Extract GridPath RA Toolkit renewable energy generation profiles from zipped CSVs.

These hourly time are organized by technology type: Wind and Solar; and level of processing: original, aggregated, and extended. This module currently only extracts the data which is both aggregated and extended, since it is the closest to being analysis ready. In the future we may extract the other versions, or re-implement the entire aggregation and extension process within PUDL, so the outputs are being derived from the more granular original data, and so a variety of different aggregations can be provided.

Alongside the Wind + Solar time series, there are also aggregation tables that describe which of the original plants & generators were combined to make the aggregated datasets. These are stored as single CSVs.

In addition there’s a table of daily weather data, which is also stored as a single CSV.

Module Contents


_extract_csv(→ pandas.DataFrame)

_extract_capacity_factor(→ pandas.DataFrame)


An asset factory for GridPath RA Toolkit hourly generation profiles.


pudl.extract.gridpathratoolkit._extract_csv(part: str, ds: pudl.workspace.datastore.Datastore) pandas.DataFrame[source]
pudl.extract.gridpathratoolkit._extract_capacity_factor(part: str, ds: pudl.workspace.datastore.Datastore) pandas.DataFrame[source]
pudl.extract.gridpathratoolkit.raw_gridpathratoolkit_asset_factory(part: str) dagster.AssetsDefinition[source]

An asset factory for GridPath RA Toolkit hourly generation profiles.

This factory works on the processed hourly profiles that store one capacity factor time series per file with the time index stored in a separate file named timestamps.csv. We extract the timestamps first and use them as the index of the dataframe, concatenating the capacity factor time series as separate columns in a (temporarily) wide-format dataframe.

The stems of the filenames are used as column labels, which are later transformed into the aggregation_group field, indicating which generators were aggregated to produce the time series based on the wind and solar capacity aggregation tables.