A Bayesian Hierarchical Model for Generating Synthetic Unbalanced Power Distribution Grids

The real-world data of power networks is often inaccessible due to privacy and security concerns, highlighting the need for tools to generate realistic synthetic network data. Existing methods leverage geographic tools like OpenStreetMap with heuristic rules to model system topology and typically focus on single-phase, balanced systems, limiting their applicability to real-world distribution systems, which are usually unbalanced. This work proposes a Bayesian Hierarchical Model to generate unbalanced 3-phase distribution systems learning from existing networks. The scheme takes as input the base topology and aggregated demand per node and outputs a 3-phase unbalanced system. The proposed scheme achieves a Mean Absolute Percentage Error (MAPE) of less than 8% when estimating the power demand accross all phases, with computation times of 20.4 seconds for model training and 3.1 seconds per sample generation. The tool is applied to learn from publicly available SMART-DS dataset and applied to generate European 906 and IEEE-123 systems. We demonstrate the transfer learning capability of the proposed tool by leveraging a model trained on an observed system to generate a synthetic network for an unobserved system. Specifically, the tool is trained using the publicly available SMART-DS dataset and subsequently applied to generate synthetic networks for the European 906-bus system and the IEEE 123-bus system. This tool allows researchers to simulate realistic unbalanced 3-phase power data with high accuracy and speed, enhancing planning and operational analysis for modern power grids.