An AI-Enabled Hybrid Cyber-Physical Framework for Adaptive Control in Smart Grids

TL;DR

This paper introduces a three-layer hybrid cyber-physical framework utilizing AI and adaptive control techniques to enhance the sustainability, scalability, and stability of smart grids, tested on IEEE 33-Bus system.

Contribution

It proposes a novel three-layer architecture integrating physical, cyber, and control layers with AI-based optimization for adaptive smart grid management.

Findings

Framework ensures grid stability and optimal dispatch.

Effective under both cloud-independent and cloud-assisted scenarios.

Simulation results demonstrate improved adaptability and performance.

Abstract

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.