Create powerplants.csv and stats by GH action

[FabianHofmann]

To avoid unnecessary work with updating the powerplants.csv, we should set up an automated pipeline via GH which takes over the whole matching process and the update of powerplants.csv.

In principle, this would only require to run

df = pm.powerplants(update=True)
df.to_csv(index_label="id")

and store that df as an GH artefact. For each git tag, the created artefact would be the data that is loaded when locally calling pm.powerplants(from_url=True) .

Besides that, another artefact should be created to monitor the quality of the current data. It should give stats on the data, like pointed out by @pz-max in #113 (however, I would actually prefer it to have it in the ppm package itself, and not outsourced). For a start, the workflow can be small. The script in https://github.com/PyPSA/powerplantmatching/blob/master/analysis/compare-with-entsoe-stats.py should give a good starting point.

If anyone is interested in starting that project, I'd be happy to support.