{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from notepad import WaterStorage, Heatpump\n",
    "from pyrecoy.forecasts import Mipf\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
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       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import cufflinks\n",
    "cufflinks.go_offline()\n",
    "from numpy.polynomial import Polynomial\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>DAM</th>\n",
       "      <th>POS</th>\n",
       "      <th>NEG</th>\n",
       "      <th>ForeNeg</th>\n",
       "      <th>ForePos</th>\n",
       "    </tr>\n",
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       "      <th>datetime</th>\n",
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       "      <th>2018-11-01 00:00:00+01:00</th>\n",
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       "<p>96 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             DAM     POS     NEG     ForeNeg     ForePos\n",
       "datetime                                                                \n",
       "2018-11-01 00:00:00+01:00  44.90   46.39   46.39   53.603333   44.623333\n",
       "2018-11-01 00:15:00+01:00  44.90   43.08   43.08   68.962000   63.177333\n",
       "2018-11-01 00:30:00+01:00  44.90   43.13   43.13   55.415333   57.922667\n",
       "2018-11-01 00:45:00+01:00  44.90   46.29   46.29   57.633333   54.712667\n",
       "2018-11-01 01:00:00+01:00  42.46   32.03   32.03   37.354000   35.400000\n",
       "...                          ...     ...     ...         ...         ...\n",
       "2018-11-01 22:45:00+01:00  50.70   54.02   54.02   71.520667   65.263333\n",
       "2018-11-01 23:00:00+01:00  50.70  202.89  202.89  143.089333  144.426000\n",
       "2018-11-01 23:15:00+01:00  50.70   85.70   85.70  101.676000   97.244000\n",
       "2018-11-01 23:30:00+01:00  50.70   38.03   38.03   50.560000   45.735333\n",
       "2018-11-01 23:45:00+01:00  50.70   30.53   30.53   45.584667   40.736000\n",
       "\n",
       "[96 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mipf = Mipf(\n",
    "    start = '2018-11-01',\n",
    "    end = '2018-11-01', \n",
    "    tidy=True, \n",
    "    include_nextQ=False,\n",
    "    folder_path=r\"C:\\Users\\Shahla Huseynova\\Recoy\\Recoy - Documents\\03 - Libraries\\12 - Data Management\\Forecast Data\"\n",
    ").data\n",
    "# mipf.columns\n",
    "price_data = mipf[['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos']]\n",
    "# price_data.index = price_data.index.tz_convert('Europe/Amsterdam')\n",
    "price_data = price_data.resample('15T').mean()\n",
    "price_data"
   ]
  },
  {
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   "execution_count": 4,
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     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "data = pd.read_excel('Demand_Data_Smurfit_Preprocessed.xlsx', sheet_name='nov2018', index_col=0)\n",
    "start, end = '2018-11-01 00:00:00', '2018-11-01 12:00:00'\n",
    "df = data[start:end]\n",
    "df.index = df.index.tz_localize('Europe/Amsterdam')\n",
    "df = df.resample('15T').mean()\n",
    "df=df.drop(['Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12'], axis=1)\n",
    "\n",
    "df.head()"
   ]
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rbByongL9tXpdu62xbuldYf1f5kER89/E2Kh77+vajwXz9rW29qO15/UKPAdsH66t/aj7dDwGmsdHa631nx2t29WPrcU+i3vil/Qddx32We/zwj4X1tHwcK4w6nAf6VCr+zOw5T7Rcry27vOYA1d4joDsfHnj5ttk0xeva7ugtWv3lQ+85+0yuWJ5ahZsQusnPv1ZJ7AetO4AMeF505euW7QO77idu6acgO39OHbDernskotkfGys5fZHtz0uH/rYp2Vqz57m7WeefmozIPv3HyUcgTlKTebKpACBOZNtXbSodoH5qd3zcut/mZC8Q372y90yMVqQ4w9ZKacfuUZOfcFqO2Aytkqew5yxhrZZTrtLskuVqkzNVmTPTFmm5stOiN4zW5LdsxXZO1uWqbmyTM2a2+tf17NRI4U4WaA1bLgPPJik0vzajW1urjJXfDQeoHBD58J2jSk99XsfQDK7ro9tPKHABA93rG+f3nHttlvYLFyt3n062zq11Gdxa2jW2rihHu4Xal0Y3LpPU+vClQ2tri2OXVxNOHQeiGk8mGae9hHKNaAfzpNaPE1ourpr9B4DLSAdtmvaeQ4IzwNe9XW0HsBe14VaFruacd4HERbV2qUfiy4Vci9paTzA5e20M2/j+S6djnO3H4u28x077kr9tXrvW94+evtxy7tOzvwPLhOe7/3pfW2DYloWb9awffsTLQHWnPXdeP65LWebTZC+6eZbnLVufeChRaH62k1flvu2bm0J2mbu67/+bXn/uy91wrj7YcYe9+JjmvObue/+0ZbIzzITmNNyFFJnYgIE5sToh7pjNzDPV2rynZ8/45xJvvPBHc7zjU88dKWcfNg+csZ/ew6Xcg61K9HvjMAcvam2GXkOs7aOxFMPl2TH48qsyQi0C8z+M9Dey5xNUNx86x1Osd6zrP55zFlZM/Ydl77FGWsC7CknniDfvvkW599uAPXuy3t2273E2oztdtZ7dm5OPnXVNXL2WWe0hGOzb/PhvUza3LZ27X5yzpmndTwL7d3OvdzaH7zbdcqMvfqaL8hbL3pTpGfnCczJ3C/Ya4oECMwpalafpZqzT3fc/5T85PE9Yl7Ea/ds2TmTfMLzV8lpL1ztPEeZj2wIEJiz0cduqyAwZ7/HZoUEZjv6HPUqc7u3RT1l6PlqK9Z1HNst6JrLsk3wfeSRbfKyU04S71gzoQmqa1avdkJpmMC875o1LWeyzTabb729eUb35w8+JGNjo2Iul/77qz/XEqq9Z5C9i+kUVP2XZYe9bNu73SOPPd720u52mJ2Ce+gmdRhIYB5UkO0zL0BgzmaLfz01L/c8ukt+/NhO57P59/GHrpITn79KTjx0lew32frcmWwq2LcqAnP2e05gzn6PCcx29DiOVY48+E0Z/d6H45i665zlw14l8ye9L3RgNoHRG1bdDd1AuOHo9c4ZWvPhDZd3ff9HLZd2tzvD7D1T224+d1/+s8PeufzPrTbfu+FbN8sfvfmCluce+wOs93Js8xzlTs9z9u7LBGb3Em6zjf+5zP4XGPNfph1FswnMUSgyR6YFCMzZae8DT+6Rex7dLXc/ulPufninrF42Kr9x0KQce+CkHLLPqBy23wopmPd94iOzAgTmzLa2uTACc/Z7TGC2o8dxrDItZ5jN2ttdJm3O/PovffaGy34Ds/9SarN/72Xfbi86XZbdKTC763DPTLd7znG7FwYLc4a506XaBOYB7zn+J6N7H0Fxnwfg3rbxgnObj9x4D9hOr9w2YGlsrliAwKy4OQGlmUutf/LYbvnBQzvk+w/tkMd3zMoL9l0mLzl0pRy7blLMK2+a5yibj6BXyU6vApV7BQjM2T8eCMzZ7zGB2Y4e27TKoBf9cs/2XnDe65zAPKwzzO5zjYN60e25w26gv+gPf1/+9bqvtTy/uNMZ5jDPYSYwB3Wlx+97n7DufWK8NzCbr9u9b5e/ke2evN5jOQxPmQCBOV0NM69s/f2Hn5Xv/8KE5J1SqdbkpMP2kRMaIXnd6vbvl0xgTlef+62WwNyvXHq2IzCnp1eDVMpzmAfRY1ttAv7AbPKH+XDfy9ibP7zPOXbPOLvPYW6XW9xXnDbztXvVav9zmG//zvfk4IPXLXoOs9n+a9+4SU5/2W8uekGtbs8ddr9navTnrXaBOeyrZLcLzLzoVwRHdrczzJ0Cs/eV3MyYTo+ERFAeUygVIDDXG7NnrvFWLObtV8xbs5i3YGncVn+v02rjbVrKMl9pfc/TYbV293RZzPslm0utT3r+PnLSYavkRetWOG8HFfRBYA4Sysb3CczZ6GO3VRCYs99js0ICsx19tmWV7V6sy/uew/4rXDu9Srbx8n7vja9/jdxz789aXiW73atNe7fp9CrZZu52Jx7dHnXKWeb77tW6/ucbe09quvP08j7M7ebjbaUGvNeEvSTbvRy73RPhzWUFV3zmWrn84gtb3gdswNLYXLFAmgPzzFxZpss1mZ4ry2yp6nyeKZvPFZktVWTvfFVm5ysyPV+WmVKt/nmuIjMl8x6lFdkzV5KpRhD2vV1ks2PFQl6WjBZkYjQvE8WC8/VoMe9c6lzIifN5pJCXQi4n5v0ynX873zO317/v3u587X1zyT6OC/P2T+ay614/CMy9iqVzPIE5nX3rpWoCcy9a6R1LYE5v76g8mwL+V8Ae9irj3L9VL/rV7ZEPt6nuK6+97a1vliMOP7TtE+u9gXm+VBn28cD+hiwwPTsvxUJBisXgs5Rxl1aq1GTndEl2zpRl59552TE93/i6JM9Ol2TXdNm5bddMWXbsLbUtx4Ta8VETckdkfCQnS8ZGZLyYkyXFEedMrAm+48WCTBTdIGy+bgRi8/1GKB4v5p3xJvxm4WPP3jlZMl6UPC/6lYV2dlzDrqkZmVze/rL8TC/cosVVK1WZni3JsqW80n2W214qVaRUqciS8dEsL5O1iciogr+/aEQ4AfeMsf/Mb7it+x/lXp69/ogj2j7Ftv+Z61sSmNsIupdhn37qSYueWO8/w7x7z8ygPWB75QLmbEXOOfMZTzA0Z353zpRkx3RJds9UnEBsAq/5zwTjXTPm3xXZZb4/W25qjeTzsmrpiExOjMjKiaLztfk8OVGQlUuKztdO+B0xwbYeik3AjWcVypsYojzzh9dIAZ8QVKkeUipXpGgubeAjswLmaphypeI80MlHdgWqtZrUqjXe2SC7LW6ubMUyHuS0oM2ql0hg7hKYzfub8Rxm1cfvUIob5JLsB5/aK9t3zjoh2Jzx3Tldlp3mrPB0WZ41n/ea5wIvnAkeGzEhuOgE3lVLR2WV87nY/LzPEnPbiOyzfFSWjY0MZf227IRLsu3oNJdkZ7/PXJKd/R6bFXJJth19ZpUIaBCwPjCbU/i33n6nvPbVZzv98J9B5lWyNRymydbQb2C+/kdPyOe++5hUq7WFEGzO/JoQvMx8HpV9zNfOf4TgZLvM20ol7T+s/ROYhyWd3H4IzMnZD3PPBOZharMvBOwWsCIwt3sFNvfaeveFve65977mkeC/7p73Ybb7TtJrYH7413vl83duk+/8/Fm5+IxD5A3H7W83YEpWzxnmlDRqwDIJzAMCpmBzAnMKmhRBiQTmCBCZAgEEQglYEZhDSTAIgQ4CvQTmr93zhGz67jY5+DkTctlvHSoHr1mCa0oECMwpadSAZRKYBwRMweYE5hQ0KYISCcwRIDIFAgiEEiAwh2JikM0CYQLzY8/MyOe/+5jctvUZ+R8vPVD+4OQDbCZL5doJzKlsW89FE5h7JkvdBgTm1LWsr4IJzH2xsRECCPQhQGDuA41N7BIICsw3/OQJ2XTnNnneygm5+IyD5IX7L7cLKCOrJTBnpJEByyAwZ7/PBObs99iskMBsR59ZJQIaBAjMGrpADaoFOgXmbc/OyqY7H5Nb7n9aLjjpALnwlANVr4PiugsQmO04QgjM2e8zgTn7PSYw29FjVrlYwH3tpQ1Hrxfzbj7dPtz3Jt54/rlyzFFHhuY07xB039at8oH3vF0mVwzvJJB5zajrv/5tef+7L5WD1um6UpPAHPrwYaCtAu0C87fufVI+/91t8twVY/LHpx4kxxy4wlaezKybwJyZVnZdCIE5+30mMGe/xwRmO3qc9VV6X1TYv9a1a/dtG1jjDszmhZJvuvkWueySi5ySPnXVNdIunJvat29/Qi4473XOGO+LJ5vt/C+g7K7PhPHNt97RXO7yZctaArI774Ubz1PVfgLzgO346o+fkOMPWSnPWzU+4ExsrlXAG5jNeyqbV8D+9/t+LW88fq285bSDtZZNXT0KEJh7BEvpcAJzShvXQ9kE5h6wUjyUS7JT3DxKXyRgguK9P73PCarjY2ORCPV6htkN42efdUbzjHS7urzjjjj80EWh2rxF74c+9ml5/e+8onkW3K1l/RFHiDcMm7E3fOtm+aM3X+Cs28z9T5/7orzmlWepOstMYB7wkDzjb+5qzmCC8wnP30eOPWiFHLyaV0cekFbN5m5gvuW/nnGeq7xq6ahsfOk6OfHQVWpqpJDBBQjMgxumYQYCcxq6NFiNBObB/NKyNYE5LZ2izjAC7YKp/wy0OWvrD6judkuWTMid/3G3syv37K4/MHcKrW59JryaM8DvuPQtzUuxzW1XfOZaufziC5sB1jtubGy07Vlo/3a9PCBgxpqPoEvOw7hGNYbAHIHkU1Pz8qOHd8oPH9kpP3xop+ydLzuzmgD94kNWyTHrVsgL9l0awZ7smKJUqcrO6bLsminJzr0l5/OO6bJMz1WkkM9JIS+Nz/WvRwp5KeTM1znJm+/nGrc5Y3NSzOcln6/Vt8nl6uMb4wru183t6tssjBN5+Mkpue6HT8jm+5+W33vJ/nLRqQdL0eyEj0wJEJgz1c6OiyEwZ7/PBObs99iskMBsR5+jXuW2Z2einjL0fOv2meg41h8o/eHV/PuRR7bJCcf/RktAdUO1G5LNJdWbvnSdczm3+fjgRz8p5jnMbtD2nj32F2O2vftHW1rOALe7BNx72XSnS8S9t59+6kkdL+1uB+K9LDyqs+2hm9RhIIF5UME22z+ztyRbHtsl9zy6S374yA55ave8M+olh6yUY9ZNytEHLJejD1j8nNdKtSZ75sqye6Yie+ZKsme2Irtny7Kn8Z/z9VxF9syUZfdcyQl2JrblTVjMNcKie5sJlbmc5BqhcdG4nEguJ5LP17evB81c/TbvfI3bco353EDqH2e2cedza3HmdfaRk4LU9zFXrsqu6XknEO80gXi6JLumzWcTkOdl50xFpufqDzho+nj+c5bIm085SE4+jLPKmvoSZS0E5ig19c5FYNbbm6gqIzBHJal7HgKz7v5ore6me5+Uv7npF0Mv7+yj95V3nfP80IHZhMa/v/pzi14Ayx9QuwVtNzC/8fWvkdvuuKvtc5G9BXU6s9vuec1u8O72nGpztnrt2v3EDczesO59LvOxG9a3XIruv0x76M1qs0MC8xC6YELhlm275d5tu+Qnj+2Wh5+edvZqXijKhGITiHfPlWVmrtJSzXgxL8vGRmT5+IgsnyjK0tG883n5WEGWjhekVstJtVYTE7TNZ6lJ8+tKVZzbzM2Vmvl+/Xs197bGNs5tIlKt1hrjalKt5hZu6zDOzGPmNPuoVhf2W79tYb7WcTXJ5XKybHxEVoyPyNKxgiw365sYkWXjRVk2lpfl40VZPl6Qpe66x833Rpw1m89mbnfO1s/1tbn7N+uuSruxDZfGurqOaaxv78y8HL7fclm9PJrnlAzhkGMXfQgQmPtAS+EmBOYUNq3HkgnMPYKldDiBOaWNS7jstJxhNkzeS7LdFwHzXwIdJjBv3/6kdHoRsTCB2VzK/YlPf7Z55tn/nON2LwwW9gxzu0u1CcwJ30m07H5q1gToKXn06WlZ2giDJggvawTIZSZAjo1w2a+ShgW9D7OSMiljQAEC84CAKdmcwJySRg1QJoF5ALwUbUpgTlGzKDVQIOg5vuaMrPlwX5XafeUBaODUAAAgAElEQVTqMIH5zNNfJmv337f56tedLnNud0m2W7h7ttj9t/v84k5nmMM+h5nAHHhoMACBdAgQmNPRp0GrJDAPKpiO7QnM6ejTIFUSmAfRS8+2BOb09IpKgwX8wdGEV/Phvn/yIIHZfR/moFDe7kW/3MpNPV/52rdk6ZIJOe/3fqf5AmDtAnMvr5Ld6cXOzH550a/g44YRCKgRIDCraUWshRCYY+VVMzmBWU0rYiuEwBwbraqJCcyq2kExAwq0O1Ns3pppas8eZ2b3eb7ma+8l0GHOMLuB2WxrgvfTzzzT9u2r2r2tlLss9xW2912zpmVbd5t+34fZ//xl3lZqwAOJzRFISoDAnJT8cPdLYB6ud1J7IzAnJT+8/RKYh2ed5J4IzEnqs++sCiT9CtXeV+DWZMyLfmnqBrWoFCAwq2xL5EURmCMnVTkhgVllWyItisAcKafayQjMaltDYSkXMGeh79u61XlrqskVy4e2GhOWr//6txe9MvjQCuiyIwKzhi5Qg2oBArPq9kRWHIE5MkrVExGYVbcnkuIIzJEwqp+EwKy+RRSIQGYECMyZaSULiUuAwByXrK55Ccy6+hFXNQTmuGT1zEtg1tOLOCshMMepy9wIIOAVIDBzPCAQIEBgtuMQITDb0WcCc/b7TGDOfo/NCgnMdvSZVSKgQYDArKEL1KBagMCsuj2RFUdgjoxS9UQEZtXtiaQ4AnMkjOonITCrbxEFIpAZAQJzZlrJQuISIDDHJatrXgKzrn7EVQ2BOS5ZPfMSmPX0Is5KCMxx6jI3Agh4BQjMHA8IBAgQmO04RAjMdvSZwJz9PhOYs99js0ICsx19ZpUIaBAgMGvoAjWoFiAwq25PZMURmCOjVD0RgVl1eyIpjsAcCaP6SQjM6ltEgQhkRoDAnJlWspC4BAjMccnqmpfArKsfcVVDYI5LVs+8BGY9vYizEgJznLrMjQACXgECM8cDAgECBGY7DhECsx19JjBnv88E5uz32KyQwGxHn1klAhoECMwaukANqgUIzKrbE1lxBObIKFVPRGBW3Z5IiiMwR8KofhICs/oWUSACmREgMGemlSwkLgECc1yyuuYlMOvqR1zVEJjjktUzL4FZTy/irITAHKcucyOAgFeAwMzxgECAAIHZjkOEwGxHnwnM2e8zgTn7PTYrJDDb0WdWiYAGAQKzhi5Qg2oBArPq9kRWHIE5MkrVExGYVbcnkuIIzJEwqp+EwKy+RRSIQGYECMyZaSULiUuAwByXrK55Ccy6+hFXNQTmuGT1zEtg1tOLOCshMMepy9wIIOAVIDBzPCAQIEBgtuMQITDb0WcCc/b7TGDOfo/NCgnMdvSZVSKgQYDArKEL1KBagMCsuj2RFUdgjoxS9UQEZtXtiaQ4AnMkjOonITCrbxEFIpAZAQJzZlrJQuISIDDHJatrXgKzrn7EVQ2BOS5ZPfMSmPX0Is5KCMxx6jI3Agh4BQjMHA8IBAgQmO04RAjMdvSZwJz9PhOYs99js0ICsx19ZpUIaBAgMGvoAjWoFiAwq25PZMURmCOjVD0RgVl1eyIpjsAcCaP6SQjM6ltEgQhkRoDAnJlWspC4BAjMccnqmpfArKsfcVVDYI5LVs+8BGY9vYizEgJznLrMjQACXgECM8cDAgECBGY7DhECsx19JjBnv88E5uz32KyQwGxHn1klAhoECMwaukANqgUIzKrbE1lxBObIKFVPRGBW3Z5IiiMwR8KofhICs/oWUSACmREgMGemlSwkLgECc1yyuuYlMOvqR1zVEJjjktUzL4FZTy/irITAHKcucyOAgFeAwMzxgECAAIHZjkOEwGxHnwnM2e8zgTn7PTYrJDDb0WdWiYAGAQKzhi5Qg2oBArPq9kRWHIE5MkrVExGYVbcnkuIIzJEwqp+EwKy+RRSIQGYECMyZaSULiUuAwByXrK55Ccy6+hFXNQTmuGT1zEtg1tOLOCshMMepy9wIIOAVIDBzPCAQIEBgtuMQITDb0WcCc/b7TGDOfo/NCgnMdvSZVSKgQYDArKEL1KBagMCsuj2RFUdgjoxS9UQEZtXtiaQ4AnMkjOonITCrbxEFIpAZAQJzZlrJQuISIDDHJatrXgKzrn7EVQ2BOS5ZPfMSmPX0Is5KCMxx6jI3Agh4BQjMHA8IBAgQmO04RAjMdvSZwJz9PhOYs99js0ICsx19ZpUIaBAgMGvoAjWoFiAwq25PZMURmCOjVD0RgVl1eyIpjsAcCaP6SQjM6ltEgQhkRoDAnJlWspC4BAjMccnqmpfArKsfcVVDYI5LVs+8BGY9vYizEgJznLrMjQACXgECM8cDAgECBGY7DhECsx19JjBnv88E5uz32KyQwGxHn1klAhoECMwaukANqgUIzKrbE1lxBObIKFVPRGBW3Z5IiiMwR8KofhICs/oWUSACmREgMGemlSwkLgECc1yyuuYlMOvqR1zVEJjjktUzL4FZTy/irITAHKcucyOAgFeAwMzxgECAAIHZjkOEwGxHnwnM2e8zgTn7PTYrJDDb0WdWiYAGAQKzhi5Qg2oBArPq9kRWHIE5MkrVExGYVbcnkuIIzJEwqp+EwKy+RRSIQGYECMyZaSULiUuAwByXrK55Ccy6+hFXNQTmuGT1zEtg1tOLOCshMMepy9wIIOAVIDBzPCAQIEBgtuMQITDb0WcCc/b7TGDOfo/NCgnMdvSZVSKgQcCqwHzj5ttk+/Yn5MKN57XY79o9JR/86Cdl+/Ynndvf+84/lWOOOrI5xmy36YvXOf8+dsN6ueySi2R8bExD/6hhCAIE5iEgK9gFgVlBE4ZQAoF5CMgJ74LAnHADhrR7AvOQoNkNAgiIFYF5y8/ul498/Eqn3WeefmpLYJ6dm5NPXXWNbDh6vZxz5mny6LbH5YrPXCuXX3yhHLTuADHbbvrSdfKB97xdJlcsl2s3fdmZxx+6OZayK0Bgzm5vvSsjMNvRZwJz9vtMYM5+j80KCcx29JlVIqBBwIrA7EK3O8NsArIJwe+49C1OIPYHaPO9tWv3c8K0+fAHaA1NpIZ4BQjM8fpqmZ3ArKUT8dZBYI7XV8PsBGYNXYi/BgJz/MbsAQEE6gLWB+Z2Adg9i3zBea9rOftswPxnoCvVGsdSxgX2Ts9JsViQ0eJIxldq9/J27ZmR5RNjki/k7YbI+Op37NorqyaXZnyVdi+vWqnK1MycTC6bsBsi46ufL5WlVKrI0iU8RS7jrZZCPpf1JbI+5QIE5p/dLzfdfEvL85L9gfnss85oPqfZH5jNH198ZFugWqtJzvyPn9eZbnS1WpNc3nSajywLmAc5+eMryx0WMQ9j16o1yfNHdqYbXauZXtckzy/nTPfZLI4HOTPfYvULJDD7nqNsOtbLGWb1HabAgQW4JHtgwlRMwCXZqWjTwEVySfbAhOon4JJs9S2KpEAuyY6EkUkQQCCEgPWBmecwhzhKLB9CYLbjACAw29FnAnP2+0xgzn6PzQoJzHb0mVUioEHA+sDMq2RrOAx110Bg1t2fqKojMEclqXseArPu/kRRHYE5CkX9cxCY9feIChHIioAVgdn7tlJu47zvtcz7MGflcI5nHQTmeFy1zUpg1taReOohMMfjqmlWArOmbsRXC4E5PltmRgCBVgErAjNNR2AQAQLzIHrp2ZbAnJ5eDVIpgXkQvXRsS2BOR58GrZLAPKgg2yOAQFgBAnNYKcZZK0BgtqP1BGY7+kxgzn6fCczZ77FZIYHZjj6zSgQ0CBCYNXSBGlQLEJhVtyey4gjMkVGqnojArLo9kRRHYI6EUf0kBGb1LaJABDIjQGDOTCtZSFwCBOa4ZHXNS2DW1Y+4qiEwxyWrZ14Cs55exFkJgTlOXeZGAAGvAIGZ4wGBAAECsx2HCIHZjj4TmLPfZwJz9ntsVkhgtqPPrBIBDQIEZg1doAbVAgRm1e2JrDgCc2SUqiciMKtuTyTFEZgjYVQ/CYFZfYsoEIHMCBCYM9NKFhKXAIE5Llld8xKYdfUjrmoIzHHJ6pmXwKynF3FWQmCOU5e5EUDAK0Bg5nhAIECAwGzHIUJgtqPPBObs95nAnP0emxUSmO3oM6tEQIMAgVlDF6hBtQCBWXV7IiuOwBwZpeqJCMyq2xNJcQTmSBjVT0JgVt8iCkQgMwIE5sy0koXEJUBgjktW17wEZl39iKsaAnNcsnrmJTDr6UWclRCY49RlbgQQ8AoQmDkeEAgQIDDbcYgQmO3oM4E5+30mMGe/x2aFBGY7+swqEdAgQGDW0AVqUC1AYFbdnsiKIzBHRql6IgKz6vZEUhyBORJG9ZMQmNW3iAIRyIwAgTkzrWQhcQkQmOOS1TUvgVlXP+KqhsAcl6yeeQnMenoRZyUE5jh1mRsBBLwCBGaOBwQCBAjMdhwiBGY7+kxgzn6fCczZ77FZIYHZjj6zSgQ0CBCYNXSBGlQLEJhVtyey4gjMkVGqnojArLo9kRRHYI6EUf0kBGb1LaJABDIjQGDOTCtZSFwCBOa4ZHXNS2DW1Y+4qiEwxyWrZ14Cs55exFkJgTlOXeZGAAGvAIGZ4wGBAAECsx2HCIHZjj4TmLPfZwJz9ntsVkhgtqPPrBIBDQIEZg1doAbVAgRm1e2JrDgCc2SUqiciMKtuTyTFEZgjYVQ/CYFZfYsoEIHMCBCYM9NKFhKXAIE5Llld8xKYdfUjrmoIzHHJ6pmXwKynF3FWQmCOU5e5EUDAK0Bg5nhAIECAwGzHIUJgtqPPBObs95nAnP0emxUSmO3oM6tEQIMAgVlDF6hBtQCBWXV7IiuOwBwZpeqJCMyq2xNJcQTmSBjVT0JgVt8iCkQgMwIE5sy0koXEJUBgjktW17wEZl39iKsaAnNcsnrmJTDr6UWclRCY49RlbgQQ8AoQmDkeEAgQIDDbcYgQmO3oM4E5+30mMGe/x2aFBGY7+swqEdAgQGDW0AVqUC1AYFbdnsiKIzBHRql6IgKz6vZEUhyBORJG9ZMQmNW3iAIRyIwAgTkzrWQhcQkQmOOS1TUvgVlXP+KqhsAcl6yeeQnMenoRZyUE5jh1mRsBBLwCBGaOBwQCBAjMdhwiBGY7+kxgzn6fCczZ77FZIYHZjj6zSgQ0CBCYNXSBGlQLEJhVtyey4gjMkVGqnojArLo9kRRHYI6EUf0kBGb1LaJABDIjQGDOTCtZSFwCBOa4ZHXNS2DW1Y+4qiEwxyWrZ14Cs55exFkJgTlOXeZGAAGvAIGZ4wGBAAECsx2HCIHZjj4TmLPfZwJz9ntsVkhgtqPPrBIBDQIEZg1doAbVAgRm1e2JrDgCc2SUqiciMKtuTyTFEZgjYVQ/CYFZfYsoEIHMCBCYM9NKFhKXAIE5Llld8xKYdfUjrmoIzHHJ6pmXwKynF3FWQmCOU5e5EUDAK0Bg5nhAIECAwGzHIUJgtqPPBObs95nAnP0emxUSmO3oM6tEQIMAgVlDF6hBtQCBWXV7IiuOwBwZpeqJCMyq2xNJcQTmSBjVT0JgVt8iCkQgMwIE5sy0koXEJUBgjktW17wEZl39iKsaAnNcsnrmJTDr6UWclRCY49RlbgQQ8AoQmDkeEAgQIDDbcYgQmO3oM4E5+30mMGe/x2aFBGY7+swqEdAgQGDW0AVqUC1AYFbdnsiKIzBHRql6IgKz6vZEUhyBORJG9ZMQmNW3iAIRyIwAgTkzrWQhcQkQmOOS1TUvgVlXP+KqhsAcl6yeeQnMenoRZyUE5jh1mRsBBLwCBGaOBwQCBEZu/BPJS9X5T6oVkVpFpFoWkYrkzL+9t9XMmLLknDGNsYVRqY2tlNr4KqmNTUptwnxeKTK2Smrj5vbGf6MrRSZWSq24jJ4kIEBgTgA9gV0SmBNAH/IuCcxDBk9odwTmhODZLQIWChCYB2x6ccu1UjrmwgFn6W3zkQe+JqP/8TciubyI5Oqfc/XPNec28+/Gbc7XZljjNu/4fMHZ3tnGncsZ787pmT+/MKZm5uvzo7bkuTL/mx/oc+vhb5abeUYm/u3VUn7usZIrjIjkCiL5kfp/uYLUjKG5zXxPzPfq36+5t5nP1ZLk5vZIrjQlMr974eu5KcnNT4mYkO39yBfrIdqE7DHC87C6Xi5XZaTQuL8Ma6fsZ+gCpVJFikXzs4+PzArURMqVqoyMmN9hfGRVoFatSbVWk4L5uc1HpgXmzrwq0+tjcfoFCMwD9mjJP58s5cNfLfPHXS4yMjHgbMGbF7dcI8V7/5eUn/8KqRz6inrYqtVEco3Pzr9NAKvVP1cX/p1r9z1z1tQda+bxbOeMb37P/3Vwre1GmNrnT3qflA97VX8TDHkr13v3K74oI6sPiWXvudIekfk9TnjOzTVCdePfUtobyz6ZdLHA7FxJxoojkssP8IgQsOoFpmfmZcnEqPo6KbB/AROk5kplGR8r9j8JW6oXMFcSmP9GR80D1nxkWaB0zEVZXh5rS4EAgXnAJo08+E0Z/d6HpbL2eCm9+DKprownVJkyR7/3URl58AYpHf0mKb3orQNWnszmxR9dKcWt18vMa74ktWX7J1NED3ud+MprpLTkebL35VfKGL+Ue5BL31AuyU5fz/qpmEuy+1FL1zZckp2ufvVbLZdk9yvHdggg0KsAgblXsTbjC7+6W0a/8wGpjU9K6bhLpbL2xAhmXZgiN7tDRu/8ayls/w+ZP/7tUj7iDZHOP+zJxr/1h1Jb8hyZO/1vh73rnvaXm35aTGCe2nCxlI88j8Dck176BhOY09ezfiomMPejlq5tCMzp6le/1RKY+5VjOwQQ6FWAwNyrWIfxuanHZeyOv5T81DaZf/HbpHz4ayOZOf/sVhm968OSm/61zJ/451I58PRI5k1ykvyvfyrjN71F5o+7TMpHvjHJUrru270c++mzvySFyQMIzGo7FU1hBOZoHLXPQmDW3qHB6yMwD26YhhkIzGnoEjUikA0BAnOUfayWZOyO90th23ektOHNUjrmjwaavfD4d2X0ex+R2vg+TliuPmfDQPNp2rh47+eluOUfZfZVm6S66nBNpTVrMWeXqysPladP/KiMFkcIzCq7FF1RBOboLDXPRGDW3J1oaiMwR+OofRYCs/YOUR8C2REgMMfQy9EffFJGtv6blA97pcyf9Bd97WHk59fL6Pc/LpX9XyLzJ7xbasvX9jWP5o3GNr/NeVum2d/+B3Vl5vY+JRPXv1bmT3in7HjeKwjM6joUfUEE5uhNNc5IYNbYlWhrIjBH66l1NgKz1s5QFwLZEyAwx9TT4n/+q5gXuKrsf5zzNkq1idWh91T8yWel+NNNUn7+OTJ//LtERsZCb5umgfmdD8v4Ny5w3partGG4b80V5ORejj3zhm/I7upSAnMQWAa+T2DOQBNDLIHAHAIp5UMIzClvYMjyCcwhoRiGAAIDCxCYBybsPEHh0Vtk9PufEBlbJnO/+ZdSXXNU4N7Mi3uNPHSjlI76Aykde3Hg+LQPGNn6FRn9wSdk9qyrpLrvsWqW41yOveoImTvjb2Vq7yyBWU1n4iuEwByfraaZCcyauhFPLQTmeFy1zUpg1tYR6kEguwIE5ph7m3/6PufS6vyuR2Tu5L+QysG/1X6PpWkZu/09Yl5xW/uLYUVNNnb7eyW3+5cy++ovRD11X/Pl9j4hE9e/XuZPfK/zHtsE5r4YU7cRgTl1LeurYAJzX2yp2ojAnKp29V0sgblvOjZEAIEeBQjMPYL1Mzw3+6zzHsrmRbxKv3GJlNZf0DJNftejMmpC49wumX/Jn3UO1f3sPAXb5GaelvGvny8V85zv4y5LvOLm5djn3ii1sUkCc+IdGU4BBObhOCe9FwJz0h2If/8E5viNNeyBwKyhC9SAgB0CBOYh9tlcnj3y869I+QWvd15Mynw47+F8+/uktmx/mX/J5aouSx4ijYw8dJOM3vlXMnf630jlgJcOc9eL9uVcjr3mKJl72Yed73GGOdF2DG3nBOahUSe6IwJzovxD2TmBeSjMie+EwJx4CygAAWsECMxDbnXxZ/8sxXv+QSprT5LKwS+X0bs+JJW1Jzpnlmsr1g25Gl27G/3eh6Xw5I9l5lX/IjIynkhxuT3bZeKrb5D53/yfUj70bAJzIl1IZqcE5mTch71XAvOwxYe/PwLz8M2T2COBOQl19omAnQIE5gT6bs6mFu++UnJzO6R82Ktk/rjLRYpLEqhE2S7LszLxjfOlst9xMn/S+xIpzr0ce/q8f2/2hDPMibRi6DslMA+dPJEdEpgTYR/qTgnMQ+VObGcE5sTo2TEC1gkQmBNqeeGJeyT/q+9L6di3JlSBzt0WfnmnjN3y5y1neIdZqXM59nOPlblTPtjcLYF5mB1Ibl8E5uTsh7lnAvMwtZPZF4E5Gfdh75XAPGxx9oeAvQIEZhG5dtOXZfOtd7QcBRsvOFfOOfM057YbN98mm754nfP1sRvWy2WXXCTjY9l8b2QNd4XRuz8lhYdvltlXfaGn968etPbc7m0y8fU3ytwpf+1cLu9+EJgHlU3H9gTmdPRp0CoJzIMK6t+ewKy/R1FUSGCOQpE5EEAgjACBuRGYDdaFG89bZLblZ/fLpi9dJx94z9tlcsVyJ1x3GhsGnDHhBMa/8SaprTig+cJb4bYabFTzcuzzbxcpFAnMg3GmbmsCc+pa1lfBBOa+2FK1EYE5Ve3qu1gCc990bIgAAj0KEJgDArMJyGvX7tc82+wP0D16MzykQP7Jn8j4zRfL/PHvkPIRvxtyq8GGmcuxK/ufIPMn/0XLRJxhHsw1LVsTmNPSqcHqJDAP5peGrQnMaejS4DUSmAc3ZAYEEAgnQGBuc0m2ezn27NycfOqqa2TD0eubgfnRbY/LFZ+5Vi6/+EI5aN0B4ZQZ1ZdAccu1UrzvX2T2lZ+X6uRBfc0RdiPzXtjjN/y+zJ32MamsO4XAHBYuQ+MIzBlqZpelEJiz32cCc/Z7bFZIYLajz6wSAQ0CBGZfF0wg/tDHPi1ve+ub5YjDD3UC89lnnSHHHHWkM9IfmJ/ZuVdDHzNbw3PveofUCqPy6xM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   ],
   "source": [
    "df['MW (VDG)'].iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waterstorage = WaterStorage(\n",
    "    name='MyStorage',\n",
    "    max_power=10,\n",
    "    min_power=-10,\n",
    "    roundtrip_eff=0.90,\n",
    "    capacity_per_volume = 50 * 1e-3,\n",
    "    volume = 1000,\n",
    "    lifetime = 25,\n",
    "    temperature = 368, #K\n",
    "    min_storagelevel = 5,\n",
    "    # max_storagelevel = 50\n",
    "    \n",
    ")\n",
    "waterstorage.set_freq('15T')\n",
    "waterstorage.set_storagelevel(15)\n",
    "waterstorage.storagelevel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process demand 25\n"
     ]
    }
   ],
   "source": [
    "Tsink = 140  #Celcius\n",
    "Tsource = 60\n",
    "Tref = 0\n",
    "hp_capacity = 31 #MW\n",
    "process_demand_MW = 25 #MW\n",
    "Cp = 4190  #J/kgK\n",
    "MWtoJs = 1000_000\n",
    "efficiency = 0.9\n",
    "Tstorage = 95\n",
    "\n",
    "print('process demand', process_demand_MW)\n",
    "# hp_capacity vs hp_load?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waterstorage.max_storage_capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process_demand_MW 25\n",
      "hp_capacity 31\n"
     ]
    }
   ],
   "source": [
    "print('process_demand_MW', process_demand_MW)\n",
    "print('hp_capacity', hp_capacity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hp_mass_flow (hp_capacity, Tsink, Tref, Cp):\n",
    "    return hp_capacity * MWtoJs /(Cp*(Tsink - Tref)) #kg/s\n",
    "\n",
    "def process_mass_flow (process_demand_MW, Tsink, Tref, Cp):\n",
    "    return process_demand_MW * MWtoJs /(Cp*(Tsink - Tref)) \n",
    "\n",
    "def cop_curve(Tsink, Tsource):\n",
    "    c0 = Tsink / (Tsink - Tsource)  \n",
    "    return Polynomial([c0])\n",
    "\n",
    "# charge_mass_flow = hp_mass_flow (hp_capacity, Tsink, Tref, Cp) - process_mass_flow (process_demand_MW, Tsink, Tref, Cp)  #kg/s\n",
    "\n",
    "# def energy_to_storage (charge_mass_flow, Cp, Tsink, Tref):\n",
    "#     return (charge_mass_flow * Cp * (Tsink - Tref)) / MWtoJs\n",
    "\n",
    "def energy_to_storage(hp_capacity, process_demand_MW):\n",
    "    return hp_capacity - process_demand_MW  #MW\n",
    "\n",
    "\n",
    "# discharged_heat = energy_to_storage(hp_capacity, process_demand_MW) #MW\n",
    "\n",
    "# def charged_heat (charge_mass_flow, Cp, Tsink, Tref):\n",
    "#     return (charge_mass_flow * Cp * (Tsink - Tref)) / MW_to_J_per_s\n",
    "\n",
    "# discharged_heat = charged_heat(charge_mass_flow, Cp, Tsink, Tref)  #MW\n",
    "\n",
    "def discharge_mass_flow (discharged_heat, Cp, Tstorage, Tref):\n",
    "    return discharged_heat*MWtoJs/(Cp*(Tstorage - Tref))\n",
    "\n",
    "def Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow):\n",
    "    return ((Tstorage * discharge_mass_flow + Tsource * process_mass_flow)\n",
    " / (discharge_mass_flow + process_mass_flow))\n",
    "\n",
    "\n",
    "# charged_heat can be also defined as hp_capacity-process_demand_MW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_to_storage(hp_capacity, process_demand_MW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Heatpump',\n",
       " 'max_th_power': 40,\n",
       " 'min_th_power': 5,\n",
       " 'cop_curve': <function __main__.cop_curve(Tsink, Tsource)>}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# heatpump = Heatpump(\"heatpump1\", 50, cop_curve, 10)\n",
    "# heatpump.set_heat_output(50, Tsource=333, Tsink=413)\n",
    "cop_curve(140, 60)\n",
    "\n",
    "heatpump = Heatpump(\n",
    "    name='Heatpump',\n",
    "    max_th_power=40,\n",
    "    min_th_power=5,\n",
    "    cop_curve=cop_curve\n",
    ")\n",
    "\n",
    "heatpump.__dict__\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.1625"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatpump.get_cop(50, Tsource=333, Tsink=413)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.8740920096852305, 20)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatpump.set_heat_output(20, Tsource=333, Tsink=41+372)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "        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>Tsource (VDG)</th>\n",
       "      <th>Tsink (VDG)</th>\n",
       "      <th>MW (VDG)</th>\n",
       "      <th>Tsource (NDG)</th>\n",
       "      <th>Tsink (NDG)</th>\n",
       "      <th>MW (NDG)</th>\n",
       "      <th>DAM</th>\n",
       "      <th>POS</th>\n",
       "      <th>NEG</th>\n",
       "      <th>ForeNeg</th>\n",
       "      <th>ForePos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:00+01:00</th>\n",
       "      <td>64.964783</td>\n",
       "      <td>142.003109</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.897433</td>\n",
       "      <td>147.731814</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.90</td>\n",
       "      <td>46.39</td>\n",
       "      <td>46.39</td>\n",
       "      <td>53.603333</td>\n",
       "      <td>44.623333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:15:00+01:00</th>\n",
       "      <td>54.578777</td>\n",
       "      <td>138.960493</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.950905</td>\n",
       "      <td>148.138964</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.90</td>\n",
       "      <td>43.08</td>\n",
       "      <td>43.08</td>\n",
       "      <td>68.962000</td>\n",
       "      <td>63.177333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:30:00+01:00</th>\n",
       "      <td>65.166672</td>\n",
       "      <td>139.885329</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.500757</td>\n",
       "      <td>147.585426</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.90</td>\n",
       "      <td>43.13</td>\n",
       "      <td>43.13</td>\n",
       "      <td>55.415333</td>\n",
       "      <td>57.922667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:45:00+01:00</th>\n",
       "      <td>65.358078</td>\n",
       "      <td>139.731901</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.203876</td>\n",
       "      <td>147.547612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.90</td>\n",
       "      <td>46.29</td>\n",
       "      <td>46.29</td>\n",
       "      <td>57.633333</td>\n",
       "      <td>54.712667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 01:00:00+01:00</th>\n",
       "      <td>64.947536</td>\n",
       "      <td>139.577871</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.702675</td>\n",
       "      <td>148.260335</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.46</td>\n",
       "      <td>32.03</td>\n",
       "      <td>32.03</td>\n",
       "      <td>37.354000</td>\n",
       "      <td>35.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 01:15:00+01:00</th>\n",
       "      <td>65.073433</td>\n",
       "      <td>139.423357</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.903652</td>\n",
       "      <td>149.186865</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.46</td>\n",
       "      <td>32.03</td>\n",
       "      <td>32.03</td>\n",
       "      <td>35.934000</td>\n",
       "      <td>31.469333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 01:30:00+01:00</th>\n",
       "      <td>47.711559</td>\n",
       "      <td>140.328730</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.574467</td>\n",
       "      <td>147.800016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.46</td>\n",
       "      <td>34.48</td>\n",
       "      <td>34.48</td>\n",
       "      <td>37.640000</td>\n",
       "      <td>35.276000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 01:45:00+01:00</th>\n",
       "      <td>29.525829</td>\n",
       "      <td>140.298902</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.065464</td>\n",
       "      <td>147.906886</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.46</td>\n",
       "      <td>32.07</td>\n",
       "      <td>32.07</td>\n",
       "      <td>31.026667</td>\n",
       "      <td>28.963333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 02:00:00+01:00</th>\n",
       "      <td>65.715569</td>\n",
       "      <td>139.991650</td>\n",
       "      <td>10.139587</td>\n",
       "      <td>49.339708</td>\n",
       "      <td>149.603741</td>\n",
       "      <td>3.333301</td>\n",
       "      <td>44.00</td>\n",
       "      <td>40.66</td>\n",
       "      <td>40.66</td>\n",
       "      <td>40.547333</td>\n",
       "      <td>38.702000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 02:15:00+01:00</th>\n",
       "      <td>65.929909</td>\n",
       "      <td>148.342325</td>\n",
       "      <td>19.585104</td>\n",
       "      <td>61.721718</td>\n",
       "      <td>155.887905</td>\n",
       "      <td>6.455359</td>\n",
       "      <td>44.00</td>\n",
       "      <td>46.04</td>\n",
       "      <td>46.04</td>\n",
       "      <td>44.696000</td>\n",
       "      <td>42.961333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Tsource (VDG)  Tsink (VDG)   MW (VDG)  \\\n",
       "2018-11-01 00:00:00+01:00      64.964783   142.003109   0.000000   \n",
       "2018-11-01 00:15:00+01:00      54.578777   138.960493   0.000000   \n",
       "2018-11-01 00:30:00+01:00      65.166672   139.885329   0.000000   \n",
       "2018-11-01 00:45:00+01:00      65.358078   139.731901   0.000000   \n",
       "2018-11-01 01:00:00+01:00      64.947536   139.577871   0.000000   \n",
       "2018-11-01 01:15:00+01:00      65.073433   139.423357   0.000000   \n",
       "2018-11-01 01:30:00+01:00      47.711559   140.328730   0.000000   \n",
       "2018-11-01 01:45:00+01:00      29.525829   140.298902   0.000000   \n",
       "2018-11-01 02:00:00+01:00      65.715569   139.991650  10.139587   \n",
       "2018-11-01 02:15:00+01:00      65.929909   148.342325  19.585104   \n",
       "\n",
       "                           Tsource (NDG)  Tsink (NDG)  MW (NDG)    DAM    POS  \\\n",
       "2018-11-01 00:00:00+01:00      19.897433   147.731814  0.000000  44.90  46.39   \n",
       "2018-11-01 00:15:00+01:00      17.950905   148.138964  0.000000  44.90  43.08   \n",
       "2018-11-01 00:30:00+01:00      33.500757   147.585426  0.000000  44.90  43.13   \n",
       "2018-11-01 00:45:00+01:00      42.203876   147.547612  0.000000  44.90  46.29   \n",
       "2018-11-01 01:00:00+01:00      18.702675   148.260335  0.000000  42.46  32.03   \n",
       "2018-11-01 01:15:00+01:00      19.903652   149.186865  0.000000  42.46  32.03   \n",
       "2018-11-01 01:30:00+01:00      19.574467   147.800016  0.000000  42.46  34.48   \n",
       "2018-11-01 01:45:00+01:00      17.065464   147.906886  0.000000  42.46  32.07   \n",
       "2018-11-01 02:00:00+01:00      49.339708   149.603741  3.333301  44.00  40.66   \n",
       "2018-11-01 02:15:00+01:00      61.721718   155.887905  6.455359  44.00  46.04   \n",
       "\n",
       "                             NEG    ForeNeg    ForePos  \n",
       "2018-11-01 00:00:00+01:00  46.39  53.603333  44.623333  \n",
       "2018-11-01 00:15:00+01:00  43.08  68.962000  63.177333  \n",
       "2018-11-01 00:30:00+01:00  43.13  55.415333  57.922667  \n",
       "2018-11-01 00:45:00+01:00  46.29  57.633333  54.712667  \n",
       "2018-11-01 01:00:00+01:00  32.03  37.354000  35.400000  \n",
       "2018-11-01 01:15:00+01:00  32.03  35.934000  31.469333  \n",
       "2018-11-01 01:30:00+01:00  34.48  37.640000  35.276000  \n",
       "2018-11-01 01:45:00+01:00  32.07  31.026667  28.963333  \n",
       "2018-11-01 02:00:00+01:00  40.66  40.547333  38.702000  \n",
       "2018-11-01 02:15:00+01:00  46.04  44.696000  42.961333  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in price_data.columns:\n",
    "    df[col] = price_data[col]\n",
    "df.head(10)\n",
    "# iki dataframeni birlesdirdik burda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df.index:\n",
    "    df.loc[i, 'hp_mass'] = hp_mass_flow(hp_capacity, df.loc[i, 'Tsink (VDG)'], Tref, Cp)\n",
    "    df.loc[i, 'process_mass'] = process_mass_flow(df.loc[i, 'MW (VDG)'], df.loc[i, 'Tsink (VDG)'],Tref, Cp)\n",
    "    df.loc[i, 'COP'] = cop_curve(df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273)\n",
    "    df.loc[i, 'charge_mass'] = df.loc[i, 'hp_mass'] - df.loc[i, 'process_mass']\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_26096/3971255130.py:31: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in double_scalars\n",
      "\n",
      "C:\\Users\\Shahla Huseynova\\python\\Encore\\Simulations\\notepad.py:319: UserWarning:\n",
      "\n",
      "Chosen heat output is out of range [5 - 40]. Heat output is being limited to the closest boundary.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# df.index = df.index.tz_localize('Europe/Amsterdam')\n",
    "for i in df.index:\n",
    "#     logic that applies IF NO STORAGE, BASELINE CASE\n",
    "\n",
    " \n",
    "    hp_load = df.loc[i, 'MW (VDG)']\n",
    "    old_COP = heatpump.get_cop(hp_load, df.loc[i,'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273)\n",
    "    hp_consumption_old = hp_load/ old_COP\n",
    "    # df.loc[i,'hp_consumption_old'] = hp_load/ old_COP\n",
    "        \n",
    "    if df.loc[i, 'ForeNeg'] <  50:\n",
    "        hp_load = heatpump.max_th_power\n",
    "        energy_2_storage = hp_load - df.loc[i, 'MW (VDG)']\n",
    "        waterstorage.charge(energy_2_storage)\n",
    "        df.loc[i, 'charged_heat'] = waterstorage.charge(energy_2_storage)\n",
    "        charge_mass = hp_mass_flow (hp_capacity,  df.loc[i, 'Tsink (VDG)']+273, Tref+273, Cp) - process_mass_flow (df.loc[i, 'MW (VDG)'], df.loc[i, 'Tsink (VDG)']+273, Tref+273, Cp)\n",
    "        df.loc[i, 'new_cl'] = waterstorage.storagelevel\n",
    "        new_COP = heatpump.get_cop(hp_load, df.loc[i,'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273)\n",
    "        df.loc[i,'hp_consumption_new'] = hp_load/ new_COP\n",
    "    elif price_data.loc[i,'ForePos'] > 50:\n",
    "        # hp_load = heatpump.set_heat_output(df.loc[i, 'MW (VDG)'], df.loc[i,'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273)[1]\n",
    "        energy_from_storage = energy_to_storage(hp_capacity ,df.loc[i, 'MW (VDG)'])\n",
    "        waterstorage.discharge(energy_from_storage)\n",
    "        df.loc[i, 'discharged_heat'] =   waterstorage.discharge(energy_from_storage)\n",
    "        df.loc[i, 'new_cl'] = waterstorage.storagelevel\n",
    "        discharge_mass = discharge_mass_flow(df.loc[i, 'discharged_heat'], Cp, Tstorage, Tref)\n",
    "        df.loc[i, 'discharge_mass'] = discharge_mass\n",
    "        process_mass = process_mass_flow (df.loc[i, 'MW (VDG)'], df.loc[i, 'Tsink (VDG)']+273, Tref+273, Cp)\n",
    "        df.loc[i, 'Tsource_new'] = Tsource_calculation(Tstorage, df.loc[i, 'discharge_mass'], df.loc[i, 'Tsource (VDG)'], process_mass)\n",
    "        heat_output = heatpump.set_heat_output(df.loc[i, 'MW (VDG)'], df.loc[i,'Tsink (VDG)']+273, df.loc[i, 'Tsource_new']+273)\n",
    "        df.loc[i,'hp_consumption_new'] = heat_output[0]\n",
    "        df.loc[i, 'new_COP'] = heatpump.get_cop(heat_output[1], df.loc[i,'Tsink (VDG)']+273, df.loc[i, 'Tsource_new']+273)\n",
    "        # df.loc[i,'hp_consumption_new'] = hp_load/ new_COP\n",
    "        # new_COP = cop_curve (df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource_new']+273)\n",
    "    else:\n",
    "        hp_load = df.loc[i, 'MW (VDG)']\n",
    "        df.loc[i, 'old_COP'] = old_COP\n",
    "        df.loc[i,'hp_consumption_old'] = hp_consumption_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in df.index:\n",
    "#     if df.loc[i, 'ForePos'] < 50:\n",
    "#         hp_load = heatpump.max_th_power\n",
    "#         old_COP = heatpump.get_cop(hp_load, df.loc[i,'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273)\n",
    "#         df.loc[i,'hp_consumption_old'] = hp_load/ (old_COP * df.loc[i, 'POS'])\n",
    "#         # df.loc[i,'hp_consumption_new'] = hp_load/df.loc[i,'new_COP'] * df.loc[i, 'POS']\n",
    "\n",
    "#     elif price_data.loc[i,'ForePos'] > 50:\n",
    "#         hp_load = heatpump.set_heat_output(df.loc[i, 'MW (VDG)'], df.loc[i,'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273)[1]\n",
    "#         new_COP = heatpump.get_cop(hp_load, df.loc[i,'Tsink (VDG)']+273, df.loc[i, 'Tsource_new']+273)\n",
    "#         df.loc[i, 'hp_consumption'] = hp_load/ (new_COP *  df.loc[i, 'POS'] )\n",
    "\n",
    "# df[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-11-01 00:00:00+01:00    0.000000\n",
       "2018-11-01 00:15:00+01:00    0.000000\n",
       "2018-11-01 00:30:00+01:00    0.000000\n",
       "2018-11-01 00:45:00+01:00    0.000000\n",
       "2018-11-01 01:00:00+01:00    0.000000\n",
       "2018-11-01 01:15:00+01:00    0.000000\n",
       "2018-11-01 01:30:00+01:00    0.000000\n",
       "2018-11-01 01:45:00+01:00    0.000000\n",
       "2018-11-01 02:00:00+01:00    1.823593\n",
       "2018-11-01 02:15:00+01:00    3.830747\n",
       "2018-11-01 02:30:00+01:00    4.001079\n",
       "2018-11-01 02:45:00+01:00    4.064893\n",
       "2018-11-01 03:00:00+01:00    4.068391\n",
       "2018-11-01 03:15:00+01:00    4.052671\n",
       "2018-11-01 03:30:00+01:00    4.061479\n",
       "2018-11-01 03:45:00+01:00    4.067838\n",
       "2018-11-01 04:00:00+01:00    4.188972\n",
       "2018-11-01 04:15:00+01:00    4.382026\n",
       "2018-11-01 04:30:00+01:00    4.524195\n",
       "2018-11-01 04:45:00+01:00    4.507751\n",
       "2018-11-01 05:00:00+01:00    4.498205\n",
       "2018-11-01 05:15:00+01:00    4.504487\n",
       "2018-11-01 05:30:00+01:00    4.516361\n",
       "2018-11-01 05:45:00+01:00    4.535025\n",
       "2018-11-01 06:00:00+01:00         NaN\n",
       "2018-11-01 06:15:00+01:00    4.502226\n",
       "2018-11-01 06:30:00+01:00    4.492637\n",
       "2018-11-01 06:45:00+01:00         NaN\n",
       "2018-11-01 07:00:00+01:00    4.488528\n",
       "2018-11-01 07:15:00+01:00    4.505183\n",
       "2018-11-01 07:30:00+01:00    4.515428\n",
       "2018-11-01 07:45:00+01:00    4.519495\n",
       "2018-11-01 08:00:00+01:00    4.489461\n",
       "2018-11-01 08:15:00+01:00         NaN\n",
       "2018-11-01 08:30:00+01:00    4.464653\n",
       "2018-11-01 08:45:00+01:00    4.484737\n",
       "2018-11-01 09:00:00+01:00    4.458603\n",
       "2018-11-01 09:15:00+01:00    4.482972\n",
       "2018-11-01 09:30:00+01:00         NaN\n",
       "2018-11-01 09:45:00+01:00    4.457915\n",
       "2018-11-01 10:00:00+01:00    4.425400\n",
       "2018-11-01 10:15:00+01:00    4.437186\n",
       "2018-11-01 10:30:00+01:00    4.455529\n",
       "2018-11-01 10:45:00+01:00         NaN\n",
       "2018-11-01 11:00:00+01:00    4.465723\n",
       "2018-11-01 11:15:00+01:00         NaN\n",
       "2018-11-01 11:30:00+01:00         NaN\n",
       "2018-11-01 11:45:00+01:00         NaN\n",
       "2018-11-01 12:00:00+01:00         NaN\n",
       "Freq: 15T, Name: hp_consumption_old, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hp_consumption_old']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('b4ddac36-4ef1-403b-afe4-bc3786c4134f');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig = px.line(df['new_cl'])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.index = df.index.tz_localize('Europe/Amsterdam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in price_data.columns:\n",
    "    df[col] = price_data[col]\n",
    "for cons in ['old', 'new']:\n",
    "    df['nomination_MWh'] = df['MW (VDG)']#MWTO mWH\n",
    "    df[f'imbalance_MWh_{cons}'] = df['nomination_MWh'] - df[f'hp_consumption_{cons}']\n",
    "    df['day-ahead costs'] = df['nomination_MWh'] * df['DAM'] \n",
    "\n",
    "    is_pos = df[f'imbalance_MWh_{cons}'] > 0\n",
    "    df.loc[is_pos, f'imbalance costs_{cons}'] = -df.loc[is_pos, f'imbalance_MWh_{cons}'] * df['POS'] \n",
    "\n",
    "    is_neg = df[f'imbalance_MWh_{cons}'] < 0\n",
    "    df.loc[is_neg, f'imbalance costs_{cons}'] = -df.loc[is_neg, f'imbalance_MWh_{cons}'] * df['NEG'] \n",
    "\n",
    "    df[f'total cost_{cons}'] = df['day-ahead costs'] + df[f'imbalance costs_{cons}']\n",
    "    # df[f'cumulative_{cons}'] =  df[f'total cost_{cons}'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Tsource (VDG)</th>\n",
       "      <th>Tsink (VDG)</th>\n",
       "      <th>MW (VDG)</th>\n",
       "      <th>Tsource (NDG)</th>\n",
       "      <th>Tsink (NDG)</th>\n",
       "      <th>MW (NDG)</th>\n",
       "      <th>DAM</th>\n",
       "      <th>POS</th>\n",
       "      <th>NEG</th>\n",
       "      <th>ForeNeg</th>\n",
       "      <th>...</th>\n",
       "      <th>nomination_MWh</th>\n",
       "      <th>imbalance_MWh_old</th>\n",
       "      <th>day-ahead costs</th>\n",
       "      <th>imbalance costs_old</th>\n",
       "      <th>total cost_old</th>\n",
       "      <th>cumulative_old</th>\n",
       "      <th>imbalance_MWh_new</th>\n",
       "      <th>imbalance costs_new</th>\n",
       "      <th>total cost_new</th>\n",
       "      <th>cumulative_new</th>\n",
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       "      <td>46.39</td>\n",
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       "      <th>2018-11-01 00:15:00+01:00</th>\n",
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       "      <th>2018-11-01 00:30:00+01:00</th>\n",
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       "                           Tsource (VDG)  Tsink (VDG)   MW (VDG)  \\\n",
       "2018-11-01 00:00:00+01:00      64.964783   142.003109   0.000000   \n",
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       "\n",
       "                           Tsource (NDG)  Tsink (NDG)  MW (NDG)    DAM    POS  \\\n",
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       "2018-11-01 02:00:00+01:00      49.339708   149.603741  3.333301  44.00  40.66   \n",
       "2018-11-01 02:15:00+01:00      61.721718   155.887905  6.455359  44.00  46.04   \n",
       "\n",
       "                             NEG    ForeNeg  ...  nomination_MWh  \\\n",
       "2018-11-01 00:00:00+01:00  46.39  53.603333  ...        0.000000   \n",
       "2018-11-01 00:15:00+01:00  43.08  68.962000  ...        0.000000   \n",
       "2018-11-01 00:30:00+01:00  43.13  55.415333  ...        0.000000   \n",
       "2018-11-01 00:45:00+01:00  46.29  57.633333  ...        0.000000   \n",
       "2018-11-01 01:00:00+01:00  32.03  37.354000  ...        0.000000   \n",
       "2018-11-01 01:15:00+01:00  32.03  35.934000  ...        0.000000   \n",
       "2018-11-01 01:30:00+01:00  34.48  37.640000  ...        0.000000   \n",
       "2018-11-01 01:45:00+01:00  32.07  31.026667  ...        0.000000   \n",
       "2018-11-01 02:00:00+01:00  40.66  40.547333  ...       10.139587   \n",
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       "\n",
       "                           imbalance_MWh_old  day-ahead costs  \\\n",
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       "\n",
       "                          imbalance costs_old  total cost_old  cumulative_old  \\\n",
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       "                           imbalance_MWh_new  imbalance costs_new  \\\n",
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       "                           total cost_new  cumulative_new  \n",
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     "metadata": {},
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    }
   ],
   "source": [
    "# df[['cumulative_old', 'cumulative_new']].iplot()"
   ]
  },
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   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7763.909915061883\n",
      "-18786.900249434475\n"
     ]
    }
   ],
   "source": [
    "print(df['total cost_old'].sum())\n",
    "print(df['total cost_new'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# discharged_heat = energy_to_storage(hp_capacity,19.585104)\n",
    "\n",
    "# print(discharged_heat)\n",
    "\n",
    "# discharge_mass = discharge_mass_flow(discharged_heat, Cp, 140+273, Tref+273)\n",
    "\n",
    "# print(discharge_mass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rng = pd.date_range('2018-11-01 00:00:00', end='2018-11-01 12:00:00', freq='15T' )\n",
    "\n",
    "# price_data = pd.DataFrame(np.random.randint(30, 110, size=(len(rng))), \n",
    "#                   columns=['ForeNeg'], \n",
    "#                   index=rng)\n",
    "\n",
    "# price_data['ForePos'] = np.random.randint(20, 50, size=(len(rng)))\n",
    "\n",
    "# price_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def test_heatpump_and_waterstorage_system(Tsink, Tsource, process_demand_MW, e_price):\n",
    "#     \"\"\"\n",
    "#     1. Follow a certain logic based on given price:\n",
    "#         - If price is low --> Heatpump at full power, and charge the heatbuffer\n",
    "#         - If price is high --> Discharge the heat buffer, and increase Tsource, which will increase COP\n",
    "#     2. Above logic should adhere to a couple of constraints:\n",
    "#         - Storage levels\n",
    "#         - Capacity of the heat pump \n",
    "#         - Process demand\n",
    "#         - ....\n",
    "#     3. This function should contain: \n",
    "#         - Heat pump \n",
    "#         - Water storage\n",
    "#         - Interactions / logic between them\n",
    "#     4. Output of the function:\n",
    "#         - Power of the heatpump (MWe)\n",
    "#         - \"New\" water storage level\n",
    "#         - (optional) Thermal output of the heatpump\n",
    "#         - (optional) In/outflow from the storage\n",
    "#     \"\"\"\n",
    "    \n",
    "#     if e_price < 50:\n",
    "#         hp_load = heatpump.max_th_power\n",
    "#         energy_to_storage = hp_load - process_demand_MW\n",
    "#         waterstorage.charge(energy_to_storage)\n",
    "#         new_cl = waterstorage.storagelevel\n",
    "#     if e_price > 100:\n",
    "#         energy_from_storage = discharged_heat\n",
    "#         waterstorage.discharge(energy_from_storage)\n",
    "#         new_cl = waterstorage.storagelevel\n",
    "        \n",
    "#         def Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow):\n",
    "#             return ((Tstorage * discharge_mass_flow(discharged_heat, Cp, Tstorage, Tref) + Tsource * process_mass_flow(process_demand_MW, Tsink, Tref, Cp))\n",
    "#                     / (discharge_mass_flow(discharged_heat, Cp, Tstorage, Tref) + process_mass_flow(process_demand_MW, Tsink, Tref, Cp)))\n",
    "#         new_COP = cop_curve (Tsink, Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow))\n",
    "#         hp_load = heatpump.set_heat_output(process_demand_MW, Tsink, Tsource) #bu da hemcinin set load assetin funksiyasidir, \n",
    "#         #heatpump da overwrite edilib. men evezinde yazdim ki set_heat_output\n",
    "#         #sen gor hansi funksiya sene lazimdir.\n",
    "\n",
    "#     return hp_load, new_cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in df.index:\n",
    "#     df.loc[i, 'hp_mass'] = hp_mass_flow(hp_capacity, df.loc[i, 'Tsink (VDG)'], Tref, Cp)\n",
    "#     df.loc[i, 'process_mass'] = process_mass_flow(df.loc[i, 'MW (VDG)'], df.loc[i, 'Tsink (VDG)'],Tref, Cp)\n",
    "#     df.loc[i, 'COP'] = cop_curve(df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273)\n",
    "#     df.loc[i, 'charge_mass'] = df.loc[i, 'hp_mass'] - df.loc[i, 'process_mass']\n",
    "#     df.loc[i, 'charged_heat'] = charged_heat(df.loc[i, 'charge_mass'], Cp, df.loc[i, 'Tsink (VDG)']+273, Tref + 273)\n",
    "#     df.loc[i, 'discharged_heat'] = charged_heat(df.loc[i, 'charge_mass'], Cp, df.loc[i, 'Tsink (VDG)']+273, Tref + 273)\n",
    "#     df.loc[i, 'discharge_mass'] = discharge_mass_flow(df.loc[i, 'discharged_heat'], Cp, Tstorage+273, Tref+273)\n",
    "#     df.loc[i, 'Tsource_new'] = Tsource_calculation(Tstorage + 273, df.loc[i, 'discharge_mass'], df.loc[i, 'Tsource (VDG)']+273, df.loc[i, 'process_mass'])\n",
    "#     df.loc[i, 'new_COP'] = cop_curve(df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource_new'])\n",
    "    \n",
    "# df.head(10)\n",
    "# # Tsource_new should be in the nested function but now it is calculated separately, need to be checked again\n",
    "# discharge_mass were checked manually,there is  very slight change in the last decimals that's why seems constant here, but it calculates correctly.\n",
    "\n",
    "# process mass results are wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for col in price_data.columns:\n",
    "#     df[col] = price_data[col]\n",
    "\n",
    "# df['nomination_MWh'] = df['DAM'] * df['MW (VDG)']\n",
    "# df['heatpump_cons_MWh'] = 9\n",
    "# df['imbalance_MWh'] = df['nomination_MWh'] - df['heatpump_cons_MWh']\n",
    "# df['day-ahead costs'] = df['nomination_MWh'] * df['DAM'] \n",
    "\n",
    "# is_pos = df['imbalance_MWh'] > 0\n",
    "# df.loc[is_pos, 'imbalance costs'] = -df.loc[is_pos, 'imbalance_MWh'] * df['POS'] \n",
    "\n",
    "# is_neg = df['imbalance_MWh'] < 0\n",
    "# df.loc[is_neg, 'imbalance costs'] = -df.loc[is_neg, 'imbalance_MWh'] * df['NEG'] \n",
    "\n",
    "# df['total cost'] = df['day-ahead costs'] + df['imbalance costs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['Total demand'] = data['MW (VDG)'] + data['MW (NDG)']\n",
    "# data = data[start:end]\n",
    "# fig_demands_nov2018 = data['Total demand'].resample('1H').mean().iplot(\n",
    "#     title='Smurfit Kappa: Heat demand in MW', \n",
    "#     yTitle='MW', \n",
    "#     asFigure=True,\n",
    "#     dimensions=(800, 400)\n",
    "# )\n",
    "# fig_demands_nov2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in df.index:\n",
    "#     if df.loc[i, 'ForePos'] < 50:\n",
    "#         hp_load = heatpump.max_th_power\n",
    "#         old_COP = heatpump.get_cop(hp_load, df.loc[i,'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273)\n",
    "#         df.loc[i,'hp_consumption_old'] = hp_load/ (old_COP * df.loc[i, 'POS'])\n",
    "#         # df.loc[i,'hp_consumption_new'] = hp_load/df.loc[i,'new_COP'] * df.loc[i, 'POS']\n",
    "\n",
    "#     elif price_data.loc[i,'ForePos'] > 50:\n",
    "#         hp_load = heatpump.set_heat_output(df.loc[i, 'MW (VDG)'], df.loc[i,'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273)[1]\n",
    "#         new_COP = heatpump.get_cop(hp_load, df.loc[i,'Tsink (VDG)']+273, df.loc[i, 'Tsource_new']+273)\n",
    "#         df.loc[i, 'hp_consumption'] = hp_load/ (new_COP *  df.loc[i, 'POS'] )\n",
    "\n",
    "# df[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VISUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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