Innovative optimization of 765 kV transmission network upgrades: Enhancing dynamic stability and efficiency in power systems

This research presents an advanced Reinforcement Learning-Graph Neural Network (RL-GNN) architecture for optimizing 765 kV transmission network upgrades. The architecture is specifically designed to address dynamic stability and efficiency challenges associated with high levels of renewable energy integration. The proposed methodology advances traditional optimization techniques through three key innovations: a dynamic reward mechanism that autonomously manages the tradeoff between stability and efficiency in real time; hierarchical graph processing methods that enable scalable implementation in large-scale systems; and adaptive control features that respond to fluctuations in renewable generation. Extensive evaluations using IEEE standard test systems and large-scale synthetic networks demonstrate significant advancements in critical metrics such as transmission efficiency, dynamic stability, and operational reliability. The architecture ensures strong compliance with N-1 security standards through topology-aware action masking and reduces computational complexity via optimized hierarchical processing. Validation against industry-standard tools and real-world grid data confirms the architecture’s effectiveness under practical operating conditions. Compared to conventional approaches, our paradigm offers superior performance in scenarios with high renewable penetration, providing grid operators with a robust and cost-effective decision-support tool for modernization and renewable integration. The architecture’s decisions are demonstrated to be interpretable and consistent with established power systems principles, while maintaining robust performance under unseen contingency scenarios. The capabilities highlight the potential for practical deployment of this architecture in contemporary power systems facing transitional challenges. These findings underscore the architecture’s potential for practical application in modern power systems experiencing transitional challenges.