Merge remote

docs/data/electricity_demand.rst

@@ -1,3 +1,92 @@
 .. _elec_demand-ref:
 
-Information about electricity demands and their spatial and temporal aggregation 
+The electricity demand considered includes demand from the residential, commercial and industrial sector. 
+The target values for scenario *eGon2035* are taken from the German grid development plan from 2021 [NEP2021]_,
+whereas the distribution on NUTS3-levels corresponds to the data from the research project *DemandRegio* [demandregio]_. 
+The following table lists the electricity demands per sector: 
+
+.. list-table:: Electricity demand per sector
+   :widths: 25 50
+   :header-rows: 1
+
+   * - Sector
+     - Annual electricity demand in TWh
+   * - residential
+     - 115.1
+   * - commercial 
+     - 123.5  
+   * - industrial
+     - 259.5
+
+A further spatial and temporal distribution of the electricity demand is needed to fullfil all requirements of the 
+subsequent grid optimization. Therefore different, sector-specific distributions methods were developed and applied. 
+
+Residential electricity demand
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The annual electricity demands of households on NUTS3-level from *DemandRegio* are scaled to meet the national target 
+values for the respective scenario in dataset :py:class:`DemandRegio <egon.data.datasets.demandregio.DemandRegio>`. 
+A further spatial and temporal distribution of residential electricity demands is performed in 
+:py:class:`HouseholdElectricityDemand <egon.data.datasets.electricity_demand.HouseholdElectricityDemand>` as described 
+in [Buettner2022]_.
+The result is a consistent dataset across aggregation levels with an hourly resolution. 
+
+.. figure:: /images/S27-3.png
+  :name: spatial_distribution_electricity_demand
+  :width: 400
+
+  Electricity demand on NUTS 3-level (upper left); Exemplary MVGD (upper right); Study region in Flensburg (20 Census cells, bottom) from [Buettner2022]_
+
+
+.. figure:: /images/S27-4a.png
+  :name: aggregation_level_electricity_demand
+  :width: 400
+
+  Electricity demand time series on different aggregation levels from [Buettner2022]_
+
+
+
+Commercial electricity demand
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The distribution of electricity demand from the commercial, trade and service (CTS) sector is also based on data from 
+*DemandRegio*, which provides annual electricity demands on NUTS3-level for Germany. In  dataset 
+:py:class:`CtsElectricityDemand <egon.data.datasets.electricity_demand.CtsElectricityDemand>` the annual electricity
+demands are further distributed to census cells (100x100m cells from [Census]_) based on the distribution of heat demands, 
+which is taken from the Pan-European Thermal Altlas version 5.0.1 [Peta]_. For further information refer to section 
+ref:`heat_demand`.
+The applied methods for a futher spatial and temporal distribution to buildings is described in [Buettner2022]_ and 
+performed in dataset :py:class:`CtsDemandBuildings <egon.data.datasets.electricity_demand_timeseries.CtsDemandBuildings>`
+
+Industrial electricity demand
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To distribute the annual industrial electricity demand OSM landuse data as well as information on industrial sites are 
+taken into account. 
+In a first step (:py:class:`CtsElectricityDemand <egon.data.datasets.electricity_demand.CtsElectricityDemand>`)
+different sources providing information about specific sites and further information on the  industry sector in which 
+the respective industrial site operates are combined. Here, the three data sources [Hotmaps]_, [sEEnergies]_ and 
+[Schmidt2018]_ are aligned and joined.  
+Based on the resulting list of industrial sites in Germany and information on industrial landuse areas from OSM [OSM]_
+which where extracted and processed in :py:class:`OsmLanduse <egon.data.datasets.loadarea.OsmLanduse>` the annual demands
+were distributed.
+The spatial and temporal distribution is performed in 
+:py:class:`IndustrialDemandCurves <egon.data.datasets.industry.IndustrialDemandCurves>`. 
+For the spatial distribution of annual electricity demands from *DemandRegio* [demandregio]_ which are available on 
+NUTS3-level are in a first step evenly split 50/50 between industrial sites and OSM-polygons tagged as industrial areas.
+Per NUTS-3 area the respective shares are then distributed linearily based on the area of the corresponding landuse polygons 
+and evenly to the identified industrial sites.
+In a next step the temporal disaggregation of the annual demands is carried out taking information about the industrial
+sectors and sector-specific standard load profiles from [demandregio]_ into account.
+Based on the resulting time series and their peak loads the corresponding grid level and grid connections point is 
+identified. 
+
+Electricity demand in neighbouring countries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The neighbouring countries considered in the model are represented in a lower spatial resolution of one or two buses per
+country. The national demand timeseries in an hourly resolution of the respective countries is taken from the Ten-Year 
+Network Development Plan, Version 2020 [TYNDP]_. In case no data for the target year is available the data is is
+interpolated linearly.  
+Refer to the corresponding dataset for detailed information: 
+:py:class:`ElectricalNeighbours <egon.data.datasets.ElectricalNeighbours>`

docs/data/electricity_supply.rst

@@ -19,6 +19,8 @@ The final distribution in the eGon2035 scenario is shown in figure :ref:`generat
 Onshore wind
 +++++++++++++
 
+
+
 Offshore wind
 ++++++++++++++
 
@@ -87,6 +89,45 @@ Disaggregation of PV rooftop scenario capacities:
   using MaStR data as basis.
 
 Hydro
-++++++
++++++
+
+In the case of hydropower plants, a distinction is made between the carrier run-of-river 
+and reservoir. 
+The methods to distribute and allocate are the same for both carriers.
+In a first step all suitable power plants (correct carrier, valid geolocation, information 
+about federal state) are selected and their installed capacity is scaled to meet the target
+values for the respective federal state and scenario. 
+Information about the voltage level the power plants are connected to is obtained. In case 
+no information is availabe the voltage level is identified using threshold values for the 
+installed capacity (see :func:`assign_voltage_level <egon.data.datasets.power_plants.assign_voltage_level>`). 
+In a next step the correct grid connection point is identified based on the voltage level
+and geolocation of the power plants (see :func:`assign_bus_id <egon.data.datasets.power_plants.assign_bus_id>`)
+The resulting list of power plants it added to table 
+:py:class:`EgonPowerPlants <egon.data.datasets.power_plants.EgonPowerPlants>`.
+
+Biomass
++++++++
+
+The allocation of biomass-based power plants follows the same method as the one for hydro
+power plants and is performed in function :func:`insert_biomass_plants <egon.data.datasets.power_plants.insert_biomass_plants>`
+
+
+
+Conventional
+++++++++++++
+
+**CHP**
+
+
+**non-chp**
+
+
+In function :func:`allocate_conventional_non_chp_power_plants <egon.data.datasets.power_plants.allocate_conventional_non_chp_power_plants>`
+capacities for conventional power plants, which are no chp plants, with carrier *oil* and 
+*gas* are allocated.
+
+
+
+
 
 

docs/data/heat_demand.rst

@@ -1,4 +1,5 @@
-Heat demands comprise space heating and dirinking hot water demands from 
+.. _heat_demand:
+Heat demands comprise space heating and drinking hot water demands from 
 residential and comertial trade and service (CTS) buildings.
 Process heat demands from the industry are, depending on the required temperature
 level, modelled as electrcity, hydrogen or methane demand. 

docs/literature.rst

@@ -30,6 +30,8 @@ Literature
 
 .. [Kleinhans] D. Kleinhans, Towards a systematic characterization of the potential of demand side management, arXiv (2014), doi: 10.48550/ARXIV.1401.4121. URL https://arxiv.org/abs/1401.4121
 
+.. [MaStR] Bundesnetzagentur für Elektrizität, Gas, Telekommunikation, Post und Eisenbahnen, Marktstammdatenregister - Datendownload (Nov. 2022). URL https://www.marktstammdatenregister.de/MaStR/Datendownload
+
 .. [MiD2017] Bundesministerium für Digitales und Verkehr, Mobilität in Deutschland 2017 (2017). URL https://daten.clearingstelle-verkehr.de/279/
 
 .. [Mueller2018] U. Mueller, L. Wienholt, D. Kleinhans, I. Cussmann, W.-D. Bunke, G. Pleßmann, J. Wendiggensen 2018 J. Phys.: Conf. Ser. 977 012003, DOI 10.1088/1742-6596/977/1/012003
@@ -47,3 +49,5 @@ Literature
 .. [Schmidt2018] D. Schmidt, Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model (Nov. 2019). doi:10.5281/zenodo.3613767. URL https://doi.org/10.5281/zenodo.3613767
 
 .. [sEEnergies] T. Fleiter, P. Manz, N. Neuwirth, F. Mildner, K. Persson, U.AND Kermeli, W. Crijns-Graus, C. Rutten, seenergies d5.1 dataset web-app.seenergies arcgis online web-apps hosted by europa-universität flensburg (2020). URL https://tinyurl.com/sEEnergies-D5-1
+
+.. [TYNDP] European Network of Transmission System Operators for Electricity, European Network of Transmission System Operators for Gas, Ten-Year Network Development Plans - “TYNDP 2020 Scenarios” (2020)