Engineering IDE for creating, simulating, and analysing power grid models.

pbsPowerGridLab is designed for power grid modelling, simulation, and complex-network research. It enables the generation of synthetic power grids using established network models such as Random, Barabási-Albert, and Small-World models, followed by power flow analysis and the simulation of different grid operating conditions. Results can be exported as structured JSON datasets representing Normal, Stress, and Blackout states, providing a foundation for further grid analysis and research.

Machine Learning Dataset Generation

pbsPowerGridModel can generate structured JSON datasets for machine learning applications under different power grid operating conditions. The generated data can represent Normal, Stress, and Blackout conditions, providing datasets for developing and evaluating machine learning models for power grid analysis. pbsPowerGridModel also supports the OPA Blackout Model for studying blackout and cascading-failure scenarios.

Supporting Academic Research

To support education and scientific research, pbsPowerGridLab is available free of charge to universities and research centres. Researchers and students can use the platform for power grid modelling, network analysis, simulation, and the development of new methods and research ideas.

Supported Network Models

pbsPowerGridLab supports the generation of power grid networks based on several established complex-network models:

• Erdős–Rényi (ER) Random Model
• Watts–Strogatz (SW) Small-World Model
• Barabási–Albert (BA) Model
• CapGrid Model
• Ring Lattice Model
• Power-Law Cluster (PLC) Model
• SA-DCSBM Model
• Bianconi–Barabási Fitness Model
• Random Geometric Graph (RGG) Model
• Geographical Threshold Graph (GTG) Model

Supported Network Indices

pbsPowerGridLab provides a comprehensive set of network indices for analysing the structural and topological properties of generated power grids:

• Average Clustering Coefficient
• Gini Coefficient
• Pearson Degree Correlation
• Newman Assortativity
• Local Efficiency
• Eigenvector Centrality
• Eigenvector Centrality using Power Iteration
• K-Core Number
• Spectral Radius (Largest Eigenvalue of the Adjacency Matrix)
• Algebraic Connectivity (Fiedler Value)
• Network Density
• Mean Degree
• Maximum Path Length
• Edge Betweenness Concentration (EBC)
• Articulation Point Density (APD)
• Structural Fragility Index

📄 Related Documents

• 📄 pbsPowerGridLab Manual

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