Enhanced GN Algorithm for Multi-Infeed HVDC Receiving-End Network Optimization

The increasing load density and higher proportion of external power feed-in in modern power systems have led to elevated short-circuit current levels in receiving-end network, posing significant challenges to power system security and operational stability. This paper presents a novel optimization approach for multi-infeed receiving-end network configurations based on complex network theory and enhanced algorithmic methods. An enhanced community detection algorithm based on complex network theory is developed to identify critical interconnection lines within the receiving-end grid, establishing an optimal pre-disconnection line set. The research formulates a comprehensive multi-objective optimization model that simultaneously addresses three aspects: short-circuit current levels, multi-feed-in short-circuit ratios, and system active power losses. The improved NSGA-II algorithm is implemented to solve the model, incorporating adaptive operators and enhanced selection criteria to generate a Pareto optimal solution set. The final optimal configuration is selected based on N-1 security verification. The proposed methodology is validated through extensive case studies on a realistic power system.