The organisation of Europe's power grid operators (ENTSO-E) is providing an open-data transparency platform with a lot of interesting data about the state of the power grid in its various member countries. This data is among others also used to power websites like https://app.electricitymaps.com/map.
There is also a Python client for this API which also converts the raw XML data into Pandas dataframes. Pandas is a Swiss army knives for dataset manipulations and one of the reasons why Python is so popular among data scientists.
The following code shows how to do simple ad-hoc analysis with the granular time-series data returned by the API. The example shows a very over-simplistic back of the envelope estimate of the idealized storage that would be needed to align the variable solar and wind energy production with the fluctuation of demand over the same time that is used in the following blog post on Moving variable renewables from "pay-as-produced" to pay-as-needed"
Producing the following output: