Generation of low-voltage synthetic grid data for energy system modeling with the pylovo tool

Abstract

The ongoing energy transition introduces challenges in designing energy systems, particularly at the low-voltage level, where additional loads like electric vehicle charging points and heat pumps are predominantly connected. Obtaining accurate grid data for modeling and analysis is hindered by availability and confidentiality concerns, especially for low-voltage distribution grids. Unlike high voltage grids, low-voltage grids lack standardized handling due to their large size and heterogeneity. This research addresses an essential gap in the availability of grid data, offering a methodological base to generate synthetic low-voltage grids based on geospatial data. This includes the consideration of multiple feeders per transformer, greenfield and brownfield transformer positioning, and variable dimensioning of equipment components. Based on data clustering methods and an algorithmic graph-based approach, the tool provides a scalable solution that is applicable to large areas such as cities, states, or even countries with hundreds of thousands of grids. The tool is designed for Germany but can be adapted to other countries with similar low voltage grid structures, provided the necessary geospatial data are available. The derived representative synthetic grid topologies support the development of more resilient and efficient future energy systems by enabling research on specific issues like grid planning and reinforcement while considering regional constraints, such as grid congestion or voltage violations. This supports a comprehensive understanding of low-voltage energy systems in the context of the energy transition.