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

TL;DR

The paper tackles the challenge of adaptive, secure control in modern smart grids with high DER and EV penetration. It proposes a harmonized three-layer cyber-physical framework that combines edge and cloud intelligence via Adaptive Dynamic Programming and multi-agent DRL (PPO and DQN), augmented by agent-based and game-theoretic models for decentralized decision-making. A resilience module including false data injection modeling and a resilience index enables evaluation under cyber-physical disturbances. Validation on the IEEE 33-bus test system demonstrates improved stability, dispatch optimization, and rapid recovery from disturbances and attacks, highlighting the framework's scalability and practicality for near-term deployment. The work offers a comprehensive, AI-enabled path toward scalable, secure, and adaptive smart-grid control with quantified resilience benefits.

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.

Figures (11)

• Figure 1: Architecture of hybrid cyber-physical modeling of smart grid for AI-based adaptive control.
• Figure 2: Illustration of the cyber-physical integration in a smart grid, showing interaction between the physical power system and the cyber infrastructure.
• Figure 3: Hybrid AI-enabled cyber-physical control architecture illustrating sensing, communication delay, packet loss, AI controllers (ADP, PPO, DQN), inverter actuation, and AC power flow dynamics.
• Figure 4: Feeder active power over time (raw and moving average).
• Figure 5: Wind generation profile over time (raw and moving average).