Merge branch 'dev' of github.com:openego/eDisGo into feature/#274-imp…

…rove-rtd-doc

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/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)
@@ -2037,6 +2037,52 @@ def check_integrity(self):
 
             logging.info("Integrity check finished. Please pay attention to warnings.")
 
+    def resample_timeseries(self, method: str = "ffill", freq: str = "15min"):
+        """
+        Resamples all generator, load and storage time series to a desired resolution.
+
+        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`
+
+        * :attr:`~.network.timeseries.TimeSeries.generators_reactive_power`
+
+        * :attr:`~.network.timeseries.TimeSeries.loads_reactive_power`
+
+        * :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 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)
+
 
 def import_edisgo_from_pickle(filename, path=""):
     abs_path = os.path.abspath(path)

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

edisgo/flex_opt/reinforce_measures.py

@@ -398,7 +398,7 @@ def reinforce_lines_voltage_issues(edisgo_obj, grid, crit_nodes):
         ]
     elif isinstance(grid, MVGrid):
         standard_line = edisgo_obj.config["grid_expansion_standard_equipment"][
-            "mv_line"
+            f"mv_line_{int(grid.nominal_voltage)}kv"
         ]
     else:
         raise ValueError("Inserted grid is invalid.")
@@ -693,40 +693,52 @@ def _replace_by_parallel_standard_lines(lines):
 
         lines_changes.update(number_parallel_lines.to_dict())
 
-    standard_line_type = edisgo_obj.config["grid_expansion_standard_equipment"][
-        "{}_line".format(voltage_level)
-    ]
-
     lines_changes = {}
 
     # chose lines of right grid level
     relevant_lines = edisgo_obj.topology.lines_df.loc[
         crit_lines[crit_lines.voltage_level == voltage_level].index
     ]
+    if not relevant_lines.empty:
+        nominal_voltage = edisgo_obj.topology.buses_df.loc[
+            edisgo_obj.topology.lines_df.loc[relevant_lines.index[0], "bus0"], "v_nom"
+        ]
+        if nominal_voltage == 0.4:
+            standard_line_type = edisgo_obj.config["grid_expansion_standard_equipment"][
+                "lv_line"
+            ]
+        else:
+            standard_line_type = edisgo_obj.config["grid_expansion_standard_equipment"][
+                f"mv_line_{int(nominal_voltage)}kv"
+            ]
 
-    # handling of standard lines
-    lines_standard = relevant_lines.loc[relevant_lines.type_info == standard_line_type]
-    if not lines_standard.empty:
-        _add_parallel_standard_lines(lines_standard.index)
+        # handling of standard lines
+        lines_standard = relevant_lines.loc[
+            relevant_lines.type_info == standard_line_type
+        ]
+        if not lines_standard.empty:
+            _add_parallel_standard_lines(lines_standard.index)
 
-    # get lines that have not been updated yet (i.e. that are not standard
-    # lines)
-    relevant_lines = relevant_lines.loc[
-        ~relevant_lines.index.isin(lines_standard.index)
-    ]
-    # handling of cables where adding one cable is sufficient
-    lines_single = (
-        relevant_lines.loc[relevant_lines.num_parallel == 1]
-        .loc[relevant_lines.kind == "cable"]
-        .loc[crit_lines.max_rel_overload < 2]
-    )
-    if not lines_single.empty:
-        _add_one_parallel_line_of_same_type(lines_single.index)
+        # get lines that have not been updated yet (i.e. that are not standard
+        # lines)
+        relevant_lines = relevant_lines.loc[
+            ~relevant_lines.index.isin(lines_standard.index)
+        ]
+        # handling of cables where adding one cable is sufficient
+        lines_single = (
+            relevant_lines.loc[relevant_lines.num_parallel == 1]
+            .loc[relevant_lines.kind == "cable"]
+            .loc[crit_lines.max_rel_overload < 2]
+        )
+        if not lines_single.empty:
+            _add_one_parallel_line_of_same_type(lines_single.index)
 
-    # handle rest of lines (replace by as many parallel standard lines as
-    # needed)
-    relevant_lines = relevant_lines.loc[~relevant_lines.index.isin(lines_single.index)]
-    if not relevant_lines.empty:
-        _replace_by_parallel_standard_lines(relevant_lines.index)
+        # handle rest of lines (replace by as many parallel standard lines as
+        # needed)
+        relevant_lines = relevant_lines.loc[
+            ~relevant_lines.index.isin(lines_single.index)
+        ]
+        if not relevant_lines.empty:
+            _replace_by_parallel_standard_lines(relevant_lines.index)
 
     return lines_changes

edisgo/io/pypsa_io.py

@@ -106,7 +106,7 @@ def _set_slack(grid):
             ],
             "Line": edisgo_object.topology.lines_df.loc[
                 :,
-                ["bus0", "bus1", "x", "r", "s_nom", "num_parallel", "length"],
+                ["bus0", "bus1", "x", "r", "b", "s_nom", "num_parallel", "length"],
             ],
             "Transformer": edisgo_object.topology.transformers_df.loc[
                 :, ["bus0", "bus1", "x_pu", "r_pu", "type_info", "s_nom"]

edisgo/network/electromobility.py

@@ -12,7 +12,7 @@
 if "READTHEDOCS" not in os.environ:
     import geopandas as gpd
 
-logger = logging.getLogger("edisgo")
+logger = logging.getLogger(__name__)
 
 COLUMNS = {
     "charging_processes_df": [

edisgo/network/timeseries.py

@@ -307,9 +307,11 @@ def reset(self):
         Resets all time series.
 
         Active and reactive power time series of all loads, generators and storage units
-        are deleted, as well as everything stored in :py:attr:`~time_series_raw`.
+        are deleted, as well as timeindex everything stored in
+        :py:attr:`~time_series_raw`.
 
         """
+        self.timeindex = pd.DatetimeIndex([])
         self.generators_active_power = None
         self.loads_active_power = None
         self.storage_units_active_power = None
@@ -2145,6 +2147,88 @@ def _check_if_components_exist(
             return set(component_names) - set(comps_not_in_network)
         return component_names
 
+    def resample_timeseries(self, method: str = "ffill", freq: str = "15min"):
+        """
+        Resamples all generator, load and storage time series to a desired resolution.
+
+        See :attr:`~.EDisGo.resample_timeseries` for more information.
+
+        Parameters
+        ----------
+        method : str, optional
+            See :attr:`~.EDisGo.resample_timeseries` for more information.
+
+        freq : str, optional
+            See :attr:`~.EDisGo.resample_timeseries` for more information.
+
+        """
+
+        # add time step at the end of the time series in case of up-sampling so that
+        # last time interval in the original time series is still included
+        attrs = self._attributes
+        freq_orig = self.timeindex[1] - self.timeindex[0]
+        df_dict = {}
+        for attr in attrs:
+            df_dict[attr] = getattr(self, attr)
+            if pd.Timedelta(freq) < freq_orig:  # up-sampling
+                new_dates = pd.DatetimeIndex([df_dict[attr].index[-1] + freq_orig])
+            else:  # down-sampling
+                new_dates = pd.DatetimeIndex([df_dict[attr].index[-1]])
+            df_dict[attr] = (
+                df_dict[attr]
+                .reindex(df_dict[attr].index.union(new_dates).unique().sort_values())
+                .ffill()
+            )
+
+        # create new index
+        if pd.Timedelta(freq) < freq_orig:  # up-sampling
+            index = pd.date_range(
+                self.timeindex[0],
+                self.timeindex[-1] + freq_orig,
+                freq=freq,
+                closed="left",
+            )
+        else:  # down-sampling
+            index = pd.date_range(
+                self.timeindex[0],
+                self.timeindex[-1],
+                freq=freq,
+            )
+
+        # set new timeindex
+        self._timeindex = index
+
+        # resample time series
+        if pd.Timedelta(freq) < freq_orig:  # up-sampling
+            if method == "interpolate":
+                for attr in attrs:
+                    setattr(
+                        self,
+                        attr,
+                        df_dict[attr].resample(freq, closed="left").interpolate(),
+                    )
+            elif method == "ffill":
+                for attr in attrs:
+                    setattr(
+                        self, attr, df_dict[attr].resample(freq, closed="left").ffill()
+                    )
+            elif method == "bfill":
+                for attr in attrs:
+                    setattr(
+                        self, attr, df_dict[attr].resample(freq, closed="left").bfill()
+                    )
+            else:
+                raise NotImplementedError(
+                    f"Resampling method {method} is not implemented."
+                )
+        else:  # down-sampling
+            for attr in attrs:
+                setattr(
+                    self,
+                    attr,
+                    df_dict[attr].resample(freq).mean(),
+                )
+
 
 class TimeSeriesRaw:
     """