{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.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-2.32.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\__init__.py\n"
     ]
    }
   ],
   "source": [
    "from datetime import timedelta, datetime\n",
    "import json\n",
    "import pprint\n",
    "from copy import deepcopy\n",
    "import pytz\n",
    "\n",
    "import cufflinks\n",
    "cufflinks.go_offline()\n",
    "import numpy as np\n",
    "from numpy.polynomial import Polynomial\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import sys\n",
    "\n",
    "import os\n",
    " \n",
    "# Path to the folder containing the alternate version of PyRecoy\n",
    "\n",
    "alternate_pyrecoy_path = 'C:\\\\Users\\\\z004yn8c\\\\OneDrive - Siemens AG\\\\Documents\\\\GitHub\\\\Mooi-Kickstart\\\\pyrecoy\\\\pyrecoy'\n",
    " \n",
    "# Add the path to sys.path\n",
    "\n",
    "if alternate_pyrecoy_path not in sys.path:\n",
    "\n",
    "    sys.path.insert(0, alternate_pyrecoy_path)\n",
    " \n",
    "# Now import PyRecoy\n",
    "\n",
    "import pyrecoy2\n",
    "\n",
    "# Check the version or path to confirm\n",
    "\n",
    "print(pyrecoy2.__file__)\n",
    "\n",
    "\n",
    "from pyrecoy2.assets import Heatpump, Eboiler, GasBoiler, HotWaterStorage\n",
    "from pyrecoy2.colors import *\n",
    "from pyrecoy2.converters import *\n",
    "from pyrecoy2.financial import calculate_eb_ode, get_tax_tables, get_tax_rate, get_grid_tariffs_electricity\n",
    "from pyrecoy2.framework import TimeFramework\n",
    "from pyrecoy2.casestudy import CaseStudy\n",
    "from pyrecoy2.plotting import ebitda_bar_chart, npv_bar_chart\n",
    "from pyrecoy2.reports import CaseReport, ComparisonReport, BusinessCaseReport, SingleFigureComparison\n",
    "from pyrecoy2.sensitivity import SensitivityAnalysis\n",
    "from pyrecoy2.prices import get_tennet_data, get_afrr_capacity_fees_nl\n",
    "from pyrecoy2.forecasts import Mipf, Forecast\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Development backlog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "* aFRR (can improve the optimisation case)\n",
    "* Report\n",
    "* COP seems to have fix values, rather than COP curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Meeting Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "##### Meeting 25-11-2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "* aFRR can help optimisation case\n",
    "* SDE++ should be included\n",
    "* Tsource sensitivity really gives interesting insights\n",
    "* Sensitivities should be verified (especially CO2, Tsink, Tsource, time period)\n",
    "* AP TNO: Update/verify COP curve\n",
    "* AP TNO: Update CAPEX\n",
    "* AP Mark: \n",
    "    * Create graphs on COP curve with different Tsource, Tsink\n",
    "    * Generate table and output in .csv (send it to Andrew)\n",
    "* Investigate opportunity to lower the COP and negative electricity prices\n",
    "    * Technically feasible, but not really needed/possible to do it in this project\n",
    "* Could be interesting to run this model on a usecase with higher delta T\n",
    "* Conclusion: Finalize this model, but not add too much extra complexity, next steps is to go towards industrial partners with the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "# Kickstart: Heatpump Optimisation Framework\n",
    "\n",
    "***\n",
    "© Shahla Huseynova  \n",
    "July 2024\n",
    "##### Cases\n",
    "In this model, 4 cases are compared:\n",
    "* **Baseline** : All heat is supplied by steam turbine\n",
    "* **Heatpump case** : All heat is supplied by heatpump\n",
    "* **Hybrid case** : Steam boiler and heatpump run in hybrid mode, and are optimised on costs in real-time\n",
    "* **Heat pump & storage cases**: Heat pump only case is compared with different setup of storage +heat pump cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Loading config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "class Config():\n",
    "    start = '2023-01-01'\n",
    "    end = '2023-12-31'\n",
    "    # year = '2019'\n",
    "    # startdate = year + '-01-01'\n",
    "    # enddate = str(year) + '-12-31'\n",
    "    # start = datetime.strptime(startdate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n",
    "    # end = datetime.strptime(enddate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n",
    "    # start = start.astimezone(pytz.UTC)\n",
    "    # end = end.astimezone(pytz.UTC)\n",
    "    \n",
    "    hp_vdg_e_power = 1.5 # MW\n",
    "    # hp_ndg_e_power = 7.7 # MW\n",
    "    hp_min_load = 0\n",
    "    hp_lifetime = 25\n",
    "    hp_capex = 200_000 # EUR/MWth\n",
    "    hp_opex = 0.01 # in % of CAPEX\n",
    "    hp_devex = 0.005 # in % of CAPEX\n",
    "    \n",
    "    gb_power = 5 # MW\n",
    "    gb_efficiency = 0.9\n",
    "    \n",
    "    storage_power = 1\n",
    "    # storage_volume = 1000 *3/50\n",
    "    # storage_cap_per_volume = 50 * 1e-3\n",
    "    rated_capacity = 3\n",
    "    storage_lifetime = 25\n",
    "    storage_temperature = 144\n",
    "    # storage_min_level = storage_volume * storage_cap_per_volume * 0.05\n",
    "    storage_min_level = rated_capacity * 0.00\n",
    "    # storage_min_level = rated_capacity\n",
    "    storage_capex_per_MW = 60_000\n",
    "    storage_capex_per_MWh = 0\n",
    "    storage_opex_perc_of_capex = 0.02\n",
    "    # storage_initial_level = 5\n",
    "    threshold = 0\n",
    "    \n",
    "    tax_bracket_g = 4 \n",
    "    tax_bracket_e = 4\n",
    "    \n",
    "    include_transport_costs = False\n",
    "    grid_operator = 'Liander'\n",
    "    connection_type = 'TS/MS'\n",
    "    \n",
    "    discount_rate = 0.1\n",
    "    project_duration = 12\n",
    "\n",
    "    forecast = 'ForeNeg'\n",
    "    gas_price_multiplier = 1\n",
    "    e_price_multiplier = 1\n",
    "    e_price_volatility_multiplier = 1\n",
    "    co2_price_multiplier = 1\n",
    "    tsource_delta = 0\n",
    "    tsink_delta = 0\n",
    "    energy_tax_multiplier = 1\n",
    "    \n",
    "    # Review the SDE implementation\n",
    "    sde_base_amount = 81\n",
    "    longterm_gas_price = 24.00\n",
    "    longterm_co2_price = 37.90\n",
    "    sde_switch_price_correction = 40\n",
    "    \n",
    "    day_ahead_buying_perc = 0.3\n",
    "    \n",
    "    afrr_capacity_fee = 25_000\n",
    "\n",
    "c = Config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "class Store():\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Model set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the file paths\n",
    "excel_file_path = r'C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\Kickstart\\V2\\Demand_Data_Smurfit_Preprocessed3.xlsx'\n",
    "output_directory = r'C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\Kickstart\\V2\\data'\n",
    "output_file_name = 'smurfit_demand_preprocessed3.csv'\n",
    "csv_file_path = os.path.join(output_directory, output_file_name)\n",
    "\n",
    "df = pd.read_excel(excel_file_path)\n",
    "# df = df.fillna(0) \n",
    "\n",
    "# # Save the DataFrame to CSV\n",
    "df.to_csv(csv_file_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def load_demand_data(c, s):\n",
    "    demand = pd.read_csv('data/smurfit_demand_preprocessed3.csv', delimiter=',', decimal='.')\n",
    "    dt_index = pd.date_range(\n",
    "        start=s.time_fw.start,\n",
    "        end=s.time_fw.start + timedelta(days=365), \n",
    "        freq='1T',\n",
    "        tz='Europe/Amsterdam')\n",
    "    dt_index = dt_index[:len(demand)]\n",
    "    demand.index = dt_index\n",
    "    demand['Total demand'] = demand['MW (VDG)']\n",
    "    \n",
    "    demand = demand[c.start:c.end]\n",
    "    return demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = Store()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testetss\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: UserWarning:\n",
      "\n",
      "The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_17424\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def setup_model(c):\n",
    "    s = Store()\n",
    "    \n",
    "    s.time_fw = TimeFramework(start=c.start, end=c.end)\n",
    "    # s.minute_ix = s.time_fw.dt_index(freq='1T')\n",
    "    mipf = Mipf(None, start=s.time_fw.start, end=s.time_fw.end, from_database=True, add_days_to_start_end=False).data\n",
    "    s.mipf = mipf\n",
    "    s.baseline = CaseStudy(time_fw=s.time_fw, freq='1T', name='Baseline')\n",
    "    s.hpcase = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump only', data=mipf)\n",
    "    #s.hpcase_sde = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + SDE', data=mipf)\n",
    "    #s.optcase1 = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation', data=mipf)\n",
    "    s.storage_case_PCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_PCM', data=mipf)\n",
    "    # s.storage_case_TCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_TCM', data=mipf)\n",
    "    # s.storage_case_battery = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_battery', data=mipf)\n",
    "    s.cases = list(CaseStudy.instances.values())\n",
    "    s.optcases = [s.hpcase, s.storage_case_PCM]\n",
    "    s.sde_cases = []\n",
    "    \n",
    "    s.demand = load_demand_data(c, s)\n",
    "    return s\n",
    "\n",
    "s = setup_model(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tsource (VDG)</th>\n",
       "      <th>Tsink (VDG)</th>\n",
       "      <th>MW (VDG)</th>\n",
       "      <th>Total demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:00:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:01:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:02:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:03:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:04:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:55:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:56:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:57:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:58:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:59:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525600 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Tsource (VDG)  Tsink (VDG)  MW (VDG)  Total demand\n",
       "2023-01-01 00:00:00+01:00             70          144         1             1\n",
       "2023-01-01 00:01:00+01:00             70          144         1             1\n",
       "2023-01-01 00:02:00+01:00             70          144         1             1\n",
       "2023-01-01 00:03:00+01:00             70          144         1             1\n",
       "2023-01-01 00:04:00+01:00             70          144         1             1\n",
       "...                                  ...          ...       ...           ...\n",
       "2023-12-31 23:55:00+01:00             70          144         1             1\n",
       "2023-12-31 23:56:00+01:00             70          144         1             1\n",
       "2023-12-31 23:57:00+01:00             70          144         1             1\n",
       "2023-12-31 23:58:00+01:00             70          144         1             1\n",
       "2023-12-31 23:59:00+01:00             70          144         1             1\n",
       "\n",
       "[525600 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.demand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Load in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def increase_volatility_by_factor(col, factor):\n",
    "    mean = col.mean()\n",
    "    diff_to_mean = col - mean\n",
    "    new_diff = diff_to_mean * factor\n",
    "    return mean + new_diff\n",
    "\n",
    "def multiply_by_factor(col, factor):\n",
    "    mean = col.mean()\n",
    "    diff_to_mean = col - mean\n",
    "    \n",
    "    cond = diff_to_mean > 0\n",
    "    diff_to_mean[cond] *= factor\n",
    "    diff_to_mean[~cond] /= factor\n",
    "    return mean + diff_to_mean"
   ]
  },
  {
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     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def load_data(c, s):\n",
    "    \n",
    "    for case in s.cases:\n",
    "        case.add_gasprices()\n",
    "        case.add_co2prices(perMWh=True)\n",
    "        \n",
    "        case.data['Gas prices (€/MWh)'] *= c.gas_price_multiplier\n",
    "        case.data['CO2 prices (€/MWh)'] *= c.co2_price_multiplier\n",
    "        case.data['CO2 prices (€/ton)'] *= c.co2_price_multiplier\n",
    "        \n",
    "    for case in s.optcases:\n",
    "        case.data['NEG'] = multiply_by_factor(case.data['NEG'], c.e_price_multiplier)\n",
    "        case.data['ForeNeg'] = multiply_by_factor(case.data['ForeNeg'], c.e_price_multiplier)\n",
    "        case.data['DAM'] = multiply_by_factor(case.data['DAM'], c.e_price_multiplier)\n",
    "        \n",
    "        case.data['NEG'] = increase_volatility_by_factor(case.data['NEG'], c.e_price_volatility_multiplier)\n",
    "        case.data['ForeNeg'] = increase_volatility_by_factor(case.data['ForeNeg'], c.e_price_volatility_multiplier)\n",
    "\n",
    "\n",
    "    s.demand['Tsource (VDG)'] += c.tsource_delta\n",
    "    s.demand['Tsink (VDG)'] += c.tsink_delta\n",
    "    for case in s.cases:\n",
    "        case.data = pd.concat([case.data, s.demand], axis=1)        \n",
    "\n",
    "    s.eb_ode_g = get_tax_rate('gas', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n",
    "    s.eb_ode_e = get_tax_rate('electricity', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n",
    "    s.grid_fees = get_grid_tariffs_electricity(c.grid_operator, 2020, c.connection_type)\n",
    "    s.grid_fee_per_MWh = s.grid_fees['kWh tarief'] * 1000\n",
    "    return s\n",
    "\n",
    "s = load_data(c, s)"
   ]
  },
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       "                            DAM     POS     NEG  ForeNeg  ForePos  \\\n",
       "2023-01-01 00:00:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:01:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
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       "...                         ...     ...     ...      ...      ...   \n",
       "2023-12-31 23:55:00+01:00  3.17   -6.81   -6.81    -7.10   -11.16   \n",
       "2023-12-31 23:56:00+01:00  3.17   -6.81   -6.81    -5.21    -8.12   \n",
       "2023-12-31 23:57:00+01:00  3.17   -6.81   -6.81    -5.61    -7.59   \n",
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       "2023-12-31 23:59:00+01:00  3.17   -6.81   -6.81    -5.70    -7.27   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-01-01 00:00:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:01:00+01:00              63.998                5.23   \n",
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       "2023-01-01 00:04:00+01:00              63.998                5.23   \n",
       "...                                       ...                 ...   \n",
       "2023-12-31 23:55:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:56:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:57:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:58:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:59:00+01:00                 NaN                 NaN   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-01-01 00:00:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:01:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:02:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:03:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:04:00+01:00                1.01             70          144   \n",
       "...                                       ...            ...          ...   \n",
       "2023-12-31 23:55:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:56:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:57:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:58:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:59:00+01:00                 NaN             70          144   \n",
       "\n",
       "                           MW (VDG)  Total demand  \n",
       "2023-01-01 00:00:00+01:00         1             1  \n",
       "2023-01-01 00:01:00+01:00         1             1  \n",
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       "2023-01-01 00:03:00+01:00         1             1  \n",
       "2023-01-01 00:04:00+01:00         1             1  \n",
       "...                             ...           ...  \n",
       "2023-12-31 23:55:00+01:00         1             1  \n",
       "2023-12-31 23:56:00+01:00         1             1  \n",
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       "2023-12-31 23:58:00+01:00         1             1  \n",
       "2023-12-31 23:59:00+01:00         1             1  \n",
       "\n",
       "[525600 rows x 12 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data"
   ]
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  {
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   "execution_count": 26,
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:56:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.21</td>\n",
       "      <td>-8.12</td>\n",
       "      <td>29.927</td>\n",
       "      <td>77.25</td>\n",
       "      <td>14.90</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:57:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.61</td>\n",
       "      <td>-7.59</td>\n",
       "      <td>29.927</td>\n",
       "      <td>77.25</td>\n",
       "      <td>14.90</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:58:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.74</td>\n",
       "      <td>-7.87</td>\n",
       "      <td>29.927</td>\n",
       "      <td>77.25</td>\n",
       "      <td>14.90</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:59:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.70</td>\n",
       "      <td>-7.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525600 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            DAM     POS     NEG  ForeNeg  ForePos  \\\n",
       "2023-01-01 00:00:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:01:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:02:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:03:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:04:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "...                         ...     ...     ...      ...      ...   \n",
       "2023-12-31 23:55:00+01:00  3.17   -6.81   -6.81    -7.10   -11.16   \n",
       "2023-12-31 23:56:00+01:00  3.17   -6.81   -6.81    -5.21    -8.12   \n",
       "2023-12-31 23:57:00+01:00  3.17   -6.81   -6.81    -5.61    -7.59   \n",
       "2023-12-31 23:58:00+01:00  3.17   -6.81   -6.81    -5.74    -7.87   \n",
       "2023-12-31 23:59:00+01:00  3.17   -6.81   -6.81    -5.70    -7.27   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-01-01 00:00:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:01:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:02:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:03:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:04:00+01:00              63.998                5.23   \n",
       "...                                       ...                 ...   \n",
       "2023-12-31 23:55:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:56:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:57:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:58:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:59:00+01:00                 NaN                 NaN   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-01-01 00:00:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:01:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:02:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:03:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:04:00+01:00                1.01             70          144   \n",
       "...                                       ...            ...          ...   \n",
       "2023-12-31 23:55:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:56:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:57:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:58:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:59:00+01:00                 NaN             70          144   \n",
       "\n",
       "                           MW (VDG)  Total demand  \n",
       "2023-01-01 00:00:00+01:00         1             1  \n",
       "2023-01-01 00:01:00+01:00         1             1  \n",
       "2023-01-01 00:02:00+01:00         1             1  \n",
       "2023-01-01 00:03:00+01:00         1             1  \n",
       "2023-01-01 00:04:00+01:00         1             1  \n",
       "...                             ...           ...  \n",
       "2023-12-31 23:55:00+01:00         1             1  \n",
       "2023-12-31 23:56:00+01:00         1             1  \n",
       "2023-12-31 23:57:00+01:00         1             1  \n",
       "2023-12-31 23:58:00+01:00         1             1  \n",
       "2023-12-31 23:59:00+01:00         1             1  \n",
       "\n",
       "[525600 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### COP curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def cop_curve_new(Tsink, Tsource):\n",
    "    Tsink += 273\n",
    "    Tsource += 273\n",
    "    Tlift = Tsink - Tsource\n",
    "\n",
    "    c0 = 0.0005426*Tlift**2 - 0.1178*Tlift + 6.962\n",
    "    c1 = 6.7058 \n",
    "    c2 = -1.79\n",
    "    \n",
    "    return Polynomial([c0, c1, c2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$x \\mapsto \\text{19.2496754} + \\text{6.7058}\\,x - \\text{1.79}\\,x^{2}$"
      ],
      "text/plain": [
       "Polynomial([19.2496754,  6.7058   , -1.79     ], domain=[-1,  1], window=[-1,  1], symbol='x')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sourceT = 63 + 273\n",
    "sinkT = 140 + 273\n",
    "cop_curve_new(sourceT, sinkT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Create and assign assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def create_and_assign_assets(c, s):\n",
    "    heatpump_vdg = Heatpump(\n",
    "        name='Heatpump VDG',\n",
    "        max_th_power=c.hp_vdg_e_power,\n",
    "        min_th_power=c.hp_vdg_e_power * c.hp_min_load,\n",
    "        cop_curve=2.5\n",
    "    )\n",
    "\n",
    "\n",
    "    capex_vdg = c.hp_capex*(heatpump_vdg.max_th_power) \n",
    "    heatpump_vdg.set_financials(capex=capex_vdg, opex=c.hp_opex*capex_vdg, devex=c.hp_devex*capex_vdg, lifetime=25)\n",
    "\n",
    "    gasboiler = GasBoiler(\n",
    "        name='Gasboiler',\n",
    "        max_th_output=c.gb_power,\n",
    "        efficiency=c.gb_efficiency\n",
    "    )\n",
    "    gasboiler.set_financials(capex=0, opex=0, devex=0, lifetime=25)\n",
    "    \n",
    "    waterstorage = HotWaterStorage(\n",
    "        name='HotWaterStorage',\n",
    "        rated_power=c.storage_power, \n",
    "        # capacity_per_volume=None,\n",
    "        # volume=None, \n",
    "        temperature=c.storage_temperature,\n",
    "        min_storagelevel=c.storage_min_level,\n",
    "        # initial_storagelevel=c.storage_initial_level\n",
    "    )\n",
    "    capex_ws = c.storage_capex_per_MW * waterstorage.max_power + c.storage_capex_per_MWh * waterstorage.capacity\n",
    "    opex_ws = c.storage_opex_perc_of_capex * capex_ws\n",
    "    waterstorage.set_financials(capex=capex_ws, opex=opex_ws, devex=0, lifetime=c.storage_lifetime)\n",
    "    \n",
    "    s.baseline.add_asset(gasboiler)\n",
    "    s.hpcase.add_asset(heatpump_vdg)\n",
    "    s.storage_case_PCM.add_asset(heatpump_vdg)\n",
    "    s.storage_case_PCM.add_asset(waterstorage)\n",
    "    return s\n",
    "\n",
    "s = create_and_assign_assets(c, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=0.0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.assets['HotWaterStorage']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def baseline_sim(case):\n",
    "    gasboiler = list(case.assets.values())[0]\n",
    "    data = case.data\n",
    "    demand = (data['MW (VDG)']).to_list()\n",
    "\n",
    "    minutes = iter(range(len(case.data)))\n",
    "    th_output = [0] * len(case.data)\n",
    "    gas_input = [0] * len(case.data)\n",
    "\n",
    "    for m in minutes:\n",
    "        th_output[m], gas_input[m] = gasboiler.set_heat_output(demand[m])\n",
    "\n",
    "    data['output_MW_th'] = np.array(th_output)\n",
    "    data['output_MWh_th'] = np.array(data['output_MW_th']/60)\n",
    "    data['gb_input_MW'] = np.array(gas_input)\n",
    "    data['gb_input_MWh'] = np.array(data['gb_input_MW']/60)\n",
    "    case.data = data.round(5)\n",
    "    return case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def hponly(case):\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    demand_vdg = case.data['MW (VDG)'].to_list()\n",
    "    Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n",
    "    Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n",
    "\n",
    "    hp_vdg_input = [0] * len(case.data)\n",
    "    hp_vdg_output = [0] * len(case.data)\n",
    "\n",
    "    minutes = iter(range(len(case.data)))\n",
    "    for m in minutes:\n",
    "        demand = demand_vdg[m]\n",
    "        if demand != 0:\n",
    "            hp_vdg_input[m], hp_vdg_output[m] = hp_vdg.set_heat_output(\n",
    "                heat_output=demand - 0.66,\n",
    "                Tsink=Tsink_vdg[m],\n",
    "                Tsource=Tsource_vdg[m]\n",
    "            )\n",
    "\n",
    "    case.data['hp_output_MW'] = np.array(hp_vdg_output)\n",
    "    case.data['hp_input_MW'] = np.array(hp_vdg_input)\n",
    "    case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n",
    "    \n",
    "    for col in case.data.columns:\n",
    "        if col.endswith('MW'):\n",
    "            case.data[col + 'h'] = case.data[col] / 60\n",
    "\n",
    "    case.data = case.data.round(3)\n",
    "    return case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hp_storage_opt_enginge(c, ws, hp_vdg, pos, neg, dam, demand_vdg, tsource_vdg, tsink_vdg):\n",
    "    \n",
    "    from_storage_vdg_MW = 0\n",
    "    to_storage_vdg_MW = 0  \n",
    "    \n",
    "    if neg < dam - c.threshold:\n",
    "        # overproduce\n",
    "        if demand_vdg != 0:\n",
    "            storage_charge_power = ws.charging_power_limit\n",
    "            if ws.chargelevel == ws.max_chargelevel:\n",
    "                storage_charge_power = 0\n",
    "\n",
    "            desired_hp_load_vdg = min(demand_vdg + storage_charge_power - 0.66, hp_vdg.max_th_power)\n",
    "            e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(desired_hp_load_vdg, tsink_vdg, tsource_vdg)\n",
    "\n",
    "            to_storage_vdg_MW = desired_hp_load_vdg - demand_vdg + 0.66\n",
    "            to_storage_vdg_MW = -ws.charge(round(to_storage_vdg_MW, 3))\n",
    "\n",
    "        else:\n",
    "            e_load_vdg, th_load_vdg = (0,0)\n",
    "            \n",
    "            \n",
    "\n",
    "    elif pos > dam + c.threshold:\n",
    "        # take from storage\n",
    "        if demand_vdg != 0:\n",
    "            from_storage_vdg_MW = demand_vdg - 0.66\n",
    "            from_storage_vdg_MW = ws.discharge(round(from_storage_vdg_MW, 3))\n",
    "            desired_hp_load_vdg = min(demand_vdg - from_storage_vdg_MW - 0.66, hp_vdg.max_th_power)\n",
    "            e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(desired_hp_load_vdg, tsink_vdg, tsource_vdg)\n",
    "        else:\n",
    "            e_load_vdg, th_load_vdg = (0,0)\n",
    "            \n",
    "        #print({f'from_storage_vdg_MW='})\n",
    "    else:\n",
    "        e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(\n",
    "            heat_output=demand_vdg - 0.66,\n",
    "            Tsink=tsink_vdg,\n",
    "            Tsource=tsource_vdg\n",
    "        )\n",
    "\n",
    "    return e_load_vdg, th_load_vdg, tsource_vdg, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_vdg\n",
    "# Neg-take price, Pos-feed price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def hponly_with_storage(case):\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    ws = case.assets['HotWaterStorage']\n",
    "    demands_vdg = case.data['MW (VDG)'].to_list()\n",
    "    Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n",
    "    Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n",
    "    dam_prices = case.data['DAM'].to_list()\n",
    "    pos_prices = case.data['POS'].to_list()\n",
    "    neg_prices = case.data['NEG'].to_list()\n",
    "\n",
    "    hp_vdg_input = [0] * len(case.data)\n",
    "    hp_vdg_output = [0] * len(case.data)\n",
    "\n",
    "    minutes = len(case.data)\n",
    "    storage_levels = [None] * minutes\n",
    "    to_storage_list = [0] * minutes\n",
    "    from_storage_vdg_list = [0] * minutes\n",
    "    to_storage_vdg_list = [0] * minutes\n",
    "\n",
    "    new_tsources_vdg = case.data['Tsource (VDG)'].to_list()\n",
    "\n",
    "    \n",
    "    ws.set_chargelevel(ws.min_chargelevel)\n",
    "    \n",
    "    for m in range(minutes):\n",
    "        tsource_vdg = Tsource_vdg[m]\n",
    "        tsink_vdg = Tsink_vdg[m]\n",
    "        demand_vdg = demands_vdg[m]\n",
    "        \n",
    "        e_load_vdg, th_load_vdg, tsource_vdg, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_vdg = hp_storage_opt_enginge(\n",
    "            c, ws, hp_vdg, pos_prices[m], neg_prices[m], dam_prices[m], demand_vdg, tsource_vdg, tsink_vdg\n",
    "        )\n",
    "            \n",
    "        hp_vdg_input[m] = e_load_vdg\n",
    "        hp_vdg_output[m] = th_load_vdg\n",
    "            \n",
    "        storage_levels[m] = ws.chargelevel\n",
    "        new_tsources_vdg[m] = tsource_vdg\n",
    "        to_storage_vdg_list[m] = to_storage_vdg_MW\n",
    "        from_storage_vdg_list[m] = from_storage_vdg_MW\n",
    "        \n",
    "    case.data['hp_output_MW'] = np.array(hp_vdg_output)\n",
    "    case.data['hp_input_MW'] = np.array(hp_vdg_input)\n",
    "    case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n",
    "    case.data['tsource_vdg'] = new_tsources_vdg\n",
    "    case.data['to_storage_vdg_MW'] = to_storage_vdg_list\n",
    "    case.data['from_storage_vdg_MW'] = from_storage_vdg_list\n",
    "    case.data['to_storage_MW'] = case.data['to_storage_vdg_MW']\n",
    "    case.data['from_storage_MW'] = case.data['from_storage_vdg_MW']\n",
    "    case.data['storage_level_MWh'] = storage_levels\n",
    "    \n",
    "    for col in case.data.columns:\n",
    "        if col.endswith('MW'):\n",
    "            case.data[col + 'h'] = case.data[col] / 60\n",
    "\n",
    "    case.data = case.data.round(3)\n",
    "    return case\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# charge_times = print(sum(s.storage_case_PCM.data['to_storage_MW']!= 0))\n",
    "# discharge_times = print(sum(s.storage_case_PCM.data['from_storage_MW']!= 0))\n",
    "# # 154476 times out of 525600, the storage is used to charge/dischage\n",
    "# # from 39 000 to 110 000 discharge time increases\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\assets.py:263: UserWarning:\n",
      "\n",
      "Chosen heat output is out of range [0.0 - 1.5]. Heat output is being limited to the closest boundary.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAM</th>\n",
       "      <th>POS</th>\n",
       "      <th>NEG</th>\n",
       "      <th>ForeNeg</th>\n",
       "      <th>ForePos</th>\n",
       "      <th>Gas prices (€/MWh)</th>\n",
       "      <th>CO2 prices (€/ton)</th>\n",
       "      <th>CO2 prices (€/MWh)</th>\n",
       "      <th>Tsource (VDG)</th>\n",
       "      <th>Tsink (VDG)</th>\n",
       "      <th>...</th>\n",
       "      <th>from_storage_vdg_MW</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>from_storage_MW</th>\n",
       "      <th>storage_level_MWh</th>\n",
       "      <th>hp_output_MWh</th>\n",
       "      <th>hp_input_MWh</th>\n",
       "      <th>to_storage_vdg_MWh</th>\n",
       "      <th>from_storage_vdg_MWh</th>\n",
       "      <th>to_storage_MWh</th>\n",
       "      <th>from_storage_MWh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-12-19 11:40:00+01:00</th>\n",
       "      <td>79.71</td>\n",
       "      <td>61.27</td>\n",
       "      <td>61.27</td>\n",
       "      <td>59.92</td>\n",
       "      <td>58.77</td>\n",
       "      <td>34.338</td>\n",
       "      <td>69.08</td>\n",
       "      <td>13.32</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-21 10:06:00+01:00</th>\n",
       "      <td>115.00</td>\n",
       "      <td>89.92</td>\n",
       "      <td>117.74</td>\n",
       "      <td>133.62</td>\n",
       "      <td>89.81</td>\n",
       "      <td>39.165</td>\n",
       "      <td>85.06</td>\n",
       "      <td>16.40</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM    POS     NEG  ForeNeg  ForePos  \\\n",
       "2023-12-19 11:40:00+01:00   79.71  61.27   61.27    59.92    58.77   \n",
       "2023-03-21 10:06:00+01:00  115.00  89.92  117.74   133.62    89.81   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-12-19 11:40:00+01:00              34.338               69.08   \n",
       "2023-03-21 10:06:00+01:00              39.165               85.06   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-12-19 11:40:00+01:00               13.32             70          144   \n",
       "2023-03-21 10:06:00+01:00               16.40             70          144   \n",
       "\n",
       "                           ...  from_storage_vdg_MW  to_storage_MW  \\\n",
       "2023-12-19 11:40:00+01:00  ...                  0.0            0.0   \n",
       "2023-03-21 10:06:00+01:00  ...                  0.0            0.0   \n",
       "\n",
       "                           from_storage_MW  storage_level_MWh  hp_output_MWh  \\\n",
       "2023-12-19 11:40:00+01:00              0.0                3.0          0.006   \n",
       "2023-03-21 10:06:00+01:00              0.0                3.0          0.006   \n",
       "\n",
       "                           hp_input_MWh  to_storage_vdg_MWh  \\\n",
       "2023-12-19 11:40:00+01:00        -0.002                 0.0   \n",
       "2023-03-21 10:06:00+01:00        -0.002                 0.0   \n",
       "\n",
       "                           from_storage_vdg_MWh  to_storage_MWh  \\\n",
       "2023-12-19 11:40:00+01:00                   0.0             0.0   \n",
       "2023-03-21 10:06:00+01:00                   0.0             0.0   \n",
       "\n",
       "                           from_storage_MWh  \n",
       "2023-12-19 11:40:00+01:00               0.0  \n",
       "2023-03-21 10:06:00+01:00               0.0  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.storage_case_PCM.data.to_csv('storage_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>hp_output_MW</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>Total demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>95.817313</td>\n",
       "      <td>93.568163</td>\n",
       "      <td>103.438997</td>\n",
       "      <td>0.341449</td>\n",
       "      <td>0.110088</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.046697</td>\n",
       "      <td>194.977533</td>\n",
       "      <td>195.696551</td>\n",
       "      <td>0.304808</td>\n",
       "      <td>0.206773</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-500.000000</td>\n",
       "      <td>-1549.470000</td>\n",
       "      <td>-1549.470000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>76.935000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>57.967500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>99.215000</td>\n",
       "      <td>84.130000</td>\n",
       "      <td>88.570000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>122.400000</td>\n",
       "      <td>114.020000</td>\n",
       "      <td>119.592500</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>463.770000</td>\n",
       "      <td>2037.740000</td>\n",
       "      <td>2037.740000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DAM            POS            NEG   hp_output_MW  \\\n",
       "count  525600.000000  525600.000000  525600.000000  525600.000000   \n",
       "mean       95.817313      93.568163     103.438997       0.341449   \n",
       "std        49.046697     194.977533     195.696551       0.304808   \n",
       "min      -500.000000   -1549.470000   -1549.470000       0.000000   \n",
       "25%        76.935000      47.000000      57.967500       0.000000   \n",
       "50%        99.215000      84.130000      88.570000       0.340000   \n",
       "75%       122.400000     114.020000     119.592500       0.340000   \n",
       "max       463.770000    2037.740000    2037.740000       0.840000   \n",
       "\n",
       "       to_storage_MW  Total demand  \n",
       "count  525600.000000      525600.0  \n",
       "mean        0.110088           1.0  \n",
       "std         0.206773           0.0  \n",
       "min         0.000000           1.0  \n",
       "25%         0.000000           1.0  \n",
       "50%         0.000000           1.0  \n",
       "75%         0.000000           1.0  \n",
       "max         0.500000           1.0  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'Total demand']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>hp_output_MW</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>from_storage_MW</th>\n",
       "      <th>Total demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-09-04 04:11:00+02:00</th>\n",
       "      <td>93.19</td>\n",
       "      <td>100.58</td>\n",
       "      <td>100.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-15 19:10:00+01:00</th>\n",
       "      <td>199.11</td>\n",
       "      <td>541.25</td>\n",
       "      <td>541.25</td>\n",
       "      <td>0.00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-25 03:32:00+01:00</th>\n",
       "      <td>-10.03</td>\n",
       "      <td>-38.00</td>\n",
       "      <td>-38.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-06 13:20:00+02:00</th>\n",
       "      <td>0.04</td>\n",
       "      <td>87.64</td>\n",
       "      <td>87.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-22 10:19:00+02:00</th>\n",
       "      <td>102.40</td>\n",
       "      <td>255.66</td>\n",
       "      <td>255.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-03 17:48:00+02:00</th>\n",
       "      <td>67.50</td>\n",
       "      <td>92.26</td>\n",
       "      <td>92.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-24 01:52:00+02:00</th>\n",
       "      <td>83.58</td>\n",
       "      <td>69.22</td>\n",
       "      <td>69.22</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-19 15:27:00+02:00</th>\n",
       "      <td>-150.00</td>\n",
       "      <td>-400.00</td>\n",
       "      <td>-400.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-08 12:51:00+02:00</th>\n",
       "      <td>80.60</td>\n",
       "      <td>72.12</td>\n",
       "      <td>72.12</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-14 20:45:00+01:00</th>\n",
       "      <td>99.90</td>\n",
       "      <td>102.52</td>\n",
       "      <td>102.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-22 11:38:00+01:00</th>\n",
       "      <td>123.96</td>\n",
       "      <td>97.75</td>\n",
       "      <td>97.75</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-09 12:36:00+01:00</th>\n",
       "      <td>125.43</td>\n",
       "      <td>117.00</td>\n",
       "      <td>117.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-09 04:19:00+02:00</th>\n",
       "      <td>85.25</td>\n",
       "      <td>88.12</td>\n",
       "      <td>88.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-28 18:31:00+02:00</th>\n",
       "      <td>50.00</td>\n",
       "      <td>-86.94</td>\n",
       "      <td>-86.94</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-08 13:08:00+01:00</th>\n",
       "      <td>135.08</td>\n",
       "      <td>484.11</td>\n",
       "      <td>484.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-22 23:28:00+02:00</th>\n",
       "      <td>127.60</td>\n",
       "      <td>115.01</td>\n",
       "      <td>115.01</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-11 23:22:00+02:00</th>\n",
       "      <td>116.16</td>\n",
       "      <td>-259.70</td>\n",
       "      <td>-259.70</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-21 01:57:00+02:00</th>\n",
       "      <td>65.61</td>\n",
       "      <td>-10.56</td>\n",
       "      <td>-10.56</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-16 15:57:00+01:00</th>\n",
       "      <td>158.50</td>\n",
       "      <td>114.45</td>\n",
       "      <td>114.45</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-28 10:39:00+02:00</th>\n",
       "      <td>-4.89</td>\n",
       "      <td>28.34</td>\n",
       "      <td>28.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM     POS     NEG  hp_output_MW  \\\n",
       "2023-09-04 04:11:00+02:00   93.19  100.58  100.58          0.00   \n",
       "2023-03-15 19:10:00+01:00  199.11  541.25  541.25          0.00   \n",
       "2023-12-25 03:32:00+01:00  -10.03  -38.00  -38.00          0.34   \n",
       "2023-08-06 13:20:00+02:00    0.04   87.64   87.64          0.00   \n",
       "2023-08-22 10:19:00+02:00  102.40  255.66  255.66          0.00   \n",
       "2023-10-03 17:48:00+02:00   67.50   92.26   92.26          0.00   \n",
       "2023-04-24 01:52:00+02:00   83.58   69.22   69.22          0.84   \n",
       "2023-04-19 15:27:00+02:00 -150.00 -400.00 -400.00          0.34   \n",
       "2023-05-08 12:51:00+02:00   80.60   72.12   72.12          0.84   \n",
       "2023-12-14 20:45:00+01:00   99.90  102.52  102.52          0.00   \n",
       "2023-02-22 11:38:00+01:00  123.96   97.75   97.75          0.34   \n",
       "2023-02-09 12:36:00+01:00  125.43  117.00  117.00          0.34   \n",
       "2023-05-09 04:19:00+02:00   85.25   88.12   88.12          0.00   \n",
       "2023-05-28 18:31:00+02:00   50.00  -86.94  -86.94          0.34   \n",
       "2023-03-08 13:08:00+01:00  135.08  484.11  484.11          0.00   \n",
       "2023-08-22 23:28:00+02:00  127.60  115.01  115.01          0.34   \n",
       "2023-10-11 23:22:00+02:00  116.16 -259.70 -259.70          0.84   \n",
       "2023-09-21 01:57:00+02:00   65.61  -10.56  -10.56          0.34   \n",
       "2023-11-16 15:57:00+01:00  158.50  114.45  114.45          0.34   \n",
       "2023-05-28 10:39:00+02:00   -4.89   28.34   28.34          0.00   \n",
       "\n",
       "                           to_storage_MW  from_storage_MW  Total demand  \n",
       "2023-09-04 04:11:00+02:00            0.0             0.34             1  \n",
       "2023-03-15 19:10:00+01:00            0.0             0.34             1  \n",
       "2023-12-25 03:32:00+01:00            0.0             0.00             1  \n",
       "2023-08-06 13:20:00+02:00            0.0             0.34             1  \n",
       "2023-08-22 10:19:00+02:00            0.0             0.34             1  \n",
       "2023-10-03 17:48:00+02:00            0.0             0.34             1  \n",
       "2023-04-24 01:52:00+02:00            0.5             0.00             1  \n",
       "2023-04-19 15:27:00+02:00            0.0             0.00             1  \n",
       "2023-05-08 12:51:00+02:00            0.5             0.00             1  \n",
       "2023-12-14 20:45:00+01:00            0.0             0.34             1  \n",
       "2023-02-22 11:38:00+01:00            0.0             0.00             1  \n",
       "2023-02-09 12:36:00+01:00            0.0             0.00             1  \n",
       "2023-05-09 04:19:00+02:00            0.0             0.34             1  \n",
       "2023-05-28 18:31:00+02:00            0.0             0.00             1  \n",
       "2023-03-08 13:08:00+01:00            0.0             0.34             1  \n",
       "2023-08-22 23:28:00+02:00            0.0             0.00             1  \n",
       "2023-10-11 23:22:00+02:00            0.5             0.00             1  \n",
       "2023-09-21 01:57:00+02:00            0.0             0.00             1  \n",
       "2023-11-16 15:57:00+01:00            0.0             0.00             1  \n",
       "2023-05-28 10:39:00+02:00            0.0             0.34             1  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'from_storage_MW','Total demand']].sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'hp_input_MW', 'hp_output_MW', 'cop', 'Total demand']]\n",
    "hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'Total demand']]\n",
    "storage_selection = s.storage_case_PCM.data[['hp_input_MW', 'hp_output_MW', 'cop', 'to_storage_MW', 'from_storage_MW']]\n",
    "\n",
    "selection = pd.concat([hp_selection, storage_selection], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAM</th>\n",
       "      <th>POS</th>\n",
       "      <th>NEG</th>\n",
       "      <th>Total demand</th>\n",
       "      <th>hp_input_MW</th>\n",
       "      <th>hp_output_MW</th>\n",
       "      <th>cop</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>from_storage_MW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-05-28 14:05:00+02:00</th>\n",
       "      <td>-400.00</td>\n",
       "      <td>-380.00</td>\n",
       "      <td>-380.00</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-11 21:59:00+02:00</th>\n",
       "      <td>118.40</td>\n",
       "      <td>88.65</td>\n",
       "      <td>88.65</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-12 06:28:00+02:00</th>\n",
       "      <td>88.22</td>\n",
       "      <td>191.87</td>\n",
       "      <td>191.87</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-11 10:54:00+01:00</th>\n",
       "      <td>131.78</td>\n",
       "      <td>-14.19</td>\n",
       "      <td>-14.19</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-22 20:23:00+01:00</th>\n",
       "      <td>170.87</td>\n",
       "      <td>143.59</td>\n",
       "      <td>143.59</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-18 13:25:00+02:00</th>\n",
       "      <td>9.57</td>\n",
       "      <td>-26.32</td>\n",
       "      <td>-26.32</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28 13:04:00+01:00</th>\n",
       "      <td>11.40</td>\n",
       "      <td>26.09</td>\n",
       "      <td>26.09</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-25 09:06:00+02:00</th>\n",
       "      <td>32.55</td>\n",
       "      <td>12.55</td>\n",
       "      <td>12.55</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-14 16:21:00+01:00</th>\n",
       "      <td>78.83</td>\n",
       "      <td>212.49</td>\n",
       "      <td>212.49</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-04 09:33:00+02:00</th>\n",
       "      <td>105.87</td>\n",
       "      <td>-17.00</td>\n",
       "      <td>-17.00</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-30 23:48:00+02:00</th>\n",
       "      <td>70.05</td>\n",
       "      <td>-259.73</td>\n",
       "      <td>85.04</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-10 18:04:00+01:00</th>\n",
       "      <td>135.10</td>\n",
       "      <td>86.38</td>\n",
       "      <td>86.38</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-02 10:38:00+01:00</th>\n",
       "      <td>170.09</td>\n",
       "      <td>-35.45</td>\n",
       "      <td>-35.45</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-19 15:42:00+01:00</th>\n",
       "      <td>84.82</td>\n",
       "      <td>71.52</td>\n",
       "      <td>71.52</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-09 00:43:00+01:00</th>\n",
       "      <td>89.20</td>\n",
       "      <td>71.84</td>\n",
       "      <td>71.84</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-05 08:42:00+02:00</th>\n",
       "      <td>180.75</td>\n",
       "      <td>98.77</td>\n",
       "      <td>98.77</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-30 20:58:00+01:00</th>\n",
       "      <td>153.93</td>\n",
       "      <td>58.00</td>\n",
       "      <td>58.00</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-05 16:56:00+02:00</th>\n",
       "      <td>91.16</td>\n",
       "      <td>94.03</td>\n",
       "      <td>94.03</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-28 13:13:00+01:00</th>\n",
       "      <td>119.90</td>\n",
       "      <td>31.80</td>\n",
       "      <td>31.80</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-07 11:33:00+02:00</th>\n",
       "      <td>85.69</td>\n",
       "      <td>1272.80</td>\n",
       "      <td>1272.80</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM      POS      NEG  Total demand  \\\n",
       "2023-05-28 14:05:00+02:00 -400.00  -380.00  -380.00             1   \n",
       "2023-05-11 21:59:00+02:00  118.40    88.65    88.65             1   \n",
       "2023-07-12 06:28:00+02:00   88.22   191.87   191.87             1   \n",
       "2023-01-11 10:54:00+01:00  131.78   -14.19   -14.19             1   \n",
       "2023-01-22 20:23:00+01:00  170.87   143.59   143.59             1   \n",
       "2023-09-18 13:25:00+02:00    9.57   -26.32   -26.32             1   \n",
       "2023-12-28 13:04:00+01:00   11.40    26.09    26.09             1   \n",
       "2023-06-25 09:06:00+02:00   32.55    12.55    12.55             1   \n",
       "2023-03-14 16:21:00+01:00   78.83   212.49   212.49             1   \n",
       "2023-10-04 09:33:00+02:00  105.87   -17.00   -17.00             1   \n",
       "2023-07-30 23:48:00+02:00   70.05  -259.73    85.04             1   \n",
       "2023-11-10 18:04:00+01:00  135.10    86.38    86.38             1   \n",
       "2023-02-02 10:38:00+01:00  170.09   -35.45   -35.45             1   \n",
       "2023-12-19 15:42:00+01:00   84.82    71.52    71.52             1   \n",
       "2023-11-09 00:43:00+01:00   89.20    71.84    71.84             1   \n",
       "2023-04-05 08:42:00+02:00  180.75    98.77    98.77             1   \n",
       "2023-11-30 20:58:00+01:00  153.93    58.00    58.00             1   \n",
       "2023-05-05 16:56:00+02:00   91.16    94.03    94.03             1   \n",
       "2023-01-28 13:13:00+01:00  119.90    31.80    31.80             1   \n",
       "2023-06-07 11:33:00+02:00   85.69  1272.80  1272.80             1   \n",
       "\n",
       "                           hp_input_MW  hp_output_MW  cop  to_storage_MW  \\\n",
       "2023-05-28 14:05:00+02:00       -0.000          0.00  NaN            0.0   \n",
       "2023-05-11 21:59:00+02:00       -0.336          0.84  2.5            0.5   \n",
       "2023-07-12 06:28:00+02:00       -0.000          0.00  NaN            0.0   \n",
       "2023-01-11 10:54:00+01:00       -0.336          0.84  2.5            0.5   \n",
       "2023-01-22 20:23:00+01:00       -0.336          0.84  2.5            0.5   \n",
       "2023-09-18 13:25:00+02:00       -0.136          0.34  2.5            0.0   \n",
       "2023-12-28 13:04:00+01:00       -0.000          0.00  NaN            0.0   \n",
       "2023-06-25 09:06:00+02:00       -0.336          0.84  2.5            0.5   \n",
       "2023-03-14 16:21:00+01:00       -0.000          0.00  NaN            0.0   \n",
       "2023-10-04 09:33:00+02:00       -0.136          0.34  2.5            0.0   \n",
       "2023-07-30 23:48:00+02:00       -0.136          0.34  2.5            0.0   \n",
       "2023-11-10 18:04:00+01:00       -0.136          0.34  2.5            0.0   \n",
       "2023-02-02 10:38:00+01:00       -0.136          0.34  2.5            0.0   \n",
       "2023-12-19 15:42:00+01:00       -0.136          0.34  2.5            0.0   \n",
       "2023-11-09 00:43:00+01:00       -0.136          0.34  2.5            0.0   \n",
       "2023-04-05 08:42:00+02:00       -0.136          0.34  2.5            0.0   \n",
       "2023-11-30 20:58:00+01:00       -0.136          0.34  2.5            0.0   \n",
       "2023-05-05 16:56:00+02:00       -0.000          0.00  NaN            0.0   \n",
       "2023-01-28 13:13:00+01:00       -0.136          0.34  2.5            0.0   \n",
       "2023-06-07 11:33:00+02:00       -0.000          0.00  NaN            0.0   \n",
       "\n",
       "                           from_storage_MW  \n",
       "2023-05-28 14:05:00+02:00             0.34  \n",
       "2023-05-11 21:59:00+02:00             0.00  \n",
       "2023-07-12 06:28:00+02:00             0.34  \n",
       "2023-01-11 10:54:00+01:00             0.00  \n",
       "2023-01-22 20:23:00+01:00             0.00  \n",
       "2023-09-18 13:25:00+02:00             0.00  \n",
       "2023-12-28 13:04:00+01:00             0.34  \n",
       "2023-06-25 09:06:00+02:00             0.00  \n",
       "2023-03-14 16:21:00+01:00             0.34  \n",
       "2023-10-04 09:33:00+02:00             0.00  \n",
       "2023-07-30 23:48:00+02:00             0.00  \n",
       "2023-11-10 18:04:00+01:00             0.00  \n",
       "2023-02-02 10:38:00+01:00             0.00  \n",
       "2023-12-19 15:42:00+01:00             0.00  \n",
       "2023-11-09 00:43:00+01:00             0.00  \n",
       "2023-04-05 08:42:00+02:00             0.00  \n",
       "2023-11-30 20:58:00+01:00             0.00  \n",
       "2023-05-05 16:56:00+02:00             0.34  \n",
       "2023-01-28 13:13:00+01:00             0.00  \n",
       "2023-06-07 11:33:00+02:00             0.34  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DAM                 95.817313\n",
       "POS                 93.568163\n",
       "NEG                103.438997\n",
       "Total demand         1.000000\n",
       "hp_input_MW         -0.136580\n",
       "hp_output_MW         0.341449\n",
       "cop                  2.500000\n",
       "to_storage_MW        0.110088\n",
       "from_storage_MW      0.109745\n",
       "dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "########################\n",
    "\n",
    "# This is where we left\n",
    "\n",
    "########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def cost_function(th_load, cop, electricity_cost, alt_heat_price, demand):\n",
    "        return (\n",
    "            th_load / cop * electricity_cost\n",
    "            + (demand - th_load) * alt_heat_price\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def hybrid_imb_optimisation(case, decimals, s):\n",
    "    gb = case.assets['Gasboiler']\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    demand_vdg = case.data['MW (VDG)'].round(decimals).to_list()\n",
    "    Tsink_vdg = case.data['Tsink (VDG)'].round(decimals).to_list()\n",
    "    Tsource_vdg = case.data['Tsource (VDG)'].round(decimals).to_list()\n",
    "    fore_neg = case.data[c.forecast].fillna(999).round(decimals).to_list()\n",
    "    gas_prices = case.data['Gas prices (€/MWh)'].round(decimals).to_list()\n",
    "    co2_prices = case.data['CO2 prices (€/MWh)'].round(decimals).to_list()\n",
    "    eb_ode_g = s.eb_ode_g\n",
    "    eb_ode_e = s.eb_ode_e\n",
    "    \n",
    "    gb_input = [0] * len(case.data)\n",
    "    gb_output = [0] * len(case.data)\n",
    "\n",
    "    minutes = range(len(case.data))\n",
    "    hp_output = [0] * len(case.data)\n",
    "    hp_input = [0] * len(case.data)\n",
    "\n",
    "    for m in tqdm(minutes):\n",
    "        dem_vgd = demand_vdg[m]\n",
    "        if dem_vgd != 0:\n",
    "            max_load = min(hp_vdg.max_th_power, dem_vgd)\n",
    "            min_load = hp_vdg.min_th_power\n",
    "            Tsink = Tsink_vdg[m]\n",
    "            Tsource = Tsource_vdg[m]\n",
    "            cop_max_load = hp_vdg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n",
    "            cop_min_load = hp_vdg.get_cop(heat_output=min_load, Tsink=Tsink_vdg[m], Tsource=Tsource_vdg[m])\n",
    "            \n",
    "            cost_full_load = cost_function(\n",
    "                th_load=max_load,\n",
    "                cop=cop_max_load,\n",
    "                electricity_cost=fore_neg[m] + eb_ode_e - c.sde_switch_price_correction,\n",
    "                alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
    "                demand=dem_vgd\n",
    "            )\n",
    "            \n",
    "            cost_min_load = cost_function(\n",
    "                th_load=min_load,\n",
    "                cop=cop_min_load,\n",
    "                electricity_cost=fore_neg[m] + eb_ode_e,\n",
    "                alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
    "                demand=dem_vgd\n",
    "            )\n",
    "            \n",
    "            if cost_full_load < cost_min_load:\n",
    "                hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(max_load, Tsink, Tsource)\n",
    "            else:\n",
    "                hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(min_load, Tsink, Tsource)\n",
    "        else:\n",
    "            hp_vdg_input, hp_vdg_output = (0, 0)\n",
    "\n",
    "\n",
    "        hp_out = hp_vdg_output\n",
    "        hp_output[m] = hp_out\n",
    "        hp_input[m] = hp_vdg_input\n",
    "        remaining_demand = max(dem_vgd+dem_ngd-hp_out, 0)\n",
    "        gb_output[m], gb_input[m] = gb.set_heat_output(remaining_demand)\n",
    "\n",
    "    case.data['hp_output_MW'] = np.array(hp_output)\n",
    "    case.data['hp_input_MW'] = np.array(hp_input)\n",
    "    case.data['gb_output_MW'] = np.array(gb_output)\n",
    "    case.data['gb_input_MW'] = np.array(gb_input)\n",
    "\n",
    "    for col in case.data.columns:\n",
    "        if col.endswith('MW'):\n",
    "            case.data[col + 'h'] = case.data[col] / 60\n",
    "\n",
    "    case.data = case.data.round(5)\n",
    "    return case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "aFRR\n",
    "* Bid in a volume (X MW) --> Strategy is to only bid in on aFRR down\n",
    "* Remaining demand is filed in by gasboiler\n",
    "* Bid price at switch price?\n",
    "* Assume direct response to 0% for now?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def calc_afrr_capacity(case):\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    \n",
    "    capacity = 0\n",
    "    for hp in hp_vdg:\n",
    "        max_th_output = hp.max_th_power\n",
    "        cop = hp.get_cop(max_th_output, Tsink=135, Tsource=60)\n",
    "        e_power = max_th_output / cop\n",
    "        capacity += e_power\n",
    "    return capacity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Run optimisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def run_optimisation(c, s):\n",
    "    s.baseline = baseline_sim(s.baseline)\n",
    "    s.hpcase = hponly(s.hpcase)\n",
    "    s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)\n",
    "#     s.hpcase_sde.assign_algorithm(hponly)\n",
    "#     s.hpcase_sde.run()\n",
    "    \n",
    "#     s.optcase1.assign_algorithm(hybrid_imb_optimisation)\n",
    "#     s.optcase1.run(decimals=2, s=s)\n",
    "    \n",
    "#     s.afrr_case.assign_algorithm(aFRR_optimisation)\n",
    "#     s.afrr_case.run(s=s)\n",
    "    \n",
    "    for case in [s.hpcase, s.storage_case_PCM]: # [s.hpcase, s.hpcase_sde, s.optcase1, s.afrr_case]:\n",
    "        case.mean_cop = case.data['hp_output_MW'].sum() / case.data['hp_input_MW'].abs().sum()\n",
    "    \n",
    "    return s\n",
    "\n",
    "s = run_optimisation(c, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4999999999999987"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data['hp_output_MW'].sum() /s.hpcase.data['hp_input_MW'].abs().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.499999999999998"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data['hp_output_MW'].sum() /s.storage_case_PCM.data['hp_input_MW'].abs().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .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>DAM</th>\n",
       "      <th>POS</th>\n",
       "      <th>NEG</th>\n",
       "      <th>ForeNeg</th>\n",
       "      <th>ForePos</th>\n",
       "      <th>Gas prices (€/MWh)</th>\n",
       "      <th>CO2 prices (€/ton)</th>\n",
       "      <th>CO2 prices (€/MWh)</th>\n",
       "      <th>Tsource (VDG)</th>\n",
       "      <th>Tsink (VDG)</th>\n",
       "      <th>MW (VDG)</th>\n",
       "      <th>Total demand</th>\n",
       "      <th>hp_output_MW</th>\n",
       "      <th>hp_input_MW</th>\n",
       "      <th>cop</th>\n",
       "      <th>hp_output_MWh</th>\n",
       "      <th>hp_input_MWh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:00:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:01:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:02:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:03:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:04:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            DAM    POS    NEG  ForeNeg  ForePos  \\\n",
       "2023-01-01 00:00:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:01:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:02:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:03:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:04:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-01-01 00:00:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:01:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:02:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:03:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:04:00+01:00              63.998                5.23   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-01-01 00:00:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:01:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:02:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:03:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:04:00+01:00                1.01             70          144   \n",
       "\n",
       "                           MW (VDG)  Total demand  hp_output_MW  hp_input_MW  \\\n",
       "2023-01-01 00:00:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:01:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:02:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:03:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:04:00+01:00         1             1          0.34       -0.136   \n",
       "\n",
       "                           cop  hp_output_MWh  hp_input_MWh  \n",
       "2023-01-01 00:00:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:01:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:02:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:03:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:04:00+01:00  2.5          0.006        -0.002  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis_data = s.storage_case_PCM.data[[\n",
    "    'storage_level_MWh', \n",
    "    #'to_storage_ndg_MW', \n",
    "    #'to_storage_vdg_MW', \n",
    "    'to_storage_MW', \n",
    "    #'from_storage_ndg_MW', \n",
    "    #'from_storage_vdg_MW',\n",
    "    'from_storage_MW',\n",
    "    'NEG'\n",
    "]]\n",
    "\n",
    "cops = pd.concat([s.hpcase.data['cop'], s.storage_case_PCM.data['cop']], axis=1)\n",
    "vis_data = pd.concat([vis_data, cops], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
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   "source": [
    "vis_data.loc['2023-10-02', :].iplot(subplots=True, shape=(6,1), dimensions=(1000, 800))"
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  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "storage_level_MWh       0.00\n",
       "to_storage_MW           0.00\n",
       "from_storage_MW         0.00\n",
       "NEG                 -1549.47\n",
       "cop                     2.50\n",
       "cop                     2.50\n",
       "dtype: float64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis_data.min()"
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  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(926.1410000000002, 1017.8269999999995)"
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     "execution_count": 201,
     "metadata": {},
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   "source": [
    "total_to_storage = s.storage_case_PCM.data['to_storage_MWh'].sum()\n",
    "total_from_storage = s.storage_case_PCM.data['from_storage_MWh'].sum()\n",
    "total_to_storage, total_from_storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh'],\n",
       "      dtype='object')"
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     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": 203,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "False"
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     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "hp_output = s.storage_case_PCM.data['hp_output_MW'].round(2)\n",
    "demand_and_to_storage = (s.storage_case_PCM.data['Total demand'] + s.storage_case_PCM.data['to_storage_MW']).round(2)\n",
    "hp_output.equals(demand_and_to_storage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
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      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_17424\\4223154146.py:1: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
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          "2023-12-30 05:00:00+01:00",
          "2023-12-30 06:00:00+01:00",
          "2023-12-30 07:00:00+01:00",
          "2023-12-30 08:00:00+01:00",
          "2023-12-30 09:00:00+01:00",
          "2023-12-30 10:00:00+01:00",
          "2023-12-30 11:00:00+01:00",
          "2023-12-30 12:00:00+01:00",
          "2023-12-30 13:00:00+01:00",
          "2023-12-30 14:00:00+01:00",
          "2023-12-30 15:00:00+01:00",
          "2023-12-30 16:00:00+01:00",
          "2023-12-30 17:00:00+01:00",
          "2023-12-30 18:00:00+01:00",
          "2023-12-30 19:00:00+01:00",
          "2023-12-30 20:00:00+01:00",
          "2023-12-30 21:00:00+01:00",
          "2023-12-30 22:00:00+01:00",
          "2023-12-30 23:00:00+01:00",
          "2023-12-31 00:00:00+01:00",
          "2023-12-31 01:00:00+01:00",
          "2023-12-31 02:00:00+01:00",
          "2023-12-31 03:00:00+01:00",
          "2023-12-31 04:00:00+01:00",
          "2023-12-31 05:00:00+01:00",
          "2023-12-31 06:00:00+01:00",
          "2023-12-31 07:00:00+01:00",
          "2023-12-31 08:00:00+01:00",
          "2023-12-31 09:00:00+01:00",
          "2023-12-31 10:00:00+01:00",
          "2023-12-31 11:00:00+01:00",
          "2023-12-31 12:00:00+01:00",
          "2023-12-31 13:00:00+01:00",
          "2023-12-31 14:00:00+01:00",
          "2023-12-31 15:00:00+01:00",
          "2023-12-31 16:00:00+01:00",
          "2023-12-31 17:00:00+01:00",
          "2023-12-31 18:00:00+01:00",
          "2023-12-31 19:00:00+01:00",
          "2023-12-31 20:00:00+01:00",
          "2023-12-31 21:00:00+01:00",
          "2023-12-31 22:00:00+01:00",
          "2023-12-31 23:00:00+01:00"
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          75,
          82.5,
          75,
          90,
          90,
          72.9,
          60,
          65.1,
          60,
          60,
          60,
          75,
          85.8,
          60,
          60,
          60,
          60,
          70.2,
          60,
          60,
          60,
          60,
          60,
          60,
          80.4,
          60,
          60,
          65.1,
          60,
          75.3,
          60,
          60,
          60,
          60,
          60,
          60,
          82.5,
          90,
          90,
          69.3,
          60,
          60,
          67.5,
          72.9,
          60,
          60,
          70.2,
          60,
          60,
          60,
          60,
          75,
          65.4,
          60,
          60,
          60,
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          70.2,
          60,
          67.5,
          62.7,
          60,
          67.5,
          83.1,
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          67.5,
          67.8,
          67.5,
          72.9,
          60,
          75,
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          67.5,
          60,
          60,
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          67.5,
          82.5,
          75,
          75,
          64.5,
          60,
          60,
          60,
          60,
          60,
          65.1,
          75.3,
          65.1,
          70.2,
          70.2,
          65.1,
          67.5,
          67.8,
          60,
          65.1,
          67.5,
          67.8,
          60,
          60,
          60,
          75,
          75.6,
          60,
          60,
          60,
          60,
          60,
          60,
          67.5,
          67.5,
          67.5,
          83.4,
          67.5,
          67.8,
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          60,
          80.4,
          60,
          60,
          60,
          67.5,
          75,
          63,
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          60,
          60,
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          60,
          60,
          60,
          60,
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          75,
          90,
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          60,
          60,
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          60,
          60,
          65.1,
          60,
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          70.2,
          65.1,
          65.1,
          60,
          60,
          60,
          60,
          60,
          65.1,
          60,
          60,
          65.1,
          75.3,
          60,
          60,
          60,
          60,
          60,
          60,
          65.1,
          60,
          60,
          60,
          60,
          60,
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          60,
          60,
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          60,
          60,
          60,
          67.5,
          75,
          75,
          73.5,
          60,
          60,
          60,
          60,
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          65.1,
          70.2,
          60,
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          60,
          60,
          67.5,
          82.5,
          90,
          61.2,
          60,
          60,
          75,
          75,
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          60,
          60,
          90,
          90,
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          60,
          60,
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          60,
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          67.5,
          60,
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          82.5,
          90,
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          60,
          60,
          60,
          60,
          82.5,
          90,
          90,
          74.4,
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          60,
          75,
          75.6,
          60,
          60,
          65.1,
          60,
          60,
          60,
          60,
          82.5,
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          68.7,
          60,
          60,
          60,
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          60,
          60,
          75,
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          75,
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          60,
          75,
          60,
          60,
          60,
          60,
          60,
          82.5,
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          90,
          90,
          90,
          90,
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          60,
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          67.5,
          80.7,
          60,
          60,
          60,
          60,
          60,
          60,
          67.5,
          82.5,
          67.5,
          75,
          89.1,
          60,
          60,
          60,
          60,
          65.1,
          67.5,
          62.7,
          72.6,
          62.7,
          75,
          60.3,
          60,
          65.1,
          60,
          67.5,
          67.8,
          70.2,
          60,
          60,
          60,
          70.2,
          60,
          67.5,
          78,
          60,
          60,
          60,
          60,
          70.2,
          70.2,
          60,
          75.3,
          60,
          65.1,
          60,
          65.1,
          60,
          65.1,
          70.2,
          70.2,
          60,
          82.5,
          63,
          60,
          60,
          65.1,
          75.3,
          60,
          65.1,
          65.1,
          60,
          60,
          60,
          90,
          75,
          82.5,
          63.9,
          60,
          65.1,
          60,
          70.2,
          60,
          60,
          67.5,
          67.5,
          60,
          75,
          75,
          75,
          75,
          82.5,
          74.7,
          60,
          60,
          75,
          75,
          60,
          90,
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          60,
          60,
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          60,
          60,
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          67.5,
          60,
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          75,
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          60,
          60,
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          90,
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          60,
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          83.1,
          60,
          70.2,
          60,
          65.1,
          60,
          65.1,
          67.5,
          72.9,
          65.1,
          65.1,
          70.2,
          65.1,
          60,
          65.1,
          60,
          60,
          67.5,
          90,
          73.5,
          67.5,
          75,
          63,
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          60,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
          75,
          75,
          67.5,
          67.5,
          82.5,
          79.2,
          60,
          60,
          60,
          60,
          60,
          60,
          70.2,
          60,
          65.1,
          65.1,
          60,
          60,
          60,
          60,
          60,
          67.5,
          67.5,
          82.5,
          60,
          67.5,
          67.5,
          90,
          69.3,
          60,
          60,
          67.5,
          75,
          78.3,
          65.1,
          60,
          80.4,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          82.5,
          67.5,
          60,
          82.5,
          67.5,
          75,
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          60,
          60,
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          75,
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          60,
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          67.5,
          60,
          90,
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          60,
          60,
          60,
          67.5,
          67.8,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          82.5,
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          60,
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          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          75,
          60,
          75,
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          67.5,
          90,
          79.8,
          60,
          60,
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          60,
          60,
          90,
          90,
          63.9,
          60,
          60,
          60,
          60,
          65.1,
          67.5,
          67.5,
          80.7,
          60,
          60,
          65.1,
          70.2,
          60,
          60,
          60,
          60,
          60,
          60,
          90,
          65.7,
          60,
          60,
          60,
          67.5,
          90,
          88.8,
          60,
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          60,
          60,
          60,
          60,
          60,
          90,
          86.1,
          60,
          65.1,
          60,
          60,
          82.5,
          63,
          60,
          60,
          70.2,
          60,
          60,
          60,
          65.1,
          65.1,
          65.1,
          60,
          60,
          60,
          60,
          60,
          82.5,
          82.5,
          60,
          60,
          67.5,
          90,
          82.5,
          67.2,
          60,
          60,
          60,
          67.5,
          67.5,
          80.7,
          67.5,
          62.7,
          65.1,
          60,
          60,
          60,
          80.4,
          60,
          60,
          60,
          60,
          60,
          80.4,
          65.1,
          70.2,
          65.1,
          60,
          60,
          60,
          60,
          67.5,
          67.5,
          60,
          67.5,
          75,
          67.5,
          90,
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          60,
          60,
          70.2,
          60,
          60,
          60,
          82.5,
          75,
          75,
          82.5,
          66.6,
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          65.1,
          65.1,
          60,
          65.1,
          70.2,
          60,
          75,
          75.6,
          60,
          60,
          67.5,
          67.5,
          90,
          76.2,
          60,
          60,
          60,
          60,
          70.2,
          60,
          60,
          60,
          75,
          82.5,
          75,
          82.5,
          66.6,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          65.1,
          60,
          67.5,
          67.5,
          60,
          60,
          82.5,
          78.6,
          60,
          60,
          70.2,
          65.1,
          60,
          65.1,
          60,
          60,
          60,
          70.2,
          60,
          60,
          75,
          65.4,
          70.2,
          60,
          60,
          70.2,
          65.1,
          60,
          60,
          60,
          60,
          60,
          60,
          75,
          75,
          67.5,
          60,
          60,
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          82.5,
          60,
          60,
          67.5,
          60,
          82.5,
          82.5,
          90,
          73.5,
          60,
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          65.1,
          70.2,
          65.1,
          60,
          60,
          60,
          65.1,
          60,
          75,
          60,
          60,
          67.5,
          60,
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          67.5,
          82.5,
          75,
          60,
          60,
          60,
          75,
          75,
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          90,
          60,
          82.5,
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          75,
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          64.8,
          67.5,
          60,
          60,
          82.5,
          70.8,
          60,
          60,
          67.5,
          75,
          78.3,
          60,
          60,
          60,
          60,
          65.1,
          65.1,
          60,
          60,
          67.5,
          62.7,
          60,
          60,
          67.5,
          67.5,
          67.5,
          67.5,
          60,
          75,
          90,
          75,
          60,
          60,
          60,
          60,
          60,
          60,
          75,
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          90,
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          67.5,
          90,
          90,
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          62.7,
          60,
          60,
          60,
          60,
          67.5,
          67.5,
          82.5,
          63.3,
          60,
          67.5,
          62.7,
          65.1,
          67.5,
          67.5,
          72.9,
          62.7,
          67.5,
          78,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          65.1,
          60,
          60,
          65.1,
          60,
          60,
          67.5,
          75,
          78.3,
          60,
          85.5,
          60,
          70.2,
          70.2,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          65.1,
          60,
          65.1,
          60,
          60,
          60,
          65.1,
          75,
          60,
          60,
          60,
          60,
          67.5,
          75,
          82.5,
          60,
          60,
          67.5,
          67.5,
          60,
          67.5,
          82.5,
          60,
          60,
          60,
          60,
          60,
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          75,
          75,
          60,
          75,
          75,
          60,
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          75,
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          75,
          60,
          60,
          60,
          60,
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          75,
          75,
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          90,
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          67.5,
          60,
          60,
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          90,
          67.5,
          90,
          67.5,
          60,
          67.5,
          75,
          75,
          75,
          90,
          82.5,
          82.5,
          75,
          73.8,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          65.1,
          60,
          60,
          60,
          60,
          65.1,
          60,
          60,
          67.5,
          82.5,
          65.7,
          60,
          82.5,
          78.3,
          60,
          60,
          65.1,
          60,
          70.2,
          60,
          60,
          70.2,
          70.2,
          60,
          60,
          60,
          60,
          60,
          80.4,
          65.1,
          60,
          60,
          67.5,
          75,
          83.4,
          60,
          70.2,
          60,
          60,
          65.1,
          60,
          60,
          60,
          60,
          70.2,
          60,
          60,
          65.1,
          67.5,
          75,
          78.3,
          60,
          60,
          60,
          60,
          60,
          60,
          75,
          67.5,
          78.3,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          82.5,
          88.5,
          65.1,
          60,
          70.2,
          60,
          60,
          75.3,
          60,
          60,
          60,
          60,
          60,
          60,
          65.1,
          60,
          65.1,
          60,
          60,
          60,
          67.5,
          88.2,
          60,
          60,
          82.5,
          78.3,
          65.1,
          60,
          65.1,
          60,
          65.1,
          60,
          67.5,
          60,
          60,
          67.5,
          60,
          75,
          67.5,
          60,
          60,
          75,
          67.5,
          75,
          60,
          60,
          60,
          60,
          67.5,
          60,
          60,
          75,
          67.5,
          67.5,
          82.5,
          90,
          90,
          90,
          90,
          90,
          65.1,
          67.5,
          62.7,
          60,
          70.2,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          75,
          75,
          70.8,
          60,
          60,
          60,
          75,
          75.6,
          60,
          60,
          60,
          80.4,
          60,
          60,
          70.2,
          60,
          60,
          90,
          65.7,
          60,
          60,
          75,
          67.5,
          90,
          73.8,
          70.2,
          67.5,
          72.9,
          60,
          60,
          67.5,
          75,
          68.1,
          60,
          60,
          67.5,
          75,
          90,
          60,
          67.5,
          75,
          82.5,
          67.5,
          67.2,
          60,
          60,
          60,
          75,
          60,
          90,
          60.9,
          65.1,
          65.1,
          65.1,
          67.5,
          67.5,
          67.5,
          60,
          60,
          67.5,
          60,
          60,
          60,
          90,
          82.5,
          82.5,
          60,
          60,
          60,
          67.5,
          67.5,
          75,
          67.5,
          60,
          75,
          75,
          82.5,
          82.5,
          60,
          67.5,
          60,
          60,
          75,
          60,
          90,
          90,
          67.5,
          67.5,
          60,
          60,
          60,
          82.5,
          75,
          90,
          82.5,
          82.5,
          90,
          76.8,
          70.2,
          65.1,
          70.2,
          60,
          60,
          60,
          65.1,
          60,
          60,
          70.2,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          67.5,
          90,
          67.5,
          90,
          71.7,
          60,
          65.1,
          60,
          60,
          60,
          60,
          67.5,
          60,
          67.5,
          67.5,
          90,
          73.8,
          67.5,
          75,
          67.5,
          60.6,
          65.1,
          60,
          60,
          60,
          75,
          67.5,
          75,
          78.6,
          60,
          65.1,
          65.1,
          60,
          70.2,
          70.2,
          60,
          60,
          80.4,
          60,
          60,
          60,
          60,
          67.5,
          82.5,
          60,
          67.5,
          60,
          60,
          60,
          60,
          60,
          67.5,
          90,
          90,
          90,
          82.5,
          90,
          66.3,
          65.1,
          60,
          60,
          80.4,
          60,
          60,
          60,
          60,
          60,
          67.5,
          82.5,
          90,
          66.3,
          60,
          60,
          60,
          60,
          75.3,
          60,
          70.2,
          60,
          65.1,
          60,
          60,
          60,
          60,
          82.5,
          63,
          70.2,
          60,
          60,
          75.3,
          60,
          60,
          60,
          60,
          60,
          75.3,
          60,
          60,
          60,
          60,
          65.1,
          60,
          60,
          65.1,
          60,
          75,
          65.4,
          70.2,
          67.5,
          62.7,
          70.2,
          75.3,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          67.5,
          62.7,
          60,
          60,
          60,
          60,
          70.2,
          60,
          60,
          70.2,
          60,
          60,
          60,
          90,
          90,
          66.3,
          65.1,
          60,
          67.5,
          67.5,
          75,
          60,
          75,
          76.2,
          60,
          60,
          60,
          80.4,
          60,
          67.5,
          67.8,
          60,
          60,
          67.5,
          88.2,
          60,
          60,
          60,
          90,
          81,
          65.1,
          65.1,
          60,
          80.4,
          60,
          60,
          60,
          60,
          67.5,
          90,
          63.3,
          60,
          60,
          60,
          82.5,
          63,
          60,
          60,
          75,
          90,
          67.5,
          60,
          82.5,
          75,
          82.2,
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          75,
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          60,
          60,
          60,
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          60,
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          60,
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          60,
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          60,
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          60,
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          60,
          60,
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          60,
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          60,
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          67.8,
          60,
          60,
          60,
          60,
          60,
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          60,
          60,
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          60,
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          60,
          60,
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          60,
          60,
          60,
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          60,
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          70.2,
          67.5,
          75,
          60,
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          67.8,
          60,
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          78,
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          60,
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          60,
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          60,
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          60,
          60,
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          60,
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          67.5,
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          60,
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          60,
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          88.2,
          60,
          60,
          60,
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          60,
          60,
          60,
          80.4,
          60,
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          60,
          60,
          75,
          75,
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          67.8,
          70.2,
          67.5,
          67.8,
          67.5,
          67.5,
          82.5,
          73.5,
          65.1,
          60,
          60,
          80.4,
          60,
          60,
          67.5,
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          65.1,
          60,
          60,
          60,
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          60,
          70.2,
          60,
          67.5,
          67.8,
          60,
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          65.1,
          60,
          60,
          60,
          60,
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          60,
          60,
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          60,
          60,
          90,
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          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
          60,
          60,
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          60,
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          75,
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          60,
          60,
          60,
          60,
          60,
          60,
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          75,
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          90,
          75,
          60,
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          60,
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          60,
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          67.5,
          82.5,
          60,
          60,
          60,
          60,
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          75,
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          60,
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          90,
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          75,
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          60,
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          60,
          65.1,
          60,
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          67.5,
          67.5,
          82.5,
          66.3,
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          60,
          67.5,
          60,
          60,
          60,
          90,
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          60,
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          70.2,
          75,
          67.5,
          68.1,
          60,
          65.1,
          70.2,
          60,
          60,
          60,
          60,
          70.2,
          65.1,
          65.1,
          60,
          60,
          60,
          60,
          60,
          70.2,
          60,
          60,
          80.4,
          60,
          60,
          75,
          60.3,
          65.1,
          60,
          60,
          60,
          60,
          75,
          70.5,
          70.2,
          70.2,
          65.1,
          60,
          60,
          60,
          65.1,
          60,
          67.5,
          62.7,
          60,
          60,
          65.1,
          65.1,
          70.2,
          70.2,
          70.2,
          65.1,
          65.1,
          65.1,
          60,
          67.5,
          82.5,
          67.5,
          78.6,
          60,
          60,
          65.1,
          60,
          60,
          60,
          67.5,
          90,
          63.3,
          60,
          60,
          70.2,
          70.2,
          70.2,
          65.1,
          67.5,
          67.5,
          75,
          67.5,
          67.5,
          67.5,
          67.5,
          75,
          82.5,
          84.9,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          65.1,
          60,
          60,
          65.1,
          60,
          60,
          65.1,
          65.1,
          70.2,
          70.2,
          70.2,
          65.1,
          70.2,
          65.1,
          60,
          65.1,
          65.1,
          60,
          67.5,
          60,
          60,
          60,
          60,
          60,
          90,
          75,
          67.5,
          75,
          82.5,
          75,
          90,
          85.8,
          60,
          60,
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          60,
          60,
          70.2,
          60,
          60,
          60,
          75,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          90,
          90,
          90,
          82.5,
          82.5,
          90,
          67.5,
          76.5,
          60,
          60,
          60,
          60,
          60,
          60,
          67.5,
          78,
          60,
          80.4,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          65.1,
          65.1,
          60,
          60,
          65.1,
          65.1,
          60,
          67.5,
          72.9,
          60,
          60,
          60,
          90,
          70.8,
          65.1,
          65.1,
          60,
          60,
          60,
          75.3,
          65.1,
          60,
          60,
          90,
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          60,
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          75,
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          75,
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          75,
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          60,
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          60,
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          60,
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          60,
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          60,
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          60,
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          60,
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          60,
          60,
          60,
          60,
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          60,
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          60,
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          60,
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          60,
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          60,
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          60,
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          60,
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          60,
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          75,
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          60,
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          60,
          60,
          60,
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          60,
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          60,
          60,
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          60,
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          60,
          60,
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          60,
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          60,
          60,
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          75,
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          75,
          75,
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          67.5,
          67.8,
          60,
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          60,
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          82.5,
          60,
          75,
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          65.1,
          60,
          60,
          60,
          60,
          60,
          70.2,
          60,
          60,
          60,
          65.1,
          60,
          60,
          82.5,
          78.3,
          60,
          60,
          60,
          65.1,
          60,
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          60,
          60,
          60,
          65.1,
          60,
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          60,
          67.5,
          67.5,
          90,
          84,
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          60,
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          75,
          75,
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          60,
          60,
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          83.4,
          60,
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          67.5,
          75.6,
          60,
          60,
          60,
          60,
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          60,
          60,
          65.1,
          60,
          70.2,
          60,
          60,
          60,
          60,
          65.1,
          75,
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          65.1,
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          60,
          60,
          60,
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          75,
          63,
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          72.9,
          60,
          60,
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          60,
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          60,
          82.5,
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          70.2,
          65.1,
          60,
          60,
          60,
          60,
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          60,
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          75,
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          65.1,
          60,
          60,
          60,
          60,
          75,
          60,
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          65.1,
          60,
          67.5,
          72.9,
          60,
          60,
          60,
          75,
          90,
          66,
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          65.1,
          60,
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          65.1,
          60,
          65.1,
          60,
          67.5,
          67.8,
          60,
          60,
          60,
          60,
          65.1,
          60,
          60,
          60,
          60,
          60,
          70.2,
          60,
          60,
          70.2,
          65.1,
          67.5,
          67.5,
          75,
          82.5,
          67.5,
          60,
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          60,
          60,
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          60,
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          60,
          60,
          60,
          60,
          67.5,
          82.5,
          60,
          60,
          82.5,
          90,
          84.6,
          60,
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          70.2,
          65.1,
          60,
          60,
          60,
          70.2,
          65.1,
          67.5,
          67.8,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          80.4,
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          62.7,
          60,
          90,
          65.7,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
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          60,
          60,
          60,
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          60,
          60,
          60,
          67.5,
          90,
          90,
          75,
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          65.1,
          60,
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          67.5,
          67.8,
          60,
          60,
          60,
          60,
          75,
          70.5,
          60,
          67.5,
          72.9,
          60,
          60,
          60,
          60,
          60,
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          60,
          60,
          60,
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          62.7,
          65.1,
          60,
          60,
          65.1,
          60,
          65.1,
          60,
          75,
          60,
          67.5,
          60,
          75,
          60,
          60,
          60,
          60,
          60,
          60,
          90,
          82.5,
          75,
          90,
          90,
          82.5,
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          70.2,
          65.1,
          60,
          75,
          60,
          75,
          81,
          60,
          60,
          60,
          60,
          65.1,
          60,
          60,
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          70.2,
          65.1,
          65.1,
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          60,
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          60,
          82.5,
          73.2,
          70.2,
          60,
          60,
          67.5,
          75,
          75,
          90,
          69,
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          75.3,
          60,
          60,
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          60,
          60,
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          60,
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          75,
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          75,
          75,
          75,
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          60,
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          60,
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          60,
          60,
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          60,
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          60,
          60,
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          60,
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          60,
          60,
          60,
          60,
          67.5,
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          60,
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          60,
          60,
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          60,
          60,
          60,
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          60,
          75,
          75,
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          60,
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          60,
          60,
          60,
          60,
          90,
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          67.5,
          90,
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          60,
          60,
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          60,
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          60,
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          75,
          75,
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          60,
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          75,
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          75,
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          75,
          75,
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          63,
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          67.5,
          60,
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          75,
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          60,
          60,
          60,
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          60,
          60,
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          60,
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          60,
          60,
          60,
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          60,
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          67.5,
          67.8,
          60,
          60,
          75,
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          60,
          60,
          60,
          80.4,
          60,
          60,
          60,
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          60,
          60,
          60,
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          60,
          60,
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          75,
          67.5,
          60,
          82.5,
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          60,
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          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
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          60,
          60,
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          60,
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          60,
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          60,
          60,
          60,
          60,
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          65.1,
          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
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          75,
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          60,
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          60,
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          60,
          60,
          60,
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          60,
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          60,
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          75,
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          75,
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          60,
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          60,
          60,
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          60,
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          67.8,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
          60,
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          78,
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          75,
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          75,
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          60,
          60,
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          60,
          60,
          67.5,
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          60,
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          75,
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          60,
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          67.8,
          60,
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          60,
          60,
          60,
          75,
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          60,
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          75,
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          60,
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          60,
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          60,
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          70.2,
          65.1,
          60,
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          75,
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          65.1,
          60,
          60,
          60,
          60,
          60,
          60,
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          65.1,
          60,
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          70.2,
          60,
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          72.6,
          62.7,
          60,
          60,
          60,
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          60,
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          60,
          60,
          60,
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          60,
          75,
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          75,
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          75,
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          60,
          75,
          75,
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          60,
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          75,
          63,
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          60,
          60,
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          60,
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          60,
          60,
          60,
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          65.1,
          65.1,
          60,
          60,
          60,
          60,
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          60,
          60,
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          60,
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          60,
          60,
          65.1,
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          60,
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          60,
          60,
          60,
          60,
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          60,
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          60,
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          60,
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          60,
          60,
          60,
          60,
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          60,
          60,
          60,
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          60,
          60,
          60,
          60,
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          60,
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          60,
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          60,
          60,
          60,
          60,
          60,
          60,
          60,
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          60,
          60,
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          75,
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          75,
          75,
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          60,
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          60,
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          60,
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          60,
          60,
          60,
          60,
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          60,
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          60,
          60,
          60,
          60,
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          60,
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          60,
          60,
          60,
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          60,
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          60,
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          60,
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          60,
          75,
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          67.5,
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          60,
          75,
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          60,
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          60,
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          78,
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          60,
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          81,
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          60,
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          60,
          60,
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          60,
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          60,
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          60,
          60,
          60,
          60,
          90,
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          90,
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          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          65.1,
          60,
          60,
          60,
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          65.1,
          65.1,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          65.1,
          60,
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          60,
          60,
          60,
          75,
          75.6,
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          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
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          60,
          60,
          67.5,
          90,
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          60,
          60,
          60,
          60,
          65.1,
          60,
          60,
          60,
          75.3,
          60,
          60,
          60,
          70.2,
          60,
          60,
          60,
          65.1,
          65.1,
          65.1,
          60,
          60,
          60,
          67.5,
          67.8,
          75.3,
          60,
          60,
          65.1,
          75.3,
          60,
          60,
          60,
          60,
          82.5,
          90,
          68.7,
          60,
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          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
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          60,
          65.1,
          60,
          60,
          60,
          60,
          80.4,
          60,
          60,
          60,
          60,
          75,
          65.4,
          60,
          60,
          60,
          70.2,
          70.2,
          60,
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          60,
          60,
          90,
          65.7,
          60,
          60,
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          60,
          60,
          60,
          60,
          90,
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          82.5,
          90,
          72,
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          60,
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          60,
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          60,
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          75,
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          60,
          60,
          60,
          60,
          75,
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          90,
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          60,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
          82.5,
          82.5,
          90,
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          60,
          60,
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          60,
          60,
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          60,
          60,
          60,
          60,
          60,
          60,
          60,
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          75,
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          90,
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          60,
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          90,
          75,
          87,
          60,
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          60,
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          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
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          60,
          60,
          75,
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          60,
          60,
          75.3,
          60,
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          67.8,
          60,
          60,
          60,
          60,
          60,
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          60,
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          60,
          60,
          60,
          60,
          60,
          60,
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          60,
          60,
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          60,
          60,
          60,
          60,
          60,
          60,
          75,
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          67.5,
          67.8,
          60,
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          65.1,
          65.1,
          60,
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          67.5,
          70.5,
          60,
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          60,
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          60,
          60,
          60,
          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
          60,
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          63,
          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
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          60,
          60,
          65.1,
          60,
          60,
          65.1,
          60,
          70.2,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          67.5,
          72.9,
          60,
          60,
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          60,
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          67.5,
          60,
          60,
          60,
          60,
          60,
          67.5,
          82.5,
          90,
          82.5,
          90,
          62.4,
          60,
          60,
          60,
          82.5,
          68.1,
          70.2,
          60,
          75.3,
          60,
          60,
          60,
          82.5,
          63,
          60,
          60,
          82.5,
          68.1,
          60,
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          60,
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          67.5,
          67.5,
          60,
          60,
          60,
          60,
          90,
          82.5,
          90,
          79.8,
          60,
          60,
          65.1,
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          75.3,
          67.5,
          67.5,
          60,
          60,
          75,
          82.5,
          60,
          60,
          67.5,
          90,
          67.5,
          67.5,
          75,
          67.5,
          90,
          90,
          68.7,
          62.7,
          60,
          60,
          60,
          70.2,
          65.1,
          65.1,
          60,
          60,
          60,
          60,
          60,
          60,
          70.2,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          67.5,
          75,
          90,
          68.7,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
          60,
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          67.5,
          67.5,
          90,
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          60,
          75,
          90,
          60,
          60,
          60,
          60,
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          75,
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          90,
          82.5,
          90,
          90,
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          60,
          60,
          60,
          82.5,
          63,
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          60,
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          60,
          60,
          60,
          60,
          67.5,
          67.8,
          60,
          60,
          60,
          60,
          70.2,
          60,
          60,
          60,
          67.5,
          83.1,
          60,
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          60,
          60,
          60,
          75,
          90,
          75,
          82.5,
          74.4,
          60,
          75,
          60,
          60,
          60,
          75,
          60,
          60,
          60,
          60,
          75,
          67.5,
          67.5,
          60,
          75,
          90,
          90,
          90,
          82.5,
          90,
          82.2,
          70.2,
          65.1,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          80.4,
          60,
          60,
          67.5,
          72.9,
          70.2,
          67.5,
          72.9,
          65.1,
          60,
          75,
          70.5,
          65.1,
          80.4,
          60,
          67.5,
          75,
          67.5,
          81,
          60,
          60,
          67.5,
          67.5,
          70.5,
          70.2,
          60,
          60,
          65.1,
          60,
          60,
          60,
          75.3,
          60,
          60,
          80.4,
          60,
          60,
          60,
          60,
          65.1,
          60,
          60,
          60,
          60,
          67.5,
          78,
          60,
          60,
          60,
          60,
          60,
          65.1,
          67.5,
          72.9,
          60,
          60,
          60,
          60,
          60,
          82.5,
          83.4,
          60,
          65.1,
          65.1,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          65.1,
          80.4,
          60,
          60,
          60,
          60,
          65.1,
          60,
          67.5,
          60,
          60,
          67.5,
          60,
          60,
          60,
          60,
          75,
          75,
          82.5,
          60,
          67.5,
          60,
          60,
          60,
          60,
          60,
          67.5,
          75,
          60,
          60,
          60,
          75,
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          75,
          75,
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          60,
          60,
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          60,
          60,
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          65.1,
          60,
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          60,
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          60,
          60,
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          63,
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          75,
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          90,
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          75,
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          60,
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          60,
          60,
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          60,
          60,
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          67.8,
          60,
          60,
          60,
          60,
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          60,
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          60,
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          75,
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          60,
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          70.2,
          67.5,
          67.8,
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          67.8,
          67.5,
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          60,
          90,
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          65.1,
          60,
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          65.1,
          60,
          60,
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          60,
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          78,
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          60,
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          67.5,
          67.8,
          60,
          60,
          75,
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          60,
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          60,
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          60,
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          60,
          60,
          60,
          60,
          60,
          60,
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          60,
          75,
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          60,
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          60,
          60,
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          60,
          60,
          60,
          60,
          60,
          60,
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          60,
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          60,
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          60,
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          60,
          60,
          60,
          60,
          60,
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          90,
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          67.5,
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          60,
          60,
          60,
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          75,
          75,
          60,
          60,
          90,
          79.2,
          65.1,
          60,
          60,
          60,
          60,
          60,
          60,
          65.1,
          60,
          65.1,
          75.3,
          60,
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          60,
          70.2,
          60,
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          75.3,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
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          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
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          60,
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          65.1,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          67.5,
          60,
          67.5,
          90,
          90,
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          60,
          60,
          70.2,
          60,
          60,
          65.1,
          65.1,
          65.1,
          70.2,
          60,
          65.1,
          60,
          60,
          60,
          60,
          60,
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          75,
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          60,
          75,
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          90,
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          60,
          60,
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          62.7,
          60,
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          72.9,
          60,
          60,
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          60,
          75,
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          60,
          60,
          67.5,
          62.7,
          60,
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          62.7,
          60,
          60,
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          65.1,
          70.2,
          65.1,
          60,
          60,
          65.1,
          60,
          60,
          60,
          60,
          60,
          60,
          67.5,
          75,
          63,
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          70.2,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
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          70.2,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          60,
          75,
          65.4,
          60,
          60,
          60,
          60,
          60,
          60,
          67.5,
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         "autorange": true,
         "gridcolor": "#E1E5ED",
         "range": [
          -5,
          95
         ],
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": ""
         },
         "type": "linear",
         "zerolinecolor": "#E1E5ED"
        }
       }
      },
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat([hp_output, demand_and_to_storage], axis=1).resample('H').sum().iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Financials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def collect_cashflows(c, s):\n",
    "    s.hpcase.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n",
    "    s.storage_case_PCM.data['nomination_MWh'] = s.hpcase.data['hp_input_MWh']\n",
    "    s.storage_case_PCM.generate_electr_market_results(nom_col='nomination_MWh', real_col='hp_input_MWh')\n",
    "\n",
    "    \n",
    "    for case in [s.baseline]: #, s.optcase1, s.afrr_case]:\n",
    "        case.add_gas_costs(gasvolumes_col='gb_input_MWh')\n",
    "        case.add_co2_costs(volume_cols='gb_input_MWh', fuel='gas')\n",
    "        case.add_eb_ode(commodity='gas', tax_bracket=c.tax_bracket_g)\n",
    "        \n",
    "    for case in [s.hpcase, s.storage_case_PCM]: #, s.hpcase_sde, s.optcase1, s.afrr_case]:\n",
    "        \n",
    "        case.add_eb_ode(commodity='electricity', tax_bracket=c.tax_bracket_e, cons_col='hp_input_MWh')\n",
    "        case.add_grid_costs(\n",
    "            power_MW_col='hp_input_MW',\n",
    "            grid_operator=c.grid_operator,\n",
    "            year=2023, \n",
    "            connection_type=c.connection_type\n",
    "        )\n",
    "        \n",
    "    for case in s.sde_cases:\n",
    "        case = calculate_sde_subsidy(c=c, s=s, case=case)\n",
    "        case.add_cashflow('SDE++ subsidy (€)', case.sde_results['corrected_amount'])\n",
    "    return s\n",
    "\n",
    "s = collect_cashflows(c, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def calculate_financials(c, s):\n",
    "    for case in s.cases:\n",
    "        case.calculate_ebitda(c.project_duration)\n",
    "\n",
    "    for case in [s.hpcase, s.storage_case_PCM]: #, s.hpcase_sde, s.optcase1, s.afrr_case]:\n",
    "        case.calculate_business_case(\n",
    "            project_duration=c.project_duration, \n",
    "            discount_rate=c.discount_rate, \n",
    "            baseline=s.baseline\n",
    "        )\n",
    "        \n",
    "    return s\n",
    "\n",
    "s = calculate_financials(c, s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Visualisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_17424\\1396106630.py:1: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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          "width": 1.3
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         "name": "Total demand",
         "text": "",
         "type": "scatter",
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    "fig_demands_over_time = s.demand['Total demand'].resample('H').mean().iplot(\n",
    "    title='Smurfit Kappa: Heat demand by Hour in MW', \n",
    "    yTitle='MW', \n",
    "    colors=recoygreen,\n",
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      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_17424\\1493830805.py:1: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
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        "title": {
         "font": {
          "color": "#4D5663"
         },
         "text": "Smurfit Kappa: Source and Sink temperatures"
        },
        "width": 800,
        "xaxis": {
         "autorange": true,
         "gridcolor": "#E1E5ED",
         "range": [
          -0.5,
          1.5
         ],
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": ""
         },
         "type": "category",
         "zerolinecolor": "#E1E5ED"
        },
        "yaxis": {
         "autorange": true,
         "gridcolor": "#E1E5ED",
         "range": [
          65.88888888888889,
          148.11111111111111
         ],
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": "Temperature in degrees C"
         },
         "type": "linear",
         "zerolinecolor": "#E1E5ED"
        }
       }
      },
      "image/png": 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",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demands_fig = s.demand[['Tsource (VDG)', 'Tsink (VDG)']].resample('H').mean().iplot(\n",
    "    kind='box',\n",
    "    title='Smurfit Kappa: Source and Sink temperatures',\n",
    "    color=recoygreen,\n",
    "    yTitle='Temperature in degrees C',\n",
    "    legend=False,\n",
    "    asFigure=True,\n",
    "    dimensions=(800, 400)\n",
    ")\n",
    "demands_fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh', 'nomination_MWh', 'Prod. vol.', 'Cons. vol.',\n",
       "       'Imb. vol.', 'Day-Ahead Result', 'POS Result', 'NEG Result',\n",
       "       'Imbalance Result', 'Combined Result'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "# temp_comparison = pd.concat([s.storage_case_PCM.data[['tsource_ndg', 'tsource_vdg']], s.storage_case_PCM.data[['Tsource (NDG)', 'Tsource (VDG)']]], axis=1)\n",
    "# temp_comparison.columns = ['Tsource (NDG) -after', 'Tsource (VDG) - after', 'Tsource (NDG) - before', 'Tsource (VDG) - before']\n",
    "# temp_comparison.resample('15T').mean().iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-01-01 00:00:00+0100', tz='Europe/Amsterdam')"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data['tsource_vdg'].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.storage_case_PCM.data.loc['2019-01-04 07:45:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " 0,\n",
       " 29.635,\n",
       " 0,\n",
       " 0,\n",
       " HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=0.0),\n",
       " Heatpump(name='Heatpump VDG', max_thermal_power=1.5, cop_curve=2.5, min_th_power=0.0))"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case = s.storage_case_PCM\n",
    "hp_vdg = case.assets['Heatpump VDG']\n",
    "ws = case.assets['HotWaterStorage']\n",
    "ws.storage_level = 47.083 + 25\n",
    "pos = 35.650\n",
    "neg = 43.850\n",
    "dam = 68.400\n",
    "demand_vdg = 0\n",
    "tsource_vdg = 29.635\n",
    "tsink_vdg = 134.840\n",
    "\n",
    "hp_storage_opt_enginge(c, ws, hp_vdg, pos, neg, dam, demand_vdg, tsource_vdg, tsink_vdg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "#e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tsource_VDG_before = s.hpcase.data['Tsource (VDG)'].round(2)\n",
    "Tsource_VDG_after = s.storage_case_PCM.data['Tsource (VDG)'].round(2)\n",
    "Tsource_VDG_before.equals(Tsource_VDG_after)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "test_hp = Heatpump(\n",
    "    name='Heatpump for Testing',\n",
    "    max_th_power=1,\n",
    "    min_th_power=0.3,\n",
    "    cop_curve=2.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "import plotly.graph_objs as go\n",
    "def create_load_trace(gas_price, Tsnk, Tsrc, name):\n",
    "    loads = []\n",
    "    eprices = list(range(1000))\n",
    "    for eprice in eprices:\n",
    "        _, load = test_hp.set_opt_load(\n",
    "            electricity_cost=eprice + s.eb_ode_e,\n",
    "            alt_heat_price=gas_price / 0.9,\n",
    "            demand=1, \n",
    "            Tsink=Tsnk,\n",
    "            Tsource=Tsrc\n",
    "        )\n",
    "        loads.append(load)\n",
    "    trace = go.Scatter(x=eprices, y=loads, name=name)\n",
    "    return trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "date = s.baseline.data.index[0].strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_steamboiler = s.baseline.data[['Total demand', 'output_MW_th']].abs().loc[date, :].iplot(\n",
    "    subplots=True, \n",
    "    title=f'Steamboiler only case on {date}',\n",
    "    subplot_titles=['Total demand', 'Steam boiler output (MW)'],\n",
    "    legend=False,\n",
    "    dimensions=(800, 400),\n",
    "    colors=[recoydarkblue, recoygreen], \n",
    "    asFigure=True,\n",
    "    shape = (2, 1)\n",
    ")\n",
    "fig_steamboiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "mode": "lines",
         "name": "Total demand",
         "text": "",
         "type": "scatter",
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   "source": [
    "fig_heatpump = s.hpcase.data[['Total demand', 'hp_output_MW']].abs().loc[date, :].iplot(\n",
    "    subplots=True, \n",
    "    title=f'Heatpump only case on {date}', \n",
    "    subplot_titles=['Total demand', 'Heatpump output (MW)'],\n",
    "    legend=False,\n",
    "    dimensions=(800, 400),\n",
    "    colors=[recoydarkblue, recoygreen], \n",
    "    asFigure=True,\n",
    "    shape = (2, 1)\n",
    ")\n",
    "fig_heatpump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\reports.py:46: FutureWarning:\n",
      "\n",
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Heatpump only</th>\n",
       "      <th>Heatpump + Storage_PCM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total net volume (MWh)</th>\n",
       "      <td>-1,051</td>\n",
       "      <td>-1,179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total exported to grid (MWh)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total consumed from grid (MWh)</th>\n",
       "      <td>1,051</td>\n",
       "      <td>1,179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total nominated volume (MWh)</th>\n",
       "      <td>-1,051</td>\n",
       "      <td>-1,051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Absolute imbalance volume (MWh)</th>\n",
       "      <td>-</td>\n",
       "      <td>807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean selling price (€/MWh)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean buying price (€/MWh)</th>\n",
       "      <td>96</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total day-ahead result (€)</th>\n",
       "      <td>-100,723</td>\n",
       "      <td>-100,723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total POS result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>69,615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total NEG result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-24,666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total imbalance result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>44,949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total combined result (€)</th>\n",
       "      <td>-100,723</td>\n",
       "      <td>-55,774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Heatpump only Heatpump + Storage_PCM\n",
       "Total net volume (MWh)                 -1,051                 -1,179\n",
       "Total exported to grid (MWh)                -                      -\n",
       "Total consumed from grid (MWh)          1,051                  1,179\n",
       "Total nominated volume (MWh)           -1,051                 -1,051\n",
       "Absolute imbalance volume (MWh)             -                    807\n",
       "Mean selling price (€/MWh)                  -                      -\n",
       "Mean buying price (€/MWh)                  96                     47\n",
       "Total day-ahead result (€)           -100,723               -100,723\n",
       "Total POS result (€)                        -                 69,615\n",
       "Total NEG result (€)                        -                -24,666\n",
       "Total imbalance result (€)                  -                 44,949\n",
       "Total combined result (€)            -100,723                -55,774"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = ComparisonReport(\n",
    "    cases=s.optcases, \n",
    "    kind='electr_market_results',\n",
    ")\n",
    "report.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\reports.py:46: FutureWarning:\n",
      "\n",
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Heatpump only</th>\n",
       "      <th>Heatpump + Storage_PCM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gasboiler OPEX (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gas consumption costs (€)</th>\n",
       "      <td>393,966</td>\n",
       "      <td>393,966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2 emission costs (€)</th>\n",
       "      <td>140,949</td>\n",
       "      <td>140,949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gas taxes (€)</th>\n",
       "      <td>33,689</td>\n",
       "      <td>33,689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heatpump VDG OPEX (€)</th>\n",
       "      <td>-3,000</td>\n",
       "      <td>-3,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Result on electricity market (€)</th>\n",
       "      <td>-100,723</td>\n",
       "      <td>-55,774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electricity taxes (€)</th>\n",
       "      <td>-999</td>\n",
       "      <td>-1,120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grid transport costs (€)</th>\n",
       "      <td>-11,391</td>\n",
       "      <td>-28,143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HotWaterStorage OPEX (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-1,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA (€)</th>\n",
       "      <td>452,492</td>\n",
       "      <td>479,367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciation (€)</th>\n",
       "      <td>-12,000</td>\n",
       "      <td>-14,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA + depr (€)</th>\n",
       "      <td>440,492</td>\n",
       "      <td>464,967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Heatpump only Heatpump + Storage_PCM\n",
       "Gasboiler OPEX (€)                           -                      -\n",
       "Gas consumption costs (€)              393,966                393,966\n",
       "CO2 emission costs (€)                 140,949                140,949\n",
       "Gas taxes (€)                           33,689                 33,689\n",
       "Heatpump VDG OPEX (€)                   -3,000                 -3,000\n",
       "Result on electricity market (€)      -100,723                -55,774\n",
       "Electricity taxes (€)                     -999                 -1,120\n",
       "Grid transport costs (€)               -11,391                -28,143\n",
       "HotWaterStorage OPEX (€)                     -                 -1,200\n",
       "EBITDA (€)                             452,492                479,367\n",
       "Depreciation (€)                       -12,000                -14,400\n",
       "EBITDA + depr (€)                      440,492                464,967"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casereport = ComparisonReport(cases = s.cases, kind='ebitda_calc', baseline=s.baseline, comparison='relative')\n",
    "casereport.show(presentation_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\styling.py:42: FutureWarning:\n",
      "\n",
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year 0</th>\n",
       "      <th>Year 1</th>\n",
       "      <th>Year 2</th>\n",
       "      <th>Year 3</th>\n",
       "      <th>Year 4</th>\n",
       "      <th>Year 5</th>\n",
       "      <th>Year 6</th>\n",
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       "      <th>Year 8</th>\n",
       "      <th>Year 9</th>\n",
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       "      <th>Year 12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CAPEX (€)</th>\n",
       "      <td>-301,500</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>Regular Earnings (€)</th>\n",
       "      <td></td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
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       "    <tr>\n",
       "      <th>Irregular Cashflows (€)</th>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>EBITDA (€)</th>\n",
       "      <td></td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
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       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciations (€) -/-</th>\n",
       "      <td></td>\n",
       "      <td>-12,000</td>\n",
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       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBIT (€)</th>\n",
       "      <td></td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income tax (Vpb.) (€)</th>\n",
       "      <td></td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOPLAT (€)</th>\n",
       "      <td></td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciations (€) +/+</th>\n",
       "      <td></td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free Cash Flow (€)</th>\n",
       "      <td>-301,500</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simple Payback Period</th>\n",
       "      <td>0.8478165594669402 years</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IRR (%)</th>\n",
       "      <td>118%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WACC (%)</th>\n",
       "      <td>10%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPV of explicit period (€)</th>\n",
       "      <td>1,928,710</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discounted residual value (€)</th>\n",
       "      <td>50,184</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPV (€)</th>\n",
       "      <td>1,928,710</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Year 0   Year 1   Year 2  \\\n",
       "CAPEX (€)                                      -301,500                     \n",
       "Regular Earnings (€)                                     452,493  452,493   \n",
       "Irregular Cashflows (€)                                        -        -   \n",
       "EBITDA (€)                                               452,493  452,493   \n",
       "Depreciations (€) -/-                                    -12,000  -12,000   \n",
       "EBIT (€)                                                 440,493  440,493   \n",
       "Income tax (Vpb.) (€)                                    -96,873  -96,873   \n",
       "NOPLAT (€)                                               343,619  343,619   \n",
       "Depreciations (€) +/+                                     12,000   12,000   \n",
       "Free Cash Flow (€)                             -301,500  355,619  355,619   \n",
       "Simple Payback Period          0.8478165594669402 years                     \n",
       "IRR (%)                                            118%                     \n",
       "WACC (%)                                            10%                     \n",
       "NPV of explicit period (€)                    1,928,710                     \n",
       "Discounted residual value (€)                    50,184                     \n",
       "NPV (€)                                       1,928,710                     \n",
       "\n",
       "                                Year 3   Year 4   Year 5   Year 6   Year 7  \\\n",
       "CAPEX (€)                                                                    \n",
       "Regular Earnings (€)           452,493  452,493  452,493  452,493  452,493   \n",
       "Irregular Cashflows (€)              -        -        -        -        -   \n",
       "EBITDA (€)                     452,493  452,493  452,493  452,493  452,493   \n",
       "Depreciations (€) -/-          -12,000  -12,000  -12,000  -12,000  -12,000   \n",
       "EBIT (€)                       440,493  440,493  440,493  440,493  440,493   \n",
       "Income tax (Vpb.) (€)          -96,873  -96,873  -96,873  -96,873  -96,873   \n",
       "NOPLAT (€)                     343,619  343,619  343,619  343,619  343,619   \n",
       "Depreciations (€) +/+           12,000   12,000   12,000   12,000   12,000   \n",
       "Free Cash Flow (€)             355,619  355,619  355,619  355,619  355,619   \n",
       "Simple Payback Period                                                        \n",
       "IRR (%)                                                                      \n",
       "WACC (%)                                                                     \n",
       "NPV of explicit period (€)                                                   \n",
       "Discounted residual value (€)                                                \n",
       "NPV (€)                                                                      \n",
       "\n",
       "                                Year 8   Year 9  Year 10  Year 11  Year 12  \n",
       "CAPEX (€)                                                                   \n",
       "Regular Earnings (€)           452,493  452,493  452,493  452,493  452,493  \n",
       "Irregular Cashflows (€)              -        -        -        -        -  \n",
       "EBITDA (€)                     452,493  452,493  452,493  452,493  452,493  \n",
       "Depreciations (€) -/-          -12,000  -12,000  -12,000  -12,000  -12,000  \n",
       "EBIT (€)                       440,493  440,493  440,493  440,493  440,493  \n",
       "Income tax (Vpb.) (€)          -96,873  -96,873  -96,873  -96,873  -96,873  \n",
       "NOPLAT (€)                     343,619  343,619  343,619  343,619  343,619  \n",
       "Depreciations (€) +/+           12,000   12,000   12,000   12,000   12,000  \n",
       "Free Cash Flow (€)             355,619  355,619  355,619  355,619  355,619  \n",
       "Simple Payback Period                                                       \n",
       "IRR (%)                                                                     \n",
       "WACC (%)                                                                    \n",
       "NPV of explicit period (€)                                                  \n",
       "Discounted residual value (€)                                               \n",
       "NPV (€)                                                                     "
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BusinessCaseReport(s.hpcase).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
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          "dash": "solid",
          "shape": "linear",
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         "mode": "lines",
         "name": "Gas prices (€/MWh)",
         "text": "",
         "type": "scatter",
         "x": [
          "2023-01-01 00:00:00+01:00",
          "2023-01-02 00:00:00+01:00",
          "2023-01-03 00:00:00+01:00",
          "2023-01-04 00:00:00+01:00",
          "2023-01-05 00:00:00+01:00",
          "2023-01-06 00:00:00+01:00",
          "2023-01-07 00:00:00+01:00",
          "2023-01-08 00:00:00+01:00",
          "2023-01-09 00:00:00+01:00",
          "2023-01-10 00:00:00+01:00",
          "2023-01-11 00:00:00+01:00",
          "2023-01-12 00:00:00+01:00",
          "2023-01-13 00:00:00+01:00",
          "2023-01-14 00:00:00+01:00",
          "2023-01-15 00:00:00+01:00",
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   "source": [
    "#price chart\n",
    "from copy import deepcopy\n",
    "\n",
    "data = deepcopy(s.baseline.data)\n",
    "data = data[data.columns[:2]]\n",
    "data[data.columns[1]] = MWh_gas_to_tonnes_CO2(data[data.columns[1]])\n",
    "data = data.rename(columns={\"CO2 prices (€/ton)\": \"CO2 prices (€/MWh)\"})\n",
    "\n",
    "data.resample('D').mean().iplot(dimensions=(800, 300), title='Gasprices vs. CO2 prices', colors=recoycolors)"
   ]
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   "cell_type": "code",
   "execution_count": 228,
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   "source": [
    "# #price chart\n",
    "# from copy import deepcopy\n",
    "\n",
    "# s.optcase1.data['DAM'].resample('D').mean().iplot(dimensions=(800, 300), title='Electricity Prices', colors=recoycolors)\n",
    "\n",
    "# _source_output = s.optcase1.data[['hp_output_MWh', 'gb_output_MWh']].resample('M').sum()\n",
    "# _total_output = _source_output.sum(axis=1)\n",
    "# _data = _source_output.divide(_total_output, axis=0).rename(\n",
    "#     columns={'hp_output_MWh':'Heat pump', 'gb_output_MWh':'Gasboiler'}\n",
    "# ) * 100\n",
    "\n",
    "# production_fig = _data.iplot(\n",
    "#     kind='bar',\n",
    "#     barmode='stack',\n",
    "#     colors=[recoydarkblue, recoygreen],\n",
    "#     title='Hybrid case: Heat production per Month by Source in % share',\n",
    "#     yTitle='Share of production in %',\n",
    "#     dimensions=(600, 400),\n",
    "#     asFigure=True\n",
    "# )\n",
    "\n",
    "# production_fig = production_fig.update_layout(legend_traceorder=\"reversed\")\n",
    "# production_fig"
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   "execution_count": 229,
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   ],
   "source": [
    "report = ComparisonReport(s.cases, kind='capex').report\n",
    "report = report[report.index.str.contains('CAPEX')].T\n",
    "capex_fig = report.iplot(\n",
    "    kind='bar', \n",
    "    barmode='relative',\n",
    "    colors=recoycolors,\n",
    "    title='CAPEX by Casestudy',\n",
    "    yTitle='CAPEX in €',\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True,\n",
    ")\n",
    "\n",
    "capex_fig = capex_fig.update_layout(legend_traceorder=\"reversed\")\n",
    "capex_fig"
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  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
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   },
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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cashflow_report = ComparisonReport(cases = s.cases, kind='cashflows')\n",
    "\n",
    "fig = cashflow_report.report.T.iplot(\n",
    "    kind='bar',\n",
    "    barmode='relative',\n",
    "    colors=recoycolors,\n",
    "    title='OPEX breakdown',\n",
    "    asFigure=True,\n",
    "    yTitle='€',\n",
    "    dimensions=(800, 600)\n",
    ")\n",
    "\n",
    "ebitda_report = ComparisonReport(cases=s.cases, kind='ebitda_calc')\n",
    "scat = go.Scatter(\n",
    "    mode='markers',\n",
    "    y=ebitda_report.report.loc['EBITDA (€)', :].values, \n",
    "    x=ebitda_report.report.columns, \n",
    "    line=go.scatter.Line(color=recoydarkgrey),\n",
    "    marker=dict(\n",
    "        color=recoydarkgrey,\n",
    "        size=20,\n",
    "        line=dict(\n",
    "            color=recoydarkgrey,\n",
    "            width=3\n",
    "        ),\n",
    "        symbol='line-ew'\n",
    "    ),\n",
    "    name='EBITDA (€)'\n",
    ")\n",
    "fig.add_trace(scat)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ebitda_report.show(comparison='relative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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   "source": [
    "_series = SingleFigureComparison(s.cases, 'ebitda', label='EBITDA').report\n",
    "ebitda_graph = _series.iplot(\n",
    "    kind='bar',\n",
    "    title='Yearly EBITDA by Casestudy in €',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400), \n",
    "    yTitle='EBITDA in €',\n",
    "    asFigure=True,\n",
    "    yrange=[_series.min() * 1.2, max(_series.max() * 2, 0)]\n",
    ")\n",
    "\n",
    "ebitda_graph.update_traces(\n",
    "    text=_series.values/1000_000, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f}M €\", \n",
    ")\n",
    "\n",
    "ebitda_graph"
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  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "tags": [
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   },
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   "source": [
    "_series = SingleFigureComparison(s.optcases, 'npv', label='NPV').report\n",
    "\n",
    "npv_graph = _series.iplot(\n",
    "    kind='bar',\n",
    "    title='NPV by Casestudy in €',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400), \n",
    "    yTitle='NPV in €',\n",
    "    asFigure=True,\n",
    "    yrange=[0, _series.max() * 1.1]\n",
    ")\n",
    "\n",
    "npv_graph.update_traces(\n",
    "    text=_series.values/1000_000, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f}M €\", \n",
    ")\n",
    "\n",
    "npv_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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",
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   "source": [
    "irr_report = (SingleFigureComparison(s.optcases, 'irr', label='IRR').report * 100)\n",
    "irr_fig = irr_report.iplot(\n",
    "    kind='bar',\n",
    "    title='IRR by Casestudy in %',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400),\n",
    "    yTitle='IRR in %',\n",
    "    asFigure=True,\n",
    "    yrange=[0, irr_report.max() * 1.2]\n",
    ")\n",
    "\n",
    "irr_fig.update_traces(\n",
    "    text=irr_report.values, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.0f} %\", \n",
    ")\n",
    "\n",
    "irr_fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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    "_series = (SingleFigureComparison(s.optcases, 'spp', label='Simple Payback Time').report)\n",
    "spt_fig = _series.iplot(\n",
    "    kind='bar',\n",
    "    title='Simple Payback Time by Casestudy in Years',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400),\n",
    "    yTitle='Years',\n",
    "    asFigure=True,\n",
    "    yrange=[0, _series.max() * 1.2]\n",
    ")\n",
    "\n",
    "spt_fig.update_traces(\n",
    "    text=_series.values, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f} years\", \n",
    ")\n",
    "\n",
    "spt_fig "
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  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
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",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_cops = (SingleFigureComparison(s.optcases, 'mean_cop', label='COP').report)\n",
    "cop_fig = mean_cops.iplot(\n",
    "    kind='bar',\n",
    "    title='Mean COP by Casestudy',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400),\n",
    "    yTitle='COP',\n",
    "    asFigure=True,\n",
    "    yrange=[0, mean_cops.max() * 1.2]\n",
    ")\n",
    "\n",
    "cop_fig.update_traces(\n",
    "    text=mean_cops.values, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f}\", \n",
    ")\n",
    "\n",
    "cop_fig "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Sensitivity analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "We have agreed that for the sensitivity analysis we will vary the following key assumptions in the flex-model:\n",
    "1. Market prices of gas and electricity. We will use the actual prices for 2019, the actual prices for 2020.\n",
    "2. aFRR prices +/- 30%\n",
    "3. CAPEX\n",
    "4. CO2 price\n",
    "5. Tsource +/- 10 degrees Celsius\n",
    "6. Tsink\n",
    "    * Roam off the peak / lower pressure\n",
    "    * Stabalize / running average per hour/ 2 hours\n",
    "7. __A scenario with a constraint on grid capacity and a scenario without grid capacity as a constraint__\n",
    "8. Energy Tax and ODE +/- 30%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def setup():\n",
    "    _c = Config()\n",
    "    s = setup_model(c=_c)\n",
    "    return s\n",
    "\n",
    "def routine(c, s):\n",
    "    s = load_data(c=c, s=s)\n",
    "    s = create_and_assign_assets(c=c, s=s)\n",
    "    #s = preprocessing(c=c, s=s)\n",
    "    s = run_optimisation(c=c, s=s)\n",
    "    #s = postprocessing(c=c, s=s)\n",
    "    s = collect_cashflows(c=c, s=s)\n",
    "    s = calculate_financials(c=c, s=s)\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "tags": [
     "exclude"
    ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testetss\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: UserWarning:\n",
      "\n",
      "The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 10.2 s\n",
      "Wall time: 25.3 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_17424\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%time _s = setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "tags": [
     "exclude"
    ]
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   "outputs": [
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     "name": "stderr",
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     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\assets.py:263: UserWarning:\n",
      "\n",
      "Chosen heat output is out of range [0.0 - 1.5]. Heat output is being limited to the closest boundary.\n",
      "\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 3.33 s\n",
      "Wall time: 7.53 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1928709.8797698324"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time result = routine(c, _s)\n",
    "npv = result.hpcase.npv\n",
    "npv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c6055b5454c452c927c19a0565b5482",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'to_list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_17424\\4147334808.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m80\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m120\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'storage_temperature'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mkpis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'npv'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'irr'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'spp'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mean_cop'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0msens\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSensitivityAnalysis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkpis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\sensitivity.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, c, s, routine, param, values, output_kpis)\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfigs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_generate_configs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[0moutput_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_output_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcases\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkpis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_sensitivities\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\sensitivity.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, s, routine, output_kpis, output_dict)\u001b[0m\n\u001b[0;32m     44\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_sensitivities\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfigs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0m_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m             \u001b[0m_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     48\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mkpi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mcase\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcases\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m                     \u001b[0moutput_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkpi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_17424\\3648020848.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(c, s)\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_and_assign_assets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[1;31m#s = preprocessing(c=c, s=s)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrun_optimisation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m     \u001b[1;31m#s = postprocessing(c=c, s=s)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcollect_cashflows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalculate_financials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_17424\\3066241253.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(c, s)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mrun_optimisation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbaseline_sim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhpcase\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhponly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhpcase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstorage_case_PCM\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhponly_with_storage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstorage_case_PCM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m#     s.hpcase_sde.assign_algorithm(hponly)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_17424\\3686723783.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(case)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbaseline_sim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mgasboiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mdemand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'MW (VDG)'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mminutes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mth_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   6295\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6296\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6297\u001b[0m         \u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6298\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6299\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'to_list'"
     ]
    }
   ],
   "source": [
    "# sensitivity:Storage temperature\n",
    "values = range(80, 120, 10)\n",
    "param = 'storage_temperature'\n",
    "kpis = ['npv','irr','spp', 'mean_cop']\n",
    "\n",
    "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='volume(m3)',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[output.min().min()*1.1, 0],\n",
    "    title='Sensitivity: Storage volume',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity: Water storage volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(_s.demand['MW (VDG)'] + _s.demand['MW (NDG)']).to_list()[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%time _s = setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = range(100, 500, 100)\n",
    "param = 'storage_volume'\n",
    "kpis = ['npv','irr','spp']\n",
    "\n",
    "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[case.name for case in s.cases][1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='volume(m3)',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[output.min().min()*1.1, 0],\n",
    "    title='Sensitivity: Storage volume',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('irr', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='volume(m3)',\n",
    "    yTitle='irr in %',\n",
    "    yrange=[output.min().min()*1.1, 0],\n",
    "    title='Sensitivity: IRR',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='volume(m3)',\n",
    "    yTitle='years',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: payback time',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='volume(m3)',\n",
    "    yTitle='COP',\n",
    "    yrange=[output.min().min()*1.1, 0],\n",
    "    title='Sensitivity: COP',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity: CAPEX per MW storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = range(4_000, 10_000, 2_000)\n",
    "param = 'storage_capex_per_MW'\n",
    "kpis = ['npv', 'irr', 'spp', 'mean_cop']\n",
    "\n",
    "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='CAPEX',\n",
    "    yTitle='NPV in Euro',\n",
    "    yrange=[output.min().min()*1.1, 0],\n",
    "    title='Sensitivity: NPV in euro',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='CAPEX/MW',\n",
    "    yTitle='years',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: payback time',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity: CAPEX per MWh storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = range(1_000, 5_000, 1000)\n",
    "param = 'storage_capex_per_MWh'\n",
    "kpis = ['npv', 'spp', 'mean_cop']\n",
    "\n",
    "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='CAPEX/MWh',\n",
    "    yTitle='NPV',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: NPV in Euro',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='CAPEX/MWh',\n",
    "    yTitle='years',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: Payback time',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity: threshold price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = range(10, 60, 10)\n",
    "param = 'threshold'\n",
    "kpis = ['npv', 'spp', 'mean_cop']\n",
    "\n",
    "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='threshold',\n",
    "    yTitle='NPV',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: NPV in Euro',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='threshold',\n",
    "    yTitle='years',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: SPP in years',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='threshold',\n",
    "    yTitle='COP',\n",
    "    yrange=[output.min().min()*1.2, 0],\n",
    "    title='Sensitivity: SOP',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Heat Pump CAPEX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0.7, 1, 1.3, 2]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.hp_capex *= value\n",
    "    configs[value*100] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "\n",
    "sens_capex = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='CAPEX factor (%)',\n",
    "    yTitle='Payback Period in years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Heat Pump CAPEX (€)',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_capex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: CO2 prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "mean = 24.86\n",
    "co2_prices = [10, 25, 50, 100]\n",
    "\n",
    "for price in co2_prices:\n",
    "    _c = Config()\n",
    "    multiplier = price / mean\n",
    "    _c.co2_price_multiplier = multiplier\n",
    "    configs[price] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('ebitda', case_names=['Baseline', 'Heatpump + SDE', 'Heatpump only', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Mean CO2 price in €',\n",
    "    yTitle='EBITDA in €',\n",
    "    yrange=[output.min().min() * 1.1, 0],\n",
    "    title='Sensitivity: CO2 prices > EBITDA',\n",
    "    colors=[recoypurple, recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_co2_spp = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Mean CO2 price in €',\n",
    "    yTitle='Payback Period in Years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: CO2 prices',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_co2_spp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Mean CO2 price in €',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[output.min().min() * 10, output.max().max()*1.1],\n",
    "    title='Sensitivity: CO2 prices > NPV',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Gas prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0.7, 1, 1.3]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.gas_price_multiplier = value\n",
    "    configs[value * 100] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Gas price factor (%)',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[output.min().min()*1.5, output.max().max()*1.1],\n",
    "    title='Sensitivity: Gas prices',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Day Ahead buying amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0, 0.5, 1]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.day_ahead_buying_perc = value\n",
    "    configs[value * 100] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Volume in %',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Volume bought on Day-Ahead market in %',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Electricity prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0.7, 1, 1.3]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.e_price_multiplier = value\n",
    "    configs[value * 100] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_eprices = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Electricity price factor in %',\n",
    "    yTitle='Payback Period in years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Electricity prices',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_eprices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Electricity price volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0.7, 1, 1.3]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.e_price_volatility_multiplier = value\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_evol = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Volatility factor',\n",
    "    yTitle='Payback period in years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: E-price volatility',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_evol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: aFRR capacity fee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [10_000, 25_000, 50_000]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.afrr_capacity_fee = value\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_affr_fee = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='aFRR capacity fee in €/MW',\n",
    "    yTitle='Payback Period in years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: aFRR capacity fee',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_affr_fee"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Energy tax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [0.7, 1, 1.3]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.energy_tax_multiplier = value\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Factor',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Energy taxes',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Tsource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [-25, -10, 0, 10, 25]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.tsource_delta = value\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.loc[-25, 'Heatpump only'] = np.nan\n",
    "sens_tsource = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Tsource delta',\n",
    "    yTitle='Payback Period in years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Tsource',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_tsource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Tsource delta',\n",
    "    yTitle='COP',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Tsource > Mean COP',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Tsink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "_s = setup()\n",
    "configs = {}\n",
    "values = [-25, -10, 0, 10, 25]\n",
    "for value in values:\n",
    "    _c = Config()\n",
    "    _c.tsink_delta = value\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sens = SensitivityAnalysis(_s, routine, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_tsink = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Tsink delta',\n",
    "    yTitle='Payback Period in Years',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Tsink',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_tsink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "sens_tsink_cop = output.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Tsink delta',\n",
    "    yTitle='COP',\n",
    "    yrange=[0, output.max().max()*1.1],\n",
    "    title='Sensitivity: Tsink > Mean COP',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "sens_tsink_cop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Sensitivity: Time period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def routine2(c, s):\n",
    "    s = setup_model(c=c)\n",
    "    s = load_data(c=c, s=s)\n",
    "    s = create_and_assign_assets(c=c, s=s)\n",
    "    s = run_optimisation(c=c, s=s)\n",
    "    s = collect_cashflows(c=c, s=s)\n",
    "    s = calculate_financials(c=c, s=s)\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "configs = {}\n",
    "start_values = ['2018-01-01', '2019-01-01', '2019-11-01']\n",
    "\n",
    "for value in start_values:\n",
    "    _c = Config()\n",
    "    _c.start = value\n",
    "    _c.end = (pd.to_datetime(value) + timedelta(days=364)).strftime('%Y-%m-%d')\n",
    "    configs[value] = _c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "sens = SensitivityAnalysis(_s, routine2, configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "result = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
    "result.iplot(\n",
    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
    "    size=10,\n",
    "    xTitle='Start date',\n",
    "    yTitle='NPV in €',\n",
    "    yrange=[0, result.max().max()*1.1],\n",
    "    title='Sensitivity: Modelled time period',\n",
    "    colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
    "    dimensions=(600, 400)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "renderer = 'svg'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Presentation Flexible Heatpumps__\n",
    "\n",
    "ENCORE meeting: 17-12-2020\n",
    "Mark Kremer\n",
    "\n",
    " \n",
    "  \n",
    "   \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Central question:__\n",
    "#### *Can Smurfit Kappi shorten the Payback Period of an investment in a Heatpump by operating it in a flexible manner?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Flexible operations on imbalance market__\n",
    "\n",
    "Benefiting from fluctuations in electricity market prices by ramping the asset up- and down (increasing and decreasing the electricity consumption)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_example_1 = s.hpcase.data[['DAM']].iloc[:60*24].iplot(\n",
    "    title='Day-Ahead prices on arbitrary day in €/MWh',\n",
    "    yrange=[-100, 200],\n",
    "    colors=recoygreen, \n",
    "    yTitle='Price in €/MWh',\n",
    "    xTitle='Time of Day',\n",
    "    dimensions=(800, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "\n",
    "fig_example_1.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imbalance prices are very volatile, with prices below -100 and above 200 €/MWh on a daily basis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_example_2 = s.hpcase.data[['DAM', 'POS']].rename(columns={'POS':'IMB'}).iloc[:60*24].iplot(\n",
    "    title='Imbalance Prices on arbitrary day in €/MWh',\n",
    "    colors=[recoygreen, recoydarkblue], \n",
    "    yTitle='Price in €/MWh',\n",
    "    xTitle='Time of Day',\n",
    "    dimensions=(800, 400),\n",
    "    asFigure=True\n",
    ")\n",
    "\n",
    "fig_example_2.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to benefit from these fluctiations, if you have __flexibility__\n",
    "* Storage options\n",
    "* Hybrid installations (e.g. with gas-powered assets)\n",
    "\n",
    "In this case we are looking at a __hybrid set-up of a Steamboiler and a Heatpump__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Simulations & mathematical modelling__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To answer the central question, we have build a simulation model\n",
    "* The model simulates the operations of a hybrid set-up of a Heatpump and a Steamboiler over the timespan of 1 years (on a 1 minute basis)\n",
    "* The goal of the model is to minimize the operating costs, in order to reach the shortest Payback Period\n",
    "* We are taking into account all major investment and operating costs, including:\n",
    "    * Asset CAPEX\n",
    "    * Commodity costs for gas and electricity\n",
    "    * Energy taxes\n",
    "    * Grid transport costs\n",
    "    * SDE++ subsidies\n",
    "    * Maintenance costs\n",
    "    * CO2 allowances\n",
    "* The output of the model is a Payback Period for an investment in an heatpump, in different scenario's"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Casestudies__\n",
    "5 main scenario's"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "1. Steamboiler only (baseline, baseload)\n",
    "2. Heatpump only (stand-alone, baseload, without SDE++)\n",
    "3. Heatpump + SDE\n",
    "4. Heatpump + SDE + Steam boiler (hybrid set-up) on Imbalance market\n",
    "5. Heatpump + SDE + Steam boiler (hybrid set-up) on aFRR (secondary reserve market)\n",
    "\n",
    "Besides that, we modelled 11 sensitivities:\n",
    "* Heatpump CAPEX\n",
    "* Gas & CO2 prices\n",
    "* Electricity prices & volatility\n",
    "* Energy taxes\n",
    "* Bidding strategies\n",
    "* Source temperatures (affecting COP)\n",
    "* Sink temperatures (affecting COP)\n",
    "* Time period (2018, 2019, 2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Smurfit Kappa case__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is based on the context of Smurfit Kappa (paper factory)\n",
    "* Currently a Steamboiler is providing the 20-30 MW of average heat demand for drying processes\n",
    "* We add a 31 MW heatpump (to make sure it can cover entire demand)\n",
    "* The steam demand must be fulfilled at all times, by either the heatpump or the gasboiler\n",
    "* The heatpump and steam boiler can both respond very quickly (within minutes) within a flexible range (30%-100% for heatpump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_demands_over_time.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Source temperatures of around 65 degrees C\n",
    "* Sink temperatures of 125-170 degrees C \n",
    "* Average Temperature lift of about 85 degrees C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "demands_fig.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Heat pump__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COP roughly between 4 and 1.5, depending on load, Tsource and Tsink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cop_curve(Tsink, Tsource):\n",
    "    Tsink += 273\n",
    "    Tsource += 273\n",
    "\n",
    "    c1 = 0.267 * Tsink / (Tsink - Tsource)\n",
    "    c2 = 0.333 * Tsink / (Tsink - Tsource)\n",
    "    \n",
    "    return Polynomial([c2, c1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sourceT = 63\n",
    "sinkT = 140\n",
    "cop_curve(sourceT, sinkT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_cop_curve.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Optimisation__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* At each moment in time, we calculate the cheapest option to produce the required heat. \n",
    "* Taking into account the COP fluctuations, due to changing Tsource and Tsink\n",
    "* Taking into account fluctuating market prices (electricity, gas, CO2)\n",
    "* We are predicting real-time electricity prices using our forecasting models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Some example days:__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steamboiler only is following demand pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_steamboiler.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar pattern for heatpump only case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_heatpump.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hybrid set-up is responding to price fluctuactions, steam boiler taking over at high prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_optcase.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_optcase2.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Business case__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CAPEX of around 6 M€ (200.000 €/MW), which need to be earned back by savings in operating costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "capex_fig.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Savings in EBITDA compared to the baseline are about 1.5 mln € without subsidy, and up to 4.5 mln € including subsidy\n",
    "* The optimisation on aFRR allows for a 30-40% improvement in EBITDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ebitda_graph.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resulting in a Payback Period of 5.4 years without subsidy, and 1.8 years with subsidy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spt_fig.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The added value of the optimisation is limited (in absolute terms), which is explained by the high COP of the heatpump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "production_fig.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The heatpump is filling in 95-98 % of the demand. \n",
    "* Because of its high COP, it is almost always cheaper to run than the steam boiler\n",
    "* Switch price is on average around 90€/MWh (excluding subsidies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Sensitivities__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If CAPEX is 200%, subsidy is needed to keep a good Payback Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_capex.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subsidies are protecting the business case againsts low CO2 prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_co2_spp.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The businesscase is quite sensitive to Tsource and Tsink differences, because they directly impact the COP\n",
    "* The Smurtfit Kappa case, with a temperature lifte of about 85 degrees C on average, looks favorable. \n",
    "* When the temperature lift is higher, the COP will decrease and the business case will degrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_tsource.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_tsink.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_tsink_cop.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sens_eprices.show(renderer=renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Conclusions__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The business case for a Heat Pump seems favourable\n",
    "* Flexible operation, using aFRR, can improve the operational results by 30-40%\n",
    "* However, this only results in a marginal improvement of the business case\n",
    "* SDE++ has a very favourable effect on the business case, but is not critical\n",
    "* The business case is notably sensitive to the temperature lift required, and is therefore strongly dependent on the specific use case."
   ]
  }
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