Documentation gas

docs/data.rst

@@ -81,7 +81,7 @@ Battery stores
 .. include:: data/batteries.rst
 
 Gas stores
------------------
+----------
 
 .. include:: data/gas_stores.rst
 

docs/data/electricity_supply.rst

@@ -1 +1,62 @@
-Information on electricity supply by carrier etc. 
+Gas turbines
+~~~~~~~~~~~~
+
+The gas turbines, or open cycle gas turbines (OCGTs) allow the production
+of electricity from methane and are modelled with unidirectional PyPSA *links*,
+which connect methane buses to power buses.
+
+The capacities of the gas turbines are invariable and considered as constant.
+The technical parameters (investment and marginal costs, efficiency, lifetime)
+comes from the PyPSA technology data [technoData]_.
+
+In Germany
+""""""""""
+
+In Germany, the gas turbines listed in the Netzentwicklungsplan [NEP2021]_
+are matched to the Marktstammdatenregister in order to get their geographical
+coordinates in :py:func:`allocate_conventional_non_chp_power_plants
+<egon.data.datasets.power_plants.allocate_conventional_non_chp_power_plants>`.
+The matched units are then associated to the corresponding power and methane
+buses.
+
+The implementation of gas turbines in the data model is detailed in the
+:py:mod:`OpenCycleGasTurbineEtrago <egon.data.datasets.power_etrago.OpenCycleGasTurbineEtrago>`
+page of our documentation.
+
+**Warning**
+
+OCGT in Germany are still missing in eGon100RE: https://github.com/openego/eGon-data/issues/983
+
+In the neighboring countries
+""""""""""""""""""""""""""""
+
+In the scenario eGon2035, the gas turbines capacities abroad comes from the
+TYNDP 2035 [TYNDP]_, the implementation is detailed in :py:func:`eGon2035.tyndp_gas_generation
+<egon.data.datasets.gas_neighbours.eGon2035.tyndp_gas_generation>`.
+
+In the scenario eGon100RE the gas turbines capacities in the neighboring
+countries are taken directly from the PyPSA-eur-sec run.
+
+Fuel cells
+~~~~~~~~~~
+
+The fuel cells allow the production of electricity from hydrogen and are
+modelled with unidirectional PyPSA *links*, which connect hydrogen buses
+to power buses.
+
+The data model contains the potentials of this technology, whose capacities
+are optimized. The technical parameters (investment and marginal costs,
+efficiency, lifetime) comes from the PyPSA technology data [technoData]_.
+
+In the eGon2035 scenario, fuel cells are modelled at every hydrogen bus
+in Germany, as well as in the eGon100RE scenario. In eGon100RE, this technology
+is also modelled in the neighboring countries. In Germany, the potentials
+are generally not limited, except when the connected buses are located more
+than 500m far from each other. In this particular case, the potential has
+an upper limit of 1 MW.
+
+The implementation of fuel cells in the data model is detailed in the
+:py:mod:`power_to_h2 <egon.data.datasets.hydrogen_etrago.power_to_h2>`
+page of our documentation.
+
+

docs/data/gas_demand.rst

@@ -1 +1,81 @@
-Information about gas demands and their spatial and temporal aggregation, including hydrogen and methane demands
+The industrial gas demand is modelled with PyPSA *loads*.
+
+In Germany
+~~~~~~~~~~
+
+In the scenario eGon2035, the industrial gas loads in Germany are taken
+directly from the eXtremos project data [eXtremos]_. They model the hourly-resolved
+industrial loads in Germany with a NUTS-3 spatial resolution, for methane
+(:math:`CH_4`), as well as for hydrogen (:math:`H_2`), for the year 2035.
+
+The spatial repartition of these loads is represented in the figure below
+(methane demand in grey and hydrogen in cyan). The size of the rounds on
+the figure corresponds to the total annual demand at the considered spot
+in the scenario eGon2035.
+
+.. image:: images/eGon2035_gas_ind_load_repartition_DE_withTitle.png
+   :width: 400
+
+In eGon100RE, the global industrial demand for methane and hydrogen (for
+whole Germany and for one year) is calculated by the PyPSA-eur-sec run.
+The spatial and the temporal repartitions used to distribute these values
+are corresponding to the spatial and temporal repartition of the hydrogen
+industrial demand from the eXtremos project [eXtremos]_ for the year 2050.
+(The same repartition is used, due to the lack of data were available for
+methane, because the eXtremos project considers that there won't be any
+industrial methane load in 2050.)
+
+.. image:: images/ind_gas_demand.png
+   :width: 400
+
+The figure above shows the temporal evolution of the methane (in grey) and
+hydrogen (in cyan) industrial demands in the year for both scenarios
+(eGon100RE in dashed).
+The total demands for whole Germany are to be found in the following table.
+
+.. list-table:: Total indutrial hydrogen and methane demands in Germany considered in eGon-data
+   :widths: 25 25 25
+   :header-rows: 1
+
+   * -
+     - eGon2035
+     - eGon100RE
+   * - :math:`CH_4` (in TWh)
+     - 195
+     - 105
+   * - :math:`H_2` (in TWh)
+     - 16
+     - 42
+
+The hydrogen loads are attributed only to the *H2_grid* buses.
+
+The implementation of these data is detailed in the :py:mod:`industrial_gas_demand
+<egon.data.datasets.industrial_gas_demand>` page of our documentation.
+
+In the neighboring countries
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In the scenario **eGon2035**, there are no hydrogen buses modelled in the
+neighboring countries. For this reason, the hydrogen industrial demand
+is there attributed to the electrical sector.
+The total demand is taken from the 'Distributed Energy' scenario of the
+Ten-Year Network Development Plan 2020 ([TYNDP]_). For the year
+2035, it has been linearly interpolated between the values for the year 2030
+and 2040. These industrial hydrogen loads are considered as constant in
+time. In other words, at each hour of the year, the same load should by
+fulfilled by electricity to supply the hydrogen industrial demand.
+
+Contrary to all the other loads described in the section, the modelled
+methane load abroad includes not only the industrial demand, but also the
+heat demand, that is supplied by methane. The total demand is again taken
+from the 'Distributed Energy' scenario of the TYNDP 2020 (linear interpolation
+between 2030 and 2040). For the temporal disaggregation of the demand in
+the year, the time series 'rural heat' from PyPSA-eur-sec is used to approximated
+the temporal profile, because the heat sector represents the biggest load.
+
+The implementation of these data is detailed in the :py:mod:`gas_neighbours.eGon2035
+<egon.data.datasets.gas_neighbours.eGon2035>` page of our documentation.
+
+In the scenario **eGon100RE**, the industrial gas loads (for methane as
+well as for hydrogen) in the neighboring countries are directly imported
+from the PyPSA-eur-sec run.

docs/data/gas_grids.rst

@@ -1,7 +1,89 @@
-Information about the gas grids and how they were created
+In the gas sector, only the transport grids are represented. They are modelled
+with bidirectional PyPSA *links* which allow to represent the energy exchange
+between distinct PyPSA *buses*. No losses are considered and the
+energy that can be transferred through a pipeline is limited by the e_nom
+parameter.
 
-Methane grid 
-~~~~~~~~~~~~
+In the scenario **eGon2035**, only the methane grid is modelled.
 
-Hydrogen grid
-~~~~~~~~~~~~~
+In the scenario **eGon100RE**, there are tree type of gas grid:
+
+* the remaining methane grid,
+* the retrofitted hydrogen grid,
+* the potential hydrogen grid extension (only present in Germany).
+
+In Germany
+~~~~~~~~~~
+
+To determine the topology of the grid, the status quo methane grid is used,
+with the data from SciGRID_gas [SciGRID_gas]_.
+
+eGon2035
+""""""""
+
+In the scenario eGon2035, the capacities of the methane pipelines are determined
+using the status quo pipeline diameters [SciGRID_gas]_ and the correspondence
+table [Kunz]_. These capacities are fixed.
+
+There are two types of hydrogen buses and none of them are connected through
+a grid:
+
+* H2_grid buses: these buses are located at the places than the methane
+  buses,
+* H2_saltcavern buses: these buses are located at the intersection of power
+  substations and potential saltcavern areas, adapted for hydrogen storage.
+
+In this scenario, there are no hydrogen bus modelled in the neighboring
+countries.
+
+The modelling of the methane grid in the scenario eGon2035 is represented
+below.
+
+.. image:: images/2035_gasgrid_enTitle_00.png
+   :width: 400
+
+The implementation of these data is detailed respectively in the
+:py:mod:`gas_grid <egon.data.datasets.gas_grid>` and :py:mod:`hydrogen_etrago.bus
+<egon.data.datasets.hydrogen_etrago.bus>` pages of our documentation.
+
+eGon100RE
+"""""""""
+
+In the scenario eGon100RE, the methane grid has the same topology than in
+the scenario eGon2035. The capacities of the methane pipelines are still
+fixed, but they are reduced, because a part of the grid is retrofitted
+into an hydrogen grid.
+
+The retrofitted hydrogen grid has the same topology than the methane grid.
+The share of the methane grid which is retrofitted is calculated by the
+PyPSA-eur-sec run.
+
+The potential hydrogen grid extensions link each hydrogen saltcavern to
+its nearest (retrofitted) hydrogen grid bus. The extension has no upper
+boundary and its technical parameters are taken from the PyPSA technology
+data [technoData]_. This option is modelled exclusively in Germany.
+
+The modelling of the methane grid in the scenario eGon100RE is represented
+below.
+
+.. image:: images/100RE_gasgrid_enTitle_00.png
+   :width: 400
+
+The implementation of these data is detailed respectively in the :py:mod:`gas_grid
+<egon.data.datasets.gas_grid>` and :py:mod:`h2_grid <egon.data.datasets.hydrogen_etrago.h2_grid>`
+pages of our documentation.
+
+
+Cross-bordering pipelines
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The cross-bordering pipelines capacities are taken respectively from the
+TYNDP [TYNDP]_ for eGon2035 and from PyPSA-eur-sec for eGon100RE.
+
+For the cross-bordering gas pipelines with Germany, the capacity is uniformly
+distributed between the all pipelines connecting one country to Germany.
+
+The implementation of these data is detailed respectively in the
+:py:func:`gas_neighbours.eGon2035.grid <egon.data.datasets.gas_neighbours.eGon2035.grid>` and
+:py:func:`insert_gas_neigbours_eGon100RE <egon.data.datasets.gas_neighbours.eGon100RE.insert_gas_neigbours_eGon100RE>`
+pages of our documentation.

docs/data/gas_stores.rst

@@ -1,7 +1,59 @@
-Description of methods and assumptions to include potential h2 stores in the system
-
 Hydrogen stores
----------------
+~~~~~~~~~~~~~~~
+
+In both scenarios is there the option to store hydrogen. In the scenario
+eGon2035, these potentials are modelled only in Germany, because the hydrogen
+sector is not modelled in the neighboring countries.
+Hydrogen is stored either in steel tanks (*overground storage*) or in saltcavern
+(*underground storage*), which are modelled by PyPSA *stores*. The technical 
+parameters (investment and marginal costs, efficiency, lifetime) comes
+from the PyPSA technology data [technoData]_. The steel tank storage potentials
+are not limited. On the contrary, the saltcavern storage potentials do have
+an upper boundary.
+
+In Germany
+""""""""""
+
+The saltcavern potentials are located at the intersection between the power
+substations and the adapted saltcavern surface areas, defined in the InSpEE-DS
+report from the Bundesanstalt für Geowissenschaften und Rohstoffe [BGR]_.
+These potentials, identical in the both scenarios, are represented in the
+figure below.
+
+.. image:: images/H2_underground_stores_withTitle.png
+   :width: 400
+
+The implementation of these data is detailed in the :py:mod:`hydrogen_etrago.storage
+<egon.data.datasets.hydrogen_etrago.storage>` page of our documentation.
+
+In eGon100RE, the retrofitted hydrogen grid can also be used as storage,
+with unvariable capacity. This implementation is detailed in the 
+:py:mod:`grid_storage <egon.data.datasets.ch4_storages>` page of our documentation.
+
+In the neighboring countries
+""""""""""""""""""""""""""""
+
+In the scenario eGon100RE the hydrogen storage potentials in the neighboring
+countries are taken directly from the PyPSA-eur-sec run.
 
 Methane stores
---------------
+~~~~~~~~~~~~~~
+
+The methane stores are modelled as PyPSA *stores* and they are invariable.
+In Germany, two types of methane stores are modelled:
+
+* the underground caverns: capacities from SciGRID_gas [SciGRID_gas]_,
+* the storage capacity of the German gas grid.
+
+The storage capacity of the gas grid (130 GWh according to the Bundesnetzagentur
+[Bna]_) is uniformly distributed between all the methane nodes in Germany
+in the scenario eGon2035.
+In the scenario eGon100RE, the retrofitted hydrogen grid take a share of
+this storage capacity over.
+
+The implementation of the methane storage in the data model is detailed in the
+:py:mod:`ch4_storages <egon.data.datasets.ch4_storages>` page of our documentation.
+
+In the neighboring countries, the methane store capacities from SciGRID_gas [SciGRID_gas]_
+are used in the eGon2035 scenario and data from PyPSA-eur-sec are used in
+the eGon100RE scenario.