An AI-Enabled Hybrid Cyber-Physical Framework for Adaptive Control in Smart Grids
This addresses the problem of flexible and adaptive control for evolving smart grids, but it appears incremental as it builds on existing methods like Adaptive Dynamic Programming and AI-based optimization.
The paper tackled the challenge of adaptive control in smart grids by proposing a hybrid cyber-physical framework, which was simulated on an IEEE 33-Bus system and showed positive results in ensuring grid stability and optimizing dispatch.
Evolving smart grids require flexible and adaptive control methods. A harmonized hybrid cyber-physical framework, which considers both physical and cyber layers and ensures adaptability, is one of the critical challenges to enable sustainable and scalable smart grids. This paper proposes a three-layer (physical, cyber, control) architecture, with an energy management system as the core of the system. Adaptive Dynamic Programming(ADP) and Artificial Intelligence-based optimization techniques are used for sustainability and scalability. The deployment is considered under two contingencies: Cloud Independent and cloud-assisted. They allow us to test the proposed model under a low-latency localized decision scenario and also under a centralized control scenario. The architecture is simulated on a standard IEEE 33-Bus system, yielding positive results. The proposed framework can ensure grid stability, optimize dispatch, and respond to ever-changing grid dynamics.