Mooi-Kickstart/Simulations/.ipynb_checkpoints/Test6-simulation-checkpoint.ipynb

{
 "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": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
       "            }\n",
       "        });\n",
       "        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": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <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",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:00+01:00</th>\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>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>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>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>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 22:45:00+01:00</th>\n",
       "      <td>50.70</td>\n",
       "      <td>54.02</td>\n",
       "      <td>54.02</td>\n",
       "      <td>71.520667</td>\n",
       "      <td>65.263333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 23:00:00+01:00</th>\n",
       "      <td>50.70</td>\n",
       "      <td>202.89</td>\n",
       "      <td>202.89</td>\n",
       "      <td>143.089333</td>\n",
       "      <td>144.426000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 23:15:00+01:00</th>\n",
       "      <td>50.70</td>\n",
       "      <td>85.70</td>\n",
       "      <td>85.70</td>\n",
       "      <td>101.676000</td>\n",
       "      <td>97.244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 23:30:00+01:00</th>\n",
       "      <td>50.70</td>\n",
       "      <td>38.03</td>\n",
       "      <td>38.03</td>\n",
       "      <td>50.560000</td>\n",
       "      <td>45.735333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 23:45:00+01:00</th>\n",
       "      <td>50.70</td>\n",
       "      <td>30.53</td>\n",
       "      <td>30.53</td>\n",
       "      <td>45.584667</td>\n",
       "      <td>40.736000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </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>0.0</td>\n",
       "      <td>19.897433</td>\n",
       "      <td>147.731814</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>17.950905</td>\n",
       "      <td>148.138964</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>33.500757</td>\n",
       "      <td>147.585426</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>42.203876</td>\n",
       "      <td>147.547612</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>18.702675</td>\n",
       "      <td>148.260335</td>\n",
       "      <td>0.0</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       0.0   \n",
       "2018-11-01 00:15:00+01:00      54.578777   138.960493       0.0   \n",
       "2018-11-01 00:30:00+01:00      65.166672   139.885329       0.0   \n",
       "2018-11-01 00:45:00+01:00      65.358078   139.731901       0.0   \n",
       "2018-11-01 01:00:00+01:00      64.947536   139.577871       0.0   \n",
       "\n",
       "                           Tsource (NDG)  Tsink (NDG)  MW (NDG)  \n",
       "2018-11-01 00:00:00+01:00      19.897433   147.731814       0.0  \n",
       "2018-11-01 00:15:00+01:00      17.950905   148.138964       0.0  \n",
       "2018-11-01 00:30:00+01:00      33.500757   147.585426       0.0  \n",
       "2018-11-01 00:45:00+01:00      42.203876   147.547612       0.0  \n",
       "2018-11-01 01:00:00+01:00      18.702675   148.260335       0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waterstorage.max_storage_capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_to_storage(hp_capacity, process_demand_MW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.1625"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatpump.get_cop(50, Tsource=333, Tsink=413)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.8740920096852305, 20)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatpump.set_heat_output(20, Tsource=333, Tsink=41+372)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 14,
     "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "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",
      "C:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_43244/3971255130.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_load/ old_COP\n",
    "        df.loc[i, 'new_cl'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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    4.496483\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    4.480298\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    4.474584\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    4.475751\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    4.464867\n",
       "2018-11-01 11:00:00+01:00    4.465723\n",
       "2018-11-01 11:15:00+01:00    4.472849\n",
       "2018-11-01 11:30:00+01:00    4.472572\n",
       "2018-11-01 11:45:00+01:00    4.488280\n",
       "2018-11-01 12:00:00+01:00    4.477443\n",
       "Freq: 15T, Name: hp_consumption_old, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hp_consumption_old']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VISUALISATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "import plotly.express as px\n",
    "from pyrecoy.colors import *\n",
    "\n",
    "\n",
    "fig_demands_over_time = df['new_cl'].iplot(\n",
    "    title='New_CL', \n",
    "    yTitle='MW', \n",
    "    colors=recoygreen,\n",
    "    asFigure=True,\n",
    "    dimensions=(800, 400)\n",
    ")\n",
    "fig_demands_over_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=df.index,\n",
    "    y=df['Tsource_new'],\n",
    "\n",
    "    xperiodalignment=\"middle\",\n",
    "    xhoverformat=\"Q%q\",\n",
    "    hovertemplate=\"%{y}%{_xother}\"\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=df.index,\n",
    "    y=df['new_cl'],\n",
    "    xperiodalignment=\"middle\",\n",
    "    hovertemplate=\"%{y}%{_xother}\"\n",
    "))\n",
    "\n",
    "fig.update_layout(hovermode=\"x unified\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_factor = 1/4\n",
    "\n",
    "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)'] * time_factor\n",
    "    df[f'imbalance_MWh_{cons}'] = df['nomination_MWh'] - (df[f'hp_consumption_{cons}']*time_factor)\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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "2018-11-01 01:00:00+01:00            NaN\n",
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       "2018-11-01 10:15:00+01:00     471.417320\n",
       "2018-11-01 10:30:00+01:00     457.979187\n",
       "2018-11-01 10:45:00+01:00     754.307416\n",
       "2018-11-01 11:00:00+01:00     506.223695\n",
       "2018-11-01 11:15:00+01:00    1173.169207\n",
       "2018-11-01 11:30:00+01:00     693.914398\n",
       "2018-11-01 11:45:00+01:00    1177.324197\n",
       "2018-11-01 12:00:00+01:00    1348.831286\n",
       "Freq: 15T, Name: total cost_old, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total cost_old']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20841.508235725\n",
      "22900.959197460757\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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