Optimization Strategies for Power Grid Security Carrying Capacity under High Renewable Energy Penetration

Ensuring the security and stability of modern power systems under high renewable energy penetration requires advancedoptimization strategies that dynamically adapt to fluctuating generation and grid constraints. Traditional optimization methods,primarily based on static capacity planning and rule-based energy dispatch, often struggle to accommodate the stochasticnature of renewable energy sources, leading to potential grid vulnerabilities. To address these challenges, this study introducesan adaptive optimization framework that integrates predictive modeling, real-time energy balancing, and dynamic reserveallocation strategies. The proposed approach leverages deep learning-based forecasting, probabilistic security assessment,and adaptive resource scheduling to enhance grid resilience under uncertain operating conditions. By incorporating data-drivenoptimization techniques, our method effectively mitigates security risks, improves carrying capacity, and optimizes system-wideperformance. Experimental results on large-scale power grid datasets validate the effectiveness of the proposed strategy, demonstrating its superiority over conventional optimization techniques in enhancing grid security and operational reliability.