"""
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.
"""
from pathlib import Path
import csv
from loguru import logger
import requests
from egon.data import config, db
from egon.data.datasets import Dataset
from egon.data.datasets.emobility.heavy_duty_transport.create_h2_buses import (
insert_hgv_h2_demand,
)
from egon.data.datasets.emobility.heavy_duty_transport.db_classes import (
EgonHeavyDutyTransportVoronoi,
)
from egon.data.datasets.emobility.heavy_duty_transport.h2_demand_distribution import ( # noqa: E501
run_egon_truck,
)
WORKING_DIR = Path(".", "heavy_duty_transport").resolve()
DATASET_CFG = config.datasets()["mobility_hgv"]
TESTMODE_OFF = (
config.settings()["egon-data"]["--dataset-boundary"] == "Everything"
)
[docs]def create_tables():
engine = db.engine()
EgonHeavyDutyTransportVoronoi.__table__.drop(bind=engine, checkfirst=True)
EgonHeavyDutyTransportVoronoi.__table__.create(
bind=engine, checkfirst=True
)
logger.debug("Created tables.")
[docs]def download_hgv_data():
sources = DATASET_CFG["original_data"]["sources"]
# Create the folder, if it does not exist
WORKING_DIR.mkdir(parents=True, exist_ok=True)
url = sources["BAST"]["url"]
file = WORKING_DIR / sources["BAST"]["file"]
response = requests.get(url)
with open(file, "w") as f:
writer = csv.writer(f)
for line in response.iter_lines():
writer.writerow(line.decode("ISO-8859-1").split(";"))
logger.debug("Downloaded BAST data.")
[docs]class HeavyDutyTransport(Dataset):
def __init__(self, dependencies):
super().__init__(
name="HeavyDutyTransport",
version="0.0.2",
dependencies=dependencies,
tasks=(
{
create_tables,
download_hgv_data,
},
run_egon_truck,
insert_hgv_h2_demand,
),
)