Shahid Beheshti University International Journal of Research and Technology in Electrical Industry 2821-0190 5 1 2026 01 01 AI-Powered Robust Protection and Energy–Flexibility Scheduling in Interconnected Local Energy Networks within Automation Cyber–Physical Systems 107390 10.48308/ijrtei.2026.245508.1129 EN Ali Riki Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran 0009-0004-2465-5017 Amir Abdollahi Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran 0009-0004-2465-5017 Ali Yazhari Kermani Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran 0009-0004-2465-5017 Journal Article 2026 07 21 The increasing integration of renewable energy resources and digital communication technologies has significantly improved the intelligence and operational flexibility of modern smart grids. However, the growing reliance on automation cyber–physical systems has also increased vulnerability to cyberattacks, threatening the reliability and security of energy management processes. Interconnected local energy networks (ILENs), which enable coordinated operation of distributed energy resources, are particularly exposed due to their dependence on sensing, communication, and control infrastructures. This paper proposes an AI-powered cyber-secure framework for robust energy and flexibility scheduling in ILENs. A robust bi-level optimization model is developed, where the upper-level operator coordinates energy and flexibility exchanges among interconnected networks, while lower-level operators optimize local energy management under uncertainty. Robust optimization is incorporated to address uncertainties in renewable generation, load demand, and electricity prices, thereby enhancing scheduling resilience. To assess cybersecurity performance, false data injection (FDI) attacks are modeled using a Gaussian probability distribution. An XGBoost-based anomaly detection and correction mechanism is employed to identify compromised measurements and recover corrupted data before scheduling decisions are executed. Simulation results demonstrate a maximum detection accuracy of 91.67%, while the proposed correction strategy effectively restores system performance to near-normal operating conditions. The proposed framework simultaneously strengthens operational robustness and cyber resilience, offering a secure and reliable solution for future automation-enabled smart grids.