3V-GM: A Tri-Layer "Point-Line-Plane" Critical Node Identification Algorithm for New Power Systems

Area of Science:

• Electrical Engineering
• Power Systems Analysis
• Network Science

Background:

• The integration of renewable energy sources introduces intermittency and stochasticity, challenging power grid stability.
• Traditional methods for identifying critical nodes often rely on incomplete network topology or power flow data, leading to inaccuracies.
• Accurate identification of critical nodes is essential for maintaining grid reliability and operational security.

Purpose of the Study:

• To develop a comprehensive model for identifying critical nodes in power grids that overcomes the limitations of existing methods.
• To improve the accuracy and completeness of critical node identification by integrating structural and electrical-physical attributes.
• To enhance the robustness and stability assessment of power grids with increasing renewable energy penetration.

Main Methods:

• The Three-Dimensional Value-Based Gravity Model (3V-GM) was proposed, integrating node topology, real-time voltage state, electrical coupling distance, and eigenvector centrality.
• Simulations were conducted on the IEEE 39 system and six other benchmark networks using Python and MATPOWER v7.1.
• Node criticality was evaluated by measuring the load loss rate upon sequential node removal, comparing 3V-GM against six baseline methods.
• Ablation experiments were performed to validate the contribution of each layer within the 3V-GM.

Main Results:

• The 3V-GM consistently identified nodes whose removal resulted in significantly higher load loss rates compared to baseline methods across all tested networks.
• The model demonstrated superior accuracy and stability in identifying critical nodes, crucial for grid operational planning.
• Ablation studies confirmed the synergistic contribution of the plane, line, and point layers to the model's overall effectiveness.

Conclusions:

• The 3V-GM offers a more accurate and comprehensive approach to critical node identification in power systems compared to traditional methods.
• This enhanced identification capability is vital for improving the stability and resilience of power grids, especially with high renewable energy integration.
• The model's multi-layered approach effectively captures complex network interdependencies, leading to better predictions of cascading failures.