Thread by @maxparzen

Do you want to create an energy system model for any part of the world? In our new preprint, we introduce you to a flexible #opensource model solution that works with high spatial and temporal resolution #opendata.

@MacroEnergySys #energytwitter @openmod
🧵0/11

PyPSA-Earth enables large-scale collaboration by being applicable worldwide. The model is derived from PyPSA-Eur (EU model) using new data and functions. It is suitable for operational as well as combined generation, storage, transmission expansion studies.

See Nigeria:
1/11

The model architecture is built around `Snakemake` as a workflow management tool. It makes the model flexible and 'easy' to use, while having other benefits.

Curious about the other benefits? You can read more in the Snakemake paper: f1000research.com/articles/10-33…
2/11

The model includes a lot of open data from @openstreetmap and @geofabrik. We extract at the moment generators and transmission lines. Here is an image from our network coverage:
3/11

Surprisingly, when looking at Africa our open grid data was on average better than the @worldbankdata. Probably because the WB data is outdated (2018) and we leverage daily updated OSM data.

Table 2 compares the different data stats.
Worldbank data: datacatalog.worldbank.org/search/dataset…
4/11

#Energy system models require aggregation. We divide countries based on the original grid structure (b) or different levels of administrative zones (a). These regions can be further clustered if required
5/11

We fill these shapes with e.g. hourly time-series from solar PV and wind power. Everything is done in a data-driven way e.g. satellite, reanlaysis, bathymetry data.

#Atlite is a great open-source tool for this job. joss.theoj.org/papers/10.2110…
6/11

If you are interested to learn more details about the model design and scope, the data validation, our 2060 demonstration study of #Nigeria and the role of the @pypsameetsearth initiative then please check out our preprint:

arxiv.org/abs/2209.04663
7/11

Big thanks also to @HighsOpt for providing us with a suitable #opensource optimization solver. It helped us to explore a sustainable energy system in Nigeria. #HiGHS was slower than Gurobi but at least solved our problem -- which is important for people non-academics.
8/11

Big thanks also to @nworbmot & the rest of the pypsa.org team as well as all the amazing contributors from the PyPSA meets Earth initiative.
9/11

There is still a lot of work waiting e.g. we can implement better data, validate more regions, create new open data, add more features, improve the optimization speed, address better uncertainty, make the model sector-coupled etc. Maybe you want to support us?
10/11

All our meetings are #open and #free. For general updates, I recommend the monthly exchange. If you want to join the developments, check out the weekly exchanges:
- List of meetings bit.ly/3LakVg0
- Discord server to chat with us: bit.ly/3BBSBjt
11/11