{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Internal Datastructure: Bus branch model, Admittance and Jacobian Matrix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This jupyter notebooks explains how to access and interpret the internal datastructure with relevant matrices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internal Datastructure\n",
    "\n",
    "We use the simple example network from the create_simple tutorial as an example for how to access internal calculation parameters:\n",
    "\n",
    "<img src=\"pics/example_network_simple.png\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:35.863414Z",
     "start_time": "2024-03-30T10:51:31.939777Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightSimBackend import error: No module named 'grid2op'\n",
      "PhysicalLawChecker import error: No module named 'grid2op'\n",
      "TimeSerie import error: cannot import name 'TimeSerie' from 'lightsim2grid.timeSerie' (C:\\ProgramData\\miniconda3\\envs\\py3.11\\Lib\\site-packages\\lightsim2grid\\timeSerie.py)\n",
      "ContingencyAnalysis import error: cannot import name 'ContingencyAnalysis' from 'lightsim2grid.contingencyAnalysis' (C:\\ProgramData\\miniconda3\\envs\\py3.11\\Lib\\site-packages\\lightsim2grid\\contingencyAnalysis.py)\n",
      "rewards import error: No module named 'grid2op'\n",
      "This pandapower network includes the following parameter tables:\n",
      "   - bus (7 elements)\n",
      "   - load (1 element)\n",
      "   - sgen (1 element)\n",
      "   - gen (1 element)\n",
      "   - switch (8 elements)\n",
      "   - shunt (1 element)\n",
      "   - ext_grid (1 element)\n",
      "   - line (4 elements)\n",
      "   - trafo (1 element)\n"
     ]
    }
   ],
   "source": [
    "import pandapower as pp\n",
    "import pandapower.networks as nw\n",
    "\n",
    "net = nw.example_simple()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we run a power flow in this network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.154189Z",
     "start_time": "2024-03-30T10:51:35.865424Z"
    }
   },
   "outputs": [],
   "source": [
    "pp.runpp(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When a power flow is carried out, the element based grid model is translated into a bus-branch model. That bus-branch model is stored in a data structure that is based on the PYPOWER/MATPOWER casefile (with some extensions). This ppc can be accesed after power flow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false,
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.172601Z",
     "start_time": "2024-03-30T10:51:40.155198Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "{'baseMVA': 1,\n 'version': 2,\n 'bus': array([[  0.        ,   3.        ,   0.        ,   0.        ,\n           0.        ,   0.        ,   1.        ,   1.02      ,\n          50.        , 110.        ,   1.        ,   2.        ,\n           0.        ,   0.        ,   0.        ,   0.        ],\n        [  1.        ,   1.        ,   0.        ,   0.        ,\n           0.        ,   0.96      ,   1.        ,   1.02082951,\n          50.03241357, 110.        ,   1.        ,   2.        ,\n           0.        ,   0.        ,   0.        ,   0.        ],\n        [  2.        ,   1.        ,   0.        ,   0.        ,\n           0.        ,   0.        ,   1.        ,   1.02456241,\n         -98.19715207,  20.        ,   1.        ,   2.        ,\n           0.        ,   0.        ,   0.        ,   0.        ],\n        [  3.        ,   2.        ,   0.        ,   0.        ,\n           0.        ,   0.        ,   1.        ,   1.03      ,\n         -98.12954543,  20.        ,   1.        ,   2.        ,\n           0.        ,   0.        ,   0.        ,   0.        ],\n        [  4.        ,   1.        ,  -0.8       ,   2.9       ,\n           0.        ,   0.        ,   1.        ,   1.02320534,\n         -98.04777562,  20.        ,   1.        ,   2.        ,\n           0.        ,   0.        ,   0.        ,   0.        ],\n        [  5.        ,   1.        ,   0.        ,   0.        ,\n           0.        ,   0.        ,   1.        ,   1.030007  ,\n         -98.13016747,  20.        ,   1.        ,   1.1       ,\n           0.9       ,   0.        ,   0.        ,   0.        ],\n        [  6.        ,   4.        ,   0.        ,   0.        ,\n           0.        ,   0.        ,   1.        ,          nan,\n                  nan, 110.        ,   1.        ,   2.        ,\n           0.        ,   0.        ,   0.        ,   0.        ],\n        [  7.        ,   4.        ,   0.        ,   0.        ,\n           0.        ,   0.        ,   1.        ,          nan,\n                  nan,  20.        ,   1.        ,   2.        ,\n           0.        ,   0.        ,   0.        ,   0.        ]]),\n 'branch': array([[ 0.00000000e+00+0.j        ,  1.00000000e+00+0.j        ,\n          4.95867769e-05+0.j        ,  1.19008264e-04+0.j        ,\n          5.47391104e+00+0.j        ,  0.00000000e+00+0.j        ,\n          2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n          1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n          3.60000000e+02+0.j        , -6.74111530e+00+0.j        ,\n         -7.14688297e+00+0.j        ,  6.74416214e+00+0.j        ,\n          1.45450502e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ],\n        [ 2.00000000e+00+0.j        ,  3.00000000e+00+0.j        ,\n          6.10000000e-04+0.j        ,  5.60000000e-04+0.j        ,\n          7.64035333e-02+0.j        ,  0.00000000e+00+0.j        ,\n          2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n          1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n          3.60000000e+02+0.j        , -5.97238973e+00+0.j        ,\n         -3.48162502e+00+0.j        ,  5.99999998e+00+0.j        ,\n          3.42634241e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ],\n        [ 3.00000000e+00+0.j        ,  5.00000000e+00+0.j        ,\n          5.19662500e-03+0.j        ,  3.25500000e-03+0.j        ,\n          4.17831823e-03+0.j        ,  0.00000000e+00+0.j        ,\n          2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n          1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n          3.60000000e+02+0.j        ,  2.40627158e-08+0.j        ,\n         -4.43279288e-03+0.j        ,  0.00000000e+00+0.j        ,\n         -0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ],\n        [ 4.00000000e+00+0.j        ,  2.00000000e+00+0.j        ,\n          7.62500000e-04+0.j        ,  7.00000000e-04+0.j        ,\n          9.55044167e-02+0.j        ,  0.00000000e+00+0.j        ,\n          2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n          1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n          3.60000000e+02+0.j        ,  8.00000000e-01+0.j        ,\n         -2.90000000e+00+0.j        , -7.93618189e-01+0.j        ,\n          2.80573774e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ],\n        [ 1.00000000e+00+0.j        ,  2.00000000e+00+0.j        ,\n          1.63923679e-04+0.j        ,  4.79726336e-03+0.j        ,\n         -1.05000908e-02-0.01399964j,  0.00000000e+00+0.j        ,\n          2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n          1.00000000e+00+0.j        ,  1.50000000e+02+0.j        ,\n          1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n          3.60000000e+02+0.j        , -6.74416214e+00+0.j        ,\n         -4.54095852e-01+0.j        ,  6.76600792e+00+0.j        ,\n          6.75887284e-01+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n          0.00000000e+00+0.j        ]]),\n 'tcsc': array([], shape=(0, 17), dtype=float64),\n 'svc': array([], shape=(0, 11), dtype=float64),\n 'ssc': array([], shape=(0, 10), dtype=float64),\n 'gen': array([[ 0.00000000e+00, -6.74111530e+00, -7.14688297e+00,\n          0.00000000e+00,  0.00000000e+00,  1.02000000e+00,\n          1.00000000e+00,  1.00000000e+00,  1.00000000e+09,\n         -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00,  1.00000000e+00],\n        [ 3.00000000e+00,  6.00000000e+00,  3.42190962e+00,\n          3.00000000e+00, -3.00000000e+00,  1.03000000e+00,\n                     nan,  1.00000000e+00,  1.00000000e+09,\n         -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00,  0.00000000e+00]]),\n 'internal': {'Ybus': <6x6 sparse matrix of type '<class 'numpy.complex128'>'\n  \twith 16 stored elements in Compressed Sparse Row format>,\n  'Yf': <5x6 sparse matrix of type '<class 'numpy.complex128'>'\n  \twith 10 stored elements in Compressed Sparse Row format>,\n  'Yt': <5x6 sparse matrix of type '<class 'numpy.complex128'>'\n  \twith 10 stored elements in Compressed Sparse Row format>,\n  'branch_is': array([ True,  True,  True,  True,  True]),\n  'gen_is': array([ True,  True]),\n  'DLF': array([], dtype=complex128),\n  'buses_ord_bfs_nets': array([], dtype=float64),\n  'svc_is': array([], dtype=bool),\n  'ssc_is': array([], dtype=bool),\n  'tcsc_is': array([], dtype=bool),\n  'ref_gens': array([0], dtype=int64),\n  'baseMVA': 1,\n  'bus': array([[  0.        ,   3.        ,   0.        ,   0.        ,\n            0.        ,   0.        ,   1.        ,   1.02      ,\n           50.        , 110.        ,   1.        ,   2.        ,\n            0.        ,   0.        ,   0.        ,   0.        ],\n         [  1.        ,   1.        ,   0.        ,   0.        ,\n            0.        ,   0.96      ,   1.        ,   1.02082951,\n           50.03241357, 110.        ,   1.        ,   2.        ,\n            0.        ,   0.        ,   0.        ,   0.        ],\n         [  2.        ,   1.        ,   0.        ,   0.        ,\n            0.        ,   0.        ,   1.        ,   1.02456241,\n          -98.19715207,  20.        ,   1.        ,   2.        ,\n            0.        ,   0.        ,   0.        ,   0.        ],\n         [  3.        ,   2.        ,   0.        ,   0.        ,\n            0.        ,   0.        ,   1.        ,   1.03      ,\n          -98.12954543,  20.        ,   1.        ,   2.        ,\n            0.        ,   0.        ,   0.        ,   0.        ],\n         [  4.        ,   1.        ,  -0.8       ,   2.9       ,\n            0.        ,   0.        ,   1.        ,   1.02320534,\n          -98.04777562,  20.        ,   1.        ,   2.        ,\n            0.        ,   0.        ,   0.        ,   0.        ],\n         [  5.        ,   1.        ,   0.        ,   0.        ,\n            0.        ,   0.        ,   1.        ,   1.030007  ,\n          -98.13016747,  20.        ,   1.        ,   1.1       ,\n            0.9       ,   0.        ,   0.        ,   0.        ]]),\n  'gen': array([[ 0.00000000e+00, -6.74111530e+00, -7.14688297e+00,\n           0.00000000e+00,  0.00000000e+00,  1.02000000e+00,\n           1.00000000e+00,  1.00000000e+00,  1.00000000e+09,\n          -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,\n           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n           0.00000000e+00,  1.00000000e+00],\n         [ 3.00000000e+00,  6.00000000e+00,  3.42190962e+00,\n           3.00000000e+00, -3.00000000e+00,  1.03000000e+00,\n                      nan,  1.00000000e+00,  1.00000000e+09,\n          -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,\n           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n           0.00000000e+00,  0.00000000e+00]]),\n  'branch': array([[ 0.00000000e+00+0.j        ,  1.00000000e+00+0.j        ,\n           4.95867769e-05+0.j        ,  1.19008264e-04+0.j        ,\n           5.47391104e+00+0.j        ,  0.00000000e+00+0.j        ,\n           2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n           1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n           3.60000000e+02+0.j        , -6.74111530e+00+0.j        ,\n          -7.14688297e+00+0.j        ,  6.74416214e+00+0.j        ,\n           1.45450502e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ],\n         [ 2.00000000e+00+0.j        ,  3.00000000e+00+0.j        ,\n           6.10000000e-04+0.j        ,  5.60000000e-04+0.j        ,\n           7.64035333e-02+0.j        ,  0.00000000e+00+0.j        ,\n           2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n           1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n           3.60000000e+02+0.j        , -5.97238973e+00+0.j        ,\n          -3.48162502e+00+0.j        ,  5.99999998e+00+0.j        ,\n           3.42634241e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ],\n         [ 3.00000000e+00+0.j        ,  5.00000000e+00+0.j        ,\n           5.19662500e-03+0.j        ,  3.25500000e-03+0.j        ,\n           4.17831823e-03+0.j        ,  0.00000000e+00+0.j        ,\n           2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n           1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n           3.60000000e+02+0.j        ,  2.40627158e-08+0.j        ,\n          -4.43279288e-03+0.j        ,  0.00000000e+00+0.j        ,\n          -0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ],\n         [ 4.00000000e+00+0.j        ,  2.00000000e+00+0.j        ,\n           7.62500000e-04+0.j        ,  7.00000000e-04+0.j        ,\n           9.55044167e-02+0.j        ,  0.00000000e+00+0.j        ,\n           2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n           1.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n           3.60000000e+02+0.j        ,  8.00000000e-01+0.j        ,\n          -2.90000000e+00+0.j        , -7.93618189e-01+0.j        ,\n           2.80573774e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ],\n         [ 1.00000000e+00+0.j        ,  2.00000000e+00+0.j        ,\n           1.63923679e-04+0.j        ,  4.79726336e-03+0.j        ,\n          -1.05000908e-02-0.01399964j,  0.00000000e+00+0.j        ,\n           2.50000000e+02+0.j        ,  2.50000000e+02+0.j        ,\n           1.00000000e+00+0.j        ,  1.50000000e+02+0.j        ,\n           1.00000000e+00+0.j        , -3.60000000e+02+0.j        ,\n           3.60000000e+02+0.j        , -6.74416214e+00+0.j        ,\n          -4.54095852e-01+0.j        ,  6.76600792e+00+0.j        ,\n           6.75887284e-01+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ,  0.00000000e+00+0.j        ,\n           0.00000000e+00+0.j        ]]),\n  'ref': array([0], dtype=int64),\n  'pv': array([3], dtype=int64),\n  'pq': array([1, 2, 4, 5], dtype=int64),\n  'Bbus': <6x6 sparse matrix of type '<class 'numpy.complex128'>'\n  \twith 16 stored elements in Compressed Sparse Column format>,\n  'Bf': <5x6 sparse matrix of type '<class 'numpy.complex128'>'\n  \twith 10 stored elements in Compressed Sparse Row format>,\n  'Pbusinj': array([   0.      +0.j, -545.726528+0.j,  545.726528+0.j,    0.      +0.j,\n            0.      +0.j,    0.      +0.j]),\n  'Pfinj': array([   0.      +0.j,    0.      +0.j,    0.      +0.j,    0.      +0.j,\n         -545.726528+0.j]),\n  'Cft': <5x6 sparse matrix of type '<class 'numpy.float64'>'\n  \twith 10 stored elements in Compressed Sparse Row format>,\n  'shift': array([  0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j, 150.+0.j]),\n  'V': array([ 0.65564336+0.78136533j,  0.65573406+0.78237186j,\n         -0.14608184-1.01409479j, -0.14565409-1.01964939j,\n         -0.1432475 -1.01312848j, -0.14566615-1.01965475j]),\n  'J': <9x9 sparse matrix of type '<class 'numpy.float64'>'\n  \twith 41 stored elements in Compressed Sparse Column format>,\n  'Vm_it': None,\n  'Va_it': None,\n  'svc': array([], shape=(0, 11), dtype=float64),\n  'tcsc': array([], shape=(0, 17), dtype=float64),\n  'ssc': array([], shape=(0, 10), dtype=float64),\n  'Sbus': array([-6.8+0.j ,  0. +0.j ,  0. +0.j ,  6. +0.j ,  0.8-2.9j,  0. +0.j ]),\n  'r_theta_kelvin_per_mw': None,\n  'T': None},\n 'success': True,\n 'et': 2.052844199934043,\n 'iterations': 3}"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net._ppc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For information on how this datastructure is defined, please refer to the MATPOWER documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** For linear power flow (DC load flow) 'Ybus' is no longer created, but 'Bbus' as a new 'internal' key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.193610Z",
     "start_time": "2024-03-30T10:51:40.174610Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 8402.77777778+0.j, -8402.77777778+0.j,     0.        +0.j,\n            0.        +0.j,     0.        +0.j,     0.        +0.j],\n       [-8402.77777778+0.j,  8611.22995659+0.j,  -208.45217882+0.j,\n            0.        +0.j,     0.        +0.j,     0.        +0.j],\n       [    0.        +0.j,  -208.45217882+0.j,  3422.7378931 +0.j,\n        -1785.71428571+0.j, -1428.57142857+0.j,     0.        +0.j],\n       [    0.        +0.j,     0.        +0.j, -1785.71428571+0.j,\n         2092.93394777+0.j,     0.        +0.j,  -307.21966206+0.j],\n       [    0.        +0.j,     0.        +0.j, -1428.57142857+0.j,\n            0.        +0.j,  1428.57142857+0.j,     0.        +0.j],\n       [    0.        +0.j,     0.        +0.j,     0.        +0.j,\n         -307.21966206+0.j,     0.        +0.j,   307.21966206+0.j]])"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.rundcpp(net)\n",
    "net._ppc['internal']['Bbus'].A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nodal Point Admittance Matrix\n",
    "\n",
    "The nodal point admittance matrix is saved in the ppc and can be accessed directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.224863Z",
     "start_time": "2024-03-30T10:51:40.194632Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "matrix([[ 2983.234714  -7157.02635809j, -2983.234714  +7159.76331361j,\n             0.           +0.j        ,     0.           +0.j        ,\n             0.           +0.j        ,     0.           +0.j        ],\n        [-2983.234714  +7159.76331361j,  2990.35626947-7364.28068082j,\n           -97.94315042 -183.87162407j,     0.           +0.j        ,\n             0.           +0.j        ,     0.           +0.j        ],\n        [    0.           +0.j        ,   110.26592227 -176.75706842j,\n          1608.40491554-1678.15899439j,  -889.60186671 +816.68368091j,\n          -711.68149336 +653.34694473j,     0.           +0.j        ],\n        [    0.           +0.j        ,     0.           +0.j        ,\n          -889.60186671 +816.68368091j,  1027.81021222 -903.21268537j,\n             0.           +0.j        ,  -138.20834551  +86.56929539j],\n        [    0.           +0.j        ,     0.           +0.j        ,\n          -711.68149336 +653.34694473j,     0.           +0.j        ,\n           711.68149336 -653.29919252j,     0.           +0.j        ],\n        [    0.           +0.j        ,     0.           +0.j        ,\n             0.           +0.j        ,  -138.20834551  +86.56929539j,\n             0.           +0.j        ,   138.20834551  -86.56720623j]])"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.runpp(net)\n",
    "net._ppc[\"internal\"][\"Ybus\"].todense()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the nodal point admittance matrix is given in per unit values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jacobian Matrix\n",
    "\n",
    "The jacobian Matrix J in the last iteration step is also stored in the ppc and can be accessed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.232737Z",
     "start_time": "2024-03-30T10:51:40.225872Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "matrix([[ 9.54796429e+02,  0.00000000e+00, -8.62952847e+02,\n          0.00000000e+00, -9.18435819e+01,  0.00000000e+00,\n         -9.15296722e+02,  0.00000000e+00, -1.42353628e+02],\n        [ 0.00000000e+00,  7.67426446e+03, -2.17432759e+02,\n          0.00000000e+00,  0.00000000e+00,  3.05264391e+03,\n         -1.38258877e+01,  0.00000000e+00,  0.00000000e+00],\n        [-8.60737366e+02, -2.17892547e+02,  1.76161072e+03,\n         -6.82980810e+02,  0.00000000e+00, -6.95207957e-01,\n          1.64791122e+03, -7.30904816e+02,  0.00000000e+00],\n        [ 0.00000000e+00,  0.00000000e+00, -6.86871045e+02,\n          6.86871045e+02,  0.00000000e+00,  0.00000000e+00,\n         -7.26450957e+02,  7.28978161e+02,  0.00000000e+00],\n        [-9.18403982e+01,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00,  9.18403982e+01,  0.00000000e+00,\n          0.00000000e+00,  0.00000000e+00,  1.42355564e+02],\n        [ 0.00000000e+00, -3.11622898e+03,  1.41654849e+01,\n          0.00000000e+00,  0.00000000e+00,  7.51767501e+03,\n         -2.12220120e+02,  0.00000000e+00,  0.00000000e+00],\n        [ 9.39812505e+02,  7.09688796e-01, -1.68838790e+03,\n          7.47865710e+02,  0.00000000e+00, -2.13446561e+02,\n          1.71937863e+03, -6.67491444e+02,  0.00000000e+00],\n        [ 0.00000000e+00,  0.00000000e+00,  7.44294346e+02,\n         -7.44294346e+02,  0.00000000e+00,  0.00000000e+00,\n         -6.70404297e+02,  6.65624991e+02,  0.00000000e+00],\n        [ 1.46627228e+02,  0.00000000e+00,  0.00000000e+00,\n          0.00000000e+00, -1.46627228e+02,  0.00000000e+00,\n          0.00000000e+00,  0.00000000e+00,  8.91648289e+01]])"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net._ppc[\"internal\"][\"J\"].todense()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The jacobian matrix is also given in per unit values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping the Buses\n",
    "\n",
    "The pandapower indices are not equal to the ppc indices for several reasons. Some buses are fused together in case of closed bus-bus switches and auxiliary buses are created for elements like extended wards or three winding transformers.\n",
    "See here for more details: https://pandapower.readthedocs.io/en/latest/elements/switch.html\n",
    "\n",
    "There is however a mapping between pandapower indices and ppc indices that is created during the conversion to keep track of the dependencies that is also stored in the net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.241491Z",
     "start_time": "2024-03-30T10:51:40.234750Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([0, 1, 1, 2, 2, 3, 4], dtype=int64)"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net._pd2ppc_lookups[\"bus\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a ppc index from the pandapower index, simply call the lookup like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.249297Z",
     "start_time": "2024-03-30T10:51:40.242504Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "pandapower_bus_idx = 3\n",
    "ppc_index = net._pd2ppc_lookups[\"bus\"][pandapower_bus_idx]\n",
    "print(ppc_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, pandapower bus index 3 corresponds to ppc bus index 2. So if we would like to find the diagonal entry of the Ybus matrix for bus 2, we could now access it with that internal index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.258281Z",
     "start_time": "2024-03-30T10:51:40.251310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(2990.356269470474-7364.280680821408j)"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ybus = net._ppc[\"internal\"][\"Ybus\"]\n",
    "int_idx = net._pd2ppc_lookups[\"bus\"][ppc_index]\n",
    "Ybus[int_idx, int_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see that some buses are mapped to the same internal bus, such as bus 1 and bus 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.267788Z",
     "start_time": "2024-03-30T10:51:40.261350Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(net._pd2ppc_lookups[\"bus\"][1])\n",
    "print(net._pd2ppc_lookups[\"bus\"][2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is because buses 1 and 2 are connected by a closed bus-bus switch and are therefore represented internally as the same bus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.276542Z",
     "start_time": "2024-03-30T10:51:40.268796Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "bus           1\nelement       2\net            b\ntype         CB\nclosed     True\nname       None\nz_ohm       0.0\nin_ka       NaN\nName: 0, dtype: object"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.switch.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtaining Jacobian Entries of Generators\n",
    "\n",
    "As an example we show how to obtain the Jacobian entries of generator buses using the pandapower -> ppc bus mapping. First we get buses of the in-service generators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.284228Z",
     "start_time": "2024-03-30T10:51:40.277557Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandapower gen bus: [5]\n"
     ]
    }
   ],
   "source": [
    "gen_buses = net.gen.loc[net.gen.in_service.values, \"bus\"].values\n",
    "print(f\"pandapower gen bus: {gen_buses}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, we geht the Jacobian matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.291248Z",
     "start_time": "2024-03-30T10:51:40.285169Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jacobian shape: (9, 9)\n"
     ]
    }
   ],
   "source": [
    "J = net._ppc[\"internal\"][\"J\"]\n",
    "print(f\"Jacobian shape: {J.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why has the Jacobian the shape 9x9?\n",
    "It consists of the partial derivatives J11 = dP_dVa, J12 = dP_Vm, J21 = dQ_dVa, J22 = dQ_dVm. Except the reference bus, all PV- and PQ-buses are included in J. Vm is constant for PV nodes and dS/dVm is 0 for PV-buses (gens in pandapower) and Q is a variable. \n",
    "\n",
    "In our case we have 1 reference bus (at bus 0), 1 gen at bus 5, and 3 pq buses (at buses 1, 2, 4)\n",
    "\n",
    "This is the reason why J11 to J22  have these shapes:\n",
    "\n",
    "J11 = pvpq x pvpq (dP_dVa)\n",
    "\n",
    "J12 = pvpq x pq (dP_dVm)\n",
    "\n",
    "J21 = pq x pvpq (dQ_dVa)\n",
    "\n",
    "J22 = pq x pq (dQ_dVm)\n",
    "\n",
    "Only J11 contains values relevant for gens."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Now we get the \"internal\" ppc buses with the lookup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.300214Z",
     "start_time": "2024-03-30T10:51:40.292195Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandapower to ppc lookup: [0 1 1 2 2 3 4]\n",
      "pandapower gen bus: [5] maps to ppc gen bus: [3]\n"
     ]
    }
   ],
   "source": [
    "bus_lookup = ppc_index = net._pd2ppc_lookups[\"bus\"]\n",
    "print(f\"pandapower to ppc lookup: {bus_lookup}\")\n",
    "ppc_gen_buses = bus_lookup[gen_buses]\n",
    "print(f\"pandapower gen bus: {gen_buses} maps to ppc gen bus: {ppc_gen_buses}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need the pv and pq entries in J to obtain the Jacobian sub-matrices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.320713Z",
     "start_time": "2024-03-30T10:51:40.302226Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pv and pq nodes as in the newtonpf() function\n",
      "pv buses: [3]\n",
      "pq buses: [1 2 4 5]\n",
      "pvpq buses: [3 1 2 4 5]\n",
      "shape of J sub-matrices:\n",
      "j11 = (5, 5)\n",
      "j12 = (5, 4)\n",
      "j21 = (4, 5)\n",
      "j22 = (4, 4)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# get pv and pq values from newtonpf()\n",
    "pv = net._ppc[\"internal\"][\"pv\"]\n",
    "pq = net._ppc[\"internal\"][\"pq\"]\n",
    "# stack these as done in newtonpf()\n",
    "pvpq = np.hstack((pv, pq))\n",
    "\n",
    "print(\"pv and pq nodes as in the newtonpf() function\")\n",
    "print(f\"pv buses: {pv}\\npq buses: {pq}\\npvpq buses: {pvpq}\")\n",
    "\n",
    "# get len of pv and pq\n",
    "n_pvpq = len(pvpq)\n",
    "n_pq = len(pq)\n",
    "n_pv = len(pv)\n",
    "# get J11, J12, J21, and J22\n",
    "j11 = J[:n_pvpq, :n_pvpq]\n",
    "j12 = J[:n_pvpq, n_pvpq:]\n",
    "j21 = J[n_pvpq:, :n_pvpq]\n",
    "j22 = J[n_pvpq:, n_pvpq:]\n",
    "\n",
    "print(\"shape of J sub-matrices:\")\n",
    "print(f\"j11 = {j11.shape}\")\n",
    "print(f\"j12 = {j12.shape}\")\n",
    "print(f\"j21 = {j21.shape}\")\n",
    "print(f\"j22 = {j22.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we finally get the generator entries in J:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.332920Z",
     "start_time": "2024-03-30T10:51:40.322679Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "J11 indices: m = [ True False False False False], n = [ True False False False False]\n",
      "pandapower gen [5] entries (ppc PV nodes [3]) in J11 (=dP/dVa): [[954.79642892]]\n"
     ]
    }
   ],
   "source": [
    "# j11 gen entries\n",
    "m = np.isin(pvpq, pv)\n",
    "n = m\n",
    "\n",
    "j11_gen_entries = j11[m, n]\n",
    "print(f\"J11 indices: m = {m}, n = {n}\")\n",
    "print(f\"pandapower gen {gen_buses} entries (ppc PV nodes {ppc_gen_buses}) in J11 (=dP/dVa): {j11_gen_entries}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visiualizing the Matrices\n",
    "You can easily visualize the matrices with matplotlib and the plot function spy(). Let's look at an example.\n",
    "\n",
    "First, we define a plot function to visiualize the sparse Ybus matrix (works also with any other matrix like J) including some labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.342410Z",
     "start_time": "2024-03-30T10:51:40.334935Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as mpl\n",
    "import numpy as np\n",
    "\n",
    "import pandapower as pp\n",
    "import pandapower.networks as nw\n",
    "import pandapower.plotting as plt\n",
    "from pandapower.plotting import get_collection_sizes\n",
    "\n",
    "def plot_ybus(net=None, ax=None, ybus=None):\n",
    "    if ax is None:\n",
    "        fig, ax = mpl.subplots(1, 2)\n",
    "    if ybus is None:\n",
    "        ybus = net._ppc[\"internal\"][\"Ybus\"]\n",
    "    ax.spy(ybus)\n",
    "    ax.set_title(\"Ybus shape {}\\n\".format(str(ybus.shape)))\n",
    "    ax.set_xticks(np.arange(ybus.shape[0]))\n",
    "    ax.set_xticklabels(np.arange(ybus.shape[0]))\n",
    "    ax.set_yticklabels(np.arange(ybus.shape[1]))\n",
    "    ax.set_yticks(np.arange(ybus.shape[1]))\n",
    "\n",
    "    ax.grid(which=\"both\", linestyle=\"dotted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, we define a function to plot the power system including some bus labels and other collections:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.352770Z",
     "start_time": "2024-03-30T10:51:40.344422Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_net(net, ax=None):\n",
    "    import geojson\n",
    "    if ax is None:\n",
    "        fig, ax = mpl.subplots(1, 1, figsize=(10, 8))\n",
    "\n",
    "    sizes = get_collection_sizes(net)\n",
    "\n",
    "    # create collections for elements\n",
    "    collections = list()\n",
    "    collections.append(plt.create_bus_collection(net, size=sizes[\"bus\"]))\n",
    "    collections.append(plt.create_line_collection(net, use_bus_geodata=True))\n",
    "    collections.append(plt.create_trafo_collection(net, size=sizes[\"trafo\"]))\n",
    "    collections.append(plt.create_ext_grid_collection(net, size=sizes[\"ext_grid\"], orientation=1.5))\n",
    "    collections.append(plt.create_bus_bus_switch_collection(net, size=sizes[\"switch\"]))\n",
    "    collections.append(\n",
    "        plt.create_line_switch_collection(net, distance_to_bus=sizes[\"switch_distance\"], size=sizes[\"switch\"]))\n",
    "    collections.append(plt.create_load_collection(net, size=sizes[\"load\"]))\n",
    "\n",
    "    # add labels for each bus\n",
    "    for idx in net.bus.geo.index:\n",
    "        x, y = geojson.loads(net.bus.loc[idx, 'geo']).coordinates \n",
    "        y += sizes[\"bus\"] * 1.\n",
    "        ax.text(x, y, str(idx), fontsize=12, color=\"r\")\n",
    "\n",
    "    plt.draw_collections(collections, ax=ax)\n",
    "    mpl.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Third, we create a plot function with three subplots. The first subplots shows the power system including the bus labels. The second and third plot shows the Ybus matrix with different settings. If you just call runpp() the buses are fused internally and the Ybus matrix has a lower dimension. You can also use the parameter `r_switch` to set impedance values for switches. In this case, the buses are not fused and the Ybus matrix has a higher dimension. This also changes your power flow result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:40.360146Z",
     "start_time": "2024-03-30T10:51:40.353781Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_overview(net):\n",
    "    fig, axes = mpl.subplots(1, 3, figsize=(16, 8))\n",
    "    pp.runpp(net)\n",
    "    plot_net(net, ax=axes[0])\n",
    "    # plot y bus with max. const shape (no fused switches)\n",
    "    pp.runpp(net, check_connectivity=False, r_switch=0.1, init=\"flat\", neglect_open_switch_branches=True)\n",
    "    plot_ybus(net, ax=axes[1])\n",
    "    # plot ybus for power flow (with fused switches)\n",
    "    pp.runpp(net)\n",
    "    plot_ybus(net, ax=axes[2])\n",
    "    mpl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we call the overview function with the cigre example power system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T10:51:41.739192Z",
     "start_time": "2024-03-30T10:51:40.361157Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mvogt\\AppData\\Local\\Temp\\ipykernel_17836\\3533381797.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(np.arange(ybus.shape[1]))\n",
      "C:\\Users\\mvogt\\AppData\\Local\\Temp\\ipykernel_17836\\3533381797.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(np.arange(ybus.shape[1]))\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1600x800 with 3 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = nw.create_cigre_network_mv()\n",
    "plot_overview(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each dot in the spy plot is ea"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}