventional cybersecurity techniques and standard EMS strategies will be performed using comprehensive simulation studies. The proposed approach is expected to significantly improve attack detection accuracy, reduce false alarm rates, and enhance the resilience of smart microgrids without sacrificing operational efficiency. The developed framework represents an important step toward secure and intelligent next-generation energy management systems.
August 4, 2026 212 4
