{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "30ec6b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tespy.components import *\n",
    "from tespy.connections import Connection\n",
    "from tespy.networks import Network\n",
    "import CoolProp.CoolProp as CP\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from tespy_functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f299f3c8",
   "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>Outside Temp</th>\n",
       "      <th>Demand OWD [MW]</th>\n",
       "      <th>Supply Temp OWD</th>\n",
       "      <th>Return Temp OWD</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>Demand TUD [MW]</th>\n",
       "      <th>Supply Temp TUD</th>\n",
       "      <th>sum demand</th>\n",
       "      <th>flow TUD [m³/h]</th>\n",
       "      <th>flow OWD [m³/h]</th>\n",
       "      <th>flow sum</th>\n",
       "      <th>Return Temp TUD</th>\n",
       "      <th>flow bypass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.16</td>\n",
       "      <td>83.00</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0040</td>\n",
       "      <td>83.40</td>\n",
       "      <td>39.1640</td>\n",
       "      <td>139.11</td>\n",
       "      <td>853.36</td>\n",
       "      <td>992.47</td>\n",
       "      <td>58.67</td>\n",
       "      <td>617.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.9</td>\n",
       "      <td>31.62</td>\n",
       "      <td>83.75</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3.9875</td>\n",
       "      <td>83.48</td>\n",
       "      <td>35.6075</td>\n",
       "      <td>136.93</td>\n",
       "      <td>781.80</td>\n",
       "      <td>918.73</td>\n",
       "      <td>58.73</td>\n",
       "      <td>543.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.6</td>\n",
       "      <td>31.14</td>\n",
       "      <td>83.75</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4.1470</td>\n",
       "      <td>83.72</td>\n",
       "      <td>35.2870</td>\n",
       "      <td>143.56</td>\n",
       "      <td>769.93</td>\n",
       "      <td>913.49</td>\n",
       "      <td>58.93</td>\n",
       "      <td>538.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.8</td>\n",
       "      <td>30.18</td>\n",
       "      <td>83.75</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3.8555</td>\n",
       "      <td>83.56</td>\n",
       "      <td>34.0355</td>\n",
       "      <td>132.77</td>\n",
       "      <td>746.20</td>\n",
       "      <td>878.97</td>\n",
       "      <td>58.80</td>\n",
       "      <td>503.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.1</td>\n",
       "      <td>30.84</td>\n",
       "      <td>83.75</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4.1415</td>\n",
       "      <td>84.12</td>\n",
       "      <td>34.9815</td>\n",
       "      <td>143.19</td>\n",
       "      <td>754.48</td>\n",
       "      <td>897.67</td>\n",
       "      <td>59.27</td>\n",
       "      <td>522.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>10.0</td>\n",
       "      <td>22.59</td>\n",
       "      <td>77.00</td>\n",
       "      <td>46</td>\n",
       "      <td>365</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>1.8975</td>\n",
       "      <td>77.00</td>\n",
       "      <td>24.4875</td>\n",
       "      <td>68.88</td>\n",
       "      <td>626.10</td>\n",
       "      <td>694.98</td>\n",
       "      <td>53.33</td>\n",
       "      <td>319.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>9.6</td>\n",
       "      <td>20.10</td>\n",
       "      <td>77.25</td>\n",
       "      <td>46</td>\n",
       "      <td>365</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>1.9470</td>\n",
       "      <td>77.32</td>\n",
       "      <td>22.0470</td>\n",
       "      <td>70.52</td>\n",
       "      <td>551.39</td>\n",
       "      <td>621.91</td>\n",
       "      <td>53.60</td>\n",
       "      <td>246.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>9.0</td>\n",
       "      <td>19.41</td>\n",
       "      <td>77.25</td>\n",
       "      <td>46</td>\n",
       "      <td>365</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>1.9965</td>\n",
       "      <td>77.80</td>\n",
       "      <td>21.4065</td>\n",
       "      <td>72.07</td>\n",
       "      <td>524.43</td>\n",
       "      <td>596.50</td>\n",
       "      <td>54.00</td>\n",
       "      <td>221.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>8.9</td>\n",
       "      <td>18.66</td>\n",
       "      <td>78.25</td>\n",
       "      <td>46</td>\n",
       "      <td>365</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0185</td>\n",
       "      <td>77.88</td>\n",
       "      <td>20.6785</td>\n",
       "      <td>71.72</td>\n",
       "      <td>497.13</td>\n",
       "      <td>568.85</td>\n",
       "      <td>54.07</td>\n",
       "      <td>193.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>8.5</td>\n",
       "      <td>18.69</td>\n",
       "      <td>78.25</td>\n",
       "      <td>46</td>\n",
       "      <td>365</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>1.9690</td>\n",
       "      <td>78.20</td>\n",
       "      <td>20.6590</td>\n",
       "      <td>70.73</td>\n",
       "      <td>497.93</td>\n",
       "      <td>568.66</td>\n",
       "      <td>54.33</td>\n",
       "      <td>193.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Outside Temp  Demand OWD [MW]  Supply Temp OWD  Return Temp OWD  Day  \\\n",
       "0              2.0            35.16            83.00               48    1   \n",
       "1              1.9            31.62            83.75               49    1   \n",
       "2              1.6            31.14            83.75               49    1   \n",
       "3              1.8            30.18            83.75               49    1   \n",
       "4              1.1            30.84            83.75               49    1   \n",
       "...            ...              ...              ...              ...  ...   \n",
       "8755          10.0            22.59            77.00               46  365   \n",
       "8756           9.6            20.10            77.25               46  365   \n",
       "8757           9.0            19.41            77.25               46  365   \n",
       "8758           8.9            18.66            78.25               46  365   \n",
       "8759           8.5            18.69            78.25               46  365   \n",
       "\n",
       "      Hour  weekday  Demand TUD [MW]  Supply Temp TUD  sum demand  \\\n",
       "0        0        7           4.0040            83.40     39.1640   \n",
       "1        1        7           3.9875            83.48     35.6075   \n",
       "2        2        7           4.1470            83.72     35.2870   \n",
       "3        3        7           3.8555            83.56     34.0355   \n",
       "4        4        7           4.1415            84.12     34.9815   \n",
       "...    ...      ...              ...              ...         ...   \n",
       "8755    19        7           1.8975            77.00     24.4875   \n",
       "8756    20        7           1.9470            77.32     22.0470   \n",
       "8757    21        7           1.9965            77.80     21.4065   \n",
       "8758    22        7           2.0185            77.88     20.6785   \n",
       "8759    23        7           1.9690            78.20     20.6590   \n",
       "\n",
       "      flow TUD [m³/h]  flow OWD [m³/h]  flow sum  Return Temp TUD  flow bypass  \n",
       "0              139.11           853.36    992.47            58.67       617.47  \n",
       "1              136.93           781.80    918.73            58.73       543.73  \n",
       "2              143.56           769.93    913.49            58.93       538.49  \n",
       "3              132.77           746.20    878.97            58.80       503.97  \n",
       "4              143.19           754.48    897.67            59.27       522.67  \n",
       "...               ...              ...       ...              ...          ...  \n",
       "8755            68.88           626.10    694.98            53.33       319.98  \n",
       "8756            70.52           551.39    621.91            53.60       246.91  \n",
       "8757            72.07           524.43    596.50            54.00       221.50  \n",
       "8758            71.72           497.13    568.85            54.07       193.85  \n",
       "8759            70.73           497.93    568.66            54.33       193.66  \n",
       "\n",
       "[8760 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demand = pd.read_excel('Demand_model.xlsx')\n",
    "\n",
    "'''\n",
    "### to adjust temperatures\n",
    "a = 2.5\n",
    "df_demand['Supply Temp OWD'] = df_demand['Supply Temp OWD']-a\n",
    "df_demand['Supply Temp TUD'] = df_demand['Supply Temp TUD']-a\n",
    "del df_demand['flow TUD [m³/h]']\n",
    "del df_demand['flow OWD [m³/h]']\n",
    "del df_demand['flow sum']\n",
    "\n",
    "for index, row in df_demand.iterrows():\n",
    "    if row['Supply Temp TUD'] > row['Supply Temp OWD']:\n",
    "        df_demand.at[index, 'flow TUD [m³/h]'] = np.round(((row['Demand TUD [MW]'] * 1000) / (4190 * (row['Supply Temp TUD'] - row['Return Temp TUD']))) * 3600, 2)\n",
    "        df_demand.at[index, 'flow OWD [m³/h]'] = np.round(((row['Demand OWD [MW]'] * 1000) / (4190 * (row['Supply Temp TUD'] - row['Return Temp OWD']))) * 3600, 2)\n",
    "    else:\n",
    "        df_demand.at[index, 'flow TUD [m³/h]'] = np.round(((row['Demand TUD [MW]'] * 1000) / (4190 * (row['Supply Temp OWD'] - row['Return Temp TUD']))) * 3600, 2)\n",
    "        df_demand.at[index, 'flow OWD [m³/h]'] = np.round(((row['Demand OWD [MW]'] * 1000) / (4190 * (row['Supply Temp OWD'] - row['Return Temp OWD']))) * 3600, 2)\n",
    "    df_demand.at[index, 'flow sum'] = df_demand.at[index, 'flow TUD [m³/h]'] + df_demand.at[index, 'flow OWD [m³/h]']\n",
    "\n",
    "# Move the TUD return temp column to the end (because of order used in tespy model later)\n",
    "temperature = df_demand.pop('Return Temp TUD')\n",
    "df_demand['Return Temp TUD'] = temperature\n",
    "###\n",
    "'''\n",
    "\n",
    "### to adjust demands\n",
    "a = 0.55\n",
    "b = 3\n",
    "\n",
    "df_demand['Demand OWD [MW]'] = df_demand['Demand OWD [MW]']*b\n",
    "df_demand['Demand TUD [MW]'] = df_demand['Demand TUD [MW]']*a\n",
    "df_demand['sum demand'] = df_demand['Demand OWD [MW]'] + df_demand['Demand TUD [MW]']\n",
    "del df_demand['flow TUD [m³/h]']\n",
    "del df_demand['flow OWD [m³/h]']\n",
    "del df_demand['flow sum']\n",
    "\n",
    "for index, row in df_demand.iterrows():\n",
    "    if row['Supply Temp TUD'] > row['Supply Temp OWD']:\n",
    "        df_demand.at[index, 'flow TUD [m³/h]'] = np.round(((row['Demand TUD [MW]'] * 1000) / (4190 * (row['Supply Temp TUD'] - row['Return Temp TUD']))) * 3600, 2)\n",
    "        df_demand.at[index, 'flow OWD [m³/h]'] = np.round(((row['Demand OWD [MW]'] * 1000) / (4190 * (row['Supply Temp TUD'] - row['Return Temp OWD']))) * 3600, 2)\n",
    "    else:\n",
    "        df_demand.at[index, 'flow TUD [m³/h]'] = np.round(((row['Demand TUD [MW]'] * 1000) / (4190 * (row['Supply Temp OWD'] - row['Return Temp TUD']))) * 3600, 2)\n",
    "        df_demand.at[index, 'flow OWD [m³/h]'] = np.round(((row['Demand OWD [MW]'] * 1000) / (4190 * (row['Supply Temp OWD'] - row['Return Temp OWD']))) * 3600, 2)\n",
    "    df_demand.at[index, 'flow sum'] = df_demand.at[index, 'flow TUD [m³/h]'] + df_demand.at[index, 'flow OWD [m³/h]']\n",
    "\n",
    "# Move the TUD return temp column to the end (because of order used in tespy model later)\n",
    "temperature = df_demand.pop('Return Temp TUD')\n",
    "df_demand['Return Temp TUD'] = temperature\n",
    "\n",
    "###\n",
    "df_year = df_demand.copy()\n",
    "df_year['flow bypass'] = df_year['flow sum'] - 375\n",
    "df_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "095d4d45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration Hour: 0, 0.0 % mode 2\n",
      "Iteration Hour: 1, 0.0 % mode 2\n",
      "Iteration Hour: 2, 0.0 % mode 2\n",
      "Iteration Hour: 3, 0.0 % mode 2\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[1;32mIn [4]\u001b[0m, in \u001b[0;36m<cell line: 10>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     42\u001b[0m     mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m---> 43\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43moption_base_mode2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq_owd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mq_tud\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_supply\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_return_tud\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_return_owd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mq_condenser\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     44\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mresults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConnection\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mv \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m result\u001b[38;5;241m.\u001b[39mresults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConnection\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m7\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mv \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m     45\u001b[0m         mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m6\u001b[39m\n",
      "File \u001b[1;32mc:\\Users\\Admin\\Documents\\Uni Delft Umwi\\MA\\Abgabe\\tespy_functions.py:316\u001b[0m, in \u001b[0;36moption_base_mode2\u001b[1;34m(q_owd, q_tud, t_supply, t_return_tud, t_return_owd, q_condenser, iterinfo)\u001b[0m\n\u001b[0;32m    313\u001b[0m c34\u001b[38;5;241m.\u001b[39mset_attr(p\u001b[38;5;241m=\u001b[39mp_eva, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, fluid\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNH3\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;241m1\u001b[39m})\n\u001b[0;32m    315\u001b[0m nw\u001b[38;5;241m.\u001b[39mset_attr(iterinfo\u001b[38;5;241m=\u001b[39miterinfo)\n\u001b[1;32m--> 316\u001b[0m \u001b[43mnw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdesign\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    318\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nw\u001b[38;5;241m.\u001b[39mresults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConnection\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m6\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mv \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    320\u001b[0m     me_bypass \u001b[38;5;241m=\u001b[39m Merge(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMerge Bypass\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\tespy\\networks\\network.py:1993\u001b[0m, in \u001b[0;36mNetwork.solve\u001b[1;34m(self, mode, init_path, design_path, max_iter, min_iter, init_only, init_previous, use_cuda, print_results, prepare_fast_lane)\u001b[0m\n\u001b[0;32m   1990\u001b[0m     logger\u001b[38;5;241m.\u001b[39merror(msg)\n\u001b[0;32m   1991\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m-> 1993\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpostprocessing\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1995\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprogress:\n\u001b[0;32m   1996\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   1997\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe solver does not seem to make any progress, aborting \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   1998\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcalculation. Residual value is \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2001\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtheir feasible range.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   2002\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\tespy\\networks\\network.py:2448\u001b[0m, in \u001b[0;36mNetwork.postprocessing\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2446\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpostprocessing\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m   2447\u001b[0m     \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Calculate connection, bus and component parameters.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 2448\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess_connections\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2449\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocess_components()\n\u001b[0;32m   2450\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocess_busses()\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\tespy\\networks\\network.py:2461\u001b[0m, in \u001b[0;36mNetwork.process_connections\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2458\u001b[0m c\u001b[38;5;241m.\u001b[39mgood_starting_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m   2459\u001b[0m c\u001b[38;5;241m.\u001b[39mcalc_results()\n\u001b[1;32m-> 2461\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConnection\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mloc[c\u001b[38;5;241m.\u001b[39mlabel] \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   2462\u001b[0m     [\n\u001b[0;32m   2463\u001b[0m         _ \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m fpd\u001b[38;5;241m.\u001b[39mkeys()\n\u001b[0;32m   2464\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m [c\u001b[38;5;241m.\u001b[39mget_attr(key)\u001b[38;5;241m.\u001b[39mval, c\u001b[38;5;241m.\u001b[39mget_attr(key)\u001b[38;5;241m.\u001b[39munit]\n\u001b[0;32m   2465\u001b[0m     ] \u001b[38;5;241m+\u001b[39m [\n\u001b[0;32m   2466\u001b[0m         c\u001b[38;5;241m.\u001b[39mfluid\u001b[38;5;241m.\u001b[39mval[fluid] \u001b[38;5;28;01mif\u001b[39;00m fluid \u001b[38;5;129;01min\u001b[39;00m c\u001b[38;5;241m.\u001b[39mfluid\u001b[38;5;241m.\u001b[39mval \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39mnan\n\u001b[0;32m   2467\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m fluid \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mall_fluids\n\u001b[0;32m   2468\u001b[0m     ]\n\u001b[0;32m   2469\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:716\u001b[0m, in \u001b[0;36m_LocationIndexer.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    713\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_valid_setitem_indexer(key)\n\u001b[0;32m    715\u001b[0m iloc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miloc\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39miloc\n\u001b[1;32m--> 716\u001b[0m \u001b[43miloc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setitem_with_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1685\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_with_indexer\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m   1682\u001b[0m     indexer, missing \u001b[38;5;241m=\u001b[39m convert_missing_indexer(indexer)\n\u001b[0;32m   1684\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m missing:\n\u001b[1;32m-> 1685\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setitem_with_indexer_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1686\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m   1688\u001b[0m \u001b[38;5;66;03m# align and set the values\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:2025\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_with_indexer_missing\u001b[1;34m(self, indexer, value)\u001b[0m\n\u001b[0;32m   2023\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_mgr \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39m_mgr\n\u001b[0;32m   2024\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2025\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_append\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m_mgr\n\u001b[0;32m   2026\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_maybe_update_cacher(clear\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:9075\u001b[0m, in \u001b[0;36mDataFrame._append\u001b[1;34m(self, other, ignore_index, verify_integrity, sort)\u001b[0m\n\u001b[0;32m   9072\u001b[0m     row_df \u001b[38;5;241m=\u001b[39m other\u001b[38;5;241m.\u001b[39mto_frame()\u001b[38;5;241m.\u001b[39mT\n\u001b[0;32m   9073\u001b[0m     \u001b[38;5;66;03m# infer_objects is needed for\u001b[39;00m\n\u001b[0;32m   9074\u001b[0m     \u001b[38;5;66;03m#  test_append_empty_frame_to_series_with_dateutil_tz\u001b[39;00m\n\u001b[1;32m-> 9075\u001b[0m     other \u001b[38;5;241m=\u001b[39m \u001b[43mrow_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer_objects\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrename_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m   9076\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, \u001b[38;5;28mlist\u001b[39m):\n\u001b[0;32m   9077\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m other:\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:324\u001b[0m, in \u001b[0;36mrewrite_axis_style_signature.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    322\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[0;32m    323\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Callable[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, Any]:\n\u001b[1;32m--> 324\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:1313\u001b[0m, in \u001b[0;36mNDFrame.rename_axis\u001b[1;34m(self, mapper, **kwargs)\u001b[0m\n\u001b[0;32m   1309\u001b[0m non_mapper \u001b[38;5;241m=\u001b[39m is_scalar(mapper) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   1310\u001b[0m     is_list_like(mapper) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_dict_like(mapper)\n\u001b[0;32m   1311\u001b[0m )\n\u001b[0;32m   1312\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m non_mapper:\n\u001b[1;32m-> 1313\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_axis_name\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1314\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1315\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `.rename` to alter labels with a mapper.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:1391\u001b[0m, in \u001b[0;36mNDFrame._set_axis_name\u001b[1;34m(self, name, axis, inplace)\u001b[0m\n\u001b[0;32m   1388\u001b[0m idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis(axis)\u001b[38;5;241m.\u001b[39mset_names(name)\n\u001b[0;32m   1390\u001b[0m inplace \u001b[38;5;241m=\u001b[39m validate_bool_kwarg(inplace, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minplace\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1391\u001b[0m renamed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m inplace \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1392\u001b[0m renamed\u001b[38;5;241m.\u001b[39mset_axis(idx, axis\u001b[38;5;241m=\u001b[39maxis, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m   1393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m inplace:\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6032\u001b[0m, in \u001b[0;36mNDFrame.copy\u001b[1;34m(self, deep)\u001b[0m\n\u001b[0;32m   5926\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m   5927\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcopy\u001b[39m(\u001b[38;5;28mself\u001b[39m: NDFrameT, deep: bool_t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[0;32m   5928\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   5929\u001b[0m \u001b[38;5;124;03m    Make a copy of this object's indices and data.\u001b[39;00m\n\u001b[0;32m   5930\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   6030\u001b[0m \u001b[38;5;124;03m    dtype: object\u001b[39;00m\n\u001b[0;32m   6031\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 6032\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdeep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdeep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6033\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_clear_item_cache()\n\u001b[0;32m   6034\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcopy\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py:603\u001b[0m, in \u001b[0;36mBaseBlockManager.copy\u001b[1;34m(self, deep)\u001b[0m\n\u001b[0;32m    600\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    601\u001b[0m     new_axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n\u001b[1;32m--> 603\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcopy\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdeep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdeep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    605\u001b[0m res\u001b[38;5;241m.\u001b[39maxes \u001b[38;5;241m=\u001b[39m new_axes\n\u001b[0;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    608\u001b[0m     \u001b[38;5;66;03m# Avoid needing to re-compute these\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py:304\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[1;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[0;32m    302\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    303\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 304\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(b, f)(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    305\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mNotImplementedError\u001b[39;00m):\n\u001b[0;32m    306\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ignore_failures:\n",
      "File \u001b[1;32mc:\\Users\\Admin\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:643\u001b[0m, in \u001b[0;36mBlock.copy\u001b[1;34m(self, deep)\u001b[0m\n\u001b[0;32m    641\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m    642\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[1;32m--> 643\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    644\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)(values, placement\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr_locs, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "df_results = pd.DataFrame()\n",
    "\n",
    "q_condenser = 10 # MW limit HP\n",
    "limit_q = 19 # MW guessed limit to decide between peak and medium demand\n",
    "\n",
    "# Parameters for buffer\n",
    "tank_volume = 1200 # m³\n",
    "t_buffer_0 = 10 #60 # initial temperature in buffer\n",
    "\n",
    "for row in df_year.itertuples():\n",
    "    try:\n",
    "        q_owd = row[2]\n",
    "        q_tud = row[8]\n",
    "        t_supply = row[3] if row[3] > row[9] else row[9]\n",
    "        t_return_owd = row[4]\n",
    "        t_return_tud = row[14]\n",
    "        t_return_mixed = t_return_owd if row[12] > 375 else (row[12]*t_return_owd + (375-row[12])*t_return_tud)/375\n",
    "        q_gtd_no_hp = np.round(4.190 * 375/3600 * (74.5-t_return_mixed), 2)\n",
    "        flow_bypass = abs(row[15])\n",
    "        flow_sum = row[13]\n",
    "\n",
    "        # check if HP is needed   \n",
    "        #if q_gtd_no_hp >= q_owd+q_tud:\n",
    "         #   mode = 0\n",
    "          #  result = option_base_mode0(q_owd, q_tud, t_supply, t_return_tud, t_return_owd)\n",
    "           # if result.results['Connection'].loc['1'].v > 375:\n",
    "            #    mode = 6\n",
    "             #   result = option_base_mode6(q_owd, q_tud, t_supply, t_return_tud, t_return_owd)\n",
    "        # check if flow OWD is > 375 m³/h\n",
    "        #else:\n",
    "        if row[12] >= 375:\n",
    "            # check if buffer can be discharged\n",
    "            t_into_buffer = ((row[12]-375)*(t_return_owd+1)+ row[11]*(t_return_tud+1)) / t_return_tud # calculate mixed return temperature from OWD and TUD\n",
    "            if t_buffer_0 > t_into_buffer:\n",
    "                mode = 1\n",
    "                result = option_base_mode1(q_owd, q_tud, t_supply, t_return_tud, t_return_owd, q_condenser, t_buffer_0)\n",
    "                flow_buffer = result.results['Connection'].loc['23'].v\n",
    "                k1 = t_buffer_0 - t_into_buffer\n",
    "                t_buffer = t_into_buffer + k1 * np.exp(-flow_buffer / tank_volume)\n",
    "                t_buffer_0 = t_buffer\n",
    "            else:\n",
    "                mode = 2\n",
    "                result = option_base_mode2(q_owd, q_tud, t_supply, t_return_tud, t_return_owd, q_condenser)\n",
    "                if result.results['Connection'].loc['2'].v < 0 or result.results['Connection'].loc['7'].v < 0:\n",
    "                    mode = 6\n",
    "                    result = option_base_mode6(q_owd, q_tud, t_supply, t_return_tud, t_return_owd)\n",
    "        else:\n",
    "            # check if sum flow is > 375 m³/h\n",
    "            if  flow_sum >= 375:\n",
    "                t_into_buffer = t_return_tud+1 # return temperature TUD only\n",
    "                # check if sum demand is > baseload\n",
    "                if row[10] >= limit_q:\n",
    "                    # check if buffer can be discharged\n",
    "                    if t_buffer_0 > t_into_buffer:   \n",
    "                        mode = 3                 \n",
    "                        result = option_base_mode3(q_owd, q_tud, t_supply, t_return_tud, t_return_owd, q_condenser, t_buffer_0) \n",
    "                        if result.results['Connection'].loc['2'].v < 0:\n",
    "                            mode = 6\n",
    "                            result = option_base_mode6(q_owd, q_tud, t_supply, t_return_tud, t_return_owd)\n",
    "                        elif result.results['Connection'].loc['22'].v < 0:\n",
    "                            mode = 4\n",
    "                            result = option_base_mode4(q_owd, q_tud, t_supply, t_return_tud, t_return_owd, q_condenser)\n",
    "                            if result.results['Connection'].loc['2'].v < 0:\n",
    "                                mode = 6\n",
    "                                result = option_base_mode6(q_owd, q_tud, t_supply, t_return_tud, t_return_owd)\n",
    "                        else:   \n",
    "                            flow_buffer = result.results['Connection'].loc['23'].v\n",
    "                            k1 = t_buffer_0 - t_into_buffer\n",
    "                            t_buffer = t_into_buffer + k1 * np.exp(-flow_buffer / tank_volume)\n",
    "                            t_buffer_0 = t_buffer\n",
    "                    else:\n",
    "                        mode = 4\n",
    "                        result = option_base_mode4(q_owd, q_tud, t_supply, t_return_tud, t_return_owd, q_condenser)\n",
    "                        if result.results['Connection'].loc['2'].v < 0:\n",
    "                            mode = 6\n",
    "                            result = option_base_mode6(q_owd, q_tud, t_supply, t_return_tud, t_return_owd)\n",
    "                else:\n",
    "                    # check if buffer can be loaded\n",
    "                    #t_into_buffer = t_supply+1\n",
    "                    t_into_buffer = 5\n",
    "                    if t_buffer_0 < t_into_buffer:\n",
    "                        mode = 5\n",
    "                        result = option_base_mode5(q_owd, q_tud, t_supply, t_return_tud, t_return_owd, q_condenser, t_buffer_0)\n",
    "                        if result.results['Connection'].loc['22'].v < 0:\n",
    "                            mode = 6\n",
    "                            result = option_base_mode6(q_owd, q_tud, t_supply, t_return_tud, t_return_owd)\n",
    "                            if abs(result.results['Condenser'].loc['Condenser'].Q/1e6) > q_condenser:\n",
    "                                mode = 4\n",
    "                                result = option_base_mode4(q_owd, q_tud, t_supply, t_return_tud, t_return_owd, q_condenser)\n",
    "                        else:\n",
    "                            flow_buffer = result.results['Connection'].loc['23'].v\n",
    "                            k1 = t_buffer_0 - t_into_buffer\n",
    "                            t_buffer = t_into_buffer + k1 * np.exp(-flow_buffer / tank_volume)\n",
    "                            t_buffer_0 = t_buffer\n",
    "                    else:\n",
    "                        mode = 6\n",
    "                        result = option_base_mode6(q_owd, q_tud, t_supply, t_return_tud, t_return_owd)\n",
    "                        if abs(result.results['Condenser'].loc['Condenser'].Q/1e6) > q_condenser:\n",
    "                            mode = 4\n",
    "                            result = option_base_mode4(q_owd, q_tud, t_supply, t_return_tud, t_return_owd, q_condenser)\n",
    "            else:\n",
    "                # check if buffer can be loaded\n",
    "                #t_into_buffer = t_supply+1\n",
    "                t_into_buffer = 5\n",
    "                if t_buffer_0 < t_into_buffer:\n",
    "                    mode = 7              \n",
    "                    result = option_base_mode7(q_owd, q_tud, t_supply, t_return_tud, t_return_owd, t_buffer_0)\n",
    "                    if result.results['Connection'].loc['22'].v < 0:\n",
    "                        mode = 8\n",
    "                        result = option_base_mode8(q_owd, q_tud, t_supply, t_return_tud, t_return_owd)\n",
    "                    else:\n",
    "                        flow_buffer = result.results['Connection'].loc['23'].v\n",
    "                        k1 = t_buffer_0 - t_into_buffer\n",
    "                        t_buffer = t_into_buffer + k1 * np.exp(-flow_buffer / tank_volume)\n",
    "                        t_buffer_0 = t_buffer\n",
    "                else:\n",
    "                    mode = 8\n",
    "                    result = option_base_mode8(q_owd, q_tud, t_supply, t_return_tud, t_return_owd, flow_sum)\n",
    "                                \n",
    "        connection_numbers = list(range(35))\n",
    "        for conn_number in connection_numbers:\n",
    "            try:\n",
    "                flow = result.results['Connection'].loc[str(conn_number)].loc['v']\n",
    "                if flow < 0:\n",
    "                    print(f\"Negative flow detected in connection {conn_number} with flow {round(result.results['Connection'].loc[str(conn_number)].loc['v'], 2)}m³/h\")\n",
    "            except KeyError:\n",
    "                continue  # Skip connections that do not exist\n",
    "            \n",
    "        print(f\"Iteration Hour: {row[0]}, {row[0]/87.60:.1f} % mode {mode}\")\n",
    "        new_row = {\n",
    "            'Hour': row[0],\n",
    "            'P Compressor [MW]': 0 if mode == 0 else round(result.results['Compressor'].loc['Compressor'].P/1e6, 2),\n",
    "            'Q Condenser [MW]': 0 if mode == 0 else round(result.results['Condenser'].loc['Condenser'].Q/1e6, 2),\n",
    "            'T DAP sink [C]': round(result.results['Connection'].loc['1'].loc['T'], 2),\n",
    "            'Q DAP [MW]': round(result. results['HeatExchanger'].loc['DAP'].Q/-1e6, 2),\n",
    "            'Q WKC [MW]': 0 if mode in (0, 5,6,7,8) else round(result.results['SimpleHeatExchanger'].loc['WKC'].Q/-1e6, 2),\n",
    "            'flow DAP': round(result.results['Connection'].loc['1'].loc['v'], 2),\n",
    "            'Q Evaporator [MW]': 0 if mode == 0 else round(result.results['HeatExchanger'].loc['Evaporator'].Q/1e6, 2),\n",
    "            #'flow buffer': 0 if mode in (2, 4, 6, 8) else round(result.results['Connection'].loc['22'].loc['v'], 2),\n",
    "            #'Q Buffer': 0 if mode in (2, 4, 6, 8) else round(result.results['SimpleHeatExchanger'].loc['Buffer'].Q/1e6, 2),\n",
    "            #'T Buffer': round(t_buffer_0, 2),\n",
    "            'Mode': mode\n",
    "        }\n",
    "        #result.print_results()\n",
    "        df_results = pd.concat([df_results, pd.DataFrame([new_row])], ignore_index=True)\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"Error occurred: {e}. Skipping this data point.\")\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "efe7ff2c",
   "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>Hour</th>\n",
       "      <th>P Compressor [MW]</th>\n",
       "      <th>Q Condenser [MW]</th>\n",
       "      <th>T DAP sink [C]</th>\n",
       "      <th>Q DAP [MW]</th>\n",
       "      <th>Q WKC [MW]</th>\n",
       "      <th>flow DAP</th>\n",
       "      <th>Q Evaporator [MW]</th>\n",
       "      <th>Mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.55</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>33.41</td>\n",
       "      <td>18.29</td>\n",
       "      <td>-18.32</td>\n",
       "      <td>367.22</td>\n",
       "      <td>-7.45</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.53</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>34.35</td>\n",
       "      <td>17.89</td>\n",
       "      <td>-15.19</td>\n",
       "      <td>367.34</td>\n",
       "      <td>-7.47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.53</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>34.35</td>\n",
       "      <td>17.89</td>\n",
       "      <td>-14.87</td>\n",
       "      <td>367.34</td>\n",
       "      <td>-7.47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.53</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>34.35</td>\n",
       "      <td>17.89</td>\n",
       "      <td>-13.62</td>\n",
       "      <td>367.34</td>\n",
       "      <td>-7.47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.54</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>34.39</td>\n",
       "      <td>17.88</td>\n",
       "      <td>-14.56</td>\n",
       "      <td>367.34</td>\n",
       "      <td>-7.46</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>8755</td>\n",
       "      <td>2.39</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>31.02</td>\n",
       "      <td>19.31</td>\n",
       "      <td>-2.79</td>\n",
       "      <td>366.94</td>\n",
       "      <td>-7.61</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>8756</td>\n",
       "      <td>2.40</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>31.05</td>\n",
       "      <td>19.29</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>366.94</td>\n",
       "      <td>-7.60</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>8757</td>\n",
       "      <td>2.33</td>\n",
       "      <td>-9.71</td>\n",
       "      <td>31.55</td>\n",
       "      <td>19.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>367.00</td>\n",
       "      <td>-7.39</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>8758</td>\n",
       "      <td>2.12</td>\n",
       "      <td>-8.99</td>\n",
       "      <td>32.79</td>\n",
       "      <td>18.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>367.15</td>\n",
       "      <td>-6.86</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>8759</td>\n",
       "      <td>2.12</td>\n",
       "      <td>-8.97</td>\n",
       "      <td>32.82</td>\n",
       "      <td>18.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>367.15</td>\n",
       "      <td>-6.85</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Hour  P Compressor [MW]  Q Condenser [MW]  T DAP sink [C]  Q DAP [MW]  \\\n",
       "0        0               2.55            -10.00           33.41       18.29   \n",
       "1        1               2.53            -10.00           34.35       17.89   \n",
       "2        2               2.53            -10.00           34.35       17.89   \n",
       "3        3               2.53            -10.00           34.35       17.89   \n",
       "4        4               2.54            -10.00           34.39       17.88   \n",
       "...    ...                ...               ...             ...         ...   \n",
       "8755  8755               2.39            -10.00           31.02       19.31   \n",
       "8756  8756               2.40            -10.00           31.05       19.29   \n",
       "8757  8757               2.33             -9.71           31.55       19.08   \n",
       "8758  8758               2.12             -8.99           32.79       18.56   \n",
       "8759  8759               2.12             -8.97           32.82       18.54   \n",
       "\n",
       "      Q WKC [MW]  flow DAP  Q Evaporator [MW]  Mode  \n",
       "0         -18.32    367.22              -7.45     2  \n",
       "1         -15.19    367.34              -7.47     2  \n",
       "2         -14.87    367.34              -7.47     2  \n",
       "3         -13.62    367.34              -7.47     2  \n",
       "4         -14.56    367.34              -7.46     2  \n",
       "...          ...       ...                ...   ...  \n",
       "8755       -2.79    366.94              -7.61     2  \n",
       "8756       -0.35    366.94              -7.60     2  \n",
       "8757        0.00    367.00              -7.39     6  \n",
       "8758        0.00    367.15              -6.86     6  \n",
       "8759        0.00    367.15              -6.85     6  \n",
       "\n",
       "[8760 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d0e93c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df_results.copy()\n",
    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(15, 7))\n",
    "\n",
    "# Plot Q Condenser\n",
    "axes[0,0].plot(df['Hour'], -(df['Q Condenser [MW]']), color='darkorange')\n",
    "axes[0,0].set_xlabel('Hour')\n",
    "axes[0,0].set_ylabel('Heat Flux [MW]')\n",
    "axes[0,0].set_title('Q Condenser Over Time')\n",
    "axes[0,0].grid(True)\n",
    "\n",
    "# Plot for P Compressor\n",
    "axes[0,1].plot(df['Hour'], df['P Compressor [MW]'], color='green')\n",
    "axes[0,1].set_xlabel('Hour')\n",
    "axes[0,1].set_ylabel('P Compressor [MW]')\n",
    "axes[0,1].set_title('P Compressor Over Time')\n",
    "axes[0,1].grid(True)\n",
    "\n",
    "# Plot for Q DAP \n",
    "axes[0,2].plot(df['Hour'], df['Q DAP [MW]'])\n",
    "axes[0,2].set_xlabel('Hour')\n",
    "axes[0,2].set_ylabel('Q DAP [MW]')\n",
    "axes[0,2].set_title('Q DAP Over Time')\n",
    "axes[0,2].grid(True)\n",
    "\n",
    "# Plot for Q WKC\n",
    "axes[1,0].plot(df['Hour'], -df['Q WKC [MW]'])\n",
    "axes[1,0].set_xlabel('Hour')\n",
    "axes[1,0].set_ylabel('Q WKC [MW]')\n",
    "axes[1,0].set_title('Q WKC over Time')\n",
    "axes[1,0].grid(True)\n",
    "\n",
    "# Plot for T DAP\n",
    "axes[1,1].plot(df['Hour'], df['T DAP sink [C]'], color='grey')\n",
    "axes[1,1].set_xlabel('Hour')\n",
    "axes[1,1].set_ylabel('T DAP sink [C]')\n",
    "axes[1,1].set_title('T DAP sink Over Time')\n",
    "axes[1,1].grid(True)\n",
    "\n",
    "# Plot for Mode\n",
    "axes[1,2].scatter(df['Hour'], df['Mode'], s=5, color='black')\n",
    "axes[1,2].set_xlabel('Hour')\n",
    "axes[1,2].set_ylabel('Mode')\n",
    "axes[1,2].set_title('Mode')\n",
    "axes[1,2].grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "#fig.savefig(\"figures/all_plots.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "328b0c1d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}