Deep Generative Graph Learning for Power Grid Synthesis

Power system studies require the topological structures of real-world power networks; however, such data may be 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 paper, we cast PGS into a graph distribution learning (GDL) problem where the probability density functions (PDFs) of the nodes (buses) and edges (lines) are captured. A novel deep GDL (DeepGDL) algorithm 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.