Add documentation for mobility demand

docs/data/mobility_demand.rst

@@ -1 +1,71 @@
-Information about demands in the mobility sector. 
+Motorized individual travel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The electricity demand data of motorized individual travel (MIT) for both the eGon2035
+and eGon100RE scenario is set up
+in the :py:class:`MotorizedIndividualTravel<egon.data.datasets.emobility.motorized_individual_travel.MotorizedIndividualTravel>`
+dataset.
+
+The profiles are generated using a modified version of
+`SimBEV v0.1.3 <https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5>`_.
+SimBEV generates driving profiles for battery electric vehicles (BEVs) and
+plug-in hybrid electric vehicles (PHEVs) based on MID survey data [MiD2017]_ per
+RegioStaR7 region type [RegioStaR7_2020]_.
+These profiles include driving, parking and (user-oriented) charging times.
+Different vehicle classes are taken
+into account whose assumed technical data is given in table :ref:`ev-types-data`.
+Moreover, charging probabilities for multiple types of charging
+infrastructure are presumed based on [NOW2020]_ and [Helfenbein2021]_.
+Given these assumptions, a pool of 33.000 EVs-types is pre-generated and provided through the data bundle
+(see :ref:`data-bundle-ref`) as well as written
+to table :py:class:`EgonEvTrip<egon.data.datasets.emobility.motorized_individual_travel.db_classes.EgonEvTrip>`.
+The complete tech data and assumptions of the run can be found in the
+metadata_simbev_run.json file, that is provided along with the trip data.
+
+.. csv-table:: EV types
+    :header: "Tecnnology", "Size", "Max. charging capacity slow in kW", "Max. charging capacity fast in kW", "Battery capacity in kWh", "Energy consumption in kWh/km"
+    :widths: 10, 10, 30, 30, 25, 10
+    :name: ev-types-data
+
+    "BEV", "mini", 11, 120, 60, 0.1397
+    "BEV", "medium", 22, 350, 90, 0.1746
+    "BEV", "luxury", 50, 350, 110, 0.2096
+    "PHEV", "mini", 3.7, 40, 14, 0.1425
+    "PHEV", "medium", 11, 40, 20, 0.1782
+    "PHEV", "luxury", 11, 120, 30, 0.2138
+
+Heavy-duty transport
+~~~~~~~~~~~~~~~~~~~~~
+
+In the context of the eGon project, it is assumed that all e-trucks will be
+completely hydrogen-powered. The hydrogen demand data of all e-trucks is set up
+in the :py:class:`HeavyDutyTransport<egon.data.datasets.emobility.heavy_duty_transport.HeavyDutyTransport>`
+dataset for both the eGon2035 and eGon100RE scenario.
+
+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 %
+(see `here <https://www.energy.gov/eere/fuelcells/doe-technical-targets-hydrogen-delivery>`_).
+
+For the eGon2035 scenario the ramp-up figures are taken from the
+`network development plan (version 2021) <https://www.netzentwicklungsplan.de/sites/default/files/paragraphs-files/NEP_2035_V2021_2_Entwurf_Teil1.pdf>`_
+(Scenario C 2035). 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.
+
+For the eGon100RE scenario it is assumed that the heavy-duty transport is
+completely hydrogen-powered. The total freight traffic with 40 Billion km is
+taken from the
+`BMWK Langfristszenarien <https://www.langfristszenarien.de/enertile-explorer-wAssets/docs/LFS3_Langbericht_Verkehr_final.pdf#page=17>`_
+for heavy-duty vehicles larger 12 t allowed total weight (SNF > 12 t zGG).
+
+The total hydrogen demand is spatially distributed on the basis of traffic volume data from [BASt]_.
+For this purpose, first a voronoi partition of Germany using the traffic measuring points is created.
+Afterwards, the spatial shares of the Voronoi regions in each NUTS3 area are used to allocate
+hydrogen demand to the NUTS3 regions and are then aggregated per NUTS3 region.
+The refuelling is assumed to take place at a constant rate.
+Finally, to
+determine the hydrogen bus where the hydrogen demand is allocated to, the centroid
+of each NUTS3 region is used to determine the respective hydrogen Voronoi cell (see
+:py:class:`GasAreaseGon2035<egon.data.datasets.gas_areas.GasAreaseGon2035>` and
+:py:class:`GasAreaseGon100RE<egon.data.datasets.gas_areas.GasAreaseGon100RE>`) it is
+located in.