kugelfish

In some of the major European power grids, the penetration of variable renewable energy sources  (VRES) has reached a level where their variability can no longer be easily absorbed by the grid as minor noise in the daily fluctuations of supply and demand. Taking for example the German grid  (~ 60 GW load), the spot market prices now routinely "flat-line" at or below zero during nights and week-end on particularly blustery days. This forces wind farms to shut down (curtailment) during times when they would be at their most productive. Because the production potential of wind & solar is each highly synchronised across a relatively large geographic area, this trend will only increase as more wind and solar capacity is added to the same grid and profitably selling renewable energy "as-produced" will become increasingly difficulty. For the next stages of integration into the grid, wind & solar resources might have to be combined with  storage, moving away from de

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 . In order to access the REST API, one needs to register a user account on the site and request API access via an email to the support help-desk following the instructions here . 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 pro