Deep Learning-Based Modeling and Optimization of Power Grid Carrying Capacity for Renewable Energy Integration

The increasing penetration of renewable energy sources in modern power systems presents significant challenges due to their inherent variability and uncertainty. Traditional grid management approaches, which primarily rely on static capacity planning and rule-based optimization, often struggle to accommodate fluctuating renewable generation while ensuring grid stability and efficiency. To address these limitations, this study introduces a deep learning-driven modeling and optimization framework that dynamically evaluates and enhances the carrying capacity of power grids for renewable energy integration. The proposed approach leverages advanced forecasting techniques, adaptive energy dispatch strategies, and probabilistic stability assessments to improve grid resilience under variable renewable generation conditions. By incorporating real-time data and intelligent control mechanisms, the method optimally balances power supply and demand, mitigates fluctuations, and maximizes renewable utilization. The framework integrates uncertainty quantification and reinforcement learning-based decision-making to enhance adaptability to dynamic grid conditions. The key contribution of this work lies in the development of a scalable, data-driven solution that combines hybrid deep learning architectures with dynamic optimization to accurately assess power grid carrying capacity and support reliable renewable energy integration across diverse grid scenarios. Experimental results on real-world power system datasets validate the effectiveness of the proposed approach, demonstrating superior performance compared to traditional capacity planning techniques.