Deep Generative Graph Distribution Learning for Synthetic Power Grids
This addresses the need for accessible power grid data for system studies, offering a domain-specific solution for power grid synthesis.
The paper tackles the problem of generating realistic synthetic power grids due to confidentiality of real data by proposing DeepGDL, a deep generative model that learns topological patterns and physical features, resulting in synthetic networks that closely resemble the original grid with significant accuracy in topological metrics and power flow measurements.
Power system studies require the topological structures of real-world power networks; however, such data is confidential due to important security concerns. Thus, power grid synthesis (PGS), i.e., creating realistic power grids that imitate actual power networks, has gained significant attention. In this letter, we cast PGS into a graph distribution learning (GDL) problem where the probability distribution functions (PDFs) of the nodes (buses) and edges (lines) are captured. A novel deep GDL (DeepGDL) model is proposed to learn the topological patterns of buses/lines with their physical features (e.g., power injection and line impedance). Having a deep nonlinear recurrent structure, DeepGDL understands complex nonlinear topological properties and captures the graph PDF. Sampling from the obtained PDF, we are able to create a large set of realistic networks that all resemble the original power grid. Simulation results show the significant accuracy of our created synthetic power grids in terms of various topological metrics and power flow measurements.