Power Grid Model is a high-performance Python/C++ library for steady-state distribution of power system analysis that offers optimized algorithms for the characteristics of the distribution grid. 

The project also offers full support of three-phase asymmetric calculation and efficient C++ implementation with native parallel computing support.

This technology can be used for Monte-Carlo low voltage grid simulations, to analyze potential bottlenecks in the grid, and to simulate different grid expansion plans in profile calculations over future decades. Additionally, real-time what-if analysis can be conducted on the current grid state, such as component failure or other anomalies.

The project consists of two main libraries: power-grid-model and power-grid-model-io. 

The library power-grid-model is a C++ calculation library with native shared-memory multi-threading for parallelisation in batch calculations. It has a C-API and a user-friendly Python API, and is compatible across Windows (x64), Linux (x64/arm64), and macOS (x64/arm64). Binary Python packages are available on official PyPI.

The library power-grid-model-io is a Python library which can handle the conversion between Power Grid Model format and other common grid data formats, such as Vision and pandapower.

The library has been validated against Power Grid Model reference models, with 80 or more test cases. Continuous validation is part of the CI pipeline in GitHub Actions. The calculation core is also integrated into GridCal, with plans to be integrated into pandapower too.