Bug fix hand analyze parameters to get_most_critical_time_steps

edisgo/flex_opt/reinforce_grid.py

@@ -157,15 +157,6 @@ def reinforce_grid(
                     f"Input {timesteps_pfa} for timesteps_pfa is not valid."
                 )
 
-    if reduced_analysis:
-        timesteps_pfa = get_most_critical_time_steps(
-            edisgo,
-            timesteps=timesteps_pfa,
-            num_steps_loading=kwargs.get("num_steps_loading", None),
-            num_steps_voltage=kwargs.get("num_steps_voltage", None),
-            percentage=kwargs.get("percentage", 1.0),
-            use_troubleshooting_mode=kwargs.get("use_troubleshooting_mode", True),
-        )
     iteration_step = 1
     lv_grid_id = kwargs.get("lv_grid_id", None)
     scale_timeseries = kwargs.get("scale_timeseries", None)
@@ -176,6 +167,19 @@ def reinforce_grid(
     else:
         analyze_mode = mode
 
+    if reduced_analysis:
+        timesteps_pfa = get_most_critical_time_steps(
+            edisgo,
+            mode=analyze_mode,
+            timesteps=timesteps_pfa,
+            lv_grid_id=lv_grid_id,
+            scale_timeseries=scale_timeseries,
+            num_steps_loading=kwargs.get("num_steps_loading", None),
+            num_steps_voltage=kwargs.get("num_steps_voltage", None),
+            percentage=kwargs.get("percentage", 1.0),
+            use_troubleshooting_mode=kwargs.get("use_troubleshooting_mode", True),
+        )
+
     edisgo.analyze(
         mode=analyze_mode,
         timesteps=timesteps_pfa,

edisgo/tools/temporal_complexity_reduction.py

@@ -396,7 +396,13 @@ def _scored_most_critical_voltage_issues_time_interval(
     return time_intervals_df
 
 
-def _troubleshooting_mode(edisgo_obj, timesteps=None):
+def _troubleshooting_mode(
+    edisgo_obj,
+    mode=None,
+    timesteps=None,
+    lv_grid_id=None,
+    scale_timeseries=None,
+):
     """
     Handles non-convergence issues in power flow by iteratively reducing load and
     feed-in until the power flow converges.
@@ -409,16 +415,31 @@ def _troubleshooting_mode(edisgo_obj, timesteps=None):
     -----------
     edisgo_obj : :class:`~.EDisGo`
         The eDisGo API object
+    mode : str or None
+        Allows to toggle between power flow analysis for the whole network or just
+        the MV or one LV grid. See parameter `mode` in function
+        :attr:`~.EDisGo.analyze` for more information.
     timesteps : :pandas:`pandas.DatetimeIndex<DatetimeIndex>` or \
         :pandas:`pandas.Timestamp<Timestamp>`
         Timesteps specifies from which time steps to select most critical ones. It
         defaults to None in which case all time steps in
         :attr:`~.network.timeseries.TimeSeries.timeindex` are used.
+    lv_grid_id : int or str
+        ID (e.g. 1) or name (string representation, e.g. "LVGrid_1") of LV grid
+        to analyze in case mode is 'lv'. Default: None.
+    scale_timeseries : float or None
+        See parameter `scale_timeseries` in function :attr:`~.EDisGo.analyze` for more
+        information.
 
     """
     try:
         logger.debug("Running initial power flow for temporal complexity reduction.")
-        edisgo_obj.analyze(timesteps=timesteps)
+        edisgo_obj.analyze(
+            mode=mode,
+            timesteps=timesteps,
+            lv_grid_id=lv_grid_id,
+            scale_timeseries=scale_timeseries,
+        )
     # Exception is used, as non-convergence can also lead to RuntimeError, not only
     # ValueError
     except Exception:
@@ -429,13 +450,17 @@ def _troubleshooting_mode(edisgo_obj, timesteps=None):
             "not all time steps converged. Power flow is run again with reduced "
             "network load."
         )
-        for fraction in np.arange(0.8, 0.0, step=-0.1):
+        if isinstance(scale_timeseries, float):
+            iter_start = scale_timeseries - 0.1
+        else:
+            iter_start = 0.8
+        for fraction in np.arange(iter_start, 0.0, step=-0.1):
             try:
                 edisgo_obj.analyze(
+                    mode=mode,
                     timesteps=timesteps,
-                    troubleshooting_mode="iteration",
-                    range_start=fraction,
-                    range_num=1,
+                    lv_grid_id=lv_grid_id,
+                    scale_timeseries=fraction,
                 )
                 logger.info(
                     f"Power flow fully converged for a reduction factor "
@@ -627,7 +652,10 @@ def get_most_critical_time_intervals(
 
 def get_most_critical_time_steps(
     edisgo_obj: EDisGo,
+    mode=None,
     timesteps=None,
+    lv_grid_id=None,
+    scale_timeseries=None,
     num_steps_loading=None,
     num_steps_voltage=None,
     percentage: float = 1.0,
@@ -640,11 +668,21 @@ def get_most_critical_time_steps(
     -----------
     edisgo_obj : :class:`~.EDisGo`
         The eDisGo API object
+    mode : str or None
+        Allows to toggle between power flow analysis for the whole network or just
+        the MV or one LV grid. See parameter `mode` in function
+        :attr:`~.EDisGo.analyze` for more information.
     timesteps : :pandas:`pandas.DatetimeIndex<DatetimeIndex>` or \
         :pandas:`pandas.Timestamp<Timestamp>`
         Timesteps specifies from which time steps to select most critical ones. It
         defaults to None in which case all time steps in
         :attr:`~.network.timeseries.TimeSeries.timeindex` are used.
+    lv_grid_id : int or str
+        ID (e.g. 1) or name (string representation, e.g. "LVGrid_1") of LV grid
+        to analyze in case mode is 'lv'. Default: None.
+    scale_timeseries : float or None
+        See parameter `scale_timeseries` in function :attr:`~.EDisGo.analyze` for more
+        information.
     num_steps_loading : int
         The number of most critical overloading events to select. If None, `percentage`
         is used. Default: None.
@@ -675,7 +713,12 @@ def get_most_critical_time_steps(
         edisgo_obj = _troubleshooting_mode(edisgo_obj, timesteps=timesteps)
     else:
         logger.debug("Running initial power flow for temporal complexity reduction.")
-        edisgo_obj.analyze(timesteps=timesteps)
+        edisgo_obj.analyze(
+            mode=mode,
+            timesteps=timesteps,
+            lv_grid_id=lv_grid_id,
+            scale_timeseries=scale_timeseries,
+        )
 
     # Select most critical steps based on current violations
     loading_scores = _scored_most_critical_loading(edisgo_obj)