Add PV rooftop documentation

docs/data/electricity_supply.rst

@@ -17,18 +17,70 @@ The final distribution in the eGon2035 scenario is shown in figure :ref:`generat
   Generator park in the eGon2035 scenario
 
 Onshore wind
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 Offshore wind
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 PV ground mounted
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 PV rooftop
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+
+In a first step, the target capacity in the eGon2035 and eGon100RE scenarios is distributed
+to all MV grid districts linear to the residential and CTS electricity demands in the
+grid district (done in function
+:func:`pv_rooftop_per_mv_grid<egon.data.datasets.power_plants.pv_rooftop.pv_rooftop_per_mv_grid>`).
+
+Afterwards, the PV rooftop capacity per MV grid district is disaggregated
+to individual buildings inside the grid district (done in function
+:func:`pv_rooftop_to_buildings<egon.data.datasets.power_plants.pv_rooftop_buildings.pv_rooftop_to_buildings>`).
+The basis for this is data from the MaStR, which is first cleaned and missing information
+inferred, and then allocated to specific buildings. New PV plants are in a last step
+added based on the capacity distribution from MaStR.
+These steps are in more detail described in the following.
+
+MaStR data cleaning and inference:
+
+* Drop duplicates and entries with missing critical data.
+* Determine most plausible capacity from multiple values given in MaStR data.
+* Drop generators that don't have a plausible capacity (23.5 MW > P > 0.1 kW).
+* Randomly and weighted add a start-up date if it is missing.
+* Extract zip and municipality from 'site' given in MaStR data.
+* Geocode unique zip and municipality combinations with Nominatim (1 sec
+  delay). Drop generators for which geocoding failed or which are located
+  outside the municipalities of Germany.
+* Add some visual sanity checks for cleaned data.
+
+Allocation of MaStR plants to buildings:
+
+* Allocate each generator to an existing building from OSM or a synthetic building
+  (see :ref:`building-data-ref`).
+* Determine the quantile each generator and building is in depending on the
+  capacity of the generator and the area of the polygon of the building.
+* Randomly distribute generators within each municipality preferably within
+  the same building area quantile as the generators are capacity wise.
+* If not enough buildings exist within a municipality and quantile additional
+  buildings from other quantiles are chosen randomly.
+
+Disaggregation of PV rooftop scenario capacities:
+
+* The scenario data per federal state is linearly distributed to the MV grid
+  districts according to the PV rooftop potential per MV grid district.
+* The rooftop potential is estimated from the building area given from the OSM
+  buildings.
+* Grid districts, which are located in several federal states, are allocated
+  PV capacity according to their respective roof potential in the individual
+  federal states.
+* The disaggregation of PV plants within a grid district respects existing
+  plants from MaStR, which did not reach their end of life.
+* New PV plants are randomly and weighted generated using the capacity distribution of
+  PV rooftop plants from MaStR.
+* Plant metadata (e.g. plant orientation) is also added randomly and weighted
+  using MaStR data as basis.
 
 Hydro
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