net_scenario.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pandapower in /usr/local/lib/python3.7/dist-packages (2.1.0)\n",
      "Requirement already satisfied: networkx in /home/efrost/.local/lib/python3.7/site-packages (from pandapower) (2.5)\n",
      "Requirement already satisfied: numpy>=0.11 in /home/efrost/.local/lib/python3.7/site-packages (from pandapower) (1.21.6)\n",
      "Requirement already satisfied: pandas>=0.17 in /usr/local/lib/python3.7/dist-packages (from pandapower) (0.24.2)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from pandapower) (1.4.1)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from pandapower) (19.0)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.17->pandapower) (2.6.1)\n",
      "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.7/dist-packages (from pandas>=0.17->pandapower) (2017.3)\n",
      "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx->pandapower) (4.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->pandapower) (2.2.0)\n",
      "Requirement already satisfied: six in /usr/lib/python3/dist-packages (from packaging->pandapower) (1.14.0)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.3; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mDefaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (0.24.2)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.6.1)\n",
      "Requirement already satisfied: numpy>=1.12.0 in /home/efrost/.local/lib/python3.7/site-packages (from pandas) (1.21.6)\n",
      "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.7/dist-packages (from pandas) (2017.3)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.5.0->pandas) (1.14.0)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.3; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mDefaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: numpy in /home/efrost/.local/lib/python3.7/site-packages (1.21.6)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.3; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mDefaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: numba in /home/efrost/.local/lib/python3.7/site-packages (0.55.1)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba) (41.0.1)\n",
      "Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in /home/efrost/.local/lib/python3.7/site-packages (from numba) (0.38.0)\n",
      "Requirement already satisfied: numpy<1.22,>=1.18 in /home/efrost/.local/lib/python3.7/site-packages (from numba) (1.21.6)\n",
      "\u001b[33mWARNING: You are using pip version 22.0.3; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# Installation der Pakete. Dies muss nur einmal ganz am Anfang durchgeführt werden.\n",
    "!pip install pandapower\n",
    "!pip install pandas\n",
    "!pip install numpy\n",
    "!pip install numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importieren abgeschlossen\n"
     ]
    }
   ],
   "source": [
    "# Importieren der benötigten Funktionen\n",
    "from util_function import create_net, run_simulation, plot_bus_values, plot_load_values, plot_generator_values, plot_sgen2_values, plot_schedule_values\n",
    "print('Importieren abgeschlossen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This pandapower network includes the following parameter tables:\n",
      "   - bus (7 elements)\n",
      "   - load (5 elements)\n",
      "   - sgen (5 elements)\n",
      "   - switch (1 element)\n",
      "   - ext_grid (1 element)\n",
      "   - line (6 elements)\n",
      "   - trafo (1 element)\n",
      "   - bus_geodata (7 elements)\n"
     ]
    }
   ],
   "source": [
    "# Erstellung des Netzes.\n",
    "# Wenn switch_is_closed = True, dann ist der Schalter geschlossen\n",
    "# Wenn switch_is_closed = False, dann ist der Schalter geöffnet\n",
    "switch_is_closed = True\n",
    "\n",
    "net = create_net(switch_is_closed)\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aktueller Zeitschritt:  08:00\n",
      "Die Lastflussrechnung ist nicht konvergiert!\n",
      "\n",
      "\n",
      "Aktueller Zeitschritt:  08:15\n",
      "Die Lastflussrechnung ist nicht konvergiert!\n",
      "\n",
      "\n",
      "Aktueller Zeitschritt:  08:30\n",
      "Die Lastflussrechnung ist nicht konvergiert!\n",
      "\n",
      "\n",
      "Aktueller Zeitschritt:  08:45\n",
      "Die Lastflussrechnung ist nicht konvergiert!\n",
      "\n",
      "\n",
      "Aktueller Zeitschritt:  09:00\n",
      "Die Lastflussrechnung ist nicht konvergiert!\n",
      "\n",
      "\n",
      "Aktueller Zeitschritt:  09:15\n",
      "Die Lastflussrechnung ist nicht konvergiert!\n",
      "\n",
      "\n",
      "Aktueller Zeitschritt:  09:30\n",
      "Die Lastflussrechnung ist nicht konvergiert!\n",
      "\n",
      "\n",
      "Aktueller Zeitschritt:  09:45\n",
      "Die Lastflussrechnung ist nicht konvergiert!\n",
      "\n",
      "\n",
      "Aktueller Zeitschritt:  10:00\n",
      "Die Lastflussrechnung ist nicht konvergiert!\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Nun kann die Simulation mit dem Netz gestartet werden\n",
    "date_idx, sgen_values, bus_values, load_values, sgen_2_values, power = run_simulation(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nun können wir uns die Ergebnisse anschauen. Zunächst die Spannungswerte der Netzknoten im Durchschnitt.\n",
    "# Der Wert sollte immer im Bereich von 0.85 und 1.15 liegen.\n",
    "plot_bus_values(date_idx, bus_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Durchschnittliche Leistungswerte der Lasten über die Zeit\n",
    "plot_load_values(date_idx, load_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Durchschnittliche Leistungswerte der Generatoren über die Zeit\n",
    "plot_generator_values(date_idx, sgen_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEWCAYAAABIVsEJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAZL0lEQVR4nO3debgldX3n8feHbkA2WVuEbtomAc3AmEHmDiiiw4RFULEdIRNwI4mG+CgZFYxikhFEHZdxxHE0joyoHVQWMY6tkiAu6ChK+jai2AjS4tLN2uy0KOs3f1RdPFxv9z237nLutd+v5znPPVX1q6pvHZrzOfX71amTqkKSpInabNAFSJLmJgNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggmnFJViU5ZNB1SJocA0SdJflZksMmul5V7VtVl46z7SVJKsn8zgXOcl1fv1HbOC7J5Ul+meTW9vmrk2Sq6pwqSS5N8sop3N6WSc5O8vMk9ya5MslRU7V9jc8AkWbYVIViklOA/wX8D+CJwK7Aq4BnAltMxT4mUMu0Bn0ao9+v5gNrgP8IbA/8HXBBkiXTWYt6VJUPH50ewM+Awzaw7PnAlcBdwGXAH461HnAAMAzcA9wCvK+d/wuggPXt4xnA6cAne7azpG0zv52+FHgb8G3gXuDLwC497V8O/By4HfhvfdaxDDilfb6w3d9r2unfB+4ANuvzmN8E/AC4HzgXeAT4VXt8b2zbPb1d9y7g+8AhG3h9twd+CRwzzn+jLYH3tq/nLcD/AbZqlx0CrAVOAW4FbgL+bILrvgm4GTgH2BH4IrAOuLN9vqht/w7gYeDX7fF+sJ1/ELACuLv9e1DP/i9t1/t2+zrt1ce/yR+M95r4mML3gEEX4GPuPthAgABPa9+QDgTmASe0bbccvR7wHeBl7fNtgae3z5fQEw7tvNMZP0B+AjwZ2Kqdfle7bJ/2jetgmk/n7wUe7KOOPwe+0D5/cbv983uWfX4Cx3wlsEfPm/BjXj+agLodeC5N78Dh7fSCMV7jI4GHel+fDfw3OhNYDuwEbAd8AXhnu+yQdhtnAJu3+70P2HEC676bJmi2AnYGjgG2btt/Bvh/PbVcCryyZ3onmqB5Gc3ZxPHt9M497X8B7Nsu33ycY92VJqD+YND/b2wqD7uwNB1OBD5SVZdX1cNVtYzmU/fTx2j7ILBXkl2qan1VfXeS+/54Vf24qn4FXADs184/liYIvlVVDwBvoQmf8er4BnBw233ybOA9NF1E0HSdfGMCx/yBqlrT1jaWlwIXVdVFVfVIVV1Cc1b03DHa7gLcVlUPjcxIclmSu5L8Ksmz23GQE4HXV9UdVXUv8N+B40Yd9xlV9WBVXUQTsk/pc91HgNOq6v6q+lVV3V5Vn62q+9r272hfow15HnBdVZ1TVQ9V1bnANcDRPW0+UVWr2uUPbmhDSTYHPgUsq6prNrJPTSEDRNPhScAp7ZvZXUnuovnkvfsYbV9Bc8ZwTZIVSZ4/yX3f3PP8PpqzCdp9rxlZUFX30Xy632gdVfUTmq6i/YBn0XTL3JjkKTw2QPo55jVs3JOAPx61jYOB3cZoezuwS+/YQ1UdVFU7tMs2AxbQnA2s7NneP7fzH91Obwjxm9esn3XXVdWvRyaSbJ3kI+2g9j3AN4EdkszbwPHuTtOl2OvnNGdiI8Z7zWjD/RzgAeCk8dpr6vzOXuGigVoDvKOq3jFew6q6Dji+fRN4EXBhkp157NnBiF/SvKmNeOIEaroJeMrIRJKRLpeN1lFVv6QJiWOBLarqhiTfoOmi2pGmWwr6O+bRxzR6eg1wTlX9RR/H8x2aM5ylwGc30OY2mrGDfavqhj62OdF1R9d/Cs1rfGBV3ZxkP+B7QDbQ/kaa0Oy1mCaoNrSPx2jPlM6m6b567sbOUjT1PAPRZG2e5HE9j/nA/wVeleTA9uqZbZI8L8l2o1dO8tIkC6rqEZqBY2i6Rta1f3+vp/mVwLOTLE6yPfDmCdR5IXB0koOSbEEznvLopa4bqQOaADmJ5hM1NH3zJwHfqqqH23l9H3OPW0Yd3yfbGp+TZF77eh6SZNHoFavqLuCtwN8nOTbJdkk2a9+0t2nbPNLWdWaSJ7THuTDJc8Z7sTquux1N6NyVZCfgtHGO9yLgyUlenGR+kj+hGav64nj19fgw8G+AozfSNahpYoBosi6iedMYeZxeVcPAXwAfpBkUXQ386QbWPxJYlWQ9zSWpx7X96ffRXoHTdqE8vR0TOJ/mSpuVTOCNpqpWAX8FnEdzNrKeZtD7/o3V0S77Bs2b40iAfIvmTGhkmgke84h3An/XHt8bqmoNzRnF39AE6Brgr9nA/6dV9R7gZOCNNG/OtwAfobky6rK22ZvaWr7bdit9hZ4zsXFMdN330wym3wZ8l8eeSUDzuh6b5M4kH6iq22muXDuFptvtjcDzq+q2fopL8iTgL2m6F29Osr59vKTP49MkpcoflNKmJ8m2NGcae1fVTwddjzQXeQaiTUaSo9uB3m1oLuO9iuZSWkkdGCDalCylGbi9EdibppvKU3CpI7uwJEmdeAYiSepkk/oeyC677FJLliwZdBmSNKesXLnytqpaMHr+JhUgS5YsYXh4eNBlSNKckmT0HQMAu7AkSR0ZIJKkTgwQSVInBogkqRMDRJLUiQEiSerEAJEkdWKASJI6MUAkSZ0YIJKkTgwQSVInBogkqRMDRJLUiQEiSerEAJEkdWKASJI6MUAkSZ0YIJKkTgwQSVInBogkqRMDRJLUiQEiSerEAJEkdWKASJI6MUAkSZ0MNECSHJnk2iSrk5w6xvItk5zfLr88yZJRyxcnWZ/kDTNVsySpMbAASTIP+BBwFLAPcHySfUY1ewVwZ1XtBZwJvHvU8vcB/zTdtUqSftsgz0AOAFZX1fVV9QBwHrB0VJulwLL2+YXAoUkCkOSFwE+BVTNUrySpxyADZCGwpmd6bTtvzDZV9RBwN7Bzkm2BNwFvHW8nSU5MMpxkeN26dVNSuCRp7g6inw6cWVXrx2tYVWdV1VBVDS1YsGD6K5OkTcT8Ae77BmCPnulF7byx2qxNMh/YHrgdOBA4Nsl7gB2AR5L8uqo+OP1lS5JgsAGyAtg7yZ40QXEc8OJRbZYDJwDfAY4FvlZVBTxrpEGS04H1hockzayBBUhVPZTkJOBiYB7wsapaleQMYLiqlgNnA+ckWQ3cQRMykqRZIM0H+k3D0NBQDQ8PD7oMSZpTkqysqqHR8+fqILokacAMEElSJwaIJKkTA0SS1IkBIknqxACRJHVigEiSOjFAJEmdGCCSpE4MEElSJwaIJKkTA0SS1IkBIknqxACRJHVigEiSOjFAJEmdGCCSpE4MEElSJwaIJKkTA0SS1IkBIknqxACRJHVigEiSOjFAJEmdGCCSpE4MEElSJwaIJKkTA0SS1IkBIknqxACRJHUy0ABJcmSSa5OsTnLqGMu3THJ+u/zyJEva+YcnWZnkqvbvH8107ZK0qRtYgCSZB3wIOArYBzg+yT6jmr0CuLOq9gLOBN7dzr8NOLqqngqcAJwzM1VLkkYM8gzkAGB1VV1fVQ8A5wFLR7VZCixrn18IHJokVfW9qrqxnb8K2CrJljNStSQJGGyALATW9EyvbeeN2aaqHgLuBnYe1eYY4Iqqun+a6pQkjWH+oAuYjCT70nRrHbGRNicCJwIsXrx4hiqTpN99gzwDuQHYo2d6UTtvzDZJ5gPbA7e304uAzwEvr6qfbGgnVXVWVQ1V1dCCBQumsHxJ2rQNMkBWAHsn2TPJFsBxwPJRbZbTDJIDHAt8raoqyQ7Al4BTq+rbM1axJOlRAwuQdkzjJOBi4EfABVW1KskZSV7QNjsb2DnJauBkYORS35OAvYC3JLmyfTxhhg9BkjZpqapB1zBjhoaGanh4eNBlSNKckmRlVQ2Nnu830SVJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjqZ30+jJFcBo3956m5gGHh7Vd0+1YVJkma3vgIE+CfgYeDT7fRxwNbAzcAngKOnvDJJ0qzWb4AcVlX790xfleSKqto/yUunozBJ0uzW7xjIvCQHjEwk+Q/AvHbyoSmvSpI06/V7BvJK4GNJtgUC3AO8Msk2wDunqzhJ0uzVV4BU1QrgqUm2b6fv7ll8wXQUJkma3fq9CmtL4BhgCTA/CQBVdca0VSZJmtX67cL6PM1luyuB+6evHEnSXNFvgCyqqiOntRJJ0pzS71VYlyV56rRWIkmaU/o9AzkY+NMkP6XpwgpQVfWH01aZJGlW6zdAjprWKiRJc06/ATL6PliSpE1cvwHyJZoQCfA4YE/gWmDfaapLkjTL9ftFwscMoCfZH3j1tFQkSZoTOv0eSFVdARw42Z0nOTLJtUlWJzl1jOVbJjm/XX55kiU9y97czr82yXMmW4skaWL6/Sb6yT2TmwH7AzdOZsdJ5gEfAg4H1gIrkiyvqqt7mr0CuLOq9kpyHPBu4E+S7ENzS/l9gd2BryR5clU9PJmaJEn963cMZLue5w/RjIl8dpL7PgBYXVXXAyQ5D1gK9AbIUuD09vmFwAfT3EdlKXBeVd0P/DTJ6nZ735lkTWN66xdWcfWN90zHpiVp2u2z++M57eipH7LuN0CurqrP9M5I8sfAZzbQvh8LgTU902v57W6xR9tU1UNJ7gZ2bud/d9S6C8faSZITgRMBFi9ePIlyJUm9+g2QN/PbYTHWvFmnqs4CzgIYGhrqdDnydCS3JM11Gw2QJEcBzwUWJvlAz6LHM/kfkroB2KNnelE7b6w2a5PMB7YHbu9zXUnSNBrvKqwbgWHg1zR34h15LAcme+XTCmDvJHsm2YJmUHz5qDbLgRPa58cCX6uqaucf116ltSewN/Avk6xHkjQBGz0DqarvA99P8umqehAgyY7AHlV152R23I5pnARcTPPzuB+rqlVJzgCGq2o5cDZwTjtIfgdNyNC2u4BmwP0h4DVegSVJMyvNB/pxGiWXAi+gCZyVwK3AZVX1+mmtbooNDQ3V8PDwoMuQpDklycqqGho9v98vEm5fVfcALwL+oaoOBA6dygIlSXNLvwEyP8luwH8BvjiN9UiS5oh+A+QMmrGK1VW1IsnvAddNX1mSpNmu35spfoae73y03x4/ZrqKkiTNfuN9D+SNVfWeJP+bMX4TpKr+67RVJkma1cY7A/lR+9dLlyRJjzHe90C+0P5dBpBk66q6byYKkyTNbn0Noid5RpKrgWva6X+X5O+ntTJJ0qzW71VY76e5dcnt8Og31J89XUVJkma/vn+RsKrWjJrlrUMkaRPW7+3c1yQ5CKgkmwOv5TcD7JKkTVC/ZyCvAl5D86NNNwD7Aa+erqIkSbNfv18kvA14Se+8JK+jGRuRJG2C+h4DGcPJU1aFJGnOmUyAZMqqkCTNOZMJkE6/Ly5J+t0w3r2w7mXsoAiw1bRUJEmaE8a7lcl2M1WIJGlumUwXliRpE2aASJI6MUAkSZ0YIJKkTgwQSVInBogkqRMDRJLUiQEiSerEAJEkdWKASJI6MUAkSZ0YIJKkTgYSIEl2SnJJkuvavztuoN0JbZvrkpzQzts6yZeSXJNkVZJ3zWz1kiQY3BnIqcBXq2pv4Kvt9GMk2Qk4DTgQOAA4rSdo3ltVfwA8DXhmkqNmpmxJ0ohBBchSYFn7fBnwwjHaPAe4pKruqKo7gUuAI6vqvqr6OkBVPQBcASyagZolST0GFSC7VtVN7fObgV3HaLMQWNMzvbad96gkOwBH05zFSJJm0EZ/UGoyknwFeOIYi/62d6KqKsmEfx43yXzgXOADVXX9RtqdCJwIsHjx4onuRpK0AdMWIFV12IaWJbklyW5VdVOS3YBbx2h2A3BIz/Qi4NKe6bOA66rq/ePUcVbblqGhIX/HXZKmyKC6sJYDJ7TPTwA+P0abi4EjkuzYDp4f0c4jyduB7YHXzUCtkqQxDCpA3gUcnuQ64LB2miRDST4KUFV3AG8DVrSPM6rqjiSLaLrB9gGuSHJlklcO4iAkaVOWqk2nV2doaKiGh4cHXYYkzSlJVlbV0Oj5fhNdktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicDCZAkOyW5JMl17d8dN9DuhLbNdUlOGGP58iQ/nP6KJUmjDeoM5FTgq1W1N/DVdvoxkuwEnAYcCBwAnNYbNEleBKyfmXIlSaMNKkCWAsva58uAF47R5jnAJVV1R1XdCVwCHAmQZFvgZODtM1CrJGkMgwqQXavqpvb5zcCuY7RZCKzpmV7bzgN4G/A/gfvG21GSE5MMJxlet27dJEqWJPWaP10bTvIV4IljLPrb3omqqiQ1ge3uB/x+Vb0+yZLx2lfVWcBZAENDQ33vR5K0cdMWIFV12IaWJbklyW5VdVOS3YBbx2h2A3BIz/Qi4FLgGcBQkp/R1P+EJJdW1SFIkmbMoLqwlgMjV1WdAHx+jDYXA0ck2bEdPD8CuLiqPlxVu1fVEuBg4MeGhyTNvEEFyLuAw5NcBxzWTpNkKMlHAarqDpqxjhXt44x2niRpFkjVpjMsMDQ0VMPDw4MuQ5LmlCQrq2po9Hy/iS5J6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjoxQCRJnRggkqRODBBJUicGiCSpEwNEktRJqmrQNcyYJOuAn3dcfRfgtiksZ6pY18RY18RY18T8rtb1pKpaMHrmJhUgk5FkuKqGBl3HaNY1MdY1MdY1MZtaXXZhSZI6MUAkSZ0YIP07a9AFbIB1TYx1TYx1TcwmVZdjIJKkTjwDkSR1YoBIkjoxQMaR5Mgk1yZZneTUQdczIsnHktya5IeDrqVXkj2SfD3J1UlWJXntoGsCSPK4JP+S5PttXW8ddE0jksxL8r0kXxx0Lb2S/CzJVUmuTDI86HpGJNkhyYVJrknyoyTPmAU1PaV9nUYe9yR53aDrAkjy+vbf/A+TnJvkcVO2bcdANizJPODHwOHAWmAFcHxVXT3QwoAkzwbWA/9QVf920PWMSLIbsFtVXZFkO2Al8MJBv2ZJAmxTVeuTbA58C3htVX13kHUBJDkZGAIeX1XPH3Q9I5L8DBiqqln1xbgky4D/X1UfTbIFsHVV3TXouka07xs3AAdWVdcvLk9VLQtp/q3vU1W/SnIBcFFVfWIqtu8ZyMYdAKyuquur6gHgPGDpgGsCoKq+Cdwx6DpGq6qbquqK9vm9wI+AhYOtCqqxvp3cvH0M/NNTkkXA84CPDrqWuSDJ9sCzgbMBquqB2RQerUOBnww6PHrMB7ZKMh/YGrhxqjZsgGzcQmBNz/RaZsGb4VyRZAnwNODywVbSaLuKrgRuBS6pqtlQ1/uBNwKPDLqQMRTw5SQrk5w46GJaewLrgI+33X4fTbLNoIsa5Tjg3EEXAVBVNwDvBX4B3ATcXVVfnqrtGyCaFkm2BT4LvK6q7hl0PQBV9XBV7QcsAg5IMtCuvyTPB26tqpWDrGMjDq6q/YGjgNe03aaDNh/YH/hwVT0N+CUwm8YmtwBeAHxm0LUAJNmRptdkT2B3YJskL52q7RsgG3cDsEfP9KJ2njaiHWP4LPCpqvrHQdczWtvl8XXgyAGX8kzgBe1Yw3nAHyX55GBL+o320ytVdSvwOZou3UFbC6ztOXu8kCZQZoujgCuq6pZBF9I6DPhpVa2rqgeBfwQOmqqNGyAbtwLYO8me7SeL44DlA65pVmsHq88GflRV7xt0PSOSLEiyQ/t8K5oLI64ZZE1V9eaqWlRVS2j+bX2tqqbs0+FkJNmmvQiCtovoCGDgV/xV1c3AmiRPaWcdCgz8opYexzNLuq9avwCenmTr9v/NQ2nGJafE/Kna0O+iqnooyUnAxcA84GNVtWrAZQGQ5FzgEGCXJGuB06rq7MFWBTSfql8GXNWONwD8TVVdNMCaAHYDlrVXyGwGXFBVs+qy2VlmV+BzzXsO84FPV9U/D7akR/0V8Kn2Q931wJ8NuB7g0aA9HPjLQdcyoqouT3IhcAXwEPA9pvC2Jl7GK0nqxC4sSVInBogkqRMDRJLUiQEiSerEAJEkdWKASNMsyX8edafWK5M8kuSojaxzWft3SZIXz1y1Uv+8jFeaYe19pV4C/Keq2ug9sJIcArxhNt2lVxrhGYg0g5I8GXgL8LKqeiTJXydZkeQHvb9RkmTkzsHvAp7VnrW8fhA1SxtigEgzpL1H2KeBU6rqF0mOAPamucfUfsC/H+OGhafS/PbFflV15sxWLG2ctzKRZs7bgFVVdX47fUT7+F47vS1NoHxzALVJE2aASDOgHcs4hsfeOTbAO6vqIwMpSpoku7Ckadb+JsPHgZe3v9I44mLgz9vfTiHJwiRPGLX6vcB2M1OpNDEGiDT9XgU8Afhw76W8wI40YyLfSXIVzW9bjA6LHwAPJ/m+g+iabbyMV5LUiWcgkqRODBBJUicGiCSpEwNEktSJASJJ6sQAkSR1YoBIkjr5V+VTD0XPNppZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tatsächlich gefahrene Leistungswerte der Anlage, dessen Werte verändert werden können (Generator 2)\n",
    "plot_sgen2_values(date_idx, sgen_2_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Eigentlich eingetragene Werte für Generator 2\n",
    "plot_schedule_values(date_idx, power)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}