Documentation/rli datasets

.github/workflows/build.yml

@@ -25,12 +25,9 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - "3.7"
           - "3.8"
         platform: [ubuntu-latest, macos-latest]
         exclude:
-          - platform: macos-latest
-            python-version: "3.7"
           - platform: macos-latest
             python-version: "3.8"
       max-parallel: 4

.readthedocs.yml

@@ -8,3 +8,8 @@ python:
     - requirements: docs/requirements.txt
     - method: pip
       path: .
+# Set the version of Python
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.8"

CHANGELOG.rst

@@ -29,11 +29,12 @@ Added
   returns the current configuration settings. See `PR #159`_ for more
   details.
 * You can now use tasks which are not part of a ``Dataset``, i.e. which
-  are unversioned, as dependencies of a dataset. See `PR #318`_ for more
+  are unversioned, as dependencies of a dataset. See
+  `#318 <https://github.com/openego/eGon-data/issues/318>`_ for more
   details.
 * You can now force the tasks of a ``Dataset`` to be always executed by
-  giving the version of the ``Dataset`` a ``".dev"`` suffix. See `PR
-  #318`_ for more details.
+  giving the version of the ``Dataset`` a ``".dev"`` suffix. See
+  `#318 <https://github.com/openego/eGon-data/issues/318>`_ for more details.
 * OSM data import as done in open_ego
   `#1 <https://github.com/openego/eGon-data/issues/1>`_
   which was updated to the latest long-term data set of the 2021-01-01

docs/conf.py

@@ -30,10 +30,10 @@
     "pr": ("https://github.com/openego/eGon-data/pull/%s", "PR #"),
 }
 # on_rtd is whether we are on readthedocs.org
-on_rtd = os.environ.get("READTHEDOCS", None) == "True"
+# on_rtd = os.environ.get("READTHEDOCS", None) == "True"
 
-if not on_rtd:  # only set the theme if we're building docs locally
-    html_theme = "sphinx_rtd_theme"
+#if not on_rtd:  # only set the theme if we're building docs locally
+html_theme = "sphinx_rtd_theme"
 
 html_use_smartypants = True
 html_last_updated_fmt = "%b %d, %Y"

docs/data.rst

@@ -15,7 +15,7 @@ and their processing within the eGon-data pipeline are described here.
 Grid models
 ===========
 
-Power grid models of different voltage levels form a central part of the eGon data model, which is required for cross-grid-level optimization.
+Power grid models of different voltage levels form a central part of the eGon-data model, which is required for cross-grid-level optimization.
 In addition, sector coupling necessitates the representation of the gas grid infrastructure, which is also described in this section.
 
 Electricity grid
@@ -41,6 +41,8 @@ Electricity
 
 .. include:: data/electricity_demand.rst
 
+.. _heat-demand-ref:
+
 Heat
 ----
 
@@ -97,8 +99,10 @@ Dynamic line rating
 
 .. include:: data/DLR.rst
 
-E-Mobility
-----------
+.. _flexible-charging-ref:
+
+Flexible charging of EVs
+---------------------------
 
 .. include:: data/e-mobility.rst
 

docs/data/DLR.rst

@@ -1,10 +1,6 @@
-====================================================
-Methods to include dynamic line rating in our model
-====================================================
-
-To calculate the transmission capacity of each transmission line in the model, 
+To calculate the transmission capacity of each transmission line in the model,
 the procedure suggested in the **Principles for the Expansion Planning of the
-German Transmission Network** [NEP2021] where used:
+German Transmission Network** [NEP2021a]_ where used:
 
 1. Import the temperature and wind temporal raster layers from ERA-5. Hourly
 resolution data from the year 2011 was used. Raster resolution
@@ -16,8 +12,8 @@ the Expansion Planning. See Figure 1.
 .. image:: images/regions_DLR.png
   :width: 400
   :alt: regions DLR
-  
-Figure 1: Representative regions in Germany for DLR analysis [NEP2021]
+
+Figure 1: Representative regions in Germany for DLR analysis [NEP2021a]_
 
 3. Find the lowest wind speed in each region. To perform this, for each
 independent region, the wind speed of every cell in the raster layer should be
@@ -35,13 +31,11 @@ Figure 2.
 .. image:: images/table_max_capacity_DLR.png
   :width: 400
   :alt: table_max_capacity_DLR
-  
-Figure 2: transmission capacity based on max temperature and min wind speed [NEP2021]
+
+Figure 2: transmission capacity based on max temperature and min wind speed [NEP2021a]_
 
 6. Assign the maximum capacity of the corresponding region to each transmission
 line inside each one of them. Crossborder lines and underground lines receive
 no values. It means that their capacities are static and equal to their nominal
-values. Lines that cross borders between regions receive the lowest 
+values. Lines that cross borders between regions receive the lowest
 capacity per hour of the regions containing the line.
-
-.. [NEP2021] Principles for the Expansion Planning of the German Transmission Network https://www.netzentwicklungsplan.de/

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>`_.