Curves

The ETM uses hourly curves to model (electricity, hydrogen and gas) demand and supply. The hourly demand/supply is determined using the annual demand/supply and a curve.

Hourly hydrogen demand chart

Example of hourly demand - hydrogen demand

In 2019 we made an inventory of all curves available and updated all ETM-curves and their documentation. This project was carried out in close collaboration with the modelling community. On the 3th of July we closed the project with a mini-symposium. In this mini-symposium we shared our findings, struggles and discussed possibilities of further improvement of curves used in energy modelling.

Overview of curves

We define demand curves, supply curves and time curves. The tables below show a brief overview of the sources and methods currently used.

Demand

SectorSub-sectorSourceMethodComment
HouseholdsSpace heatingTNOTNO curves fitted to temperature and irradiance which enables to generate curves for all years. Curves have been smoothed to show the average load of a cluster of 300 houses rather than an individual house. This results in lower and more realistic total demand peaks.Update with TNO heat loss calculation when data becomes available
Hot waterJordan (2001)Distribution function based on average Dutch household-
CoolingNEDUE1A curveArgumentation of method, update with TNO heat loss calculation when data becomes available
AppliancesNEDUE1A curve-
BuildingsSpace heatingNEDUG2AUpdate with TNO heat loss calculation when data becomes available
CoolingNEDUE3A curveArgumentation of method, update with TNO heat loss calculation when data becomes available
AppliancesNEDUE3A curve-
TransportElectric vehiclesMovares and ELaadProfiles available:
Movares: week and weekend days for
1) charging everywhere
2) charging at home
3) fast charging.
ELaad: repeating average day for
4) smart charging
5) regular charging
Default curve for cars is charging everywhere.
-
Passenger trains, trams/metro, electric bicycle, motorcyclesMovaresCharging everywhereAim to update with measured data (Pro Rail)
Electric busses, electric trucks, freight trainsMovaresCharging at home (curve peaks during night)Update when specific data becomes available
Hydrogen trucks, hydrogen busses, hydrogen cars-Flat curve-
IndustryAll sectors except "food", "paper" and "other"-Flat curve
Food, Paper and OtherGasterraG2C profile-
AgricultureElectricityNEDUE3A curve-
HeatNEDUG2AUpdate when specific data becomes available

Supply

SectorSourceMethod
Solar PV"Open Power System Data platform"Profile from measured data, adjusted to match country specific full load hours
Solar Thermal"KNMI"Profile from measured data, adjusted to solar-thermal behaviour
Wind"Open Power System Data platform"Profile from measured data, adjusted to match country specific full load hours
OtherRiverFlat curve
Dispatchable technologiesProduction determined by merit order

For NL2015 the OPSD data is incomplete (< 98% of data points available) Hence, different sources (SoDa: Solar Radiation Data for PV and Ecofys data for wind) have been used to generate this curve.

Time curves

Time curves define how the national production of energy carriers changes over the years (up to 2040)

They are documented in on ETDataset in the source analyses of the specific datasets. Example for The Netherlands - 2015

For the Netherlands the time curves are based on:

For all other countries the time curves are based on the Primes reference scenario 2016.

Details

ETDataset - curves contains all raw data, scripts and further explanations.

Discussion

Feedback on the curves we use is very welcome! If you have a comment or a better source please let us know, you can: