Hybrid AI-Quantum-Based Energy Management Architecture for Distributed Smart Grids Under Uncertainty

The increasing penetration of renewable energy sources and energy storage systems introduces significant uncertainty and complexity in modern smart energy systems. Traditional optimization methods often fail to provide efficient real-time solutions under high-dimensional and stochastic operating conditions. This study proposes a multi-level hybrid energy management framework that integrates deep learning, reinforcement learning, and quantum optimization techniques. The architecture combines neural-network-based forecasting, reinforcement learning-driven decision-making, and quantum-assisted optimization to address complex resource allocation and control tasks. The energy management problem is formulated as a stochastic optimization task with operational and network constraints. The proposed hybrid AI-quantum approach enhances system adaptability, improves energy efficiency, reduces operational losses and carbon emissions, and supports scalable deployment in smart grids, microgrids, and intelligent urban energy infrastructures.