From 43552cfd726e3f714fa26784f08dc09b72328093 Mon Sep 17 00:00:00 2001
From: birgits <birgit.schachler@rl-institut.de>
Date: Sun, 22 Oct 2023 14:54:54 +0200
Subject: [PATCH] Change LV reniforcement

---
 edisgo/flex_opt/reinforce_grid.py | 67 ++++++++++++++++++++++++-------
 1 file changed, 53 insertions(+), 14 deletions(-)

diff --git a/edisgo/flex_opt/reinforce_grid.py b/edisgo/flex_opt/reinforce_grid.py
index f7ff6513..7f4fe80d 100644
--- a/edisgo/flex_opt/reinforce_grid.py
+++ b/edisgo/flex_opt/reinforce_grid.py
@@ -883,7 +883,7 @@ def enhanced_reinforce_grid(
     logger.info("Run initial grid reinforcement for single LV grids.")
     for lv_grid in list(edisgo_object.topology.mv_grid.lv_grids):
         logger.info(f"Check initial convergence for {lv_grid=}.")
-        _, ts_not_converged = edisgo_object.analyze(
+        ts_converged, ts_not_converged = edisgo_object.analyze(
             mode="lv", raise_not_converged=False, lv_grid_id=lv_grid.id
         )
         if len(ts_not_converged) > 0:
@@ -902,12 +902,31 @@ def enhanced_reinforce_grid(
                 )
         try:
             logger.info(f"Try initial mode 'lv' reinforcement for {lv_grid=}.")
-            edisgo_object.reinforce(
-                mode="lv",
-                lv_grid_id=lv_grid.id,
-                catch_convergence_problems=True,
-                **kwargs,
-            )
+            if len(ts_not_converged) > 0:
+                # if there are time steps that did not converge, run reinforcement
+                # first on converged time steps
+                edisgo_object.reinforce(
+                    mode="lv",
+                    lv_grid_id=lv_grid.id,
+                    catch_convergence_problems=False,
+                    timesteps_pfa=ts_converged,
+                    **kwargs,
+                )
+                # run reinforcement again in catch-convergence mode with all time steps
+                edisgo_object.reinforce(
+                    mode="lv",
+                    lv_grid_id=lv_grid.id,
+                    catch_convergence_problems=True,
+                    **kwargs,
+                )
+            else:
+                # if all time steps converged, run normal reinforcement
+                edisgo_object.reinforce(
+                    mode="lv",
+                    lv_grid_id=lv_grid.id,
+                    catch_convergence_problems=False,
+                    **kwargs,
+                )
             logger.info(f"Initial mode 'lv' reinforcement for {lv_grid} successful.")
         except (ValueError, RuntimeError, exceptions.MaximumIterationError):
             logger.warning(f"Initial mode 'lv' reinforcement for {lv_grid} failed.")
@@ -940,7 +959,7 @@ def enhanced_reinforce_grid(
 
         for lv_grid in list(edisgo_object.topology.mv_grid.lv_grids):
             logger.info(f"Check convergence for {lv_grid=}.")
-            _, ts_not_converged = edisgo_object.analyze(
+            ts_converged, ts_not_converged = edisgo_object.analyze(
                 mode="lv", raise_not_converged=False, lv_grid_id=lv_grid.id
             )
             if len(ts_not_converged) > 0:
@@ -961,12 +980,32 @@ def enhanced_reinforce_grid(
                     )
             try:
                 logger.info(f"Try mode 'lv' reinforcement for {lv_grid=}.")
-                edisgo_object.reinforce(
-                    mode="lv",
-                    lv_grid_id=lv_grid.id,
-                    catch_convergence_problems=True,
-                    **kwargs,
-                )
+                if len(ts_not_converged) > 0:
+                    # if there are time steps that did not converge, run reinforcement
+                    # first on converged time steps
+                    edisgo_object.reinforce(
+                        mode="lv",
+                        lv_grid_id=lv_grid.id,
+                        catch_convergence_problems=False,
+                        timesteps_pfa=ts_converged,
+                        **kwargs,
+                    )
+                    # run reinforcement again in catch-convergence mode with all time
+                    # steps
+                    edisgo_object.reinforce(
+                        mode="lv",
+                        lv_grid_id=lv_grid.id,
+                        catch_convergence_problems=True,
+                        **kwargs,
+                    )
+                else:
+                    # if all time steps converged, run normal reinforcement
+                    edisgo_object.reinforce(
+                        mode="lv",
+                        lv_grid_id=lv_grid.id,
+                        catch_convergence_problems=False,
+                        **kwargs,
+                    )
                 logger.info(f"Mode 'lv' reinforcement for {lv_grid} successful.")
             except (ValueError, RuntimeError, exceptions.MaximumIterationError):
                 logger.info(f"Mode 'lv' reinforcement for {lv_grid} failed.")