  Energy system models are increasingly being used to explore scenarios with
large shares of variable renewables. This requires input data of high spatial
and temporal resolution and places a considerable preprocessing burden on the
modeling team. Here we present a new code set with an open source license for
automatic generation of input data for large-scale energy system models for
arbitrary regions of the world, including sub-national regions, along with an
associated generic capacity expansion model of the electricity system. We use
ECMWF ERA5 global reanalysis data along with other public geospatial datasets
to generate detailed supply curves and hourly capacity factors for solar
photovoltaic power, concentrated solar power, onshore and offshore wind power,
and existing and future hydropower. Further, we use a machine learning approach
to generate synthetic hourly electricity demand series that describe current
demand, which we extend to future years using regional SSP scenarios. Finally,
our code set automatically generates costs and losses for HVDC interconnections
between neighboring regions. The usefulness of our approach is demonstrated by
several different case studies based on input data generated by our code. We
show that our model runs of a future European electricity system with high
share of renewables are in line with results from more detailed models, despite
our use of global datasets and synthetic demand.
