Merge branch 'features/#261-emob-tests' into features/#276-improve-si…

…mple-example

doc/api/edisgo.tools.rst

@@ -9,14 +9,6 @@ edisgo.tools.config module
     :undoc-members:
     :show-inheritance:
 
-edisgo.tools.edisgo\_run module
---------------------------------
-
-.. automodule:: edisgo.tools.edisgo_run
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
 edisgo.tools.geo module
 ------------------------
 
@@ -33,6 +25,14 @@ edisgo.tools.geopandas\_helper module
     :undoc-members:
     :show-inheritance:
 
+edisgo.tools.logger module
+----------------------------------------
+
+.. automodule:: edisgo.tools.logger
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 edisgo.tools.networkx\_helper module
 ----------------------------------------
 

doc/conf.py

@@ -92,7 +92,7 @@
     "shapely": ("https://shapely.readthedocs.io/en/latest/manual.html#%s", "shapely."),
     "ding0": ("https://dingo.readthedocs.io/en/dev/api/ding0.html#%s", "Ding0"),
     "pypsa": ("https://pypsa.readthedocs.io/en/latest/components.html#%s", "pypsa"),
-    "plotly": ("https://plotly.com/python-api-reference/generated/#%s.html", "plotly"),
+    "plotly": ("https://plotly.com/python-api-reference/generated/%s.html", "plotly"),
 }
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]

doc/features_in_detail.rst

@@ -264,57 +264,57 @@ It is also possible to curtail specific generators internally, though a user fri
 .. _electromobility-integration-label:
 
 Electromobility integration
---------------------
+----------------------------
 
 The import and integration of electromobility data is implemented in :py:func:`~edisgo.io.electromobility_import`.
 
 Allocation of charging demand
-^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The allocation of charging processes to charging stations is implemented in 
+The allocation of charging processes to charging stations is implemented in
 :py:func:`~edisgo.io.electromobility_import.distribute_charging_demand`.
-After electromobility data is loaded, the charging demand from SimBEV is allocated to potential charging parks 
-from TracBEV. The allocation of the charging processes to the charging infrastructure is carried out with the 
-help of the weighting factor of the potential charging parks determined by TracBEV. This involves a random and 
+After electromobility data is loaded, the charging demand from SimBEV is allocated to potential charging parks
+from TracBEV. The allocation of the charging processes to the charging infrastructure is carried out with the
+help of the weighting factor of the potential charging parks determined by TracBEV. This involves a random and
 weighted selection of one charging park per charging process. In the case of private charging infrastructure, a
-separate charging point is set up for each EV. All charging processes of the respective EV and charging use case
-are assigned to this charging point. The allocation of private charging processes to charging stations is 
+separate charging point is set up for each electric vehicle (EV). All charging processes of the respective EV and charging use case
+are assigned to this charging point. The allocation of private charging processes to charging stations is
 implemented in :py:func:`~edisgo.io.electromobility_import.distribute_private_charging_demand`.
 
-For the public charging infrastructure, the allocation is made explicitly per charging process. For each charging
-process it is determined whether a suitable charging point is already available. For this purpose it is checked 
-if the charging point is occupied by another EV in the corresponding period and whether it can provide the 
-corresponding charging capacity. If no suitable charging point is available, a charging point is determined 
-randomly and weighted in the same way as for private charging. The allocation of public charging processes to 
+For public charging infrastructure, the allocation is made explicitly per charging process. For each charging
+process it is determined whether a suitable charging point is already available. For this purpose it is checked
+whether the charging point is occupied by another EV in the corresponding period and whether it can provide the
+corresponding charging capacity. If no suitable charging point is available, a charging point is determined
+randomly and weighted in the same way as for private charging. The allocation of public charging processes to
 charging stations is implemented in :py:func:`~edisgo.io.electromobility_import.distribute_public_charging_demand`.
 
+.. _charging_strategies-label:
+
 Charging strategies
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
-eDisGo right now provides three charging strategy methodologies called 'dumb', 'reduced' and 'residual', that 
-are implemented in :py:mod:`~edisgo.flex_opt.charging_strategies`.  
-The aim of the charging strategies is to generate the most grid-friendly charging behavior possible without
-restricting the convenience for end users. Therefore, the boundary condition of all charging strategies is that 
-the charging requirement of each charging process must be fully covered. This means that charging processes can 
-only be used as a flexibility if the EV can be fully charged while it is stationary. Furthermore, only private 
-charging processes can be used as a flexibility, since the fulfillment of the service is the priority for public 
-charging processes. In order to be able to evaluate the three charging strategies, a reference charging strategy 
-is also examined.
+eDisGo right now provides three charging strategy methodologies called 'dumb', 'reduced' and 'residual', that
+are implemented in :py:mod:`~edisgo.flex_opt.charging_strategies`.
+The aim of the charging strategies 'reduced' and 'residual' is to generate the most grid-friendly charging behavior possible without
+restricting the convenience for end users. Therefore, the boundary condition of all charging strategies is that
+the charging requirement of each charging process must be fully covered. This means that charging processes can
+only be flexibilised if the EV can be fully charged while it is stationary. Furthermore, only private
+charging processes can be used as a flexibility, since the fulfillment of the service is the priority for public
+charging processes.
 
 'dumb'
 """"""""""""""""""
-Is the default charging strategy and corresponds to the reference charging.
-The cars are charged directly after arrival with the maximum possible charging capacity.
+In this charging strategy the cars are charged directly after arrival with the maximum possible charging capacity.
 
 'reduced'
 """"""""""""""""""
-Is a preventive charging strategy. The cars are charged directly after arrival with the minimum possible 
-charging power. The minimum possible charging power is determined by the parking time and the parameter 
+This is a preventive charging strategy. The cars are charged directly after arrival with the minimum possible
+charging power. The minimum possible charging power is determined by the parking time and the parameter
 minimum_charging_capacity_factor.
 
 'residual'
 """"""""""""""""""
-Is an active charging strategy. The cars are charged when the residual load in the MV grid is lowest 
+This is an active charging strategy. The cars are charged when the residual load in the MV grid is lowest
 (high generation and low consumption). Charging processes with a low flexibility are given priority.
 
 

doc/usage_details.rst

@@ -266,7 +266,8 @@ The charging strategies can be invoked as follows:
 
     edisgo.apply_charging_strategy()
 
-See :attr:`~.edisgo.EDisGo.apply_charging_strategy` for more information.
+See function docstring of :attr:`~.edisgo.EDisGo.apply_charging_strategy` or
+documentation section :ref:`charging_strategies-label` for more information.
 
 Reactive power time series
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -349,27 +350,24 @@ Further information on the grid reinforcement methodology can be found in sectio
 Electromobility
 -----------------
 
-Electromobility data including charging processes necessary to apply different
-charging strategies, as well as information on potential charging sites and
-integrated charging parks can be integrated into the grid and are stored in
-the :pandas:`pandas.DataFrames<DataFrame>` or  :geopandas:`GeoDataFrame` in the 
+Electromobility data including charging processes as well as information on potential charging sites and
+integrated charging parks are stored in the
 :class:`~.network.electromobility.Electromobility` object.
 
-You can access those dataframes/geodataframes as follows:
+You can access these data as follows:
 
 .. code-block:: python
 
-    # Access DataFrame with all SimBEV charging processes.
+    # Access DataFrame with all SimBEV charging processes
     edisgo.electromobility.charging_processes_df
 
-    # Access GeoDataFrame with all TracBEV potential charging parks.
+    # Access GeoDataFrame with all TracBEV potential charging parks
     edisgo.electromobility.potential_charging_parks_gdf
-    
-    # Access DataFrame with all charging parks that got integrated.
+
+    # Access DataFrame with all charging parks that got integrated
     edisgo.electromobility.integrated_charging_parks_df
-    
-The integrated charging points are then also stored in the 
-:pandas:`pandas.DataFrames<DataFrame>` in the :class:`~.network.topology.Topology` 
+
+The integrated charging points are also stored in the :class:`~.network.topology.Topology`
 object and can be accessed as follows:
 
 .. code-block:: python
@@ -378,21 +376,23 @@ object and can be accessed as follows:
     edisgo.topology.charging_points_df
 
 
-So far, adding electormobility data to an eDisGo object requires electromobility
+So far, adding electromobility data to an eDisGo object requires electromobility
 data from `SimBEV <https://github.com/rl-institut/simbev>`_ (required version:
 `3083c5a <https://github.com/rl-institut/simbev/commit/
-86076c936940365587c9fba98a5b774e13083c5a>`_) 
+86076c936940365587c9fba98a5b774e13083c5a>`_)
 and `TracBEV <https://github.com/rl-institut/tracbev>`_ (required version:
 `14d864c <https://github.com/rl-institut/tracbev/commit/
-03e335655770a377166c05293a966052314d864c>`_) to be stored in the directories 
-specified through the parameters simbev_directory and tracbev_directory. 
-SimBEV provides data on standing times, charging demand, etc. per vehicle, 
+03e335655770a377166c05293a966052314d864c>`_) to be stored in the directories
+specified through the parameters simbev_directory and tracbev_directory.
+SimBEV provides data on standing times, charging demand, etc. per vehicle,
 whereas TracBEV provides potential charging point locations.
 
 .. todo:: Add information on how to retrieve SimBEV and TracBEV data
 
-Here is a small examples on how to import electromobility data and apply a
-charging strategy to the charging processes.
+Here is a small example on how to import electromobility data and apply a
+charging strategy. A more extensive example can be found in
+the example jupyter notebook
+`electromobility_example <https://github.com/openego/eDisGo/blob/dev/examples/electromobility_example.ipynb>`_.
 
 .. code-block:: python
 
@@ -407,25 +407,27 @@ charging strategy to the charging processes.
     )
     edisgo.set_time_series_active_power_predefined(
         fluctuating_generators_ts="oedb",
-        dispatchable_generators_ts=pd.DataFrame(data=1, columns=["other"], index=timeindex),
+        dispatchable_generators_ts=pd.DataFrame(
+            data=1, columns=["other"], index=timeindex),
         conventional_loads_ts="demandlib",
     )
 
     edisgo.set_time_series_reactive_power_control()
-    
-    # Resample edisgo timeseries to 15-minute resolution to match simBEV and tracBEV data
+
+    # Resample edisgo timeseries to 15-minute resolution to match with SimBEV and
+    # TracBEV data
     edisgo.resample_timeseries()
-    
+
     # Import electromobility data
     edisgo.import_electromobility(
         simbev_directory=simbev_path,
         tracbev_directory=tracbev_path,
     )
-    
+
     # Apply charging strategy
     edisgo.apply_charging_strategy(strategy="dumb")
 
-Further information on the electromobility integration methodology and the charging 
+Further information on the electromobility integration methodology and the charging
 strategies can be found in section :ref:`electromobility-integration-label`.
 
 
@@ -565,18 +567,18 @@ line loading and node voltages in the MV grid or as a histograms.
 
     # plot voltage histogram
     edisgo.histogram_voltage()
-    
+
     # draw a plotly html figure
     draw_plotly(edisgo)
 
-    # plot relative loading and voltage deviation, with grid coordinates 
+    # plot relative loading and voltage deviation, with grid coordinates
     # modified to have the station in the origin
     draw_plotly(
-        edisgo, G=edisgo_obj.topology.mv_grid.graph, 
-        line_color="relative_loading", node_color="voltage_deviation", 
+        edisgo, G=edisgo_obj.topology.mv_grid.graph,
+        line_color="relative_loading", node_color="voltage_deviation",
         grid=edisgo.topology.mv_grid
     )
-    
+
 See :class:`~.EDisGo` class for more plots and plotting options.
 
 Results

doc/whatsnew/v0-2-0.rst

@@ -9,11 +9,13 @@ Changes
 * added pre-commit hooks (flake8, black, isort, pyupgrade) `#229 <https://github.com/openego/eDisGo/pull/229>`_
 * added issue and pull request templates `#220 <https://github.com/openego/eDisGo/issues/220>`_
 * added Windows installation yml and documentation
+* added functionality to set up different loggers with individual logging levels and where to write output `#295 <https://github.com/openego/eDisGo/issues/295>`_
 * added integrity checks of eDisGo object `#231 <https://github.com/openego/eDisGo/issues/231>`_
 * added functionality to save to and load from zip archive `#216 <https://github.com/openego/eDisGo/pull/216>`_
 * added option to not raise error in case power flow did not converge `#207 <https://github.com/openego/eDisGo/issues/207>`_
 * added pyplot `#214 <https://github.com/openego/eDisGo/pull/214>`_
 * added functionality to create geopandas dataframes `#224 <https://github.com/openego/eDisGo/issues/224>`_
+* added functionality to resample time series `#269 <https://github.com/openego/eDisGo/pull/269>`_
 * added tests
 * major refactoring of loads and time series
 

edisgo/config/config_grid_expansion_default.cfg

@@ -13,7 +13,8 @@
 # Standard equipment for grid expansion measures. Source: Rehtanz et. al.: "Verteilnetzstudie für das Land Baden-Württemberg", 2017.
 hv_mv_transformer = 40 MVA
 mv_lv_transformer = 630 kVA
-mv_line = NA2XS2Y 3x1x185 RM/25
+mv_line_10kv = NA2XS2Y 3x1x185 RM/25
+mv_line_20kv = NA2XS2Y 3x1x240
 lv_line = NAYY 4x1x150
 
 [grid_expansion_allowed_voltage_deviations]

edisgo/edisgo.py

@@ -116,7 +116,7 @@ class EDisGo:
     def __init__(self, **kwargs):
 
         # load configuration
-        self._config = Config(config_path=kwargs.get("config_path", None))
+        self._config = Config(config_path=kwargs.get("config_path", "default"))
 
         # instantiate topology object and load grid data
         self.topology = Topology(config=self.config)
@@ -2039,30 +2039,46 @@ def check_integrity(self):
 
     def resample_timeseries(self, method: str = "ffill", freq: str = "15min"):
         """
-        Returns timeseries resampled from hourly resolution to 15 minute resolution.
+        Resamples all generator, load and storage time series to a desired resolution.
 
-        Parameters
-        ----------
-        method : str, optional
-            Method to choose from to fill missing values when upsampling. Possible
-            options are:
+        The following time series are affected by this:
+
+        * :attr:`~.network.timeseries.TimeSeries.generators_active_power`
+
+        * :attr:`~.network.timeseries.TimeSeries.loads_active_power`
+
+        * :attr:`~.network.timeseries.TimeSeries.storage_units_active_power`
 
-            * 'ffill': propagate last valid observation forward to next valid
-            observation. See
-            https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ffill.html
-            'ffill' is the Default.
+        * :attr:`~.network.timeseries.TimeSeries.generators_reactive_power`
 
-            * 'bfill': use next valid observation to fill gap. See
-            https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.bfill.html
+        * :attr:`~.network.timeseries.TimeSeries.loads_reactive_power`
 
-            * 'interpolate': Fill NaN values using an interpolation method. See
-            https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.interpolate.html
+        * :attr:`~.network.timeseries.TimeSeries.storage_units_reactive_power`
+
+        Both up- and down-sampling methods are possible.
+
+        Parameters
+        ----------
+        method : str, optional
+            Method to choose from to fill missing values when resampling.
+            Possible options are:
 
+            * 'ffill'
+                Propagate last valid observation forward to next valid
+                observation. See :pandas:`pandas.DataFrame.ffill<DataFrame.ffill>`.
+            * 'bfill'
+                Use next valid observation to fill gap. See
+                :pandas:`pandas.DataFrame.bfill<DataFrame.bfill>`.
+            * 'interpolate'
+                Fill NaN values using an interpolation method. See
+                :pandas:`pandas.DataFrame.interpolate<DataFrame.interpolate>`.
+
+            Default: 'ffill'.
         freq : str, optional
-            Frequency that timeseries is resampled to. Can be any frequency up to one
-            hour. Offset aliases can be found here:
-            https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
-            15 minutes is the default.
+            Frequency that time series is resampled to. Offset aliases can be found
+            here:
+            https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases.
+            Default: '15min'.
 
         """
         self.timeseries.resample_timeseries(method=method, freq=freq)

edisgo/equipment/equipment-parameters_LV_cables.csv

@@ -1,10 +1,10 @@
-name,U_n,I_max_th,R_per_km,L_per_km
-#-,kV,kA,ohm/km,mH/km
-NAYY 4x1x300,0.4,0.419,0.1,0.279
-NAYY 4x1x240,0.4,0.364,0.125,0.254
-NAYY 4x1x185,0.4,0.313,0.164,0.256
-NAYY 4x1x150,0.4,0.275,0.206,0.256
-NAYY 4x1x120,0.4,0.245,0.253,0.256
-NAYY 4x1x95,0.4,0.215,0.320,0.261
-NAYY 4x1x50,0.4,0.144,0.449,0.270
-NAYY 4x1x35,0.4,0.123,0.868,0.271
+name,U_n,I_max_th,R_per_km,L_per_km,C_per_km
+#-,kV,kA,ohm/km,mH/km,uF/km
+NAYY 4x1x300,0.4,0.419,0.1,0.279,0
+NAYY 4x1x240,0.4,0.364,0.125,0.254,0
+NAYY 4x1x185,0.4,0.313,0.164,0.256,0
+NAYY 4x1x150,0.4,0.275,0.206,0.256,0
+NAYY 4x1x120,0.4,0.245,0.253,0.256,0
+NAYY 4x1x95,0.4,0.215,0.320,0.261,0
+NAYY 4x1x50,0.4,0.144,0.449,0.270,0
+NAYY 4x1x35,0.4,0.123,0.868,0.271,0

edisgo/flex_opt/charging_strategies.py

@@ -32,7 +32,7 @@
     ],
 }
 
-logger = logging.getLogger("edisgo")
+logger = logging.getLogger(__name__)
 
 
 # TODO: the dummy timeseries should be as long as the simulated days and not