Merge pull request #403 from openego/bugfix/documentation-updates

Bugfix/documentation updates

doc/conf.py

@@ -128,16 +128,23 @@ def setup(sphinx):
         "shapely.%s",
     ),
     "ding0": ("https://dingo.readthedocs.io/en/dev/api/ding0.html#%s", "ding0.%s"),
-    "pypsa": ("https://pypsa.readthedocs.io/en/latest/components.html#%s", "pypsa.%s"),
+    "pypsa": (
+        "https://pypsa.readthedocs.io/en/latest/user-guide/components.html#%s",
+        "pypsa.%s",
+    ),
     "plotly": (
         "https://plotly.com/python-api-reference/generated/%s.html",
         "plotly.%s",
     ),
 }
 # ignore the following external links when checking the links
-# stackoverflow is listed here because for some reason the link check fails for these
+# stackoverflow and gurobi is listed here because for some reason
+# the link check fails for these
 # in the github action, even though the link is correct
-linkcheck_ignore = [r"https://stackoverflow.com*"]
+linkcheck_ignore = [
+    r"https://stackoverflow.com*",
+    r"https://support.gurobi.com/*",
+]
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]

doc/dev_notes.rst

@@ -24,7 +24,7 @@ following commands within your eDisGo directory:
 
 .. code-block:: bash
 
-    python -m pip install -e .[full]  # install eDisGo from source
+    python -m pip install -e .[dev]  # install eDisGo from source
     pre-commit install  # install pre-commit hooks
 
 

doc/index.rst

@@ -15,7 +15,7 @@ The toolbox currently includes:
 
   * `ding0 <https://github.com/openego/ding0>`_ tool for synthetic medium and low
     voltage grid topologies for the whole of Germany
-  * `OpenEnergy DataBase (oedb) <https://openenergy-platform.org/dataedit/schemas>`_ for
+  * `OpenEnergy DataBase (oedb) <https://openenergyplatform.org/dataedit/schemas>`_ for
     feed-in time series of fluctuating renewables and scenarios for future
     power plant park of Germany
   * `demandlib <https://github.com/oemof/demandlib>`_ for electrical load time series

doc/quickstart.rst

@@ -3,11 +3,11 @@
 Getting started
 ================
 
+.. warning:: Make sure to use python 3.8 or higher!
+
 Installation using Linux
 -------------------------
 
-.. warning:: Make sure to use python 3.8 or higher!
-
 Install latest eDisGo version through pip. Therefore, we highly recommend using
 a virtual environment and its pip.
 
@@ -21,8 +21,6 @@ You may also consider installing a developer version as detailed in
 Installation using Windows
 --------------------------
 
-.. warning:: Make sure to use python 3.8 or higher!
-
 For Windows users we recommend using Anaconda and to install the geo stack
 using the conda-forge channel prior to installing eDisGo. You may use the provided
 `eDisGo_env.yml file <https://github.com/openego/eDisGo/blob/dev/eDisGo_env.yml>`_
@@ -44,117 +42,29 @@ Installation using MacOS
 We don't have any experience with our package on MacOS yet! If you try eDisGo on MacOS
 we would be happy if you let us know about your experience!
 
-Requirements for edisgoOPF package
-----------------------------------
-
-.. warning:: The non-linear optimal power flow is currently not maintained and might not work out of the box!
-
-To use the multiperiod optimal power flow that is provided in the julia package
-edisgoOPF in eDisGo you additionally need to install julia version 1.1.1.
-Download julia from
-`julia download page <https://julialang.org/downloads/oldreleases/>`_ and
-add it to your path (see
-`platform specific instructions <https://julialang.org/downloads/platform/>`_
-for more information).
-
-Before using the edisgoOPF julia package for the first time you need to
-instantiate it. Therefore, in a terminal change directory to the edisgoOPF
-package located in eDisGo/edisgo/opf/edisgoOPF and call julia from there.
-Change to package mode by typing
-
-.. code-block:: bash
-
-    ]
+Additional requirements for Optimal Power Flow
+---------------------------------------------------
 
-Then activate the package:
+In order to use the optimal power flow, you additionally need:
 
-.. code-block:: bash
-
-    (v1.0) pkg> activate .
-
-And finally instantiate it:
-
-.. code-block:: bash
-
-    (SomeProject) pkg> instantiate
+1. **Julia**: Version 1.6.7 is required.
+2. **Gurobi Optimizer**: A powerful optimization solver.
 
-.. _prerequisites:
+Installation Steps
+^^^^^^^^^^^^^^^^^^^
 
-Additional linear solver
-^^^^^^^^^^^^^^^^^^^^^^^^^
+1. Install Julia 1.6.7
 
-As with the default linear solver in Ipopt (local solver used in the OPF)
-the limit for prolem sizes is reached quite quickly, you may want to instead use
-the solver HSL_MA97.
-The steps required to set up HSL  are also described in the
-`Ipopt Documentation <https://coin-or.github.io/Ipopt/INSTALL.html#DOWNLOAD_HSL>`_.
-Here is a short version for reference:
 
-First, you need to obtain an academic license for HSL Solvers.
-Under https://licences.stfc.ac.uk/product/coin-hsl download the sources for Coin-HSL Full (Stable).
-You will need to provide an institutional e-mail to gain access.
+Download Julia 1.6.7 from the `Julia LTS releases page <https://julialang.org/downloads/#long_term_support_release>`_.
 
-Unpack the tar.gz:
-
-.. code-block:: bash
-
-    tar -xvzf coinhsl-2014.01.10.tar.gz
-
-To install the solver, clone the Ipopt Third Party HSL tools:
-
-.. code-block:: bash
-
-    git clone https://github.com/coin-or-tools/ThirdParty-HSL.git
-    cd ThirdParty-HSL
-
-
-Under `ThirdParty-HSL`, create a folder for the HSL sources named `coinhsl` and
-copy the contents of the HSL archive into it.
-Under Ubuntu, you'll need BLAS, LAPACK and GCC for Fortran. If you don't have them, install them via:
-
-.. code-block:: bash
-
-    sudo apt-get install libblas-dev liblapack-dev gfortran
-
-You can then configure and install your HSL Solvers:
-
-.. code-block:: bash
-
-    ./configure
-    make
-    sudo make install
-
-To make Ipopt pick up the solver, you need to add it to your path.
-During install, there will be an output that tells you where the libraries have
-been put. Usually like this:
-
-.. code-block:: bash
-
-    Libraries have been installed in:
-        /usr/local/lib
-
-
-Add this path to the variable `LD_LIBRARY_PATH`:
-
-.. code-block:: bash
-
-    export LD_LIBRARY="/usr/local/bin":$LD_LIBRARY_PATH
-
-You might also want to add this to your .bashrc to make it persistent.
-
-For some reason, Ipopt looks for a library named `libhsl.so`, which is not what
-the file is named, so we'll also need to provide a symlink:
-
-.. code-block:: bash
+Install Julia by following the instructions in the `Julia installation guide <https://julialang.org/downloads/platform/#linux_and_freebsd>`_. Make sure to add Julia to your system path.
 
-    cd /usr/local/lib
-    ln -s libcoinhsl.so libhsl.so
 
-MA97 should now work and can be called from Julia with:
+2. Install Gurobi
 
-.. code-block:: julia
 
-    JuMP.setsolver(pm.model,IpoptSolver(linear_solver="ma97"))
+Follow the `Gurobi installation guide <https://support.gurobi.com/hc/en-us/articles/14799677517585-Getting-Started-with-Gurobi-Optimizer>`_ to install Gurobi and add it to your system path.
 
 Prerequisites
 -------------
@@ -188,8 +98,8 @@ Aside from grid topology data you may eventually need a dataset on future
 installation of power plants. You may therefore use the scenarios developed in
 the `open_eGo <https://openegoproject.wordpress.com>`_ project that
 are available in the
-`OpenEnergy DataBase (oedb) <https://openenergy-platform.org/dataedit/schemas>`_
-hosted on the `OpenEnergy Platform (OEP) <https://openenergy-platform.org/>`_.
+`OpenEnergy DataBase (oedb) <https://openenergyplatform.org/dataedit/schemas>`_
+hosted on the `OpenEnergy Platform (OEP) <https://openenergyplatform.org/>`_.
 eDisGo provides an interface to the oedb using the package
 `ego.io <https://github.com/openego/ego.io>`_. ego.io gives you a python
 SQL-Alchemy representations of the oedb and access to it by using the

doc/usage_details.rst

@@ -241,7 +241,7 @@ This mode can be invoked as follows:
 For the following components you can use existing time series:
 
 * Fluctuating generators: Feed-in time series for solar and wind power plants can be
-  retrieved from the `OpenEnergy DataBase <https://openenergy-platform.org/dataedit/schemas>`_.
+  retrieved from the `OpenEnergy DataBase <https://openenergyplatform.org/dataedit/schemas>`_.
 * Conventional loads: Standard load profiles for the different sectors residential,
   commercial, agricultural and industrial are generated using the oemof
   `demandlib <https://github.com/oemof/demandlib/>`_.

edisgo/edisgo.py

@@ -417,7 +417,7 @@ def set_time_series_active_power_predefined(
                 Technology- and weather cell-specific hourly feed-in time series are
                 obtained from the
                 `OpenEnergy DataBase
-                <https://openenergy-platform.org/dataedit/schemas>`_. See
+                <https://openenergyplatform.org/dataedit/schemas>`_. See
                 :func:`edisgo.io.timeseries_import.feedin_oedb` for more information.
 
                 This option requires that the parameter `engine` is provided in case
@@ -478,7 +478,7 @@ def set_time_series_active_power_predefined(
 
                 Sets active power demand time series using individual hourly electricity
                 load time series for one year obtained from the `OpenEnergy DataBase
-                <https://openenergy-platform.org/dataedit/schemas>`_.
+                <https://openenergyplatform.org/dataedit/schemas>`_.
 
                 This option requires that the parameters `engine` and `scenario` are
                 provided. For further settings, the parameter `timeindex` can also be
@@ -933,7 +933,7 @@ def import_generators(self, generator_scenario=None, **kwargs):
         Gets generator park for specified scenario and integrates generators into grid.
 
         The generator data is retrieved from the
-        `open energy platform <https://openenergy-platform.org/>`_. Decommissioned
+        `open energy platform <https://openenergyplatform.org/>`_. Decommissioned
         generators are removed from the grid, generators with changed capacity
         updated and new generators newly integrated into the grid.
 
@@ -1003,7 +1003,7 @@ def analyze(
         Conducts a static, non-linear power flow analysis.
 
         Conducts a static, non-linear power flow analysis using
-        `PyPSA <https://pypsa.readthedocs.io/en/latest/power_flow.html#\
+        `PyPSA <https://pypsa.readthedocs.io/en/latest/user-guide/power-flow.html#\
         full-non-linear-power-flow>`_
         and writes results (active, reactive and apparent power as well as
         current on lines and voltages at buses) to :class:`~.network.results.Results`
@@ -1928,7 +1928,7 @@ def import_electromobility(
         Imports electromobility data and integrates charging points into grid.
 
         Electromobility data can be obtained from the `OpenEnergy DataBase
-        <https://openenergy-platform.org/dataedit/schemas>`_ or from self-provided
+        <https://openenergyplatform.org/dataedit/schemas>`_ or from self-provided
         data. In case you want to use self-provided data, it needs to be generated
         using the tools
         `SimBEV <https://github.com/rl-institut/simbev>`_ (required version:
@@ -1960,7 +1960,7 @@ def import_electromobility(
             * "oedb"
 
                 Electromobility data is obtained from the `OpenEnergy DataBase
-                <https://openenergy-platform.org/dataedit/schemas>`_.
+                <https://openenergyplatform.org/dataedit/schemas>`_.
 
                 This option requires that the parameters `scenario` and `engine` are
                 provided.
@@ -2143,7 +2143,7 @@ def import_heat_pumps(self, scenario, engine, timeindex=None, import_types=None)
         between two scenarios: 'eGon2035' and 'eGon100RE'.
 
         The data is retrieved from the
-        `open energy platform <https://openenergy-platform.org/>`_.
+        `open energy platform <https://openenergyplatform.org/>`_.
 
         # ToDo Add information on scenarios and from which tables data is retrieved.
 
@@ -2305,7 +2305,7 @@ def apply_heat_pump_operating_strategy(
     def import_dsm(self, scenario: str, engine: Engine, timeindex=None):
         """
         Gets industrial and CTS DSM profiles from the
-        `OpenEnergy DataBase <https://openenergy-platform.org/dataedit/schemas>`_.
+        `OpenEnergy DataBase <https://openenergyplatform.org/dataedit/schemas>`_.
 
         Profiles comprise minimum and maximum load increase in MW as well as maximum
         energy pre- and postponing in MWh. The data is written to the
@@ -2356,7 +2356,7 @@ def import_home_batteries(
         between two scenarios: 'eGon2035' and 'eGon100RE'.
 
         The data is retrieved from the
-        `open energy platform <https://openenergy-platform.org/>`_.
+        `open energy platform <https://openenergyplatform.org/>`_.
 
         The batteries are integrated into the grid (added to
         :attr:`~.network.topology.Topology.storage_units_df`) based on their building

edisgo/io/dsm_import.py

@@ -28,7 +28,7 @@ def oedb(
 ):
     """
     Gets industrial and CTS DSM profiles from the
-    `OpenEnergy DataBase <https://openenergy-platform.org/dataedit/schemas>`_.
+    `OpenEnergy DataBase <https://openenergyplatform.org/dataedit/schemas>`_.
 
     Profiles comprise minimum and maximum load increase in MW as well as maximum energy
     pre- and postponing in MWh.

edisgo/io/generators_import.py

@@ -42,9 +42,9 @@ def oedb_legacy(edisgo_object, generator_scenario, **kwargs):
     The importer uses SQLAlchemy ORM objects. These are defined in
     `ego.io <https://github.com/openego/ego.io/tree/dev/egoio/db_tables/>`_.
     The data is imported from the tables
-    `conventional power plants <https://openenergy-platform.org/dataedit/\
+    `conventional power plants <https://openenergyplatform.org/dataedit/\
     view/supply/ego_dp_conv_powerplant>`_ and
-    `renewable power plants <https://openenergy-platform.org/dataedit/\
+    `renewable power plants <https://openenergyplatform.org/dataedit/\
     view/supply/ego_dp_res_powerplant>`_.
 
     When the generator data is retrieved, the following steps are conducted:

edisgo/io/heat_pump_import.py

@@ -583,7 +583,7 @@ def _grid_integration(
 def efficiency_resistive_heaters_oedb(scenario, engine):
     """
     Get efficiency of resistive heaters from the
-    `OpenEnergy DataBase <https://openenergy-platform.org/dataedit/schemas>`_.
+    `OpenEnergy DataBase <https://openenergyplatform.org/dataedit/schemas>`_.
 
     Parameters
     ----------

edisgo/io/timeseries_import.py

@@ -74,7 +74,7 @@ def _timeindex_helper_func(
 def feedin_oedb_legacy(edisgo_object, timeindex=None):
     """
     Import feed-in time series data for wind and solar power plants from the
-    `OpenEnergy DataBase <https://openenergy-platform.org/dataedit/schemas>`_.
+    `OpenEnergy DataBase <https://openenergyplatform.org/dataedit/schemas>`_.
 
     Parameters
     ----------
@@ -158,7 +158,7 @@ def feedin_oedb(
 ):
     """
     Import feed-in time series data for wind and solar power plants from the
-    `OpenEnergy DataBase <https://openenergy-platform.org/dataedit/schemas>`_.
+    `OpenEnergy DataBase <https://openenergyplatform.org/dataedit/schemas>`_.
 
     Parameters
     ----------
@@ -319,7 +319,7 @@ def load_time_series_demandlib(edisgo_obj, timeindex=None):
 def cop_oedb(edisgo_object, engine, weather_cell_ids, timeindex=None):
     """
     Get COP (coefficient of performance) time series data from the
-    `OpenEnergy DataBase <https://openenergy-platform.org/dataedit/schemas>`_.
+    `OpenEnergy DataBase <https://openenergyplatform.org/dataedit/schemas>`_.
 
     Parameters
     ----------
@@ -377,7 +377,7 @@ def cop_oedb(edisgo_object, engine, weather_cell_ids, timeindex=None):
 def heat_demand_oedb(edisgo_obj, scenario, engine, timeindex=None):
     """
     Get heat demand profiles for heat pumps from the
-    `OpenEnergy DataBase <https://openenergy-platform.org/dataedit/schemas>`_.
+    `OpenEnergy DataBase <https://openenergyplatform.org/dataedit/schemas>`_.
 
     Heat demand data is returned for all heat pumps in the grid.
     For more information on how individual heat demand profiles are obtained see
@@ -498,7 +498,7 @@ def electricity_demand_oedb(
 ):
     """
     Get electricity demand profiles for all conventional loads from the
-    `OpenEnergy DataBase <https://openenergy-platform.org/dataedit/schemas>`_.
+    `OpenEnergy DataBase <https://openenergyplatform.org/dataedit/schemas>`_.
 
     Conventional loads comprise conventional electricity applications in the
     residential, CTS and industrial sector.