Features/#1460 integrate new mit lgv dataset

CHANGELOG.rst

@@ -15,10 +15,18 @@ Added
 * Use MaStR data for home_batteries for allocation for all scenarios (status + future)
   `#1470 <https://github.com/openego/eGon-data/issues/1470>`_
 * Distinguish grid-scale battery storage (new carrier 'BESS') from home batteries
-  throughout the pipeline: real MaStR-based BESS carry-forward (aged, all scenarios) 
-  alongside home batteries, and separate eTraGo carriers/cost parameters 
+  throughout the pipeline: real MaStR-based BESS carry-forward (aged, all scenarios)
+  alongside home batteries, and separate eTraGo carriers/cost parameters
   ('BESS' vs'home_battery') instead of one generic 'battery' carrier
   `#1478 <https://github.com/openego/eGon-data/issues/1478>`_
+* Write flexibility diagnostics alongside the eTraGo model for the new
+  eMobility methodology: the grid-side dumb charging load, its flexible
+  share and the driving load per MV grid district
+  (`demand.egon_ev_mit_lgv_flex_timeseries`), an annual energy balance
+  per grid district, charging use case and vehicle type
+  (`demand.egon_ev_mit_lgv_energy_balance`) and the charging load per
+  use case (`demand.egon_ev_mit_lgv_charging_profile_use_case`)
+  `#1460 <https://github.com/openego/eGon-data/issues/1460>`_
 
 
 Changed
@@ -39,8 +47,8 @@ Changed
   factor columns to the configured scenarios, and remove obsolete
   status2019/status2023/eGon100RE handling
   `#1433 <https://github.com/openego/eGon-data/issues/1433>`_
-* Adapt scenario_capacities to new scenarios; implementing the 
-  new Kraftwerksliste from the NEP2025; remove obsolete 
+* Adapt scenario_capacities to new scenarios; implementing the
+  new Kraftwerksliste from the NEP2025; remove obsolete
   scenario (status2019/status2023/eGon100RE) handling
   `#1415 <https://github.com/openego/eGon-data/issues/1415>`_
 * Adapt heat_demand TaskGroup to new scenarios: generalize district
@@ -84,6 +92,19 @@ Changed
   `#1483 <https://github.com/openego/eGon-data/issues/1483>`_
 * Allign Methodology for laoding NEP target values for Battery storage for all scenarios
   `#1471 <https://github.com/openego/eGon-data/issues/1471>`_
+* Replace the eMobility dataset for motorized individual travel by a new
+  one covering vehicle class M1 (passenger cars) and N1 (light
+  commercial vehicles < 3.5 t). Vehicle pool, events, vehicle counts per
+  municipality, charging locations and the allocation of vehicles to
+  municipalities are delivered as input data and downloaded from Zenodo
+  instead of being derived from KBA registration statistics; the
+  charging infrastructure is generated together with the vehicles, so
+  events, vehicles and charging points are mutually consistent.
+  `eGon2035` keeps the previous methodology and input data. All tables
+  are renamed to the `egon_ev_mit_lgv_` prefix and the foreign key
+  column `egon_ev_pool_ev_id` to `ev_id`; `demand.egon_ev_mit_lgv_metadata`
+  now stores the simBEV and GeoLIS run configurations whole, as JSONB
+  `#1460 <https://github.com/openego/eGon-data/issues/1460>`_
 
 
 Bug Fixes

docs/data/e-mobility.rst

@@ -1,19 +1,25 @@
-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.
+The flexibility potential of EVs is determined on the basis of the event data
+of the vehicle profiles (see :ref:`mobility-demand-mit-ref`).
+It is assumed that only charging where the vehicle has reliable access to its
+own charging point can be flexibilized. For the new methodology these are the
+charging use cases ``depot``, ``home_detached``, ``home_apartment`` and
+``work``; ``street``, ``retail``, ``urban_fast`` and ``highway_fast`` are
+assumed not to provide demand-side flexibility. The legacy ``eGon2035``
+scenario uses its own taxonomy and flexibilizes charging at home and at the
+workplace.
 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
+not possible. In the generation of the event 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
+It is set up for every configured scenario that models flexible charging (cf.
+:py:func:`is_flexible<egon.data.datasets.emobility.motorized_individual_travel.model_timeseries.is_flexible>`);
+status quo scenarios get a plain load instead. Its parametrization is 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>`.
@@ -25,6 +31,13 @@ Further, the charging infrastructure is represented by unidirectional links from
 buses to EV buses. Its maximum charging power per hour is set to the available charging power
 of grid-connected EVs.
 
+Note that the load written for a flexible scenario carries the *driving* energy,
+while the one written for its lowflex counterpart and for status quo scenarios
+carries the grid-side *charging* energy. Both references are exported
+explicitly for the new methodology, together with the recipe for deriving the
+realised load shift from an eTraGo result; see
+:ref:`mit-reference-points-ref`.
+
 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