bayesgrid: An Open-Source Python Tool for Generating Probabilistic Synthetic Transmission-Distribution Grids Using Bayesian Hierarchical Models

TL;DR

bayesgrid is an open-source Python tool that generates probabilistic synthetic power transmission and distribution networks worldwide using Bayesian Hierarchical Models, enabling diverse applications including reliability analysis and hosting capacity estimation.

Contribution

The paper introduces bayesgrid, a novel Bayesian Hierarchical Model-based toolbox for creating realistic, probabilistic synthetic power grids applicable globally, with transfer learning capabilities and compatibility with open-source platforms.

Findings

Successfully generates diverse synthetic networks for various regions.

Includes reliability indices like failure frequency and duration.

Demonstrates applications in probabilistic hosting capacity analysis.

Abstract

In this work, we present bayesgrid, an open-source python toolbox for generating synthetic power transmission-distribution systems for any geographical location worldwide, using the publicly available data from OpenStreetMap (OSM). The toolbox is based on Bayesian Hierarchical Models (BHM) which is trained on existing distribution network databases to develop a probabilistic model and can be applied to any geographical location worldwide, leveraging transfer learning. Thanks to the BHM, the tool is capable of generating multiple instances of the distribution system for a same region. The generated networks contain three-phase phase-consistent unbalanced networks, radial topology and information on the nodal demand distributions. The generated network also contain the critical reliability indices, specifically the interruption duration and frequency of failure for individual grid components, allowing its application in reliability-related studies. The tool is demonstrated for different case studies generating synthetic network datasets for different geographical regions around the world. The framework allows saving the generated networks into open-source platforms: PandaPower and OpenDSS. We also present an application for computation of probabilistic hosting capacity using the synthetic networks.