Add emobility documentation

docs/data/e-mobility.rst

@@ -1 +1,35 @@
-What flexibilities does e-mobility provide to the system. How did we implement it?
+The flexibility potential of  EVs is determined on the basis of the trip data
+created with SimBEV (see :ref:`mobility-demand-mit-ref`).
+It is assumed, that only charging at private charging points, comprising charging points at
+home and at the workplace, can be flexibilized. Public fast (e.g. gas stations) and slow charging (e.g. schools
+and shopping facilities) stations are assumed not to provide demand-side flexibility.
+Further, vehicle-to-grid is not considered and it is assumed that charging can only be shifted
+within a charging event. Shifting charging demand to a later charging event, for example
+from charging at work during working hours to charging at home in the evening, is therefore
+not possible. In the generation of the trip data itself it is already considered, that
+EVs are not charged everytime a charging point is available, but only if a certain
+lower state of charge (SoC) is reached or the energy level is not sufficient for the next ride.
+
+In `eTraGo <https://github.com/openego/eTraGo>`_, the flexibility of the EVs is modeled
+using a storage model based on [Brown2018]_ and [Wulff2020]_.
+The used model is visualised in the upper right in figure :ref:`mit-model`.
+Its parametrization is for both the eGon2035 and eGon100RE scenario conducted in the
+:py:class:`MotorizedIndividualTravel<egon.data.datasets.emobility.motorized_individual_travel.MotorizedIndividualTravel>`
+dataset in the function
+:py:func:`generate_load_time_series<egon.data.datasets.emobility.motorized_individual_travel.model_timeseries.generate_load_time_series>`.
+The model consists of loads for static driving demands and stores for the fleet’s batteries.
+The stores are constrained by hourly lower and upper SoC limits.
+The lower SoC limit represents the inflexible charging demand while the
+SoC band between the lower and upper SoC limit represents the flexible charging demand.
+Further, the charging infrastructure is represented by unidirectional links from electricity
+buses to EV buses. Its maximum charging power per hour is set to the available charging power
+of grid-connected EVs.
+
+In `eDisGo <https://github.com/openego/eDisGo>`_, the flexibility potential for
+controlled charging is modeled using
+so-called flexibility bands. These bands comprise an upper and lower power band for
+the charging power and an upper and lower energy band for the energy to be recharged
+for each charging point in an hourly resolution. These flexibility bands are not
+set up in eGon-data but in eDisGo, using the trip data from eGon-data.
+For further information on the flexibility bands see
+`eDisGo documentation <https://edisgo.readthedocs.io/en/dev/features_in_detail.html#charging-strategies>`_.

docs/data/mobility_demand.rst

@@ -7,18 +7,33 @@ The electricity demand data of motorized individual travel (MIT) for both the eG
 and eGon100RE scenario is set up in the
 :py:class:`MotorizedIndividualTravel<egon.data.datasets.emobility.motorized_individual_travel.MotorizedIndividualTravel>`
 dataset.
-In a first step, pre-generated trip data, including information on driving, parking and
-(user-oriented) charging times is downloaded and written to the database.
+For the eGon2035, the workflow is visualised in figure :ref:`mit-model`. The workflow
+for the eGon100RE scenario is analogous to the workflow for the eGon2035 scenario.
+In a first step, pre-generated SimBEV trip data, including information on driving, parking and
+(user-oriented) charging times is downloaded.
 In the second step, the number of EVs in each MV grid district in the future scenarios is determined.
 Last, based on the trip data and the EV numbers, charging time series as well as
 time series to model the flexibility of EVs are set up.
 In the following, these steps are explained in more detail.
 
+.. figure:: /images/eGon_emob_MIT_model.png
+  :name: mit-model
+  :width: 800
+
+  Workflow to set up charging demand data for MIT in the eGon2035 scenario
+
+
 The trip data 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
+SimBEV generates driving and parking 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]_.
+The data contain information on energy consumption during the drive, as well as on
+the availability of charging points at the parking
+location and in case of an available charging point the corresponding charging demand,
+charging power and charging point use case
+(home charging point, workplace charging point, public charging point and fast charging
+point).
 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
@@ -62,13 +77,15 @@ The results are written to table
 :py:class:`EgonEvMvGridDistrict<egon.data.datasets.emobility.motorized_individual_travel.db_classes.EgonEvMvGridDistrict>`.
 
 On the basis of the assigned EVs per MV grid district and the trip data, charging demand
-time series in each MV grid district can be determined. For inflexible charging it is
+time series in each MV grid district can be determined. For inflexible charging
+(see lower right in figure :ref:`mit-model`) it is
 assumed that the EVs are charged with full power as soon as they arrive at a charging
 station until they are fully charged. The respective charging power and demand is obtained
 from the trip data. The individual charging demand time series per EV are summed up
 to obtain the charging time series per MV grid district.
-The generation of time series to model flexible charging of EVs is described in section
-:ref:`flexible-charging-ref`.
+The generation of time series to model flexible charging of EVs (upper right in figure
+:ref:`mit-model`) is described in section :ref:`flexible-charging-ref`.
+
 
 Heavy-duty transport
 +++++++++++++++++++++