{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from notepad import WaterStorage, Heatpump\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "        </script>\n",
       "        "
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     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "import pandas as pd\n",
    "import cufflinks\n",
    "cufflinks.go_offline()"
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   "execution_count": 3,
   "metadata": {},
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       "                     Tsource (VDG)  Tsink (VDG)   MW (VDG)  Tsource (NDG)  \\\n",
       "2018-11-01 00:00:00      64.964783   142.003109   0.000000      19.897433   \n",
       "2018-11-01 00:15:00      54.578777   138.960493   0.000000      17.950905   \n",
       "2018-11-01 00:30:00      65.166672   139.885329   0.000000      33.500757   \n",
       "2018-11-01 00:45:00      65.358078   139.731901   0.000000      42.203876   \n",
       "2018-11-01 01:00:00      64.947536   139.577871   0.000000      18.702675   \n",
       "2018-11-01 01:15:00      65.073433   139.423357   0.000000      19.903652   \n",
       "2018-11-01 01:30:00      47.711559   140.328730   0.000000      19.574467   \n",
       "2018-11-01 01:45:00      29.525829   140.298902   0.000000      17.065464   \n",
       "2018-11-01 02:00:00      65.715569   139.991650  10.139587      49.339708   \n",
       "2018-11-01 02:15:00      65.929909   148.342325  19.585104      61.721718   \n",
       "\n",
       "                     Tsink (NDG)  MW (NDG)  \n",
       "2018-11-01 00:00:00   147.731814  0.000000  \n",
       "2018-11-01 00:15:00   148.138964  0.000000  \n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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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 = df.resample('15T', origin=start).mean()\n",
    "df=df.drop(['Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12'], axis=1)\n",
    "df[:10]"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
   ]
  },
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   "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",
    "    energy_density = 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": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waterstorage.max_storage_capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hp_mass_flow (hp_capacity, Tsink, Tref, Cp):\n",
    "    return hp_capacity /(Cp*(Tsink - Tref)) \n",
    "\n",
    "def process_mass_flow (demand, Tsink, Tref, Cp):\n",
    "    return demand /(Cp*(Tsink - Tref)) \n",
    "\n",
    "def COP_calculation(Tsink, Tsource):\n",
    "    return Tsink / (Tsink - Tsource)\n",
    "\n",
    "from numpy.polynomial import Polynomial\n",
    "\n",
    "def cop_curve(Tsink, Tsource):\n",
    "    c0 = Tsink / (Tsink - Tsource)  \n",
    "    return Polynomial([c0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.1625"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatpump.get_cop(50, Tsource=333, Tsink=413)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.400000000000001"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tsink = 140  #Celcius\n",
    "Tsource = 60\n",
    "Tref = 0\n",
    "hp_capacity = 31 #MW\n",
    "demand = 25 #MW\n",
    "Cp = 4190  #J/kgK\n",
    "MW_to_J_per_s = 1000_000\n",
    "hp_capacity *= MW_to_J_per_s\n",
    "demand *= MW_to_J_per_s\n",
    "efficiency = 0.9\n",
    "Tstorage = 95\n",
    "\n",
    "# charge_mass_flow = hp_mass_flow (hp_capacity, Tsink, Tref, Cp) - process_mass_flow (demand, Tsink, Tref, Cp)\n",
    "# charged_heat = (charge_mass_flow * Cp * (Tsink - Tref)) / MW_to_J_per_s\n",
    "# charged_heat\n",
    "charge_mass_flow = hp_mass_flow (hp_capacity, Tsink, Tref, Cp) - process_mass_flow (demand, Tsink, Tref, Cp)\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",
    "\n",
    "discharged_heat = charged_heat(charge_mass_flow, Cp, Tsink, Tref) * efficiency  #MW\n",
    "def discharge_mass_flow (discharged_heat, Cp, Tstorage, Tref):\n",
    "    return discharged_heat * MW_to_J_per_s /(Cp*(Tstorage - Tref))\n",
    "# discharge_mass_flow = discharged_heat * MW_to_J_per_s /(Cp*(Tstorage - Tref))\n",
    "# discharge_mass_flow\n",
    "# process_mass_flow\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)/ (discharge_mass_flow(discharged_heat, Cp, Tstorage, Tref) + process_mass_flow)\n",
    "            \n",
    "\n",
    "Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow (demand, Tsink, Tref, Cp))\n",
    "discharged_heat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
    "        # Tstorage = 95\n",
    "        energy_from_storage = discharged_heat\n",
    "        waterstorage.discharge(energy_from_storage)\n",
    "        new_cl = waterstorage.storagelevel\n",
    "        def Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow):\n",
    "            return (\n",
    "                (Tstorage * discharge_mass_flow + Tsource * process_mass_flow)\n",
    "                / (discharge_mass_flow + process_mass_flow)\n",
    "            )\n",
    "        new_Tsource = Tsource_calculation(Tstorage, discharge_mass_flow(discharged_heat, Cp, Tstorage, Tref), Tsource, process_mass_flow (demand, Tsink, Tref, Cp))\n",
    "        new_COP = COP_calculation (Tsink, new_Tsource)\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": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hp_load, new_cl = test_heatpump_and_waterstorage_system(\n",
    "#     Tsink = 140+273, \n",
    "#     Tsource = 60+273, \n",
    "#     process_demand_MW = 25, \n",
    "#     e_price = 30\n",
    "# )\n",
    "\n",
    "# hp_load, new_cl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# waterstorage.get_soc (30, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df.index:\n",
    "    # df.loc[i, 'MWe'] = test_heatpump_and_waterstorage_system(df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273, df.loc[i, 'MW (VDG)'], 130)[0][0]\n",
    "    df.loc[i, 'new_cl'] = test_heatpump_and_waterstorage_system(df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273, df.loc[i, 'MW (VDG)'], 30)[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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       "      <th>MW (VDG)</th>\n",
       "      <th>Tsource (NDG)</th>\n",
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       "                     Tsource (VDG)  Tsink (VDG)   MW (VDG)  Tsource (NDG)  \\\n",
       "2018-11-01 00:00:00      64.964783   142.003109   0.000000      19.897433   \n",
       "2018-11-01 00:15:00      54.578777   138.960493   0.000000      17.950905   \n",
       "2018-11-01 00:30:00      65.166672   139.885329   0.000000      33.500757   \n",
       "2018-11-01 00:45:00      65.358078   139.731901   0.000000      42.203876   \n",
       "2018-11-01 01:00:00      64.947536   139.577871   0.000000      18.702675   \n",
       "2018-11-01 01:15:00      65.073433   139.423357   0.000000      19.903652   \n",
       "2018-11-01 01:30:00      47.711559   140.328730   0.000000      19.574467   \n",
       "2018-11-01 01:45:00      29.525829   140.298902   0.000000      17.065464   \n",
       "2018-11-01 02:00:00      65.715569   139.991650  10.139587      49.339708   \n",
       "2018-11-01 02:15:00      65.929909   148.342325  19.585104      61.721718   \n",
       "\n",
       "                     Tsink (NDG)  MW (NDG)  new_cl  \n",
       "2018-11-01 00:00:00   147.731814  0.000000   28.75  \n",
       "2018-11-01 00:15:00   148.138964  0.000000   38.75  \n",
       "2018-11-01 00:30:00   147.585426  0.000000   47.50  \n",
       "2018-11-01 00:45:00   147.547612  0.000000   47.50  \n",
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       "2018-11-01 02:00:00   149.603741  3.333301   47.50  \n",
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    "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",
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    "fig_demands_nov2018"
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       "                            \n",
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      ]
     },
     "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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost_function(th_load, cop, e_price, alt_heat_price, process_demand_MW):\n",
    "        return (\n",
    "            th_load / cop * e_price\n",
    "            + (demand - th_load) * alt_heat_price\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_load = min(heatpump.max_th_power, process_demand_MW)\n",
    "min_load = heatpump.min_th_power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "999999150.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_full_load = cost_function(\n",
    "    th_load=max_load,\n",
    "    cop=new_COP,\n",
    "    e_price = 30,\n",
    "    alt_heat_price=40,\n",
    "    process_demand_MW=25\n",
    ")\n",
    "cost_full_load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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