An Autonomic Resilient Electrical Grid for a University Campus

This paper proposes an autonomic cyber-physical electrical grid for a university campus, designed to optimize energy generation, storage, and consumption through autonomic self-management. The system integrates real-time monitoring, predictive analytics, and automated decision-making within a multi-layered MAPE-K architecture. Smart meters and Internet-of-Things (IoT) sensors collect data on usage, occupancy, and environmental conditions, which inform control strategies that dynamically adjust loads, prioritize critical systems, and manage battery storage. The autonomic manager balances supply and demand while integrating renewable sources such as solar and wind. Challenges include minimizing latency through edge computing, ensuring interoperability across legacy and modern systems, preserving user privacy, handling cybersecurity concerns, managing battery degradation, and maintaining user comfort. By combining edge and cloud computing, the system ensures low-latency control and long-term optimization. This approach supports bi-directional energy flow and demonstrates how autonomous energy systems can improve grid resilience, efficiency, and sustainability, serving as a potential model for future campus-scale and other smart grid deployments.