Optimizing Smart Grids with a Hybrid Classical-Quantum Architecture: Efficiency, Scalability, and Real-Time Resilience
Binghao Li · 2024
Modern smart grids demand robust computational solutions to manage real-time data processing, complex optimization, and fault resilience, especially with the integration of distributed and renewable energy resources (DERs). This study introduces a hybrid classical-quantum architecture tailored for these needs, leveraging classical computing for routine processing while employing quantum computing for high-complexity tasks. Experimental simulations demonstrate that the hybrid architecture reduces computation time by 40–41% and enhances energy efficiency by approximately 30% compared to traditional configurations. Additionally, its task allocation system improves scalability and resilience, making this architecture a viable solution for optimizing dynamic, large-scale smart grids. The findings offer a pathway toward a scalable and sustainable grid management system, bridging classical reliability with quantum advancements.