docs/examples/pandapower_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PandaPower conversion\n",
    "\n",
    "This example illustrates conversion from PandaPower to power-grid-model input data. \n",
    "We can then calculate power-flow with it or convert to a different formats like PGM JSON.\n",
    "\n",
    "**NOTE:** To run this example, the optional `examples` dependencies are required:\n",
    "\n",
    "```sh\n",
    "pip install .[examples]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load the PandaPower Data\n",
    "\n",
    "For this example we will construct a minimal pandapower network.\n",
    "\n",
    "\n",
    "                                                      \n",
    "      (ext_grid #1)      shunt - [104]  - trafo_3w - [105] - (sym_gen + asym_gen + asym_load + ward + motor)\n",
    "       |                                    |\n",
    "      [101] ---trafo- [102] ------------- [103]\n",
    "       |                                    |\n",
    "      -/-                               (load #31)\n",
    "       |\n",
    "      [106]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import pandapower as pp\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", module=\"pandapower\", category=FutureWarning) # Hide warnings related to pandas\n",
    "\n",
    "def pandapower_simple_grid():\n",
    "    net = pp.create_empty_network(f_hz=50)\n",
    "    pp.create_bus(net, index=101, vn_kv=110)\n",
    "    pp.create_bus(net, index=102, vn_kv=20)\n",
    "    pp.create_bus(net, index=103, vn_kv=20)\n",
    "    pp.create_bus(net, index=104, vn_kv=30.1)\n",
    "    pp.create_bus(net, index=105, vn_kv=60)\n",
    "    pp.create_bus(net, index=106, vn_kv=110)\n",
    "    pp.create_ext_grid(net, index=1, in_service=True, bus=101, vm_pu=1, s_sc_max_mva=1e10, rx_max=0, va_degree=0)\n",
    "    pp.create_transformer_from_parameters(net,index=101,hv_bus=101,lv_bus=102,i0_percent=3.0,pfe_kw=11.6,vkr_percent=10.22,sn_mva=40,vn_lv_kv=20.0,vn_hv_kv=110.0,vk_percent=17.8,vector_group=\"Dyn\",shift_degree=30,tap_side=\"hv\",tap_pos=2,tap_min=-1,tap_max=3,tap_step_percent=2,tap_neutral=1,parallel=2,)\n",
    "    pp.create_line(net, index=101, from_bus=103, to_bus=102, length_km=1.23, parallel=2, df=0.2, std_type=\"NAYY 4x150 SE\")\n",
    "    pp.create_load(net, index=101, bus=103, p_mw=2.5, q_mvar=0.24, const_i_percent=26.0, const_z_percent=51.0, cos_phi=2)\n",
    "    pp.create_switch(net, index=101, et=\"l\", bus=103, element=101, closed=True)\n",
    "    pp.create_switch(net, index=3021, et=\"b\", bus=101, element=106, closed=True)\n",
    "    pp.create_switch(net, index=321, et=\"t\", bus=101, element=101, closed=True)\n",
    "    pp.create_shunt(net, index=1201, in_service=True, bus=104, p_mw=0.1, q_mvar=0.55, step=3)\n",
    "    pp.create_sgen(net, index=31, bus=105, p_mw=1.21, q_mvar=0.81)\n",
    "    pp.create_asymmetric_sgen(net, index=32, bus=105, p_a_mw=0.1, p_b_mw=0.2, p_c_mw=3, q_a_mvar=0.01, q_b_mvar=0.01, q_c_mvar=0.01)\n",
    "    pp.create_asymmetric_load(net, index=33, bus=105, p_a_mw=0.1, p_b_mw=0.2, p_c_mw=3, q_a_mvar=0.01, q_b_mvar=0.01, q_c_mvar=0.01)\n",
    "    pp.create_ward(net, index=34, bus=105, ps_mw=0.1, qs_mvar=0.1, pz_mw=0.1, qz_mvar=0.1)\n",
    "    pp.create_motor(net, bus=105, index=12, pn_mech_mw=0.1, cos_phi=0.9, loading_percent=80, efficiency_percent=90, scaling=0.8)\n",
    "    pp.create_transformer3w_from_parameters(net,index=102,hv_bus=103,mv_bus=105,lv_bus=104,in_service=True,vn_hv_kv=20.0,vn_mv_kv=60.0,vn_lv_kv=30.1,sn_hv_mva=40,sn_mv_mva=100,sn_lv_mva=50,vk_hv_percent=10,vk_mv_percent=11,vk_lv_percent=12,vkr_hv_percent=1,vkr_mv_percent=2,vkr_lv_percent=4,i0_percent=0.1,pfe_kw=10,vector_group=\"Dyny\",shift_mv_degree=30,shift_lv_degree=30,tap_side=\"lv\",tap_pos=2,tap_min=1,tap_max=3,tap_step_percent=3,tap_neutral=2)\n",
    "    return net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate the converter. The converter assumes that all the parameters  (eg. `r_ohm_per_km`) are already present in the respective component dataframes. If they are not present but a `std_type` is mentioned, then it is recommended that the user refers `pandapower.add_zero_impedance_parameters()` or `pandapower.load_std_type()` to include those parameters to the pandapower net.\n",
    "\n",
    "Then use `load_input_data()` to load the data and convert it to power-grid-model data.\n",
    "The additional information that is not used in the powerflow calculation but may be useful to link the results to the source data is stored in `extra_info`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture cap --no-stderr\n",
    "from power_grid_model_io.converters import PandaPowerConverter\n",
    "\n",
    "pp_net = pandapower_simple_grid()\n",
    "converter = PandaPowerConverter()\n",
    "input_data, extra_info = converter.load_input_data(pp_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's investigate the data we have converted, for one of the components: `lines`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(6, 2, 1, 1, 1, 0.12792, 0.0492, 6.4206e-07, 0., nan, nan, nan, nan, 108.)],\n",
       "      dtype={'names': ['id', 'from_node', 'to_node', 'from_status', 'to_status', 'r1', 'x1', 'c1', 'tan1', 'r0', 'x0', 'c0', 'tan0', 'i_n'], 'formats': ['<i4', '<i4', '<i4', 'i1', 'i1', '<f8', '<f8', '<f8', '<f8', '<f8', '<f8', '<f8', '<f8', '<f8'], 'offsets': [0, 4, 8, 12, 13, 16, 24, 32, 40, 48, 56, 64, 72, 80], 'itemsize': 88, 'aligned': True})"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{6: {'id_reference': {'table': 'line', 'index': 101},\n",
       "  'pgm_input': {'from_node': 2, 'to_node': 1, 'i_n': 108.0}}}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.set_option('future.no_silent_downcasting', True)  # enable behaviour of pandas 3.x\n",
    "\n",
    "# The node data is stored as a numpy structured array in input_data[\"line\"]\n",
    "display(input_data[\"line\"])\n",
    "\n",
    "# We can use pandas to display the data in a convenient tabular format\n",
    "# display(pd.DataFrame(input_data[\"line\"]))\n",
    "\n",
    "# The original indices are stored in the extra_data dictionary\n",
    "display({i: extra_info[i] for i in input_data[\"line\"][\"id\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Validate the data\n",
    "Before we run a power flow calculation, it is wise validate the data. The most basic method is to use `assert_valid_input_data()`, which will raise a `ValueError` when the data is invalid. For more details on data validation, please consult the [validation Example](https://github.com/PowerGridModel/power-grid-model/blob/main/docs/examples/Validation%20Examples.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from power_grid_model import CalculationType\n",
    "from power_grid_model.validation import assert_valid_input_data\n",
    "\n",
    "assert_valid_input_data(input_data, calculation_type=CalculationType.power_flow, symmetric=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Run the calculation\n",
    "\n",
    "Run powerflow calculation with the `input_data` and show the results for `nodes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>energized</th>\n",
       "      <th>u_pu</th>\n",
       "      <th>u</th>\n",
       "      <th>u_angle</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>109999.999962</td>\n",
       "      <td>-1.798666e-10</td>\n",
       "      <td>1.798666e+06</td>\n",
       "      <td>3.476629e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.973746</td>\n",
       "      <td>19474.919873</td>\n",
       "      <td>-5.239008e-01</td>\n",
       "      <td>1.408570e-07</td>\n",
       "      <td>-3.370807e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.973014</td>\n",
       "      <td>19460.275746</td>\n",
       "      <td>-5.237224e-01</td>\n",
       "      <td>-2.414573e+06</td>\n",
       "      <td>-2.317990e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.969550</td>\n",
       "      <td>29183.446690</td>\n",
       "      <td>-1.045187e+00</td>\n",
       "      <td>-8.295649e-08</td>\n",
       "      <td>1.430200e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.971998</td>\n",
       "      <td>58319.874965</td>\n",
       "      <td>-1.044829e+00</td>\n",
       "      <td>9.444109e+05</td>\n",
       "      <td>5.810813e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>109999.999962</td>\n",
       "      <td>-1.798666e-10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  energized      u_pu              u       u_angle             p  \\\n",
       "0   0          1  1.000000  109999.999962 -1.798666e-10  1.798666e+06   \n",
       "1   1          1  0.973746   19474.919873 -5.239008e-01  1.408570e-07   \n",
       "2   2          1  0.973014   19460.275746 -5.237224e-01 -2.414573e+06   \n",
       "3   3          1  0.969550   29183.446690 -1.045187e+00 -8.295649e-08   \n",
       "4   4          1  0.971998   58319.874965 -1.044829e+00  9.444109e+05   \n",
       "5   5          1  1.000000  109999.999962 -1.798666e-10  0.000000e+00   \n",
       "\n",
       "              q  \n",
       "0  3.476629e+06  \n",
       "1 -3.370807e-07  \n",
       "2 -2.317990e+05  \n",
       "3  1.430200e-07  \n",
       "4  5.810813e+05  \n",
       "5 -0.000000e+00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from power_grid_model import PowerGridModel\n",
    "\n",
    "pgm = PowerGridModel(input_data=input_data)\n",
    "output_data = pgm.calculate_power_flow()\n",
    "\n",
    "display(pd.DataFrame(output_data[\"node\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross referencing objects\n",
    "The converter has generated unique numerical IDs for all the components in the pandapower net, in fact for some special components like `loads` , multiple PGM components have been created, each with their own numerical ID. To find out which component belongs to which id, some helper functions have been defined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PGM object #4: {'table': 'bus', 'index': 105}\n",
      "Trafo with index=101: 12\n"
     ]
    }
   ],
   "source": [
    "print(\"PGM object #4:\", converter.lookup_id(4))\n",
    "\n",
    "print(\"Trafo with index=101:\", converter.get_id(\"trafo\", 101))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the data as a JSON file\n",
    "The data can be stored in a json file using the PgmJsonConverter. The file will be saved in the `destination_file` path supplied in the constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from power_grid_model_io.converters import PgmJsonConverter\n",
    "    \n",
    "input_file = \"data/pandapower/example_simple_input.json\"\n",
    "output_file = \"data/pandapower/example_simple_output.json\"\n",
    "\n",
    "PgmJsonConverter(destination_file=input_file).save(data=input_data, extra_info=extra_info)\n",
    "PgmJsonConverter(destination_file=output_file).save(data=output_data, extra_info=extra_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Converting output data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we convert the output data, lets run the powerflow in pandapower so we can compare results for demostration purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vm_pu</th>\n",
       "      <th>va_degree</th>\n",
       "      <th>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.798661</td>\n",
       "      <td>-3.476701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.973746</td>\n",
       "      <td>-30.017299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0.973014</td>\n",
       "      <td>-30.007075</td>\n",
       "      <td>2.414572</td>\n",
       "      <td>0.231799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.969519</td>\n",
       "      <td>-59.886045</td>\n",
       "      <td>0.281990</td>\n",
       "      <td>1.550945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.972063</td>\n",
       "      <td>-59.860441</td>\n",
       "      <td>-0.944398</td>\n",
       "      <td>-0.581069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        vm_pu  va_degree      p_mw    q_mvar\n",
       "101  1.000000   0.000000 -1.798661 -3.476701\n",
       "102  0.973746 -30.017299  0.000000  0.000000\n",
       "103  0.973014 -30.007075  2.414572  0.231799\n",
       "104  0.969519 -59.886045  0.281990  1.550945\n",
       "105  0.972063 -59.860441 -0.944398 -0.581069\n",
       "106  1.000000   0.000000  0.000000  0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.runpp(pp_net,  trafo_model='pi', trafo_loading='power', calculate_voltage_angles=True, numba=False)\n",
    "display(pp_net.res_bus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the results of powerflow in the pandapower net, convert the result from power-grid-model powerflow ie. `output_data` from previous section to the pandapower `res_*` dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vm_pu</th>\n",
       "      <th>va_degree</th>\n",
       "      <th>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.030560e-08</td>\n",
       "      <td>-1.798666e+00</td>\n",
       "      <td>-3.476629e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.973746</td>\n",
       "      <td>-3.001731e+01</td>\n",
       "      <td>3.026798e-14</td>\n",
       "      <td>3.019813e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0.973014</td>\n",
       "      <td>-3.000708e+01</td>\n",
       "      <td>2.414573e+00</td>\n",
       "      <td>2.317990e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.969550</td>\n",
       "      <td>-5.988481e+01</td>\n",
       "      <td>2.820080e-01</td>\n",
       "      <td>1.551044e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.971998</td>\n",
       "      <td>-5.986427e+01</td>\n",
       "      <td>-9.444109e-01</td>\n",
       "      <td>-5.810813e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.030560e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        vm_pu     va_degree          p_mw        q_mvar\n",
       "101  1.000000 -1.030560e-08 -1.798666e+00 -3.476629e+00\n",
       "102  0.973746 -3.001731e+01  3.026798e-14  3.019813e-13\n",
       "103  0.973014 -3.000708e+01  2.414573e+00  2.317990e-01\n",
       "104  0.969550 -5.988481e+01  2.820080e-01  1.551044e+00\n",
       "105  0.971998 -5.986427e+01 -9.444109e-01 -5.810813e-01\n",
       "106  1.000000 -1.030560e-08  0.000000e+00  0.000000e+00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "converted_output_data = converter.convert(output_data)\n",
    "\n",
    "display(converted_output_data[\"res_bus\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus we can see that the results of powerflow match. We can then replace the dataframes of results in the pandapower net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for table in converted_output_data.keys():\n",
    "    pp_net[table] = converted_output_data[table]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Asymmetrical Calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simulating the asymmetric calculation, we shall use the same grid as used in `unbalanced_minimal.ipynb` tutorial of pandapower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandapower_simple_asym_grid():\n",
    "    net = pp.create_empty_network()\n",
    "    b1  = pp.create_bus(net, 20.0)\n",
    "    b2  = pp.create_bus(net, 0.4)\n",
    "    b3  = pp.create_bus(net, 0.4)\n",
    "    pp.create_ext_grid(net, b1, s_sc_max_mva=1000, rx_max=0.1, x0x_max=1.0,\n",
    "                       r0x0_max=0.1)\n",
    "    pp.create_transformer_from_parameters(net, b1, b2, sn_mva=0.63,\n",
    "                                          vn_hv_kv=20., vn_lv_kv=0.4,\n",
    "                                          vkr_percent=0.1, vk_percent=6,\n",
    "                                          vk0_percent=6, vkr0_percent=0.78125,\n",
    "                                          mag0_percent=100, mag0_rx=0.,\n",
    "                                          pfe_kw=0.1, i0_percent=0.1,\n",
    "                                          vector_group=\"Dyn\", shift_degree=150,\n",
    "                                          si0_hv_partial=0.9)\n",
    "    pp.create_line_from_parameters(net, b2, b3, length_km=0.1, r0_ohm_per_km=0.0848,\n",
    "                                   x0_ohm_per_km=0.4649556, c0_nf_per_km=230.6,\n",
    "                                   max_i_ka=0.963, r_ohm_per_km=0.0212,\n",
    "                                   x_ohm_per_km=0.1162389, c_nf_per_km= 230)\n",
    "    pp.create_asymmetric_load(net, b3, p_a_mw=0.25, p_b_mw=0.18, p_c_mw=0.20, type=\"wye\")\n",
    "    return net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert to get input data. Run asymmetric powerflow calculation similarly. Then convert the asymmetric PGM output data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2m2024-02-07 12:15:14\u001b[0m [\u001b[33m\u001b[1mwarning  \u001b[0m] \u001b[1mZero sequence parameters given in trafo shall be ignored:vkr0_percent, mag0_percent, mag0_rx, si0_hv_partial\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "pp_net_3ph  = pandapower_simple_asym_grid()\n",
    "asym_input_data, asym_extra_info = converter.load_input_data(pp_net_3ph)\n",
    "asym_pgm = PowerGridModel(input_data=asym_input_data)\n",
    "asym_output_data = asym_pgm.calculate_power_flow(symmetric=False)\n",
    "converted_asym_output_data = converter.convert(asym_output_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the keys to pandapower net if required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for table in converted_asym_output_data.keys():\n",
    "    pp_net_3ph[table] = converted_asym_output_data[table]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture cap --no-stderr\n",
    "\n",
    "from power_grid_model import PowerGridModel, CalculationType\n",
    "from power_grid_model.validation import assert_valid_input_data\n",
    "from power_grid_model_io.converters import PandaPowerConverter\n",
    "\n",
    "output_file = \"data/pandapower/example_simple_output.json\"\n",
    "\n",
    "pp_net = pandapower_simple_grid()\n",
    "converter = PandaPowerConverter()\n",
    "input_data, extra_info = converter.load_input_data(pp_net)\n",
    "assert_valid_input_data(input_data, calculation_type=CalculationType.power_flow, symmetric=True)\n",
    "pgm = PowerGridModel(input_data=input_data)\n",
    "output_data = pgm.calculate_power_flow()\n",
    "json_converter = PgmJsonConverter(destination_file=output_file)\n",
    "json_converter.save(data=output_data, extra_info=extra_info)\n",
    "converted_output_data = converter.convert(output_data)\n",
    "for table in converted_output_data.keys():\n",
    "    pp_net[table] = converted_output_data[table]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "6191baaf994abc6c3e7d918f70d2cb3a487f327a3f3fca1519684915289f78b6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}