  Information on power grids is confidential and thus real data is often
inaccessible. This necessitates the use of synthetic power grid models in
research. So far the models used, for example, in machine learning had to be
very simple and homogeneous to produce large ensembles of robust grids. We
present a modular framework to generate synthetic power grids that considers
the heterogeneity of real power grid dynamics but remains simple and tractable.
This enables the generation of large sets of synthetic grids for a wide range
of applications. We also include the major drivers of fluctuations on
short-time scales. The synthetic grids generated are robust and show good
synchronization under all evaluated scenarios, as should be expected for
realistic power grids. This opens the door to future research that studies
grids under severe stress due to extreme events which could lead to
destabilization and black-outs. A software package that includes an efficient
Julia implementation of the framework is released as a companion to the paper.

Adaptation and Self-Organizing Systems