heavy_duty_transport¶
Heavy Duty Transport / Heavy Goods Vehicle (HGV)
Main module for preparation of model data (static and timeseries) for heavy duty transport.
Contents of this module * Creation of DB tables * Download and preprocessing of vehicle registration data from BAST * Calculation of hydrogen demand based on a Voronoi distribution of counted truck
traffic among NUTS 3 regions.
- Write results to DB
- Map demand to H2 buses and write to DB
Configuration
The config of this dataset can be found in datasets.yml in section mobility_hgv.
Scenarios and variations
Assumptions can be changed within the datasets.yml.
In the context of the eGon project, it is assumed that e-trucks will be completely hydrogen-powered and in both scenarios the hydrogen consumption is assumed to be 6.68 kgH2 per 100 km with an additional [supply chain leakage rate of 0.5 %]( https://www.energy.gov/eere/fuelcells/doe-technical-targets-hydrogen-delivery).
### Scenario NEP C 2035
The ramp-up figures are taken from [Scenario C 2035 Grid Development Plan 2021-2035]( https://www.netzentwicklungsplan.de/sites/default/files/paragraphs-files/ NEP_2035_V2021_2_Entwurf_Teil1.pdf). According to this, 100,000 e-trucks are expected in Germany in 2035, each covering an average of 100,000 km per year. In total this means 10 Billion km.
### Scenario eGon100RE
In the case of the eGon100RE scenario it is assumed that the HGV traffic is completely hydrogen-powered. The total freight traffic with 40 Billion km is taken from the [BMWk Langfristszenarien GHG-emission free scenarios (SNF > 12 t zGG)]( https://www.langfristszenarien.de/enertile-explorer-wAssets/docs/ LFS3_Langbericht_Verkehr_final.pdf#page=17).
## Methodology
Using a Voronoi interpolation, the censuses of the BASt data is distributed according to the area fractions of the Voronoi fields within each mv grid or any other geometries like NUTS-3.
-
class
HeavyDutyTransport(dependencies)[source]¶ Bases:
egon.data.datasets.Dataset