diff --git a/.github/workflows/github_test_action.yml b/.github/workflows/github_test_action.yml
index 6e653d7..9752354 100644
--- a/.github/workflows/github_test_action.yml
+++ b/.github/workflows/github_test_action.yml
@@ -19,7 +19,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.10', '3.11', '3.12', '3.13']
+        python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']
         # Reminder: when removing support of an old python version here, then don't forget to remove
         # it also in pyproject.toml 'requires-python'
     steps:
diff --git a/.github/workflows/test_release.yml b/.github/workflows/test_release.yml
index afeae25..41e872f 100644
--- a/.github/workflows/test_release.yml
+++ b/.github/workflows/test_release.yml
@@ -17,7 +17,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        python-version: ['3.10', '3.11', '3.12', '3.13']
+        python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']
         os:  [ ubuntu-latest, windows-latest ]
 
     steps:
diff --git a/pyproject.toml b/pyproject.toml
index 89067dc..a346c1c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,6 +31,7 @@ classifiers = [
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
 ]
 dependencies = [
     "pandapower>=3.4.0",
diff --git a/tutorials/EHVHV_powerflow_expl.ipynb b/tutorials/EHVHV_powerflow_expl.ipynb
index c4e6ee0..9be47cb 100644
--- a/tutorials/EHVHV_powerflow_expl.ipynb
+++ b/tutorials/EHVHV_powerflow_expl.ipynb
@@ -22,39 +22,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "hp.pandapower.plotting.plotly.draw_layers - INFO: Failed to import plotly - interactive plotting will not be available\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n"
-     ]
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-22T06:55:40.685108Z",
+     "iopub.status.busy": "2026-09-22T06:55:40.684919Z",
+     "iopub.status.idle": "2026-09-22T06:55:44.660556Z",
+     "shell.execute_reply": "2026-09-22T06:55:44.660156Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-22T11:42:25.235580Z",
+     "start_time": "2026-09-22T11:42:20.159455Z"
     }
-   ],
+   },
    "source": [
+    "import lightsim2grid\n",
+    "import logging\n",
+    "import sys\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import pandapower as pp\n",
     "import simbench as sb\n",
     "\n",
+    "# lf_info() reports its results via the logging module -> route INFO messages\n",
+    "# to stdout so that they are shown in this notebook\n",
+    "logging.basicConfig(level=logging.INFO, stream=sys.stdout, format=\"%(message)s\")\n",
+    "\n",
     "net = sb.get_simbench_net(\"1-EHVHV-mixed-all-0-no_sw\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 44
   },
   {
    "cell_type": "markdown",
@@ -67,9 +63,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-22T06:55:44.661676Z",
+     "iopub.status.busy": "2026-09-22T06:55:44.661609Z",
+     "iopub.status.idle": "2026-09-22T06:55:44.672118Z",
+     "shell.execute_reply": "2026-09-22T06:55:44.671681Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-22T11:42:25.304953Z",
+     "start_time": "2026-09-22T11:42:25.244094Z"
+    }
+   },
    "source": [
     "# --- convert ext_grids to gen elements and set some assumed active power setpoints\n",
     "nuclear_gens = pp.toolbox.replace_ext_grid_by_gen(net, add_cols_to_keep=[\"slack_weight\"])\n",
@@ -89,7 +94,9 @@
     "# --- apply the assumed and calculated scaling values\n",
     "for et, scale in scaling.items():\n",
     "    net[et][\"scaling\"] = scale"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 45
   },
   {
    "cell_type": "markdown",
@@ -100,16 +107,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-22T06:55:44.672967Z",
+     "iopub.status.busy": "2026-09-22T06:55:44.672917Z",
+     "iopub.status.idle": "2026-09-22T06:55:44.866862Z",
+     "shell.execute_reply": "2026-09-22T06:55:44.866472Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-22T11:42:25.533969Z",
+     "start_time": "2026-09-22T11:42:25.308119Z"
+    }
+   },
    "source": [
     "# --- perform DC power flow\n",
     "pp.rundcpp(net, distributed_slack=True)\n",
     "\n",
     "# --- log some results\n",
     "pp.toolbox.lf_info(net)"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max voltage in vm_pu:\n",
+      "  1.092 at busidx 0 (EHV Bus 1)\n",
+      "Min voltage in vm_pu:\n",
+      "  1.0 at busidx 3748 (HV2 Bus 361)\n",
+      "Max loading trafo in %:\n",
+      "  51.082044367680965 loading at trafo 34 (EHV Trafo 35)\n",
+      "  51.082044367680965 loading at trafo 35 (EHV Trafo 36)\n",
+      "Max loading line in %:\n",
+      "  92.73344755050131 loading at line 824 (EHV Line 825)\n",
+      "  85.93217745798079 loading at line 768 (EHV Line 769)\n"
+     ]
+    }
+   ],
+   "execution_count": 46
   },
   {
    "cell_type": "markdown",
@@ -125,48 +160,38 @@
     "## AC Power Flow Results\n",
     "\n",
     "In addition to a realistic load case (here equal scaling factors are applied all over the grid), several adjustments for the volt-var control are required for realistic AC power flows.\n",
-    "Again, only tap controllers for EHV-HV transformers and a distributed slack (to balance the branch losses) are applied here."
+    "Again, only tap controllers for EHV-HV transformers and a distributed slack (to balance the branch losses) are applied here. Note: lightsim2grid needs to be set to False as it does not support slack weights on non-slack buses (lighsim2grid 1.1.0)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-22T06:55:44.867947Z",
+     "iopub.status.busy": "2026-09-22T06:55:44.867872Z",
+     "iopub.status.idle": "2026-09-22T06:55:45.588032Z",
+     "shell.execute_reply": "2026-09-22T06:55:45.587677Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-22T11:42:25.871187Z",
+     "start_time": "2026-09-22T11:42:25.541270Z"
+    }
+   },
+   "source": [
+    "ehv_hv_trafos = sb.voltlvl_idx(net, \"trafo\", [3], \"lv_bus\")\n",
+    "pp.control.ContinuousTapControl(net, ehv_hv_trafos, 1.0)\n",
+    "pp.runpp(net, run_control=True, distributed_slack=True, lightsim2grid=False)"
+   ],
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "hp.pandapower.control.util.auxiliary - INFO: Creating controller 0 of type <class 'pandapower.control.controller.trafo.ContinuousTapControl.ContinuousTapControl'> \n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\pf\\create_jacobian.py:27: RuntimeWarning: invalid value encountered in divide\n",
-      "  dVm_x, dVa_x = dSbus_dV_numba_sparse(Ybus.data, Ybus.indptr, Ybus.indices, V, V / abs(V), Ibus)\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\pypower\\newtonpf.py:512: MatrixRankWarning: Matrix is exactly singular\n",
-      "  dx = -1 * spsolve(J, F, permc_spec=permc_spec, use_umfpack=use_umfpack)\n"
-     ]
-    },
-    {
-     "ename": "LoadflowNotConverged",
-     "evalue": "Power Flow nr did not converge after 10 iterations!",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mLoadflowNotConverged\u001b[0m                      Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[5], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m ehv_hv_trafos \u001b[38;5;241m=\u001b[39m sb\u001b[38;5;241m.\u001b[39mvoltlvl_idx(net, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrafo\u001b[39m\u001b[38;5;124m\"\u001b[39m, [\u001b[38;5;241m3\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlv_bus\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      2\u001b[0m pp\u001b[38;5;241m.\u001b[39mcontrol\u001b[38;5;241m.\u001b[39mContinuousTapControl(net, ehv_hv_trafos, \u001b[38;5;241m1.0\u001b[39m)\n\u001b[1;32m----> 3\u001b[0m \u001b[43mpp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunpp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_control\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistributed_slack\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\run.py:227\u001b[0m, in \u001b[0;36mrunpp\u001b[1;34m(net, algorithm, calculate_voltage_angles, init, max_iteration, tolerance_mva, trafo_model, trafo_loading, enforce_p_lims, enforce_q_lims, check_connectivity, voltage_depend_loads, consider_line_temperature, run_control, distributed_slack, tdpf, tdpf_delay_s, **kwargs)\u001b[0m\n\u001b[0;32m    225\u001b[0m     \u001b[38;5;66;03m# disable run control for inner loop to avoid infinite loop\u001b[39;00m\n\u001b[0;32m    226\u001b[0m     parameters[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_control\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m--> 227\u001b[0m     \u001b[43mrun_control\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    228\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    229\u001b[0m     passed_parameters \u001b[38;5;241m=\u001b[39m _passed_runpp_parameters(\u001b[38;5;28mlocals\u001b[39m())\n",
-      "File \u001b[1;32m~\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\control\\run_control.py:284\u001b[0m, in \u001b[0;36mrun_control\u001b[1;34m(net, ctrl_variables, max_iter, **kwargs)\u001b[0m\n\u001b[0;32m    281\u001b[0m control_initialization(controller_order)\n\u001b[0;32m    283\u001b[0m \u001b[38;5;66;03m# initial power flow (takes time, but is not needed for every kind of controller)\u001b[39;00m\n\u001b[1;32m--> 284\u001b[0m ctrl_variables \u001b[38;5;241m=\u001b[39m \u001b[43mnet_initialization\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctrl_variables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    286\u001b[0m \u001b[38;5;66;03m# run each controller step in given controller order\u001b[39;00m\n\u001b[0;32m    287\u001b[0m control_implementation(net, controller_order, ctrl_variables, max_iter, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32m~\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\control\\run_control.py:152\u001b[0m, in \u001b[0;36mnet_initialization\u001b[1;34m(net, ctrl_variables, **kwargs)\u001b[0m\n\u001b[0;32m    150\u001b[0m run_funct \u001b[38;5;241m=\u001b[39m ctrl_variables[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m initial_run:\n\u001b[1;32m--> 152\u001b[0m     \u001b[43mrun_funct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# run can be runpp, runopf or whatever\u001b[39;00m\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    154\u001b[0m     net[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconverged\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m  \u001b[38;5;66;03m# assume that the initial state is valid\u001b[39;00m\n",
-      "File \u001b[1;32m~\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\run.py:242\u001b[0m, in \u001b[0;36mrunpp\u001b[1;34m(net, algorithm, calculate_voltage_angles, init, max_iteration, tolerance_mva, trafo_model, trafo_loading, enforce_p_lims, enforce_q_lims, check_connectivity, voltage_depend_loads, consider_line_temperature, run_control, distributed_slack, tdpf, tdpf_delay_s, **kwargs)\u001b[0m\n\u001b[0;32m    240\u001b[0m _check_bus_index_and_print_warning_if_high(net)\n\u001b[0;32m    241\u001b[0m _check_gen_index_and_print_warning_if_high(net)\n\u001b[1;32m--> 242\u001b[0m \u001b[43m_powerflow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\powerflow.py:70\u001b[0m, in \u001b[0;36m_powerflow\u001b[1;34m(net, **kwargs)\u001b[0m\n\u001b[0;32m     68\u001b[0m result \u001b[38;5;241m=\u001b[39m _run_pf_algorithm(ppci, net[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_options\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     69\u001b[0m \u001b[38;5;66;03m# read the results (=ppci with results) to net\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m \u001b[43m_ppci_to_net\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnet\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\powerflow.py:174\u001b[0m, in \u001b[0;36m_ppci_to_net\u001b[1;34m(result, net)\u001b[0m\n\u001b[0;32m    172\u001b[0m     algorithm \u001b[38;5;241m=\u001b[39m net[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_options\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malgorithm\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    173\u001b[0m     max_iteration \u001b[38;5;241m=\u001b[39m net[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_options\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_iteration\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m--> 174\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m LoadflowNotConverged(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPower Flow \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m did not converge after \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    175\u001b[0m                                \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m iterations!\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(algorithm, max_iteration))\n\u001b[0;32m    176\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    177\u001b[0m     net[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ppc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m result\n",
-      "\u001b[1;31mLoadflowNotConverged\u001b[0m: Power Flow nr did not converge after 10 iterations!"
+      "Creating controller 0 of type <class 'pandapower.control.controller.trafo.ContinuousTapControl.ContinuousTapControl'> \n"
      ]
     }
    ],
-   "source": [
-    "ehv_hv_trafos = sb.voltlvl_idx(net, \"trafo\", [3], \"lv_bus\")\n",
-    "pp.control.ContinuousTapControl(net, ehv_hv_trafos, 1.0)\n",
-    "pp.runpp(net, run_control=True, distributed_slack=True)"
-   ]
+   "execution_count": 47
   },
   {
    "cell_type": "markdown",
@@ -178,50 +203,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-22T06:55:45.589181Z",
+     "iopub.status.busy": "2026-09-22T06:55:45.589124Z",
+     "iopub.status.idle": "2026-09-22T06:55:45.838153Z",
+     "shell.execute_reply": "2026-09-22T06:55:45.837672Z"
+    },
+    "ExecuteTime": {
+     "end_time": "2026-09-22T11:42:26.948719Z",
+     "start_time": "2026-09-22T11:42:25.878114Z"
+    }
+   },
+   "source": [
+    "pp.toolbox.lf_info(net)\n",
+    "\n",
+    "# --- plot voltages and loadings of the AC power flow results\n",
+    "fig, axs = plt.subplots(ncols=2)\n",
+    "net.res_bus.vm_pu.rename(\"bus\").plot(kind=\"box\", ax=axs[0])\n",
+    "branch_loadings = pd.concat([\n",
+    "    net.res_line.loading_percent, net.res_trafo.loading_percent],\n",
+    "    axis=1, keys=[\"line\", \"trafo\"])\n",
+    "branch_loadings.plot(kind=\"box\", ax=axs[1])\n",
+    "axs[0].set_ylabel(\"vm in pu\")\n",
+    "axs[1].set_ylabel(\"loading in %\")\n",
+    "plt.tight_layout()"
+   ],
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "hp.pandapower.toolbox.result_info - INFO: Max voltage in vm_pu:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   1.1070887174089667 at busidx 56 (EHV Bus 57)\n",
-      "hp.pandapower.toolbox.result_info - INFO: Min voltage in vm_pu:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   0.9933431959987652 at busidx 3477 (HV2 Bus 90)\n",
-      "hp.pandapower.toolbox.result_info - INFO: Max loading trafo in %:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   48.65530642371104 loading at trafo 34 (EHV Trafo 35)\n",
-      "hp.pandapower.toolbox.result_info - INFO:   48.65530642371104 loading at trafo 35 (EHV Trafo 36)\n",
-      "hp.pandapower.toolbox.result_info - INFO: Max loading line in %:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   101.5984172475295 loading at line 768 (EHV Line 769)\n",
-      "hp.pandapower.toolbox.result_info - INFO:   91.14251627977873 loading at line 824 (EHV Line 825)\n"
+      "Max voltage in vm_pu:\n",
+      "  1.1069970886569855 at busidx 3123 (HV1 Bus 33)\n",
+      "Min voltage in vm_pu:\n",
+      "  1.0132503981783685 at busidx 1856 (EHV Bus 1857)\n",
+      "Max loading trafo in %:\n",
+      "  48.65602880269026 loading at trafo 34 (EHV Trafo 35)\n",
+      "  48.65602880269026 loading at trafo 35 (EHV Trafo 36)\n",
+      "Max loading line in %:\n",
+      "  101.59806013006747 loading at line 768 (EHV Line 769)\n",
+      "  91.14247616036923 loading at line 824 (EHV Line 825)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
-      ]
+      ],
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "source": [
-    "pp.lf_info(net)\n",
-    "\n",
-    "# --- plot voltages and loadings of the AC power flow results\n",
-    "fig, axs = plt.subplots(ncols=2)\n",
-    "net.res_bus.vm_pu.rename(\"bus\").plot(kind=\"box\", ax=axs[0])\n",
-    "branch_loadings = pd.concat([\n",
-    "    net.res_line.loading_percent, net.res_trafo.loading_percent],\n",
-    "    axis=1, keys=[\"line\", \"trafo\"])\n",
-    "branch_loadings.plot(kind=\"box\", ax=axs[1])\n",
-    "axs[0].set_ylabel(\"vm in pu\")\n",
-    "axs[1].set_ylabel(\"loading in %\")\n",
-    "plt.tight_layout()"
-   ]
+   "execution_count": 48
   }
  ],
  "metadata": {
@@ -240,7 +276,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.7"
+   "version": "3.14.3"
   },
   "vscode": {
    "interpreter": {
diff --git a/tutorials/simbench_converter_usage.ipynb b/tutorials/simbench_converter_usage.ipynb
index 4d27135..4046e9f 100644
--- a/tutorials/simbench_converter_usage.ipynb
+++ b/tutorials/simbench_converter_usage.ipynb
@@ -20,9 +20,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:13.357859Z",
+     "start_time": "2026-09-22T08:19:12.219717Z"
+    }
+   },
+   "source": [
+    "import pandapower.networks as nw\n",
+    "import simbench as sb\n",
+    "import os\n",
+    "\n",
+    "# let's have a look at the SimBench csv format appearance\n",
+    "net = nw.simple_mv_open_ring_net()\n",
+    "csv_data = sb.pp2csv_data(net)\n",
+    "list(csv_data.keys())"
+   ],
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -57,16 +152,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "import pandapower.networks as nw\n",
-    "import simbench as sb\n",
-    "import os\n",
-    "\n",
-    "# let's have a look at the SimBench csv format appearance\n",
-    "net = nw.simple_mv_open_ring_net()\n",
-    "csv_data = sb.pp2csv_data(net)\n",
-    "list(csv_data.keys())"
-   ]
+   "execution_count": 1
   },
   {
    "cell_type": "markdown",
@@ -84,9 +170,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:13.724768Z",
+     "start_time": "2026-09-22T08:19:13.360047Z"
+    }
+   },
    "source": [
     "# determine relevant paths\n",
     "test_network_path = os.path.join(sb.sb_dir, \"test\", \"converter\", \"test_network\")\n",
@@ -97,7 +186,66 @@
     "\n",
     "# convert pp net to csv files\n",
     "sb.pp2csv(net, test_output_folder_path, export_pp_std_types=False)"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n",
+      "/Users/jakob/src/github/simbench-jk/.venv/lib/python3.14/site-packages/pandapower/toolbox/data_modification.py:113: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  element_type.loc[element_type == short] = complete\n"
+     ]
+    }
+   ],
+   "execution_count": 2
   },
   {
    "cell_type": "markdown",
diff --git a/tutorials/simbench_grids_basics_and_usage.ipynb b/tutorials/simbench_grids_basics_and_usage.ipynb
index 8877cd3..2e5d88b 100644
--- a/tutorials/simbench_grids_basics_and_usage.ipynb
+++ b/tutorials/simbench_grids_basics_and_usage.ipynb
@@ -23,29 +23,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "hp.pandapower.plotting.plotly.draw_layers - INFO: Failed to import plotly - interactive plotting will not be available\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:48.150850Z",
+     "start_time": "2026-09-22T08:19:47.253246Z"
     }
-   ],
+   },
    "source": [
     "# Let's do some necessary imports\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import os\n",
+    "import logging\n",
+    "import sys\n",
     "\n",
     "import pandapower as pp\n",
     "import pandapower.topology as top\n",
     "import pandapower.plotting as plot\n",
-    "import simbench as sb"
-   ]
+    "import simbench as sb\n",
+    "\n",
+    "# lf_info() reports its results via the logging module -> route INFO messages\n",
+    "# to stdout so that they are shown in this notebook\n",
+    "logging.basicConfig(level=logging.INFO, stream=sys.stdout, format=\"%(message)s\")"
+   ],
+   "outputs": [],
+   "execution_count": 1
   },
   {
    "cell_type": "markdown",
@@ -128,14 +131,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:48.162163Z",
+     "start_time": "2026-09-22T08:19:48.157075Z"
+    }
+   },
    "source": [
     "# get lists of simbench codes\n",
     "all_simbench_codes = sb.collect_all_simbench_codes()\n",
     "all_simbench_code_with_LV_as_lower_voltage_level = sb.collect_all_simbench_codes(lv_level=\"LV\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 2
   },
   {
    "cell_type": "markdown",
@@ -146,13 +154,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:48.171355Z",
+     "start_time": "2026-09-22T08:19:48.163994Z"
+    }
+   },
    "source": [
     "complete_data_sb_codes = [\"1-complete_data-mixed-all-%i-sw\" % scenario for scenario in [0, 1, 2]]\n",
     "complete_grid_sb_codes = [\"1-EHVHVMVLV-mixed-all-%i-sw\" % scenario for scenario in [0, 1, 2]]"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -165,43 +178,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\simbench\\converter\\csv_pp_converter.py:874: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  output_data[output_name] = pd.concat([output_data[output_name], input_data[\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:50.564173Z",
+     "start_time": "2026-09-22T08:19:48.175384Z"
     }
-   ],
+   },
    "source": [
     "sb_code1 = \"1-MV-rural--0-sw\"  # rural MV grid of scenario 0 with full switchs\n",
     "net = sb.get_simbench_net(sb_code1)\n",
     "\n",
     "sb_code2 = \"1-HVMV-urban-all-0-no_sw\"  # urban hv grid with one connected mv grid which has the subnet 2.202\n",
     "multi_voltage_grid = sb.get_simbench_net(sb_code2)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 4
   },
   {
    "cell_type": "markdown",
@@ -214,8 +205,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:50.604777Z",
+     "start_time": "2026-09-22T08:19:50.587555Z"
+    }
+   },
+   "source": [
+    "net"
+   ],
    "outputs": [
     {
      "data": {
@@ -238,9 +236,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "net"
-   ]
+   "execution_count": 5
   },
   {
    "cell_type": "markdown",
@@ -251,15 +247,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:50.675628Z",
+     "start_time": "2026-09-22T08:19:50.614714Z"
+    }
+   },
+   "source": [
+    "# plot the grid to show the open ring systems\n",
+    "plot.simple_plot(net)"
+   ],
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
-      ]
+      ],
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -270,15 +274,12 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# plot the grid to show the open ring systems\n",
-    "plot.simple_plot(net)"
-   ]
+   "execution_count": 6
   },
   {
    "cell_type": "markdown",
@@ -289,34 +290,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\pf\\create_jacobian.py:27: RuntimeWarning: invalid value encountered in divide\n",
-      "  dVm_x, dVa_x = dSbus_dV_numba_sparse(Ybus.data, Ybus.indptr, Ybus.indices, V, V / abs(V), Ibus)\n",
-      "C:\\Users\\tbanze\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\pypower\\newtonpf.py:512: MatrixRankWarning: Matrix is exactly singular\n",
-      "  dx = -1 * spsolve(J, F, permc_spec=permc_spec, use_umfpack=use_umfpack)\n"
-     ]
-    },
-    {
-     "ename": "LoadflowNotConverged",
-     "evalue": "Power Flow nr did not converge after 10 iterations!",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mLoadflowNotConverged\u001b[0m                      Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[6], line 14\u001b[0m\n\u001b[0;32m     11\u001b[0m net\u001b[38;5;241m.\u001b[39mswitch\u001b[38;5;241m.\u001b[39mloc[loop_switches_1_5, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclosed\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;66;03m# run a simple power flow\u001b[39;00m\n\u001b[1;32m---> 14\u001b[0m \u001b[43mpp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunpp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     16\u001b[0m \u001b[38;5;66;03m# analyze maximal loaded lines\u001b[39;00m\n\u001b[0;32m     17\u001b[0m feeder_1_5_lines \u001b[38;5;241m=\u001b[39m net\u001b[38;5;241m.\u001b[39mline\u001b[38;5;241m.\u001b[39msubnet\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeeder1\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m|\u001b[39m net\u001b[38;5;241m.\u001b[39mline\u001b[38;5;241m.\u001b[39msubnet\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeeder5\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[1;32m~\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\run.py:242\u001b[0m, in \u001b[0;36mrunpp\u001b[1;34m(net, algorithm, calculate_voltage_angles, init, max_iteration, tolerance_mva, trafo_model, trafo_loading, enforce_p_lims, enforce_q_lims, check_connectivity, voltage_depend_loads, consider_line_temperature, run_control, distributed_slack, tdpf, tdpf_delay_s, **kwargs)\u001b[0m\n\u001b[0;32m    240\u001b[0m _check_bus_index_and_print_warning_if_high(net)\n\u001b[0;32m    241\u001b[0m _check_gen_index_and_print_warning_if_high(net)\n\u001b[1;32m--> 242\u001b[0m \u001b[43m_powerflow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\powerflow.py:70\u001b[0m, in \u001b[0;36m_powerflow\u001b[1;34m(net, **kwargs)\u001b[0m\n\u001b[0;32m     68\u001b[0m result \u001b[38;5;241m=\u001b[39m _run_pf_algorithm(ppci, net[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_options\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     69\u001b[0m \u001b[38;5;66;03m# read the results (=ppci with results) to net\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m \u001b[43m_ppci_to_net\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnet\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\miniconda3\\envs\\dave_13\\Lib\\site-packages\\pandapower\\powerflow.py:174\u001b[0m, in \u001b[0;36m_ppci_to_net\u001b[1;34m(result, net)\u001b[0m\n\u001b[0;32m    172\u001b[0m     algorithm \u001b[38;5;241m=\u001b[39m net[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_options\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malgorithm\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    173\u001b[0m     max_iteration \u001b[38;5;241m=\u001b[39m net[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_options\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_iteration\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m--> 174\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m LoadflowNotConverged(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPower Flow \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m did not converge after \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    175\u001b[0m                                \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m iterations!\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(algorithm, max_iteration))\n\u001b[0;32m    176\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    177\u001b[0m     net[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ppc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m result\n",
-      "\u001b[1;31mLoadflowNotConverged\u001b[0m: Power Flow nr did not converge after 10 iterations!"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:51.345824Z",
+     "start_time": "2026-09-22T08:19:50.680185Z"
     }
-   ],
+   },
    "source": [
     "# let's run a simple power flow calculation while assuming an outage of the first line in feeder 1\n",
     "outage_line = 1\n",
@@ -337,29 +316,47 @@
     "feeder_1_5_lines = net.line.subnet.str.contains(\"Feeder1\") | net.line.subnet.str.contains(\"Feeder5\")\n",
     "net.res_line.loading_percent.loc[feeder_1_5_lines].max()  # maximal loaded line of Feeder 1 and 5\n",
     "# -> maximal line loading is less than 100%\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
+   ],
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0"
+       "np.float64(68.13953789906593)"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:51.364323Z",
+     "start_time": "2026-09-22T08:19:51.353751Z"
+    }
+   },
    "source": [
     "# print number of unsupplied buses\n",
     "unsupplied_buses = top.unsupplied_buses(net)\n",
     "len(unsupplied_buses)"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 8
   },
   {
    "cell_type": "markdown",
@@ -375,11 +372,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:51.390821Z",
+     "start_time": "2026-09-22T08:19:51.372668Z"
+    }
+   },
+   "source": [
+    "# the predefined study case data are stored in pandapower within net.loadcases\n",
+    "net.loadcases"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "            pload     qload  Wind_p  PV_p  RES_p  Slack_vm\n",
+       "Study Case                                                \n",
+       "hL            1.0  1.000000    0.00  0.00    0.0     1.035\n",
+       "n1            1.0  1.000000    0.00  0.00    0.0     1.035\n",
+       "hW            1.0  1.000000    1.00  0.80    1.0     1.035\n",
+       "hPV           1.0  1.000000    0.85  0.95    1.0     1.035\n",
+       "lW            0.1  0.122543    1.00  0.80    1.0     1.015\n",
+       "lPV           0.1  0.122543    0.85  0.95    1.0     1.015"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -474,27 +489,14 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "            pload     qload  Wind_p  PV_p  RES_p  Slack_vm\n",
-       "Study Case                                                \n",
-       "hL            1.0  1.000000    0.00  0.00    0.0     1.035\n",
-       "n1            1.0  1.000000    0.00  0.00    0.0     1.035\n",
-       "hW            1.0  1.000000    1.00  0.80    1.0     1.035\n",
-       "hPV           1.0  1.000000    0.85  0.95    1.0     1.035\n",
-       "lW            0.1  0.122543    1.00  0.80    1.0     1.015\n",
-       "lPV           0.1  0.122543    0.85  0.95    1.0     1.015"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# the predefined study case data are stored in pandapower within net.loadcases\n",
-    "net.loadcases"
-   ]
+   "execution_count": 9
   },
   {
    "cell_type": "markdown",
@@ -507,34 +509,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "hp.pandapower.toolbox.result_info - INFO: Max voltage in vm_pu:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   1.0366719617122484 at busidx 2 (MV1.101 busbar1.1)\n",
-      "hp.pandapower.toolbox.result_info - INFO: Min voltage in vm_pu:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   0.9520172165178736 at busidx 15 (MV1.101 Bus 15)\n",
-      "hp.pandapower.toolbox.result_info - INFO: Max loading trafo in %:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   36.2246088399241 loading at trafo 0 (HV1-MV1.101-Trafo1)\n",
-      "hp.pandapower.toolbox.result_info - INFO:   36.2246088399241 loading at trafo 1 (HV1-MV1.101-Trafo2)\n",
-      "hp.pandapower.toolbox.result_info - INFO: Max loading line in %:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   53.858594171083354 loading at line 44 (MV1.101 Line 45)\n",
-      "hp.pandapower.toolbox.result_info - INFO:   53.221190845578306 loading at line 45 (MV1.101 Line 46)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0\n",
-      "17.256\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:51.461198Z",
+     "start_time": "2026-09-22T08:19:51.410168Z"
     }
-   ],
+   },
    "source": [
     "# calculate the absolute values:\n",
     "loadcases = sb.get_absolute_values(net, profiles_instead_of_study_cases=False)\n",
@@ -555,11 +535,22 @@
     "\n",
     "# run a power flow and log basic results\n",
     "pp.runpp(net)\n",
-    "pp.lf_info(net)\n",
+    "pp.toolbox.lf_info(net)\n",
     "\n",
     "print(net.res_sgen.p_mw.sum())\n",
     "print(net.res_load.p_mw.sum())"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n",
+      "17.256\n"
+     ]
+    }
+   ],
+   "execution_count": 10
   },
   {
    "attachments": {
@@ -579,26 +570,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "hp.pandapower.toolbox.result_info - INFO: Max voltage in vm_pu:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   1.1825673728689035 at busidx 15 (MV1.101 Bus 15)\n",
-      "hp.pandapower.toolbox.result_info - INFO: Min voltage in vm_pu:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   0.9872012257896651 at busidx 2 (MV1.101 busbar1.1)\n",
-      "hp.pandapower.toolbox.result_info - INFO: Max loading trafo in %:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   45.78060595121995 loading at trafo 0 (HV1-MV1.101-Trafo1)\n",
-      "hp.pandapower.toolbox.result_info - INFO:   45.78060595121995 loading at trafo 1 (HV1-MV1.101-Trafo2)\n",
-      "hp.pandapower.toolbox.result_info - INFO: Max loading line in %:\n",
-      "hp.pandapower.toolbox.result_info - INFO:   101.51874070183955 loading at line 36 (MV1.101 Line 37)\n",
-      "hp.pandapower.toolbox.result_info - INFO:   98.98903846301906 loading at line 37 (MV1.101 Line 38)\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:51.571918Z",
+     "start_time": "2026-09-22T08:19:51.539643Z"
     }
-   ],
+   },
    "source": [
     "# let's have a look at the low load high generation (extra high wind) case\n",
     "apply_absolute_values(net, loadcases, \"lW\")\n",
@@ -608,10 +585,12 @@
     "\n",
     "# run a power flow and log basic results\n",
     "pp.runpp(net)\n",
-    "pp.lf_info(net)\n",
+    "pp.toolbox.lf_info(net)\n",
     "assert net.res_bus.vm_pu.loc[net.bus.index.difference(feeder1_buses.union(feeder5_buses))].max() < 1.055\n",
     "assert net.res_bus.vm_pu.loc[net.bus.index.difference(feeder1_buses.union(feeder5_buses))].min() > 0.915"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 11
   },
   {
    "cell_type": "markdown",
@@ -633,8 +612,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:52.599314Z",
+     "start_time": "2026-09-22T08:19:51.609155Z"
+    }
+   },
+   "source": [
+    "# load 'net' again to have no outage again\n",
+    "net = sb.get_simbench_net(sb_code1)\n",
+    "\n",
+    "# As an example, the first sgen uses the profile:\n",
+    "net.sgen.profile.iloc[0]"
+   ],
    "outputs": [
     {
      "data": {
@@ -642,18 +632,12 @@
        "'WP4'"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# load 'net' again to have no outage again\n",
-    "net = sb.get_simbench_net(sb_code1)\n",
-    "\n",
-    "# As an example, the first sgen uses the profile:\n",
-    "net.sgen.profile.iloc[0]"
-   ]
+   "execution_count": 12
   },
   {
    "cell_type": "markdown",
@@ -666,13 +650,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:52.684372Z",
+     "start_time": "2026-09-22T08:19:52.671867Z"
+    }
+   },
    "source": [
     "# check that all needed profiles existent\n",
     "assert not sb.profiles_are_missing(net)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 13
   },
   {
    "cell_type": "markdown",
@@ -687,9 +676,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:53.562306Z",
+     "start_time": "2026-09-22T08:19:52.685399Z"
+    }
+   },
    "source": [
     "# calculate absolute profiles\n",
     "profiles = sb.get_absolute_values(net, profiles_instead_of_study_cases=True)\n",
@@ -708,7 +700,9 @@
     "    results.loc[time_step, \"Load Sum\"] = net.res_load.p_mw.sum()\n",
     "    results.loc[time_step, \"min_vm_pu\"] = net.res_bus.vm_pu.min()\n",
     "    results.loc[time_step, \"max_vm_pu\"] = net.res_bus.vm_pu.max()"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 14
   },
   {
    "cell_type": "markdown",
@@ -719,29 +713,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:53.625133Z",
+     "start_time": "2026-09-22T08:19:53.576452Z"
+    }
+   },
+   "source": [
+    "# plot the voltage extrema\n",
+    "#plt.rc('text', usetex=True)\n",
+    "fig1, ax1 = plt.subplots()\n",
+    "results[[\"min_vm_pu\", \"max_vm_pu\"]].plot(ax=ax1)\n",
+    "ax1.set_ylabel(\"Voltage Extrema [pu]\")\n",
+    "ax1.set_xlabel(\"Timesteps (quarter hours)\")\n",
+    "plt.show()"
+   ],
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
-      ]
+      ],
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "source": [
-    "# plot the voltage extrema\n",
-    "#plt.rc('text', usetex=True)\n",
-    "fig1, ax1 = plt.subplots()\n",
-    "results[[\"min_vm_pu\", \"max_vm_pu\"]].plot(ax=ax1)\n",
-    "ax1.set_ylabel(\"Voltage Extrema [pu]\")\n",
-    "ax1.set_xlabel(\"Timesteps (quarter hours)\")\n",
-    "plt.show()"
-   ]
+   "execution_count": 15
   },
   {
    "cell_type": "markdown",
@@ -752,20 +751,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:53.698771Z",
+     "start_time": "2026-09-22T08:19:53.633374Z"
     }
-   ],
+   },
    "source": [
     "# get LV grid profiles DataFrame\n",
     "load_profile_names = pd.Series(net.profiles[\"load\"].columns)\n",
@@ -785,7 +776,20 @@
     "ax2.set_ylabel(\"Active power per peak power\")\n",
     "ax1.set_xlabel(\"Timesteps (quarter hours)\")\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 16
   },
   {
    "cell_type": "markdown",
@@ -810,23 +814,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 96/96 [00:01<00:00, 79.05it/s]\n",
-      "C:\\Users\\smeinecke\\AppData\\Local\\Temp\\ipykernel_3568\\1331126851.py:21: FutureWarning: The behavior of 'to_datetime' with 'unit' when parsing strings is deprecated. In a future version, strings will be parsed as datetime strings, matching the behavior without a 'unit'. To retain the old behavior, explicitly cast ints or floats to numeric type before calling to_datetime.\n",
-      "  vm_pu = pd.read_json(vm_pu_file)\n",
-      "C:\\Users\\smeinecke\\AppData\\Local\\Temp\\ipykernel_3568\\1331126851.py:21: FutureWarning: The behavior of 'to_datetime' with 'unit' when parsing strings is deprecated. In a future version, strings will be parsed as datetime strings, matching the behavior without a 'unit'. To retain the old behavior, explicitly cast ints or floats to numeric type before calling to_datetime.\n",
-      "  vm_pu = pd.read_json(vm_pu_file)\n",
-      "C:\\Users\\smeinecke\\AppData\\Local\\Temp\\ipykernel_3568\\1331126851.py:21: FutureWarning: The behavior of 'to_datetime' with 'unit' when parsing strings is deprecated. In a future version, strings will be parsed as datetime strings, matching the behavior without a 'unit'. To retain the old behavior, explicitly cast ints or floats to numeric type before calling to_datetime.\n",
-      "  vm_pu = pd.read_json(vm_pu_file)\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:53.970992Z",
+     "start_time": "2026-09-22T08:19:53.703524Z"
     }
-   ],
+   },
    "source": [
     "import tempfile\n",
     "from pandapower.timeseries import OutputWriter\n",
@@ -853,7 +846,23 @@
     "load_p = pd.read_json(load_p_file)\n",
     "result2 = pd.concat([load_p[[\"Load Sum\"]], vm_pu[[\"min_vm_pu\", \"max_vm_pu\"]]], axis=1)\n",
     "result2.sort_index(inplace=True)"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 96/96 [00:00<00:00, 481.17it/s]\n",
+      "/var/folders/c_/n57nw635205bzymwddqmhdsc0000gn/T/ipykernel_12806/1331126851.py:21: FutureWarning: The behavior of 'to_datetime' with 'unit' when parsing strings is deprecated. In a future version, strings will be parsed as datetime strings, matching the behavior without a 'unit'. To retain the old behavior, explicitly cast ints or floats to numeric type before calling to_datetime.\n",
+      "  vm_pu = pd.read_json(vm_pu_file)\n",
+      "/var/folders/c_/n57nw635205bzymwddqmhdsc0000gn/T/ipykernel_12806/1331126851.py:21: FutureWarning: The behavior of 'to_datetime' with 'unit' when parsing strings is deprecated. In a future version, strings will be parsed as datetime strings, matching the behavior without a 'unit'. To retain the old behavior, explicitly cast ints or floats to numeric type before calling to_datetime.\n",
+      "  vm_pu = pd.read_json(vm_pu_file)\n",
+      "/var/folders/c_/n57nw635205bzymwddqmhdsc0000gn/T/ipykernel_12806/1331126851.py:21: FutureWarning: The behavior of 'to_datetime' with 'unit' when parsing strings is deprecated. In a future version, strings will be parsed as datetime strings, matching the behavior without a 'unit'. To retain the old behavior, explicitly cast ints or floats to numeric type before calling to_datetime.\n",
+      "  vm_pu = pd.read_json(vm_pu_file)\n"
+     ]
+    }
+   ],
+   "execution_count": 17
   },
   {
    "cell_type": "markdown",
@@ -866,11 +875,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:53.983131Z",
+     "start_time": "2026-09-22T08:19:53.975453Z"
+    }
+   },
+   "source": [
+    "results.head()"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   Load Sum min_vm_pu max_vm_pu\n",
+       "0   3.39125  1.022008  1.061251\n",
+       "1  3.133539  1.020201   1.05946\n",
+       "2  3.580242   1.01924  1.059461\n",
+       "3  3.315185  1.020414  1.060228\n",
+       "4  3.458423  1.020345  1.060152"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -899,62 +923,67 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>3.39125</td>\n",
-       "      <td>1.006643</td>\n",
-       "      <td>1.046464</td>\n",
+       "      <td>1.022008</td>\n",
+       "      <td>1.061251</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>3.133539</td>\n",
-       "      <td>1.004809</td>\n",
-       "      <td>1.044647</td>\n",
+       "      <td>1.020201</td>\n",
+       "      <td>1.05946</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>3.580242</td>\n",
-       "      <td>1.003833</td>\n",
-       "      <td>1.044649</td>\n",
+       "      <td>1.01924</td>\n",
+       "      <td>1.059461</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3.315185</td>\n",
-       "      <td>1.005025</td>\n",
-       "      <td>1.045427</td>\n",
+       "      <td>1.020414</td>\n",
+       "      <td>1.060228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>3.458423</td>\n",
-       "      <td>1.004955</td>\n",
-       "      <td>1.04535</td>\n",
+       "      <td>1.020345</td>\n",
+       "      <td>1.060152</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   Load Sum min_vm_pu max_vm_pu\n",
-       "0   3.39125  1.006643  1.046464\n",
-       "1  3.133539  1.004809  1.044647\n",
-       "2  3.580242  1.003833  1.044649\n",
-       "3  3.315185  1.005025  1.045427\n",
-       "4  3.458423  1.004955   1.04535"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "results.head()"
-   ]
+   "execution_count": 18
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-09-22T08:19:54.018289Z",
+     "start_time": "2026-09-22T08:19:54.008330Z"
+    }
+   },
+   "source": [
+    "result2.head()"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   Load Sum  min_vm_pu  max_vm_pu\n",
+       "0  3.391250   1.022008   1.061251\n",
+       "1  3.133539   1.020201   1.059460\n",
+       "2  3.580242   1.019240   1.059461\n",
+       "3  3.315185   1.020414   1.060228\n",
+       "4  3.458423   1.020345   1.060152"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -983,54 +1012,44 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>3.391250</td>\n",
-       "      <td>1.006643</td>\n",
-       "      <td>1.046464</td>\n",
+       "      <td>1.022008</td>\n",
+       "      <td>1.061251</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>3.133539</td>\n",
-       "      <td>1.004809</td>\n",
-       "      <td>1.044647</td>\n",
+       "      <td>1.020201</td>\n",
+       "      <td>1.059460</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>3.580242</td>\n",
-       "      <td>1.003833</td>\n",
-       "      <td>1.044649</td>\n",
+       "      <td>1.019240</td>\n",
+       "      <td>1.059461</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3.315185</td>\n",
-       "      <td>1.005025</td>\n",
-       "      <td>1.045427</td>\n",
+       "      <td>1.020414</td>\n",
+       "      <td>1.060228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>3.458423</td>\n",
-       "      <td>1.004955</td>\n",
-       "      <td>1.045350</td>\n",
+       "      <td>1.020345</td>\n",
+       "      <td>1.060152</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   Load Sum  min_vm_pu  max_vm_pu\n",
-       "0  3.391250   1.006643   1.046464\n",
-       "1  3.133539   1.004809   1.044647\n",
-       "2  3.580242   1.003833   1.044649\n",
-       "3  3.315185   1.005025   1.045427\n",
-       "4  3.458423   1.004955   1.045350"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "result2.head()"
-   ]
+   "execution_count": 19
   }
  ],
  "metadata": {