Research on Deep Learning-Based Collaborative Optimization Planning Strategy for Source-GridLoad-Storage Coordination

The integration of renewable energy sources into power systems has introduced complexities that necessitate advanced optimization strategies for efficient energy management. This paper presents a novel deep learning-based collaborative optimization planning strategy that coordinates source-grid-load-storage interactions to enhance grid stability and efficiency. By leveraging deep learning algorithms, the proposed strategy forecasts energy generation, demand, and storage utilization, enabling real-time adaptive optimization. Simulated case studies demonstrate the effectiveness of this approach, highlighting improvements in energy balance, peak load reduction, and renewable energy utilization. The results offer insights into the potential of artificial intelligence in advancing smart grid management and promoting sustainable energy systems.