Do-a-thon: Modelling power demand of the transport sector for energy models



The role of transport energy demand for future power system can be (at least) twofold: An increased power demand by electric mobility will require increased power supply by low-carbon power plant technologies. At the same time, electric mobility may act as load shifting option in future power systems.

A Ph.D. thesis at our group at the German Aersopace Center (DLR) in Stuttgart by Diego Luca di Tena investigated these described challenges and potential by providing five different hourly resolved profiles to our energy system model REMix:

  • an uncontrolled charging profile (that only exogenously adds to power demand)
  • an availability profile describing what amount of the fleet is connected to the grid
  • a profile for the actual power demand “on the street”
  • an SOC_max profile describing the upper bound of the battery state-of-charge and
  • a SOC_min profile describing the lower bound of the battery state-of-charge

I’m reproducing the tool that Diego developed called VENCO (unfortunately it is Excel-based) in Python. In the do-a-thon, I would like to give a quick introduction into the input data and how the tool works. If available through other participants it would be great to have 1-2 other group’s methods presented in order to discuss the limitations and how we could learn from each other to improve transport modelling in our research.


We use the same approach in PyPSA. I’d be very interested in how you generate the profiles and adapt them for different European countries.


Thanks for the interest, so far werely on data of the MiD 2008 (Mobility in Germany, orig.: Mobilität in Deutschland) study offering a broad spectrum of hourly transport demand profiles including info’s like aim of the trip, distance etc.
So far we’re only using data for Germany but I would also be interested in exchanging sources and data on other European countries.
I see the workshop more on the method side than the data side but maybe we can allocate 30 mins in the end on data exchange.