{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 11,
   "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\n",
    "    storage_capex_per_MW = 7_000\n",
    "    storage_capex_per_MWh = 5_000\n",
    "    storage_opex_perc_of_capex = 0.02\n",
    "    # storage_initial_level = 5\n",
    "    threshold = 20\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": 12,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "class Store():\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Model set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = Store()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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_17908\\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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.demand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Load in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
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      "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data"
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  {
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   "execution_count": 25,
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       "    <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": 25,
     "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": 38,
   "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": 39,
   "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": 39,
     "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": 47,
   "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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=3.0)"
      ]
     },
     "execution_count": 48,
     "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": 51,
   "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": 52,
   "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,\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": 54,
   "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",
    "            desired_hp_load_vdg = min(demand_vdg + ws.charging_power_limit, 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 = th_load_vdg - demand_vdg\n",
    "            to_storage_vdg_MW = -ws.charge(round(to_storage_vdg_MW, 3))\n",
    "\n",
    "            extra_charging_power_constraint = ws.max_power - to_storage_vdg_MW\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_process_massflow_vdg = power_to_mass_flow(demand_vdg, tsink_vdg, Tref, Cp)\n",
    "            try:\n",
    "                from_storage_vdg_MW = ws.discharging_power_limit\n",
    "                # tsource_vdg = Tsource_calculation(ws.temperature, from_storage_vdg_MW, tsource_vdg, from_process_massflow_vdg)\n",
    "                e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\n",
    "            except: \n",
    "                from_storage_vdg_MW = 0\n",
    "                # tsource_vdg = Tsource_calculation(ws.temperature, from_storage_vdg_MW, tsource_vdg, from_process_massflow_vdg)\n",
    "                e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\n",
    "\n",
    "            from_storage_vdg_MW = ws.discharge(round(from_storage_vdg_MW, 3))\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,\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": 55,
   "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": 56,
   "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": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>2023-12-09 17:36:00+01:00</th>\n",
       "      <td>83.44</td>\n",
       "      <td>50.34</td>\n",
       "      <td>50.34</td>\n",
       "      <td>55.98</td>\n",
       "      <td>51.96</td>\n",
       "      <td>38.123</td>\n",
       "      <td>68.56</td>\n",
       "      <td>13.22</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>0.025</td>\n",
       "      <td>-0.01</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>2023-05-31 22:13:00+02:00</th>\n",
       "      <td>94.29</td>\n",
       "      <td>56.69</td>\n",
       "      <td>56.69</td>\n",
       "      <td>54.35</td>\n",
       "      <td>53.95</td>\n",
       "      <td>25.220</td>\n",
       "      <td>78.61</td>\n",
       "      <td>15.16</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>0.025</td>\n",
       "      <td>-0.01</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",
       "2023-12-09 17:36:00+01:00  83.44  50.34  50.34    55.98    51.96   \n",
       "2023-05-31 22:13:00+02:00  94.29  56.69  56.69    54.35    53.95   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-12-09 17:36:00+01:00              38.123               68.56   \n",
       "2023-05-31 22:13:00+02:00              25.220               78.61   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-12-09 17:36:00+01:00               13.22             70          144   \n",
       "2023-05-31 22:13:00+02:00               15.16             70          144   \n",
       "\n",
       "                           ...  from_storage_vdg_MW  to_storage_MW  \\\n",
       "2023-12-09 17:36:00+01:00  ...                  0.0           -0.0   \n",
       "2023-05-31 22:13:00+02:00  ...                  0.0           -0.0   \n",
       "\n",
       "                           from_storage_MW  storage_level_MWh  hp_output_MWh  \\\n",
       "2023-12-09 17:36:00+01:00              0.0                3.0          0.025   \n",
       "2023-05-31 22:13:00+02:00              0.0                3.0          0.025   \n",
       "\n",
       "                           hp_input_MWh  to_storage_vdg_MWh  \\\n",
       "2023-12-09 17:36:00+01:00         -0.01                -0.0   \n",
       "2023-05-31 22:13:00+02:00         -0.01                -0.0   \n",
       "\n",
       "                           from_storage_vdg_MWh  to_storage_MWh  \\\n",
       "2023-12-09 17:36:00+01:00                   0.0            -0.0   \n",
       "2023-05-31 22:13:00+02:00                   0.0            -0.0   \n",
       "\n",
       "                           from_storage_MWh  \n",
       "2023-12-09 17:36:00+01:00               0.0  \n",
       "2023-05-31 22:13:00+02:00               0.0  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>525600.0</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>1.214783</td>\n",
       "      <td>0.0</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>0.247507</td>\n",
       "      <td>0.0</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>1.000000</td>\n",
       "      <td>0.0</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>1.000000</td>\n",
       "      <td>0.0</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>1.000000</td>\n",
       "      <td>0.0</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>1.500000</td>\n",
       "      <td>0.0</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>1.500000</td>\n",
       "      <td>0.0</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       1.214783   \n",
       "std        49.046697     194.977533     195.696551       0.247507   \n",
       "min      -500.000000   -1549.470000   -1549.470000       1.000000   \n",
       "25%        76.935000      47.000000      57.967500       1.000000   \n",
       "50%        99.215000      84.130000      88.570000       1.000000   \n",
       "75%       122.400000     114.020000     119.592500       1.500000   \n",
       "max       463.770000    2037.740000    2037.740000       1.500000   \n",
       "\n",
       "       to_storage_MW  Total demand  \n",
       "count       525600.0      525600.0  \n",
       "mean             0.0           1.0  \n",
       "std              0.0           0.0  \n",
       "min              0.0           1.0  \n",
       "25%              0.0           1.0  \n",
       "50%              0.0           1.0  \n",
       "75%              0.0           1.0  \n",
       "max              0.0           1.0  "
      ]
     },
     "execution_count": 59,
     "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": 60,
   "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>2023-03-17 18:23:00+01:00</th>\n",
       "      <td>139.90</td>\n",
       "      <td>140.22</td>\n",
       "      <td>140.22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-10 06:22:00+02:00</th>\n",
       "      <td>120.74</td>\n",
       "      <td>95.95</td>\n",
       "      <td>95.95</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-13 20:14:00+02:00</th>\n",
       "      <td>118.60</td>\n",
       "      <td>137.23</td>\n",
       "      <td>137.23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-20 06:16:00+01:00</th>\n",
       "      <td>48.13</td>\n",
       "      <td>91.10</td>\n",
       "      <td>91.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-28 20:38:00+02:00</th>\n",
       "      <td>125.43</td>\n",
       "      <td>91.41</td>\n",
       "      <td>91.41</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-04 03:50:00+02:00</th>\n",
       "      <td>115.61</td>\n",
       "      <td>121.50</td>\n",
       "      <td>121.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28 08:24:00+01:00</th>\n",
       "      <td>38.20</td>\n",
       "      <td>-91.37</td>\n",
       "      <td>-91.37</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-20 06:02:00+02:00</th>\n",
       "      <td>73.02</td>\n",
       "      <td>-35.86</td>\n",
       "      <td>-35.86</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-03 12:42:00+01:00</th>\n",
       "      <td>61.81</td>\n",
       "      <td>-78.75</td>\n",
       "      <td>103.12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-20 13:07:00+01:00</th>\n",
       "      <td>181.62</td>\n",
       "      <td>1143.64</td>\n",
       "      <td>1143.64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-23 16:46:00+02:00</th>\n",
       "      <td>-4.19</td>\n",
       "      <td>-51.70</td>\n",
       "      <td>160.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-13 11:48:00+02:00</th>\n",
       "      <td>34.90</td>\n",
       "      <td>150.66</td>\n",
       "      <td>150.66</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01 17:46:00+02:00</th>\n",
       "      <td>98.03</td>\n",
       "      <td>123.81</td>\n",
       "      <td>123.81</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-18 20:29:00+01:00</th>\n",
       "      <td>117.09</td>\n",
       "      <td>160.74</td>\n",
       "      <td>160.74</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-13 03:01:00+01:00</th>\n",
       "      <td>2.65</td>\n",
       "      <td>-11.68</td>\n",
       "      <td>-11.68</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-12 23:38:00+02:00</th>\n",
       "      <td>109.84</td>\n",
       "      <td>133.02</td>\n",
       "      <td>133.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-17 12:04:00+02:00</th>\n",
       "      <td>59.36</td>\n",
       "      <td>66.52</td>\n",
       "      <td>86.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-18 18:03:00+02:00</th>\n",
       "      <td>122.67</td>\n",
       "      <td>83.07</td>\n",
       "      <td>83.07</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-26 19:49:00+02:00</th>\n",
       "      <td>117.86</td>\n",
       "      <td>157.55</td>\n",
       "      <td>157.55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-13 18:43:00+02:00</th>\n",
       "      <td>79.91</td>\n",
       "      <td>112.40</td>\n",
       "      <td>112.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM      POS      NEG  hp_output_MW  \\\n",
       "2023-03-17 18:23:00+01:00  139.90   140.22   140.22           1.0   \n",
       "2023-07-10 06:22:00+02:00  120.74    95.95    95.95           1.5   \n",
       "2023-05-13 20:14:00+02:00  118.60   137.23   137.23           1.0   \n",
       "2023-12-20 06:16:00+01:00   48.13    91.10    91.10           1.0   \n",
       "2023-04-28 20:38:00+02:00  125.43    91.41    91.41           1.5   \n",
       "2023-04-04 03:50:00+02:00  115.61   121.50   121.50           1.0   \n",
       "2023-12-28 08:24:00+01:00   38.20   -91.37   -91.37           1.5   \n",
       "2023-10-20 06:02:00+02:00   73.02   -35.86   -35.86           1.5   \n",
       "2023-11-03 12:42:00+01:00   61.81   -78.75   103.12           1.0   \n",
       "2023-01-20 13:07:00+01:00  181.62  1143.64  1143.64           1.0   \n",
       "2023-07-23 16:46:00+02:00   -4.19   -51.70   160.11           1.0   \n",
       "2023-04-13 11:48:00+02:00   34.90   150.66   150.66           1.0   \n",
       "2023-10-01 17:46:00+02:00   98.03   123.81   123.81           1.0   \n",
       "2023-01-18 20:29:00+01:00  117.09   160.74   160.74           1.0   \n",
       "2023-01-13 03:01:00+01:00    2.65   -11.68   -11.68           1.0   \n",
       "2023-10-12 23:38:00+02:00  109.84   133.02   133.02           1.0   \n",
       "2023-06-17 12:04:00+02:00   59.36    66.52    86.29           1.0   \n",
       "2023-07-18 18:03:00+02:00  122.67    83.07    83.07           1.5   \n",
       "2023-03-26 19:49:00+02:00  117.86   157.55   157.55           1.0   \n",
       "2023-10-13 18:43:00+02:00   79.91   112.40   112.40           1.0   \n",
       "\n",
       "                           to_storage_MW  from_storage_MW  Total demand  \n",
       "2023-03-17 18:23:00+01:00            0.0              0.0             1  \n",
       "2023-07-10 06:22:00+02:00           -0.0              0.0             1  \n",
       "2023-05-13 20:14:00+02:00            0.0              0.0             1  \n",
       "2023-12-20 06:16:00+01:00            0.0              0.0             1  \n",
       "2023-04-28 20:38:00+02:00           -0.0              0.0             1  \n",
       "2023-04-04 03:50:00+02:00            0.0              0.0             1  \n",
       "2023-12-28 08:24:00+01:00           -0.0              0.0             1  \n",
       "2023-10-20 06:02:00+02:00           -0.0              0.0             1  \n",
       "2023-11-03 12:42:00+01:00            0.0              0.0             1  \n",
       "2023-01-20 13:07:00+01:00            0.0              0.0             1  \n",
       "2023-07-23 16:46:00+02:00            0.0              0.0             1  \n",
       "2023-04-13 11:48:00+02:00            0.0              0.0             1  \n",
       "2023-10-01 17:46:00+02:00            0.0              0.0             1  \n",
       "2023-01-18 20:29:00+01:00            0.0              0.0             1  \n",
       "2023-01-13 03:01:00+01:00            0.0              0.0             1  \n",
       "2023-10-12 23:38:00+02:00            0.0              0.0             1  \n",
       "2023-06-17 12:04:00+02:00            0.0              0.0             1  \n",
       "2023-07-18 18:03:00+02:00           -0.0              0.0             1  \n",
       "2023-03-26 19:49:00+02:00            0.0              0.0             1  \n",
       "2023-10-13 18:43:00+02:00            0.0              0.0             1  "
      ]
     },
     "execution_count": 60,
     "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": 61,
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 64,
   "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>2023-10-21 03:31:00+02:00</th>\n",
       "      <td>34.62</td>\n",
       "      <td>-16.49</td>\n",
       "      <td>-16.49</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-31 02:46:00+02:00</th>\n",
       "      <td>57.10</td>\n",
       "      <td>63.85</td>\n",
       "      <td>63.85</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-21 06:51:00+01:00</th>\n",
       "      <td>24.44</td>\n",
       "      <td>99.74</td>\n",
       "      <td>99.74</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-24 22:29:00+01:00</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-41.90</td>\n",
       "      <td>-41.90</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-25 06:10:00+01:00</th>\n",
       "      <td>79.81</td>\n",
       "      <td>107.51</td>\n",
       "      <td>107.51</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-21 00:42:00+01:00</th>\n",
       "      <td>102.75</td>\n",
       "      <td>98.10</td>\n",
       "      <td>98.10</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-25 21:15:00+01:00</th>\n",
       "      <td>121.00</td>\n",
       "      <td>124.16</td>\n",
       "      <td>124.16</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-05 22:20:00+02:00</th>\n",
       "      <td>115.56</td>\n",
       "      <td>92.58</td>\n",
       "      <td>92.58</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-29 06:12:00+02:00</th>\n",
       "      <td>103.24</td>\n",
       "      <td>45.49</td>\n",
       "      <td>45.49</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-13 01:35:00+02:00</th>\n",
       "      <td>115.00</td>\n",
       "      <td>99.64</td>\n",
       "      <td>99.64</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-08 09:57:00+02:00</th>\n",
       "      <td>83.77</td>\n",
       "      <td>101.34</td>\n",
       "      <td>101.34</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-25 17:02:00+02:00</th>\n",
       "      <td>68.41</td>\n",
       "      <td>-40.00</td>\n",
       "      <td>-40.00</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-31 20:10:00+02:00</th>\n",
       "      <td>105.50</td>\n",
       "      <td>294.73</td>\n",
       "      <td>294.73</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-21 18:17:00+02:00</th>\n",
       "      <td>142.77</td>\n",
       "      <td>104.20</td>\n",
       "      <td>104.20</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-15 23:03:00+02:00</th>\n",
       "      <td>107.89</td>\n",
       "      <td>86.31</td>\n",
       "      <td>86.31</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-30 20:15:00+02:00</th>\n",
       "      <td>84.47</td>\n",
       "      <td>581.00</td>\n",
       "      <td>581.00</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-05 09:38:00+02:00</th>\n",
       "      <td>83.55</td>\n",
       "      <td>44.82</td>\n",
       "      <td>44.82</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-31 12:57:00+01:00</th>\n",
       "      <td>106.00</td>\n",
       "      <td>102.79</td>\n",
       "      <td>102.79</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-27 10:35:00+01:00</th>\n",
       "      <td>138.00</td>\n",
       "      <td>214.23</td>\n",
       "      <td>214.23</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-25 12:08:00+02:00</th>\n",
       "      <td>117.39</td>\n",
       "      <td>86.15</td>\n",
       "      <td>114.02</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM     POS     NEG  Total demand  hp_input_MW  \\\n",
       "2023-10-21 03:31:00+02:00   34.62  -16.49  -16.49             1         -0.6   \n",
       "2023-03-31 02:46:00+02:00   57.10   63.85   63.85             1         -0.4   \n",
       "2023-12-21 06:51:00+01:00   24.44   99.74   99.74             1         -0.4   \n",
       "2023-12-24 22:29:00+01:00    0.00  -41.90  -41.90             1         -0.6   \n",
       "2023-11-25 06:10:00+01:00   79.81  107.51  107.51             1         -0.4   \n",
       "2023-11-21 00:42:00+01:00  102.75   98.10   98.10             1         -0.4   \n",
       "2023-11-25 21:15:00+01:00  121.00  124.16  124.16             1         -0.4   \n",
       "2023-05-05 22:20:00+02:00  115.56   92.58   92.58             1         -0.6   \n",
       "2023-04-29 06:12:00+02:00  103.24   45.49   45.49             1         -0.6   \n",
       "2023-10-13 01:35:00+02:00  115.00   99.64   99.64             1         -0.4   \n",
       "2023-06-08 09:57:00+02:00   83.77  101.34  101.34             1         -0.4   \n",
       "2023-04-25 17:02:00+02:00   68.41  -40.00  -40.00             1         -0.6   \n",
       "2023-05-31 20:10:00+02:00  105.50  294.73  294.73             1         -0.4   \n",
       "2023-08-21 18:17:00+02:00  142.77  104.20  104.20             1         -0.6   \n",
       "2023-08-15 23:03:00+02:00  107.89   86.31   86.31             1         -0.6   \n",
       "2023-07-30 20:15:00+02:00   84.47  581.00  581.00             1         -0.4   \n",
       "2023-06-05 09:38:00+02:00   83.55   44.82   44.82             1         -0.6   \n",
       "2023-10-31 12:57:00+01:00  106.00  102.79  102.79             1         -0.4   \n",
       "2023-11-27 10:35:00+01:00  138.00  214.23  214.23             1         -0.4   \n",
       "2023-10-25 12:08:00+02:00  117.39   86.15  114.02             1         -0.4   \n",
       "\n",
       "                           hp_output_MW  cop  to_storage_MW  from_storage_MW  \n",
       "2023-10-21 03:31:00+02:00           1.5  2.5           -0.0              0.0  \n",
       "2023-03-31 02:46:00+02:00           1.0  2.5            0.0              0.0  \n",
       "2023-12-21 06:51:00+01:00           1.0  2.5            0.0              0.0  \n",
       "2023-12-24 22:29:00+01:00           1.5  2.5           -0.0              0.0  \n",
       "2023-11-25 06:10:00+01:00           1.0  2.5            0.0              0.0  \n",
       "2023-11-21 00:42:00+01:00           1.0  2.5            0.0              0.0  \n",
       "2023-11-25 21:15:00+01:00           1.0  2.5            0.0              0.0  \n",
       "2023-05-05 22:20:00+02:00           1.5  2.5           -0.0              0.0  \n",
       "2023-04-29 06:12:00+02:00           1.5  2.5           -0.0              0.0  \n",
       "2023-10-13 01:35:00+02:00           1.0  2.5            0.0              0.0  \n",
       "2023-06-08 09:57:00+02:00           1.0  2.5            0.0              0.0  \n",
       "2023-04-25 17:02:00+02:00           1.5  2.5           -0.0              0.0  \n",
       "2023-05-31 20:10:00+02:00           1.0  2.5            0.0              0.0  \n",
       "2023-08-21 18:17:00+02:00           1.5  2.5           -0.0              0.0  \n",
       "2023-08-15 23:03:00+02:00           1.5  2.5           -0.0              0.0  \n",
       "2023-07-30 20:15:00+02:00           1.0  2.5            0.0              0.0  \n",
       "2023-06-05 09:38:00+02:00           1.5  2.5           -0.0              0.0  \n",
       "2023-10-31 12:57:00+01:00           1.0  2.5            0.0              0.0  \n",
       "2023-11-27 10:35:00+01:00           1.0  2.5            0.0              0.0  \n",
       "2023-10-25 12:08:00+02:00           1.0  2.5            0.0              0.0  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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         -0.485913\n",
       "hp_output_MW         1.214783\n",
       "cop                  2.500000\n",
       "to_storage_MW        0.000000\n",
       "from_storage_MW      0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "########################\n",
    "\n",
    "# This is where we left\n",
    "\n",
    "########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 68,
   "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": 70,
   "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": 73,
   "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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.500000000000002"
      ]
     },
     "execution_count": 74,
     "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": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5000000000000004"
      ]
     },
     "execution_count": 75,
     "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": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2.5</td>\n",
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       "      <td>-0.007</td>\n",
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       "    <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",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.007</td>\n",
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       "    <tr>\n",
       "      <th>2023-01-01 00:02:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
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       "      <td>-209.4</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
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       "      <td>144</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.007</td>\n",
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       "    <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>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.007</td>\n",
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       "    <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",
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       "      <td>1</td>\n",
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       "      <td>-0.007</td>\n",
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       "  </tbody>\n",
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      ],
      "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             1         -0.4   \n",
       "2023-01-01 00:01:00+01:00         1             1             1         -0.4   \n",
       "2023-01-01 00:02:00+01:00         1             1             1         -0.4   \n",
       "2023-01-01 00:03:00+01:00         1             1             1         -0.4   \n",
       "2023-01-01 00:04:00+01:00         1             1             1         -0.4   \n",
       "\n",
       "                           cop  hp_output_MWh  hp_input_MWh  \n",
       "2023-01-01 00:00:00+01:00  2.5          0.017        -0.007  \n",
       "2023-01-01 00:01:00+01:00  2.5          0.017        -0.007  \n",
       "2023-01-01 00:02:00+01:00  2.5          0.017        -0.007  \n",
       "2023-01-01 00:03:00+01:00  2.5          0.017        -0.007  \n",
       "2023-01-01 00:04:00+01:00  2.5          0.017        -0.007  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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",
    "]]\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": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
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       "var outputEl = gd.closest('.output');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_data.loc['2023-01-01', :].iplot(subplots=True, shape=(5,1), dimensions=(1000, 800))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "storage_level_MWh    3.0\n",
       "to_storage_MW        0.0\n",
       "from_storage_MW      0.0\n",
       "cop                  2.5\n",
       "cop                  2.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis_data.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 81,
   "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": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_17908\\4223154146.py:1: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
      "\n"
     ]
    },
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       "config": {
        "linkText": "Export to plot.ly",
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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": 86,
   "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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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=2020, \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": 88,
   "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": 90,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_17908\\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_17908\\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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      ]
     },
     "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": 92,
   "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": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-01-01 00:00:00+0100', tz='Europe/Amsterdam')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data['tsource_vdg'].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.storage_case_PCM.data.loc['2019-01-04 07:45:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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=3.0),\n",
       " Heatpump(name='Heatpump VDG', max_thermal_power=1.5, cop_curve=2.5, min_th_power=0.0))"
      ]
     },
     "execution_count": 96,
     "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": 97,
   "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": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 98,
     "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": 99,
   "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=cop_curve\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "# Tsrc_vdg, Tsnk_vdg, Tsrc_ndg, Tsnk_ndg = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].mean().to_list()\n",
    "# mean_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].mean() \n",
    "#              + s.eb_ode_g \n",
    "#              + s.optcase1.data['CO2 prices (€/MWh)'].mean()\n",
    "#             )\n",
    "\n",
    "# max_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].max() \n",
    "#              + s.eb_ode_g \n",
    "#              + s.optcase1.data['CO2 prices (€/MWh)'].max()\n",
    "#             )\n",
    "\n",
    "# min_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].min() \n",
    "#              + s.eb_ode_g \n",
    "#              + s.optcase1.data['CO2 prices (€/MWh)'].min()\n",
    "#             )\n",
    "\n",
    "# Tsrc_vdg_min, Tsnk_vdg_min, Tsrc_ndg_min, Tsnk_ndg_min = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].min().to_list()\n",
    "# Tsrc_vdg_max, Tsnk_vdg_max, Tsrc_ndg_max, Tsnk_ndg_max = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].max().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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": 102,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "# import plotly.graph_objects as go\n",
    "# import numpy as np\n",
    "\n",
    "# fig = go.Figure()\n",
    "\n",
    "# configs = {\n",
    "#     'mean': [mean_gas_price, Tsnk_vdg, Tsrc_vdg],\n",
    "#     'unfav_gas': [min_gas_price, Tsnk_vdg, Tsrc_vdg],\n",
    "#     'fav_gas': [max_gas_price, Tsnk_vdg, Tsrc_vdg],\n",
    "#     'unfav_all': [min_gas_price, Tsnk_vdg_max, Tsrc_vdg_min],\n",
    "#     'fav_all': [max_gas_price, Tsnk_vdg_min, Tsrc_vdg_max],\n",
    "# }\n",
    "\n",
    "# for name, config in configs.items():\n",
    "#     trace = create_load_trace(*config, name)\n",
    "#     fig.add_trace(trace)\n",
    "\n",
    "# fig.update_layout(title='Switch prices for different configurations')\n",
    "# fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "date = s.baseline.data.index[0].strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
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      "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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    "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",
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     "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",
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      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 106,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "# fig_optcase = s.optcase1.data[['hp_output_MW', 'gb_output_MW', 'NEG']].loc[date, :].iplot(\n",
    "#     subplots=True, \n",
    "#     title=f'Hybrid case on {date}',\n",
    "#     subplot_titles=['Heatpump output (MW)', 'Steam boiler output (MW)', 'Imbalance price (€/MWh)'],\n",
    "#     legend=False,\n",
    "#     dimensions=(800, 600),\n",
    "#     colors=[recoydarkblue, recoygreen, recoyred],\n",
    "#     asFigure=True,\n",
    "#     shape=(3,1)\n",
    "# )\n",
    "# fig_optcase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "# date = '2019-09-04'\n",
    "\n",
    "# fig_optcase2 = s.optcase1.data[['hp_output_MW', 'gb_output_MW', 'NEG']].loc[date, :].iplot(\n",
    "#     subplots=True, \n",
    "#     title=f'Hybrid case on {date}',\n",
    "#     subplot_titles=['Heatpump output (MW)', 'Steam boiler output (MW)', 'Imbalance price (€/MWh)'],\n",
    "#     legend=False,\n",
    "#     dimensions=(800, 600),\n",
    "#     colors=[recoydarkblue, recoygreen, recoyred],\n",
    "#     asFigure=True,\n",
    "#     shape=(3,1)\n",
    "# )\n",
    "# fig_optcase2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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",
       "\n",
       "    .dataframe thead th {\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>Total net volume (MWh)</th>\n",
       "      <td>-3,679</td>\n",
       "      <td>-4,357</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>3,679</td>\n",
       "      <td>4,357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total nominated volume (MWh)</th>\n",
       "      <td>-3,679</td>\n",
       "      <td>-3,679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Absolute imbalance volume (MWh)</th>\n",
       "      <td>-</td>\n",
       "      <td>677</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>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total day-ahead result (€)</th>\n",
       "      <td>-352,531</td>\n",
       "      <td>-352,531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total POS result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total NEG result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-18,246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total imbalance result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-18,246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total combined result (€)</th>\n",
       "      <td>-352,531</td>\n",
       "      <td>-370,777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Heatpump only Heatpump + Storage_PCM\n",
       "Total net volume (MWh)                 -3,679                 -4,357\n",
       "Total exported to grid (MWh)                -                      -\n",
       "Total consumed from grid (MWh)          3,679                  4,357\n",
       "Total nominated volume (MWh)           -3,679                 -3,679\n",
       "Absolute imbalance volume (MWh)             -                    677\n",
       "Mean selling price (€/MWh)                  -                      -\n",
       "Mean buying price (€/MWh)                  96                     85\n",
       "Total day-ahead result (€)           -352,531               -352,531\n",
       "Total POS result (€)                        -                      -\n",
       "Total NEG result (€)                        -                -18,246\n",
       "Total imbalance result (€)                  -                -18,246\n",
       "Total combined result (€)            -352,531               -370,777"
      ]
     },
     "execution_count": 108,
     "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": 109,
   "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",
       "\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>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>-352,531</td>\n",
       "      <td>-370,777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electricity taxes (€)</th>\n",
       "      <td>-3,495</td>\n",
       "      <td>-4,139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grid transport costs (€)</th>\n",
       "      <td>-19,632</td>\n",
       "      <td>-29,448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HotWaterStorage OPEX (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA (€)</th>\n",
       "      <td>189,947</td>\n",
       "      <td>160,801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciation (€)</th>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA + depr (€)</th>\n",
       "      <td>177,947</td>\n",
       "      <td>147,921</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 (€)      -352,531               -370,777\n",
       "Electricity taxes (€)                   -3,495                 -4,139\n",
       "Grid transport costs (€)               -19,632                -29,448\n",
       "HotWaterStorage OPEX (€)                     -                   -440\n",
       "EBITDA (€)                             189,947                160,801\n",
       "Depreciation (€)                       -12,000                -12,880\n",
       "EBITDA + depr (€)                      177,947                147,921"
      ]
     },
     "execution_count": 109,
     "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": 110,
   "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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>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",
       "      <th>Year 7</th>\n",
       "      <th>Year 8</th>\n",
       "      <th>Year 9</th>\n",
       "      <th>Year 10</th>\n",
       "      <th>Year 11</th>\n",
       "      <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",
       "      <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>Regular Earnings (€)</th>\n",
       "      <td></td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA (€)</th>\n",
       "      <td></td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</td>\n",
       "      <td>189,947</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>EBIT (€)</th>\n",
       "      <td></td>\n",
       "      <td>177,947</td>\n",
       "      <td>177,947</td>\n",
       "      <td>177,947</td>\n",
       "      <td>177,947</td>\n",
       "      <td>177,947</td>\n",
       "      <td>177,947</td>\n",
       "      <td>177,947</td>\n",
       "      <td>177,947</td>\n",
       "      <td>177,947</td>\n",
       "      <td>177,947</td>\n",
       "      <td>177,947</td>\n",
       "      <td>177,947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income tax (Vpb.) (€)</th>\n",
       "      <td></td>\n",
       "      <td>-26,692</td>\n",
       "      <td>-26,692</td>\n",
       "      <td>-26,692</td>\n",
       "      <td>-26,692</td>\n",
       "      <td>-26,692</td>\n",
       "      <td>-26,692</td>\n",
       "      <td>-26,692</td>\n",
       "      <td>-26,692</td>\n",
       "      <td>-26,692</td>\n",
       "      <td>-26,692</td>\n",
       "      <td>-26,692</td>\n",
       "      <td>-26,692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOPLAT (€)</th>\n",
       "      <td></td>\n",
       "      <td>151,255</td>\n",
       "      <td>151,255</td>\n",
       "      <td>151,255</td>\n",
       "      <td>151,255</td>\n",
       "      <td>151,255</td>\n",
       "      <td>151,255</td>\n",
       "      <td>151,255</td>\n",
       "      <td>151,255</td>\n",
       "      <td>151,255</td>\n",
       "      <td>151,255</td>\n",
       "      <td>151,255</td>\n",
       "      <td>151,255</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>163,255</td>\n",
       "      <td>163,255</td>\n",
       "      <td>163,255</td>\n",
       "      <td>163,255</td>\n",
       "      <td>163,255</td>\n",
       "      <td>163,255</td>\n",
       "      <td>163,255</td>\n",
       "      <td>163,255</td>\n",
       "      <td>163,255</td>\n",
       "      <td>163,255</td>\n",
       "      <td>163,255</td>\n",
       "      <td>163,255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simple Payback Period</th>\n",
       "      <td>1.8468056475124852 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>54%</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>737,153</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>737,153</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 (€)                                     189,947  189,947   \n",
       "Irregular Cashflows (€)                                        -        -   \n",
       "EBITDA (€)                                               189,947  189,947   \n",
       "Depreciations (€) -/-                                    -12,000  -12,000   \n",
       "EBIT (€)                                                 177,947  177,947   \n",
       "Income tax (Vpb.) (€)                                    -26,692  -26,692   \n",
       "NOPLAT (€)                                               151,255  151,255   \n",
       "Depreciations (€) +/+                                     12,000   12,000   \n",
       "Free Cash Flow (€)                             -301,500  163,255  163,255   \n",
       "Simple Payback Period          1.8468056475124852 years                     \n",
       "IRR (%)                                             54%                     \n",
       "WACC (%)                                            10%                     \n",
       "NPV of explicit period (€)                      737,153                     \n",
       "Discounted residual value (€)                    50,184                     \n",
       "NPV (€)                                         737,153                     \n",
       "\n",
       "                                Year 3   Year 4   Year 5   Year 6   Year 7  \\\n",
       "CAPEX (€)                                                                    \n",
       "Regular Earnings (€)           189,947  189,947  189,947  189,947  189,947   \n",
       "Irregular Cashflows (€)              -        -        -        -        -   \n",
       "EBITDA (€)                     189,947  189,947  189,947  189,947  189,947   \n",
       "Depreciations (€) -/-          -12,000  -12,000  -12,000  -12,000  -12,000   \n",
       "EBIT (€)                       177,947  177,947  177,947  177,947  177,947   \n",
       "Income tax (Vpb.) (€)          -26,692  -26,692  -26,692  -26,692  -26,692   \n",
       "NOPLAT (€)                     151,255  151,255  151,255  151,255  151,255   \n",
       "Depreciations (€) +/+           12,000   12,000   12,000   12,000   12,000   \n",
       "Free Cash Flow (€)             163,255  163,255  163,255  163,255  163,255   \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 (€)           189,947  189,947  189,947  189,947  189,947  \n",
       "Irregular Cashflows (€)              -        -        -        -        -  \n",
       "EBITDA (€)                     189,947  189,947  189,947  189,947  189,947  \n",
       "Depreciations (€) -/-          -12,000  -12,000  -12,000  -12,000  -12,000  \n",
       "EBIT (€)                       177,947  177,947  177,947  177,947  177,947  \n",
       "Income tax (Vpb.) (€)          -26,692  -26,692  -26,692  -26,692  -26,692  \n",
       "NOPLAT (€)                     151,255  151,255  151,255  151,255  151,255  \n",
       "Depreciations (€) +/+           12,000   12,000   12,000   12,000   12,000  \n",
       "Free Cash Flow (€)             163,255  163,255  163,255  163,255  163,255  \n",
       "Simple Payback Period                                                       \n",
       "IRR (%)                                                                     \n",
       "WACC (%)                                                                    \n",
       "NPV of explicit period (€)                                                  \n",
       "Discounted residual value (€)                                               \n",
       "NPV (€)                                                                     "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BusinessCaseReport(s.hpcase).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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
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       "data": [
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          "shape": "linear",
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         "name": "Gas prices (€/MWh)",
         "text": "",
         "type": "scatter",
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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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   "execution_count": 112,
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     "exclude"
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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": 113,
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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": 114,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('be0495fb-bb2b-4cac-9585-a497940280ce');\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": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ebitda_report.show(comparison='relative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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BAgQIECBAgAABApkElC+Z4HMu66qXnPq/XtvVF3ky4J7H3aoECBAgQIAAAQIECBAgQIAAAQIExk1A+TJuidsvAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMFIB5ctIeR2cAAECBAgQIECAAAECBAgQIECAAAECBAgQGDcB5cu4JW6/BAgQIECAAAECBAgQIECAAAECBAgQIECAwEgFlC8j5XVwAgQIECBAgAABAgQIECBAgAABAgQIECBAYNwElC/jlrj9EiBAgAABAgQIECBAgAABAgQIECBAgAABAiMVUL6MlNfBCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXETUL6MW+L2S4AAAQIECBAgQIAAAQIECBAgQIAAAQIECIxUQPkyUl4HJ0CAAAECBAgQIECAAAECBAgQIECAAAECBMZNQPkybonbLwECBAgQIECAAAECBAgQIECAAAECBAgQIDBSAeXLSHkdnAABAgQIECBAgAABAgQIECBAgAABAgQIEBg3AeXLuCVuvwQIECBAgAABAgQIECBAgAABAgQIECBAgMBIBZQvI+V1cAIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDcBJQv45a4/RIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIjFVC+jJTXwQkQIECAAAECBAgQIECAAAECBAgQIECAAIFxE1C+jFvi9kuAAAECBAgQIECAAAECBAgQIECAAAECBAiMVED5MlJeBydAgAABAgQIECBAgAABAgQIECBAgAABAgTGTUD5Mm6J2y8BAgQIECBAgAABAgQIECBAgAABAgQIECAwUgHly0h5HZwAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYNwHly7glbr8ECBAgQIAAAQIECBAgQIAAAQIECBAgQIDASAWULyPldXACBAgQIECAAAECBAgQIECAAAECBAgQIEBg3ASUL+OWuP0SIECAAAECBAgQIECAAAECBAgQIECAAAECIxVQvoyU18EJECBAgAABAgQIECBAgAABAgQIECBAgACBcRNQvoxb4vZLgAABAgQIECBAgAABAgQIECBAgAABAgQIjFRA+TJSXgcnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIExk1A+TJuidsvAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMFIB5ctIeR2cAAECBAgQIECAAAECBAgQIECAAAECBAgQGDcB5cu4JW6/BAgQIECAAAECBAgQIECAAAECBAgQIECAwEgFlC8j5XVwAgQIECBAgAABAgQIECBAgAABAgQIECBAYNwElC/jlrj9EiBAgAABAgQIECBAgAABAgQIECBAgAABAiMVUL6MlNfBCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXETUL6MW+L2S4AAAQIECBAgQIAAAQIECBAgQIAAAQIECIxUQPkyUl4HJ0CAAAECBAgQIECAAAECBAgQIECAAAECBMZNQPkybonbLwECBAgQIECAAAECBAgQIECAAAECBAgQIDBSAeXLSHkdnAABAgQIECBAgAABAgQIECBAgAABAgQIEBg3AeXLuCVuvwQIECBAgAABAgQIECBAgAABAgQIECBAgMBIBZQvI+V1cAIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDcBJQv45a4/RIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIjFVC+jJTXwQkQIECAAAECBAgQIECAAAECBAgQIECAAIFxE1C+jFvi9kuAAAECBAgQIECAAAECBAgQIECAAAECBAiMVED5MlJeBydAgAABAgQIECBAgAABAgQIECBAgAABAgTGTUD5Mm6J2y8BAgQIECBAgAABAgQIECBAgAABAgQIECAwUgHly0h5HZwAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYNwHlywCJX3nVurT20is6jzx4xfJ06omr0/x58wZ4pocQIECAAAECBAgQIECAAAECBAgQIECAAAEC4yagfJkm8Ru+cWNae9lH0plnnJZ2XbQwrVl7eecZq1cdPW7niv0SIECAAAECBAgQIECAAAECBAgQIECAAAECAwgoX6ZBKsqWvZfsmVYedkjnkd1lzADGHkKAAAECBAgQIECAAAECBAgQIECAAAECBAiMkYDyZYqw79+8OZ130Zq04gkHTpQvt9z6o3T+RWvSKSeuTsuW7jNGp4qtEiBAgAABAgQIECBAgAABAgQIECBAgAABAoMIKF8GKF9WHvrctOKg5Z1Hdpcvt//8nkGcPYYAAQIECBAgQIAAAQIECBAgQIAAAQIECDRGYPdH7tKYWSMOqnwZoHyZ6sqXppcv//KDz6Vr1n8tLdpxp4jnp5kIECBAIJPAAw/9Mj24ZUt629NfmWkCyxYCt913Z3rLl/8u7TF/IRACBAgQIDBJ4Lb7N6U3PWVV2n3+IjIZBc74/PvSjtttnx6+/cMyTmFpAgQIEIgmcNcD96Xn7f2ktHLZ06ONNtQ8ypehuLZ5sPJlGr+2v+fLFd++Nn1+w3+lx+y61+zOJM8m0AKBBx/akh586KG048N2aMFubIHA7ATu2vyL9ON7fpYuOPQ1szuQZ89KYMMv7kiv/8z709P32n9Wx/FkAm0RuH/zL9P8eQ9vy3bsg8CsBD63/r/SOw85Ie2x8yNndRxPnp3Aq69+d1q6YPe0cMedZ3cgzybQAoEHfvlg2mGH7dMO22/Xgt3YAoHZCXz35xvSH+y9PB2x/zNndyDPbrSA8mWa+G74xo1p7WUfSWeecVraddHCVJQxxcfqVUc3OvhyeOVLK2K0iZoElC81QTpMKwSULzFiVL7EyMEUcQSUL3GyMEl+AeVL/gyKCZQvMXIwRQwB5UuMHEwRQ0D5EiOH3FMoXwZI4Mqr1qW1l17ReeTBK5anU09cnebPmzfAM+M/RPkSPyMTzp2A8mXurK0UX0D5EiMj5UuMHEwRR0D5EicLk+QXUL7kz0D5EiMDU8QRUL7EycIk+QWUL/kziDCB8iVCChlnUL5kxLd0OAHlS7hIDJRRQPmSEb+ytPIlRg6miCOgfImThUnyCyhf8megfImRgSniCChf4mRhkvwCypf8GUSYQPkSIYWMMyhfMuJbOpyA8iVcJAbKKKB8yYivfImBb4qQAsqXkLEYKpOA8iUTfNeybjsWIwdTxBBQvsTIwRQxBJQvMXLIPYXyJXcCmddXvmQOwPKhBJQvoeIwTGYB5UvmALYu78qXGDmYIo6A8iVOFibJL6B8yZ9BMYHyJUYOpoghoHyJkYMpYggoX2LkkHsK5UvuBDKvr3zJHIDlQwkoX0LFYZjMAsqXzAEoX2IEYIpwAsqXcJEYKKOA8iUjfmVp5UuMHEwRQ0D5EiMHU8QQUL7EyCH3FMqX3AlkXl/5kjkAy4cSUL6EisMwmQWUL5kDUL7ECMAU4QSUL+EiMVBGAeVLRnzlSwx8U4QTUL6Ei8RAGQWULxnxAy2tfAkURo5RlC851K0ZVUD5EjUZc+UQUL7kUN92Tbcdi5GDKeIIKF/iZGGS/ALKl/wZFBO48iVGDqaIIaB8iZGDKWIIKF9i5JB7CuVL7gQyr698yRyA5UMJKF9CxWGYzALKl8wBbF1e+RIjB1PEEVC+xMnCJPkFlC/5M1C+xMjAFHEElC9xsjBJfgHlS/4MIkygfImQQsYZlC8Z8S0dTkD5Ei4SA2UUUL5kxK8srXyJkYMp4ggoX+JkYZL8AsqX/BkoX2JkYIo4AsqXOFmYJL+A8iV/BhEmUL5ESCHjDMqXjPiWDiegfAkXiYEyCihfMuIrX2LgmyKkgPIlZCyGyiSgfMkE37Ws247FyMEUMQSULzFyMEUMAeVLjBxyT6F8yZ1A5vWVL5kDsHwoAeVLqDgMk1lA+ZI5gK3Lu/IlRg6miCOgfImThUnyCyhf8mdQTKB8iZGDKWIIKF9i5GCKGALKlxg55J5C+ZI7gczrK18yB2D5UALKl1BxGCazgPIlcwDKlxgBmCKcgPIlXCQGyiigfMmIX1la+RIjB1PEEFC+xMjBFDEElC8xcsg9hfIldwKZ11e+ZA7A8qEElC+h4jBMZgHlS+YAlC8xAjBFOAHlS7hIDJRRQPmSEV/5EgPfFOEElC/hIjFQRgHlS0b8QEsrXwKFkWMU5UsOdWtGFVC+RE3GXDkElC851Ldd023HYuRgijgCypc4WZgkv4DyJX8GxQSufImRgyliCChfYuRgihgCypcYOeSeQvmSO4HM6ytfMgdg+VACypdQcRgms4DyJXMAW5dXvsTIwRRxBJQvcbIwSX4B5Uv+DJQvMTIwRRwB5UucLEySX0D5kj+DCBMoXyKkkHEG5UtGfEuHE1C+hIvEQBkFlC8Z8StLK19i5GCKOALKlzhZmCS/gPIlfwbKlxgZmCKOgPIlThYmyS+gfMmfQYQJlC8RUsg4g/IlI76lwwkoX8JFYqCMAsqXjPjKlxj4pggpoHwJGYuhMgkoXzLBdy3rtmMxcjBFDAHlS4wcTBFDQPkSI4fcUyhfcieQeX3lS+YALB9KQPkSKg7DZBZQvmQOYOvyrnyJkYMp4ggoX+JkYZL8AsqX/BkUEyhfYuRgihgCypcYOZgihoDyJUYOuadQvuROIPP6ypfMAVg+lIDyJVQchsksoHzJHIDyJUYApggnoHwJF4mBMgooXzLiV5ZWvsTIwRQxBJQvMXIwRQwB5UuMHHJPoXzJnUDm9ZUvmQOwfCgB5UuoOAyTWUD5kjkA5UuMAEwRTkD5Ei4SA2UUUL5kxFe+xMA3RTgB5Uu4SAyUUUD5khE/0NLKl0Bh5BhF+ZJD3ZpRBZQvUZMxVw4B5UsO9W3XdNuxGDmYIo6A8iVOFibJL6B8yZ9BMYErX2LkYIoYAsqXGDmYIoaA8iVGDrmnUL7kTiDz+sqXzAFYPpSA8iVUHIbJLKB8yRzA1uWVLzFyMEUcAeVLnCxMkl9A+ZI/A+VLjAxMEUdA+RInC5PkF1C+5M8gwgTKlwgpZJxB+ZIR39LhBJQv4SIxUEYB5UtG/MrSypcYOZgijoDyJU4WJskvoHzJn4HyJUYGpogjoHyJk4VJ8gsoX/JnEGEC5UuEFDLOoHzJiG/pcALKl3CRGCijgPIlI77yJQa+KUIKKF9CxmKoTALKl0zwXcu67ViMHEwRQ0D5EiMHU8QQUL7EyCH3FMqX3AlkXl/5kjkAy4cSUL6EisMwmQWUL5kD2Lq8K19i5GCKOALKlzhZmCS/gPIlfwbFBMqXGDmYIoaA8iVGDqaIIaB8iZFD7imUL7kTyLy+8iVzAJYPJaB8CRWHYTILKF8yB6B8iRGAKcIJKF/CRWKgjALKl4z4laWVLzFyMEUMAeVLjBxMEUNA+RIjh9xTKF9yJ5B5feVL5gAsH0pA+RIqDsNkFlC+ZA5A+RIjAFOEE1C+hIvEQBkFlC8Z8ZUvMfBNEU5A+RIuEgNlFFC+ZMQPtLTyJVAYOUZRvuRQt2ZUAeVL1GTMlUNA+ZJDfds13XYsRg6miCOgfImThUnyCyhf8mdQTODKlxg5mCKGgPIlRg6miCGgfImRQ+4plC+5E8i8vvIlcwCWDyWgfAkVh2EyCyhfMgewdXnlS4wcTBFHQPkSJwuT5BdQvuTPQPkSIwNTxBFQvsTJwiT5BZQv+TOIMIHyJUIKGWdQvmTEt3Q4AeVLuEgMlFFA+ZIRv7K08iVGDqaII6B8iZOFSfILKF/yZ6B8iZGBKeIIKF/iZGGS/ALKl/wZRJhA+RIhhYwzKF8y4ls6nIDyJVwkBsoooHzJiK98iYFvipACypeQsRgqk4DyJRN817JuOxYjB1PEEFC+xMjBFDEElC8xcsg9hfIldwKZ11e+ZA7A8qEElC+h4jBMZgHlS+YAti7vypcYOZgijoDyJU4WJskvoHzJn0ExgfIlRg6miCGgfImRgyliCChfYuSQewrlS+4EMq+vfMkcgOVDCShfQsVhmMwCypfMAShfYgRginACypdwkRgoo4Ay4B6eAAAgAElEQVTyJSN+ZWnlS4wcTBFDQPkSIwdTxBBQvsTIIfcUypfcCWReX/mSOQDLhxJQvoSKwzCZBZQvmQNQvsQIwBThBJQv4SIxUEYB5UtGfOVLDHxThBNQvoSLxEAZBZQvGfEDLa18CRRGjlGULznUrRlVQPkSNRlz5RBQvuRQ33ZNtx2LkYMp4ggoX+JkYZL8AsqX/BkUE7jyJUYOpoghoHyJkYMpYggoX2LkkHsK5UvuBDKvr3zJHIDlQwkoX0LFYZjMAsqXzAFsXV75EiMHU8QRUL7EycIk+QWUL/kzUL7EyMAUcQSUL3GyMEl+AeVL/gwiTKB8iZBCxhmULxnxLR1OQPkSLhIDZRRQvmTEryytfImRgyniCChf4mRhkvwCypf8GShfYmRgijgCypc4WZgkv4DyJX8GESZQvkRIIeMMypeM+JYOJ6B8CReJgTIKKF8y4itfYuCbIqSA8iVkLIbKJKB8yQTftazbjsXIwRQxBJQvMXIwRQwB5UuMHHJPoXzJnUDm9ZUvmQOwfCgB5UuoOAyTWUD5kjmArcu78iVGDqaII6B8iZOFSfILKF/yZ1BMoHyJkYMpYggoX2LkYIoYAsqXGDnknkL5kjuBzOsrXzIHYPlQAsqXUHEYJrOA8iVzAMqXGAGYIpyA8iVcJAbKKKB8yYhfWVr5EiMHU8QQUL7EyMEUMQSULzFyyD2F8iV3ApnXV75kDsDyoQSUL6HiMExmAeVL5gCULzECMEU4AeVLuEgMlFFA+ZIRX/kSA98U4QSUL+EiMVBGAeVLRvxASytfAoWRYxTlSw51a0YVUL5ETcZcOQSULznUt13Tbcdi5GCKOALKlzhZmCS/gPIlfwbFBK58iZGDKWIIKF9i5GCKGALKlxg55J5C+ZI7gczrK18yB2D5UALKl1BxGCazgPIlcwBbl1e+xMjBFHEElC9xsjBJfgHlS/4MlC8xMjBFHAHlS5wsTJJfQPmSP4MIEyhfIqSQcQblS0Z8S4cTUL6Ei8RAGQWULxnxK0srX2LkYIo4AsqXOFmYJL+A8iV/BsqXGBmYIo6A8iVOFibJL6B8yZ9BhAmULxFSyDiD8iUjvqXDCShfwkVioIwCypeM+MqXGPimCCmgfAkZi6EyCShfMsF3Leu2YzFyMEUMAeVLjBxMEUNA+RIjh9xTKF9yJ5B5feVL5gAsH0pA+RIqDsNkFlC+ZA5g6/KufImRgyniCChf4mRhkvwCypf8GRQTKF9i5GCKGALKlxg5mCKGgPIlRg65p1C+5E4g8/rKl8wBWD6UgPIlVByGySygfMkcgPIlRgCmCCegfAkXiYEyCihfMuJXlla+xMjBFDEElC8xcjBFDAHlS4wcck+hfMmdQOb1lS+ZA7B8KAHlS6g4DJNZQPmSOQDlS4wATBFOQPkSLhIDZRRQvmTEV77EwDdFOAHlS7hIDJRRQPmSET/Q0sqXQGHkGEX5kkPdmlEFlC9RkzFXDgHlSw71bdd027EYOZgijoDyJU4WJskvoHzJn0ExgStfYuRgihgCypcYOZgihoDyJUYOuadQvuROIPP6ypfMAVg+lIDyJVQchsksoHzJHMDW5ZUvMXIwRRwB5UucLEySX0D5kj8D5UuMDEwRR0D5EicLk+QXUL7kzyDCBMqXCClknEH5khHf0uEElC/hIjFQRgHlS0b8ytLKlxg5mCKOgPIlThYmyS+gfMmfgfIlRgamiCOgfImThUnyCyhf8mcQYQLlS4QUMs6gfMmIb+lwAsqXcJEYKKOA8iUjvvIlBr4pQgooX0LGYqhMAsqXTPBdy7rtWIwcTBFDQPkSIwdTxBBQvsTIIfcUypfcCWReX/mSOQDLhxJQvoSKwzCZBZQvmQPYurwrX2LkYIo4AsqXOFmYJL+A8iV/BsUEypcYOZgihoDyJUYOpoghoHyJkUPuKZQvuRPIvL7yJXMAlg8loHwJFYdhMgsoXzIHoHyJEYApwgkoX8JFYqCMAsqXjPiVpZUvMXIwRQwB5UuMHEwRQ0D5EiOH3FMoX3InkHl95UvmACwfSkD5EioOw2QWUL5kDkD5EiMAU4QTUL6Ei8RAGQWULxnxlS8x8E0RTkD5Ei4SA2UUUL5kxA+0tPIlUBg5RlG+5FC3ZlQB5UvUZMyVQ0D5kkN92zXddixGDqaII6B8iZOFSfILKF/yZ1BM4MqXGDmYIoaA8iVGDqaIIaB8iZFD7imUL9MkcOemu9JZZ5+bVh1zZFpx0PLcedW+vvKldlIHbLCA8qXB4Rm9dgHlS+2kMzqg8mVGbJ7UYgHlS4vDtbWhBZQvQ5ON5AnKl5GwOmhDBZQvDQ3O2CMRUL6MhLVxB21N+bJm7eXp6nXXTQSw6tiXpJWHHTLx57JEWb9hY+fvXn/6qyeVKVdetS6tvfSKzucOXrE8nXri6jR/3rykfGncOW1gAjMWUL7MmM4TWyigfIkRqvIlRg6miCOgfImThUnyCyhf8mdQTKB8iZGDKWIIKF9i5GCKGALKlxg55J6iFeXL/Zs3p7//0EfTkS/647TrooXpllt/lN76zgvSa05Y1SlYis+fd9GatOIJB3YKmeLz51+0Jp1y4uq0bOk+6YZv3JjWXvaRdOYZp3WeXxQ5xcfqVUcrX3KfodYnMIcCypc5xLZUeAHlS4yIlC8xcjBFHAHlS5wsTJJfQPmSPwPlS4wMTBFHQPkSJwuT5BdQvuTPIMIErShfuiF7lS3vv+RD6bUnndApV7o/X5Qtey/Zc+JKmWoZUxy7etux8kqYAw94fKecafqH2441PUHz1ymgfKlT07GaLqB8iZGg8iVGDqaII6B8iZOFSfILKF/yZ6B8iZGBKeIIKF/iZGGS/ALKl/wZRJigleVL963Cuq9sKeDLq1v+7KgjJl0VU3yuemXMokULJ8qX/R//2M5jVx763Ilblm3ZsiVCjjOe4Yqbrk2f3/Dt9Jhd95rxMTyRQFsEHnzwofTQQ1vSwx++Q1u2ZB9TCGxHZ0oB5UuME0T5EiMHU8QRUL7EycIk+QWUL/kzUL4MnkGzXzkZfJ/j/shf/vLBtP0O26Udtt9+3Cnsn0D675+vT8/Ye3k64vHPbLTGdtt59WQ2AbayfKneNqzAKcqXK6++ZuJ9XHqVL9VCpVf58rIXH57WXXf9xK3LSvTbf37PbPyzP/dffvC59NWf3Zwevcvi7LMYgAABAnMlsN3226ctDz00V8s1cp27f3lv+sm9P09nPe0VjZy/LUPfdt+d6eyvXJaetPtj2rIl+yBAgACBmgS+cvt30v/95D9Pu89fVNMRHWYmAm/4wgfS3jvvlnZ5+E4zefrYPKd48a7pv7w6NmHZKAECtQj84O6N6SmP2j+tXPb0Wo6X6yC7P3KXXEu3Yt3w5UtRnLz9nPf0xD54xfJJhUrxoKJ4uf2OOyb9fR1XvqzfsDEt2WvxxPvCtCL9lJLbjrUlSfuoQ8Btx+pQdIy2CLjyJUaSrnyJkYMp4gi48iVOFibJL+DKl/wZFBO8+up3p6ULdk8Ld9w5xkCmIJBRwG3HMuJbOpyA246FiyTLQOHLl2FUehUvxfOLK1lm+54vhz3v2WnJnntscwXNMPNFfKzyJWIqZsoloHzJJW/diALKlxipKF9i5GCKOALKlzhZmCS/gPIlfwbKlxgZmCKOgPIlThYmyS+gfMmfQYQJWlO+dN9qrIp7/+bNk97XpXpbsWVL9+nclmztZR+ZuKqleqzu94+58qp16YZvfmubK24ihDmTGZQvM1HznLYKKF/amqx9zURA+TITtfqfo3yp39QRmy2gfGl2fqavV0D5Uq/nTI/mypeZynleGwWUL21M1Z5mKqB8malcu57XivKlLEiKW4NVP6q3Jet+zOtPf3VacdDyiYcXpcraS6/o/LnX81Ydc+TE4/tdYdPEU0P50sTUzDwqAeXLqGQdt4kCypcYqSlfYuRgijgCypc4WZgkv4DyJX8GxQTKlxg5mCKGgPIlRg6miCGgfImRQ+4pBi5fpiocyitLdt9tt7R61dG592T9IQSUL0NgeWjrBZQvrY/YBocQUL4MgTXChypfRojr0I0UUL40MjZDj0hA+TIi2CEPq3wZEszDWy2gfGl1vDY3pIDyZUiwlj58oPKl+9ZbvSx6val9S81atS3lS6vitJlZCihfZgno6a0SUL7EiFP5EiMHU8QRUL7EycIk+QWUL/kzKCZQvsTIwRQxBJQvMXIwRQwB5UuMHHJPMXD58q4LL07HH3dUKt4jpddH95va596Y9QcTUL4M5uRR4yGgfBmPnO1yMAHly2BOo36U8mXUwo7fNAHlS9MSM+8oBZQvo9Qd/NjKl8GtPLL9AsqX9mdsh4MLKF8Gt2rzIwcuX846+9xUvu9J8f4oS/bcY9J7prjypZmnifKlmbmZejQCypfRuDpqMwWULzFyU77EyMEUcQSUL3GyMEl+AeVL/gyKCZQvMXIwRQwB5UuMHEwRQ0D5EiOH3FMMVL4UQxbv+VJ8FO/pUpYv5fDFG9dXP597U9YfXED5MriVR7ZfQPnS/oztcHAB5cvgVqN8pPJllLqO3UQB5UsTUzPzqASUL6OSHe64ypfhvDy63QLKl3bna3fDCShfhvNq66MHLl/u37w5nXfRmo7D/o/ZL+2379LO/33/5gfSuuuuT7fddns684zT0q6LFrbVqpX7Ur60MlabmqGA8mWGcJ7WSgHlS4xYlS8xcjBFHAHlS5wsTJJfQPmSP4NiAuVLjBxMEUNA+RIjB1PEEFC+xMgh9xQDly/loMUVLlevu27S3Ice8qzOFTE+miegfGleZiYenYDyZXS2jtw8AeVLjMyULzFyMEUcAeVLnCxMkl9A+ZI/A+VLjAxMEUdA+RInC5PkF1C+5M8gwgRDly/F0L1uOxZhM2YYXkD5MryZZ7RXQPnS3mztbHgB5cvwZqN4hvJlFKqO2WQB5UuT0zN73QLKl7pFZ3Y8V77MzM2z2imgfGlnrnY1MwHly8zc2vasGZUvbUMY5/0oX8Y5fXvvFlC+OCcI/EZA+RLjbFC+xMjBFHEElC9xsjBJfgHlS/4MigmULzFyMEUMAeVLjBxMEUNA+RIjh9xTKF9yJ5B5feVL5gAsH0pA+RIqDsNkFlC+ZA5g6/LKlxg5mCKOgPIlThYmyS+gfMmfgfIlRgamiCOgfImThUnyCyhf8mcQYQLlS4QUMs6gfMmIb+lwAsqXcJEYKKOA8iUjfmVp5UuMHEwRR0D5EicLk+QXUL7kz0D5EiMDU8QRUL7EycIk+QWUL/kziDCB8iVCChlnUL5kxLd0OAHlS7hIDJRRQPmSEV/5EgPfFCEFlC8hYzFUJgHlSyb4rmXddixGDqaIIaB8iZGDKWIIKF9i5JB7CuVL7gQyr698yRyA5UMJKF9CxWGYzALKl8wBbF3elS8xcjBFHAHlS5wsTJJfQPmSP4NiAuVLjBxMEUNA+RIjB1PEEFC+xMgh9xRDly93brornXX2uWn9ho3bzL5kr8XpzDNOS7suWph7X9YfUED5MiCUh42FgPJlLGK2yQEFlC8DQo34YcqXEQM7fOMElC+Ni8zAIxRQvowQd4hDK1+GwPLQ1gsoX1ofsQ0OIaB8GQKrxQ8dunxZs/byDsfqVUe3mGV8tqZ8GZ+s7XR6AeXL9EYeMT4CypcYWStfYuRgijgCypc4WZgkv4DyJX8GxQTKlxg5mCKGgPIlRg6miCGgfImRQ+4phipfiqte3nXhxen4445Ky5buk3t269cgoHypAdEhWiOgfGlNlDZSg4DypQbEGg6hfKkB0SFaJaB8aVWcNjNLAeXLLAFrerrypSZIh2mFgPKlFTHaRE0CypeaIBt+GOVLwwOc7fjKl9kKen6bBJQvbUrTXmYroHyZrWA9z1e+1OPoKO0RUL60J0s7mb2A8mX2hnUcQflSh6JjtEVA+dKWJO2jDgHlSx2KzT/GUOVLsd3itmN7L9kzrTzskObv3g6S8sVJQOA3AsoXZwOB3wgoX2KcDcqXGDmYIo6A8iVOFibJL6B8yZ9BMYHyJUYOpoghoHyJkYMpYggoX2LkkHuKocuXW279UfrEJ/8tvfLlx6b58+blnt/6sxRQvswS0NNbJaB8aVWcNjNLAeXLLAFrerrypSZIh2mNgPKlNVHaSA0CypcaEGs4hPKlBkSHaI2A8qU1UdpIDQLKlxoQW3CIocqX4j1fzjr73LR+w8aeW1+y1+J05hmnpV0XLWwBzXhsQfkyHjnb5WACypfBnDxqPASULzFyVr7EyMEUcQSUL3GyMEl+AeVL/gyKCZQvMXIwRQwB5UuMHEwRQ0D5EiOH3FMMVb7kHtb69QsoX+o3dcTmCihfmpudyesXUL7UbzqTIypfZqLmOW0WUL60OV17G1ZA+TKs2Gger3wZjaujNlNA+dLM3Ew9GgHly2hcm3ZU5UvTEqt5XuVLzaAO12gB5Uuj4zN8zQLKl5pBZ3g45csM4TyttQLKl9ZGa2MzEFC+zABtBE9RvowA1SEbK6B8aWx0Bh+BgPJlBKgNPKTypYGh1Tmy8qVOTcdquoDypekJmr9OAeVLnZozP5byZeZ2ntlOAeVLO3O1q5kJKF9m5lb3s5QvdYs6XpMFlC9NTs/sdQsoX+oWbebxBipfyvd6+b/+6Pnpnz75b97zpZlZ95xa+dKiMG1l1gLKl1kTOkCLBJQvMcJUvsTIwRRxBJQvcbIwSX4B5Uv+DIoJlC8xcjBFDAHlS4wcTBFDQPkSI4fcUwxUvuQe0vqjE1C+jM7WkZsnoHxpXmYmHp2A8mV0tsMcWfkyjJbHjoOA8mUcUrbHQQWUL4NKjfZxypfR+jp6swSUL83Ky7SjFVC+jNa3KUdXvjQlqRHNqXwZEazDNlJA+dLI2Aw9IgHly4hghzys8mVIMA9vvYDypfUR2+AQAsqXIbBG+FDlywhxHbpxAsqXxkVm4BEKKF9GiNugQytfGhTWKEZVvoxC1TGbKqB8aWpy5h6FgPJlFKrDH1P5MryZZ7RbQPnS7nztbjgB5ctwXqN6tPJlVLKO20QB5UsTUzPzqASUL6OSbdZxlS/Nyqv2aZUvtZM6YIMFlC8NDs/otQsoX2onndEBlS8zYvOkFgsoX1ocrq0NLaB8GZpsJE9QvoyE1UEbKqB8aWhwxh6JgPJlJKyNO6jypXGR1Tuw8qVeT0drtoDypdn5mb5eAeVLvZ4zPZryZaZyntdWAeVLW5O1r5kIKF9molb/c5Qv9Zs6YnMFlC/Nzc7k9QsoX+o3beIRlS9NTK3GmZUvNWI6VOMFlC+Nj9AGahRQvtSIOYtDKV9mgeeprRRQvrQyVpuaoYDyZYZwNT9N+VIzqMM1WkD50uj4DF+zgPKlZtCGHq6W8uXOTXels84+t0Nw5hmnpV0XLWwox/iNrXwZv8ztuL+A8sXZQeA3AsqXGGeD8iVGDqaII6B8iZOFSfILKF/yZ1BMoHyJkYMpYggoX2LkYIoYAsqXGDnknmJW5csN37gxvf2c93T2sGDBLukNrzs5LVu6T+49WX8IAeXLEFge2noB5UvrI7bBIQSUL0NgjfChypcR4jp0IwWUL42MzdAjElC+jAh2yMMqX4YE8/BWCyhfWh2vzQ0poHwZEqylD59R+bJm7eXp6nXXTZC8/vRXpxUHLW8pUbu3pXxpd752N5yA8mU4L49ut4DyJUa+ypcYOZgijoDyJU4WJskvoHzJn0ExgfIlRg6miCGgfImRgyliCChfYuSQe4qBy5fy1mLrN2zszFwULsuW/nbndmOrjjlS+ZI7yRmur3yZIZyntVJA+dLKWG1qhgLKlxnC1fw05UvNoA7XeAHlS+MjtIEaBZQvNWLO4lDKl1ngeWrrBJQvrYvUhmYhoHyZBV6LnjpQ+dLvPV3Kv1e+NPeMUL40NzuT1y+gfKnf1BGbK6B8iZGd8iVGDqaII6B8iZOFSfILKF/yZ1BMoHyJkYMpYggoX2LkYIoYAsqXGDnknmKo8mWPPXZPp564Os2fN68zt/Ild3yzX1/5MntDR2iPgPKlPVnayewFlC+zN6zjCMqXOhQdo00Cypc2pWkvsxVQvsxWsJ7nK1/qcXSUdggoX9qRo13UI6B8qcex6UcZqHypFi3FbccWLNglveF1J6dFixa67VjDzwDlS8MDNH6tAsqXWjkdrOECypcYASpfYuRgijgCypc4WZgkv4DyJX8GxQTKlxg5mCKGgPIlRg6miCGgfImRQ+4pBi5fqoOuWXt5unrddRN/Vbz/y4qDlufei/VnIKB8mQGap7RWQPnS2mhtbAYCypcZoI3gKcqXEaA6ZKMFlC+Njs/wNQsoX2oGneHhlC8zhPO0VgooX1oZq03NUED5MkO4lj1tRuVLaXDDN25Mbz/nPZ0/Hrxi+aRbkrXMqbXbUb60Nlobm4GA8mUGaJ7SWgHlS4xolS8xcjBFHAHlS5wsTJJfQPmSP4NiAuVLjBxMEUNA+RIjB1PEEFC+xMgh9xSzKl/K4cv3fin+fOYZp6VdFy3MvS/rDyigfBkQysPGQkD5MhYx2+SAAsqXAaFG/DDly4iBHb5xAsqXxkVm4BEKKF9GiDvEoZUvQ2B5aOsFlC+tj9gGhxBQvgyB1eKH1lK+tNin9VtTvrQ+YhscQkD5MgSWh7ZeQPkSI2LlS4wcTBFHQPkSJwuT5BdQvuTPoJhA+RIjB1PEEFC+xMjBFDEElC8xcsg9xdDlS/VWY8XwCxbskt7wupPTsqX75N6L9WcgoHyZAZqntFZA+dLaaG1sBgLKlxmgjeApypcRoDpkowWUL42Oz/A1Cyhfagad4eGULzOE87RWCihfWhmrTc1QQPkyQ7iWPW2o8mXN2svTt75986Rbi5W3HDvwgMen1auObhlP+7ejfGl/xnY4uIDyZXArj2y/gPIlRsbKlxg5mCKOgPIlThYmyS+gfMmfQTGB8iVGDqaIIaB8iZGDKWIIKF9i5JB7ioHLl+KKl7WXfaTne7qUBcyqY45MKw5anntP1h9CQPkyBJaHtl5A+dL6iG1wCAHlyxBYI3yo8mWEuA7dSAHlSyNjM/SIBJQvI4Id8rDKlyHBPLzVAsqXVsdrc0MKKF+GBGvpwwcuX4qrXvZesmdaedghPSmuvGpd+vH6n7j6pWEnivKlYYEZd6QCypeR8jp4wwSULzECU77EyMEUcQSUL3GyMEl+AeVL/gyKCZQvMXIwRQwB5UuMHEwRQ0D5EiOH3FMMVL7cv3lzOu+iNWnloc/te2VLcWXMlVdfk049cXWaP29e7n1Zf0AB5cuAUB42FgLKl7GI2SYHFFC+DAg14ocpX0YM7PCNE1C+NC4yA49QQPkyQtwhDq18GQLLQ1svoHxpfcQ2OISA8mUIrBY/dKDypbit2LsuvDgdf9xRadnSfXpy3HLrj9L7L/lQeu1JJ6RdFy1sMVm7tqZ8aVeedjM7AeXL7Pw8u10CypcYeSpfYuRgijgCypc4WZgkv4DyJX8GxQTKlxg5mCKGgPIlRg6miCGgfImRQ+4plC+5E8i8vvIlcwCWDyWgfAkVh2EyCyhfMgewdXnlS4wcTBFHQPkSJwuT5BdQvuTPQPkSIwNTxBFQvsTJwiT5BZQv+TOIMIHyJUIKGWdQvmTEt3Q4AeVLuEgMlFFA+ZIRv7K08iVGDqaII6B8iZOFSfILKF/yZ6B8iZGBKeIIKF/iZGGS/ALKl/wZRJhg4PLlrLPPTes3bJxy5iV7LU5nnnGa245FSHbAGZQvA0J52FgIKF/GImabHFBA+TIg1IgfpnwZMbDDN05A+dK4yAw8QgHlywhxhzi0244NgeWhrRdQvrQ+YhscQkD5MgRWix86cPky3Xu+tNio1VtTvrQ6XpsbUkD5MiSYh7daQPkSI17lS4wcTBFHQPkSJwuT5BdQvuTPoJhA+RIjB1PEEFC+xMjBFDEElC8xcsg9hfIldwKZ11e+ZA7A8qEElC+h4jBMZgHlS+YAti6vfImRgyniCChf4mRhkvwCypf8GShfYmRgijgCypc4WZgkv4DyJX8GESZQvkRIIeMMypeM+JYOJ6B8CReJgTIKKF8y4leWLsqXv1p3cVq6cI8YA5mCQGaBX/3qwfSwh+2QeQrLE4ghcOtdG9O5z3tV2mPnR8YYaEyncOXLmAZv2z0FlC9ODAK/EVC+OBsKAeXLmJ8HypcxPwFsf5KA8sUJQeA3AsqXGGfDQ1u2pE//4Evp1rtuizGQKQhkFnDlS+YALB9KYNnCxen5j35y2n677ULNNW7DKF/GLXH7nUpA+eL8IKB8cQ5MFhi4fDnr7HPT+g0bp/RbstfidOYZp6VdFy1sjfOdm+5Kxd5XHXNkWnHQ8tbsq9yI8qV1kdrQLASUL7PA89TWCShfWhepDRFohcDtP78n7f7IXVqxF5sgQKAdAsqXduRoF/UIKF/qcXSUdgi48qUdOc52FwOXL++68OJ0/HFHpWVL95ntmiN//pq1l6dvffvmSUVQWaKUBdLrT3/1pDLlyqvWpbWXXtGZ7eAVy9OpJ65O8+fNS8qXkcdlAQJhBJQvYaIwSAAB5UuAEIxAgMA2AsoXJwUBAtEElC/REjFPTgHlS059a0cTUL5ESyTPPK0rX4ri5ep116XqVTj3b96czrtoTVrxhAPTysMOSbfc+qN0/kVr0iknru6USTd848a09rKPTJQ1xTGKj9Wrjla+5DkvrUogi4DyJQu7RYMKKF+CBmMsAmMuoHwZ8xPA9gkEFFC+BAzFSNkElC/Z6C0cUED5EjCUDCO1qnwprl758fqfpKf+7hMnlSlF2fL+Sz6UXnvSCZ1bonWXMUXZsveSPTvFTPFRLWOKP1dvO1ZeCXPgAY/vlDNN/3DbsaYnaP46BZQvdWo6VtMFlC9NT9D8BNopoHxpZ652RaDJAsqXJqdn9roFlC91izpekwWUL01Or77ZW1O+FMXLDd/8Vud2YTfd/Om7ckAAACAASURBVN1J5Uv3lS0FX3l1y58ddcSkq2KKz1WvjFm0aOFE+bL/4x/beezKQ5/bmvd/Ub7U94/JkZovoHxpfoZ2UJ+A8qU+S0ciQKA+AeVLfZaORIBAPQLKl3ocHaUdAsqXduRoF/UIKF/qcWz6UQYqX6JvsihXrrz6mon3aekuW7o/36t8qRYqvcqXl7348LTuuusnbl1Wmvzi3s3Reaac7+P//X/Sl267Oe27cHGj92F4AnUIbNmS0pYtW9L2229Xx+EcI7DAdttt18naR3+Bux+4N2249470jj84ARMBAgRqE/jkv/9n+t8XXdY53qHPfEo6/X8enXaav+O0x//aN7+TTn3TBekdbzgxPf1Jv5Puu/+BdM7fXp5+vPGn6e1//aq068JdOse486570uvf9t7O/139++4Fysf913duGXqWaYf1AAIExkrgrz77/6Z9HrF7WrDjTmO172E36/vvYcWa+fiHHtqSiqy38yN1MwM0da0C39/0k/T0PX8nvXC/36/1uHN9sEfsPG+ul2zVeuHLl6I4efs57+mJfvCK5Z3CZd2116e1l16xzWPK93255dYfTroSpnjgsFe+rN+wcdL7yJSL3bf5l40+IT723c+mL/3kprTvoj0bvQ/DE6hD4KEtW1LxevwOypc6OGMfo/hhQPcyZUZ3FeXLL36W3vnsV8XO0nQECDRG4KvfvDm995J/Su94Q1GWLEjvveTjndlfddyLptxD8bxT3nB+5zFF+fL7T1neKV/e+Z5L092/uDc99xlPSn986DM6n//Xq69P11z/1XTXPfdOrNN98B/8cEN63VkXpb942cqJ5xV/9/1b16dD/uDJjfE0KAECMQROv/a9ypeBovAN+EBMDX/Qgw9uSdttn9L22peGJ2n8OgS+t2lD+r09D0yHP/bX36c29WOneQ9v6ugh5h6ofCne5+S9H/i79KpX/HnnPVN6fQzymLnacfeVL3W858thz3t2WrLnHpOusJmr/YxyHbcdG6WuYzdNwG3HmpaYeUcp4LZjo9R1bALjKXDhB/4xLdt7cTr8Bc/sAHz56zelCz/w0XTu37wmPXLRgp4o37t1Qzp/zRXp1Fe+NP3NOR9Mx71kZXreHxzcKV/edv4l6TnPODh95vqvpde+6mWd57/rvf/Q+bu//8er+h63e47xTMOuCRCoS+DXtx37rbRgx53rOqTjEGisgNuONTY6g49A4L9//pP0B3svT0fs/+vvfX2Mp8DA5UvxpvPF1R9TfZRXmvQraOaKuLt8uX/z5knv61K9rdiypfuk7seXV8WsXnV0KkqlYu+rjjmy8z4v1feWmT+v+ZddKV/m6qy0ThMElC9NSMmMcyWgfJkraesQGA+Bsix56sEHTJQvRbFy1jkfTGee/vK039K9toGofv6Ri3ZJp73p3duULy9a+az04w0/7Tx3770e1fm/i//tV+p84tOfTW8575KJtZbvv++U5c94pGOXBAjMRuDCr/xTWnfrV2dzCM8l0B6B4u4CbjnWnjztZFYC26ft01uf/RfpgN2Wzuo4ntxsgYHLlyZf+VJEVJYoZYH0+tNf3SlTyo+iVClvXVbezqwoV7rLl+LxRTlz+x13TLzHTJNPAeVLk9Mze90Cype6RR2vyQLKlyanZ3YC8QTK8qUoS578xP07A05Vvvx8093pzed8MJ2y+iWdYqb4c7/yZb9lSzqPXfiInTtXwHzvlvV9y5fu48aTMhEBAgQIEGiuwN333J92nPewNO/hD2vuJkxOgACBGgUGKl9qXM+hggkoX4IFYpysAsqXrPwWDyagfAkWiHEINEigKFVOeeMF6Se3/awz9RtPPS4d9pyndW4TNuiVL93HqG7/ve84PR34+H07xyvLnOqtxKa6nVn3lS/FscoyqEHERiVAgAABAiEFlC8hYzEUAQIZBZQvGfEjLK18iZCCGaIIKF+iJGGOCALKlwgpmIFAuwRm8p4vpcBUV750lyfTvZeM93xp13llNwQIECAQR0D5EicLkxAgEENgVuVLcfutq9dd19nJqmNfklYedkiMXZliYAHly8BUHjgGAsqXMQjZFgcWUL4MTOWBBAgMKNBdihQlSPFx0ite3Pnf4qqUj155Xc/3YKmzfCmvqnnlMX886f1nvnfLj9Ohz3rKgLvxMAIECBAgQKBbQPninCBAgMBkgYHLl+JN6t/6zgvS3Xff0ylaluy5R/riV76eijel735De8jNEVC+NCcrk45eQPkyemMrNEdA+dKcrExKoEkC1dt+/eFzn5b++pTj0k7zd5zT8qVYrCxzbrzp+521u2dpkqlZCRAgQIBAFAHlS5QkzEGAQBSBgcqX7nKlfHP66pvW3/CNG9OVV1/TijehjxLOXMyhfJkLZWs0RUD50pSkzDkXAsqXuVC2BgECwwrc/vN70u6P3GXYp3k8AQIECBAgMAcCypc5QLYEAQKNEhiofLlz013pXRdenI4/7qi0bOk+qfvPxY6LK2Pef8mH0mtPOiHtumhhoxDGeVjlyzinb+/dAsoX5wSB3wgoX5wNBAhEFFC+REzFTAQIECDQFoHuK0Pf+47TU/d7q/Xa6333P5Dedv4l6Ze/ejCdcfKfp10X7Ny5nehbzrskdR+juO3oJVd8epu/7z5u9WrZ4nODztKWLOyDAIF2CChf2pHjjHehfJkxnSe2UED50sJQbWnGAsqXGdN5IgECIxRQvowQ16EJECBAYKwFygLlqQcf0HlPtOI90s4654PpzNNfnvZbuldfm/J5n7rmC+l/PPPJk8qXj37y2nTA4x6dTll9ZOc2o8Uxz3vfFen7t25Ibz79L/oWO0VB8+Wv3zzpfeD+4RP/np7/nKelRy5aMNY52TwBAs0SUL40K6/ap1W+1E7qgA0WUL40ODyj1y6gfKmd1AEJEKhBQPlSA6JDECBAgACBHgJFMXL+mivSm09/eafg6C5j+qEVRcmyvRd3Pn39l26cVL7c8uONnb9/xlOe0Claisf+zuOWpb//x6vSSa84omf50j2HsAgQINBkAeVLk9OrYXblSw2IDtEaAeVLa6K0kRoElC81IDoEAQK1Cyhfaid1QAIECBAg0BH48tdvShd+4KOTrjYpypLi46RXvLinUvXzxW3CepUvf3zoM9KHP7Eu/cVLX5Cu+Jdr0kv+5LnpjLdd3LN86b7tWbHoG089rnMljg8CBAg0UWDg8uWss89N6zf8urHu97Fkr8XpzDNO854vDToTlC8NCsuoIxdQvoyc2AINElC+NCgsoxIYIwHlyxiFbasECBAgMKcCRfny8SuvS399ynGdW4QVH1OVL0XZUlzZUhYz/cqX4vPFcb7z/R+nVS95Qdpv2ZJ02pve3ffKl+7jzimCxQgQIFCzwEDlS81rOlwgAeVLoDCMkl1A+ZI9AgMEElC+BArDKAQITAgoX5wMBAgQIEBgdgLV92gpjvSHz31ap3D51s3fH+rKl6JQueSKT28zzGHPfmp642mr0lWf+cJEOVMtdu7fvLlv+dJ95Us5W1kGzW7nnk2AAIG5F1C+zL15qBWVL6HiMExmAeVL5gAsH0pA+RIqDsMQILBVQPniVCBAgAABAqMRmOl7vpTTTHXlS3XismDxni+jydFRCRCIJTBQ+XLnprvSuy68OB1/3FFp2dJ9Uvefiy3dcuuP0vsv+VB67UknuO1YrIynnEb50qCwjDpyAeXLyIkt0CAB5UuDwjIqgTESUL6MUdi2SoAAAQJzKlBeEfPUgw/ovMdKUcacdc4H05mnvzztt3Svzp9PeeMF6c2n/0V68hP332a2usqX4sDFVTVf/vrNk95/5h8+8e/p+c95WnrkogVz6mIxAgQIzEZA+TIbvRY8V/nSghBtoTYB5UttlA7UAgHlSwtCtAUCLRRQvrQwVFsiQIAAgTAC3bf9eu87Tp8oWuayfClAijLnLeddMmFTnSUMmEEIECAwjYDyZcxPEeXLmJ8Atj9JQPnihCDwGwHli7OBAIGIAsqXiKmYiQABAgQI/Frg7nvuTzvOe1ia9/CHISFAgACBlJLyZcxPA+XLmJ8Atq98cQ4Q6COgfHFqECAQUUD5EjEVMxEgQIAAAeWLc4AAAQK9BJQvY35eKF/G/ASwfeWLc4CA8sU5QIBAgwSULw0Ky6gECBAgMHYCrnwZu8htmACBaQSUL2N+iihfxvwEsH3li3OAgPLFOUCAQIMElC8NCsuoBAgQIDB2AsqXsYvchgkQqKt8Oevsc9P6DRunPNySvRanM884Le26aCH4hggoXxoSlDHnRMB7vswJs0UaIuC2Yw0JypgExkxA+TJmgdsuAQIECDRKQPnSqLgMS4DAHAgMdOXLHMxhiUwCypdM8JYNKaB8CRmLoTIJKF8ywVuWAIEpBZQvThACBAgQIBBXQPkSNxuTESCQR0D5ksc9zKrKlzBRGCSAgPIlQAhGCCOgfAkThUEIEKgIKF+cDgQIECBAIK6A8iVuNiYjQCCPgPIlj3uYVZUvYaIwSAAB5UuAEIwQRkD5EiYKgxAgoHxxDhAgQIAAgUYIKF8aEZMhCRCYQwHlyxxiR1xK+RIxFTPlElC+5JK3bkQB5UvEVMxEgIArX5wDBAgQIEAgroDyJW42JiNAII+A8iWPe5hVlS9hojBIAAHlS4AQjBBGQPkSJgqDECBQEVC+OB0IECBAgEBcAeVL3GxMRoBAHgHlSx73MKsqX8JEYZAAAsqXACEYIYyA8iVMFAYhQED54hwgQIAAAQKNEFC+NCImQxIgMIcCypc5xI64lPIlYipmyiWgfMklb92IAsqXiKmYiQABV744BwgQIECAQFwB5UvcbExGgEAeAeVLHvcwqypfwkRhkAACypcAIRghjIDyJUwUBiFAoCKgfHE6ECBAgACBuALKl7jZmIwAgTwCypc87mFWVb6EicIgAQSULwFCMEIYAeVLmCgMQoCA8sU5QIAAAQIEGiGgfGlETIYkQGAOBZQvc4gdcSnlS8RUzJRLQPmSS966EQWULxFTMRMBAq58cQ4QIECAAIG4AsqXuNmYjACBPALKlzzuYVZVvoSJwiABBJQvAUIwQhgB5UuYKAxCgEBFQPnidCBAgAABAnEFlC9xszEZAQJ5BJQvedzDrKp8CROFQQIIKF8ChGCEMALKlzBRGIQAAeWLc4AAAQIECDRCQPnSiJgMSYDAHAooX+YQO+JSypeIqZgpl4DyJZe8dSMKKF8ipmImAgRc+eIcIECAAAECcQWUL3GzMRkBAnkElC953MOsqnwJE4VBAggoXwKEYIQwAsqXMFEYhACBioDyxelAgAABAgTiCihf4mZjMgIE8ggoX/K4h1lV+RImCoMEEFC+BAjBCGEElC9hojAIAQLKF+cAAQIECBBohIDypRExGZIAgTkUUL7MIXbEpZQvEVMxUy4B5UsueetGFFC+REzFTAQIuPLFOUCAAAECBOIKKF/iZmMyAgTyCChf8riHWVX5EiYKgwQQUL4ECMEIYQSUL2GiMAgBAhUB5YvTgQABAgQIxBVQvsTNxmQECOQRUL7kcQ+zqvIlTBQGCSCgfAkQghHCCChfwkRhEAIElC/OAQIECBAg0AgB5UsjYjIkAQJzKKB8mUPsiEspXyKmYqZcAsqXXPLWjSigfImYipkIEHDli3OAAAECBAjEFVC+xM3GZAQI5BFQvuRxD7Oq8iVMFAYJIKB8CRCCEcIIKF/CRGEQAgQqAsoXpwMBAgQIEIgroHyJm43JCBDII6B8yeMeZlXlS5goDBJAQPkSIAQjhBFQvoSJwiAECChfnAMECBAgQKARAsqXRsRkSAIE5lBA+TKH2BGXUr5ETMVMuQSUL7nkrRtRQPkSMRUzESDgyhfnAAECBAgQiCugfImbjckIEMgjoHzJ4x5mVeVLmCgMEkBA+RIgBCOEEVC+hInCIAQIVASUL04HAgQIECAQV0D5EjcbkxEgkEdA+ZLHPcyqypcwURgkgIDyJUAIRggjoHwJE4VBCBBQvjgHCBAgQIBAIwSUL42IyZAECMyhgPJlDrEjLqV8iZiKmXIJKF9yyVs3ooDyJWIqZiJAwJUvzgECBAgQIBBXQPkSNxuTESCQR0D5ksc9zKrKlzBRGCSAgPIlQAhGCCOgfAkThUEIEKgIKF+cDgQIECBAIK6A8iVuNiYjQCCPgPIlj3uYVZUvYaIwSAAB5UuAEIwQRkD5EiYKgxAgoHxxDhAgQIAAgUYIKF8aEZMhCRCYQwHlyxxiR1xK+RIxFTPlElC+5JK3bkQB5UvEVMxEgIArX5wDBAgQIEAgroDyJW42JiNAII+A8iWPe5hVlS9hojBIAAHlS4AQjBBGQPkSJgqDECBQEVC+OB0IECBAgEBcAeVL3GxMRoBAHgHlSx73MKsqX8JEYZAAAsqXACEYIYyA8iVMFAYhQED54hwgQIAAAQKNEFC+NCImQxIgMIcCypc5xI64VFG+XPejb6TfXrh7xPHMRGBOBR56aEt6cMuW9PAdtp/TdS1GIKLAPQ/cl352/93p3YeeFHE8MxEgMKYCrnwZ0+BtmwABAgQaIaB8aURMhiRAYA4FlC9ziB1xqR/dfXt66/V/n7bfzovNEfMx09wKbNmyJRX/z7+HuXW3WkyBh7ZsSS/Y98npTx//zJgDmooAgbEUUL6MZew2TYAAAQINEVC+NCQoYxIgMGcCypc5o7YQAQLRBTY/8Kv0wC9/lRY8Yn70Uc1HgAABAgTGUkD5Mpax2zQBAgQINERA+dKQoIxJgMCcCShf5ozaQgQIRBdQvkRPyHwECBAgMO4CypdxPwPsnwABAgQiCyhfIqdjNgIEcggoX3KoW5MAgZACypeQsRiKAAECBAhMCChfnAwECBAgQCCugPIlbjYmI0Agj4DyJY+7VQkQCCigfAkYipEIECBAgEBFQPnidCBAgAABAnEFlC9xszEZAQJ5BJQv07jfuemudNbZ56ZVxxyZVhy0PE9KViVAYE4ElC9zwmwRAgQIECAwYwHly4zpPJEAAQIECIxcQPkycmILECDQMIFWlS+33Pqj9NZ3XpDuvvuetGDBLukNrzs5LVu6TyeSskRZv2Fj58+vP/3Vk8qUK69al9ZeekXncwevWJ5OPXF1mj9v3sTzlC8NO7ONS2AGAsqXGaB5CgECBAgQmEMB5cscYluKAAECBAgMKaB8GRLMwwkQaL1Aa8qXong5/6I16ZQTV08ULmV692/enM67aE1a8YQD08rDDkndj73hGzemtZd9JJ15xmlp10UL05q1l3eeunrV0cqX1v8TsME2Cdx3/wPpbedfkj51zRc623rjqcelw1/wzCm3+L1bN6RT3nhB+sltP0uL99gtvf2vX5UO2v/R6eeb7k6nvend6bf3elT661OOSzvN37FznPLxBx/4mEl/373Il79+U3rV/zpn4q8HmaVNWdgLAQIECBAYhYDyZRSqjkmAAAECBOoRUL7U4+goBAi0R6AV5UtZrqw89Lk9bw1WlC3vv+RD6bUnndApV7rLmKJs2XvJnp1ipvioljHFn6u3HSuvoDnwgMd3yhkfBAjEEbjwA//YGeakV7x4ojw56RVHpCc/cf+eQxZFylnnfDCdefrL035L90rVK1+K8uXN53wwbdp0Tzrp+BdPHKNY4zvf/3FatMvOfcuXT3z6s+l9l/1rOv8tJ3eOW3xcfd2X0n7L9p74cxw1kxAgQIAAgeYIKF+ak5VJCRAgQGD8BJQv45e5HRMgMLVAK8qX7luKFVuu3jqs+8qW4vPl1S1/dtQRk66KKT5XvTJm0aKFE+XL/o9/bOex1ZJny5YtzrExFpB+nPDv3HR3+ptz/r908uoj075bC493f+CjnQFf84ojthm0uErm7Rf8XXrRHz4z/e7WcuaBB36VHvjlr9Iuj5ifyuP9yWG/n7709ZvTyccf2bk65sP/vC49ep890403fT+9/uQ/n7giplyg1xxxlExSCmyXUtqu+P98ECBAgECjBO7YdG/abdHOjZrZsAQIECCQUvHSiZ+f238m3HPP/WnHeQ9LOz78Ye3frB0SGBOB7b14MqukW1G+9LuyZffddutcnVKUL1defc3E+7gUYt3lS7VQ6VW+vOzFh6d1110/ceuyUr347Tsf4yvgtds42d/y443pXe+9LL32VcekZXsv7gz26Ws+n772ze+kU1750jR/3q9vG1Z+bLr7nnTmO9ekm75768TfPfcZv9t57E7zdkzF5/+fv708rT728HT1tV9MT11xQNpw28/SXnv8Vud/ex33/s0PpPPf9+G07vqvTBzzJS98Xjr+6D+JA2WSjsAOO+yQfvXgQzQIECBAoGECxc9+fvepYaEZlwABAimlh+2wXXrQ99+tPxfKgs1rJa2P2gbHSOC3HrnLGO22/q2GL1+K4uTt57yn587Lq1s2bvzppNuKFQ+uXu1yy60/nPSeLsXnh73yZf2GjWnJXosn3hem/igckQCB2QgUtxA7f80V6c2nvzw9ctGCzqGK23998Wvf7nl7sO7HF1fCvOXctem3dluYTv/Ll3VuW1bcduyU1S/pHOu8912RlizePZ2y+sh01We+0Pe4xXu9fPzK66Z8P5jZ7NNzCRAgQIDAOAu47dg4p2/vBAgQIBBdwG3HoidkPgIE5logfPkyCEhx27F3XXhxOv64o9Kypft0nlK92qW7nJnJe74c9rxnpyV77rHNFTSDzOcxBAjUK1C878olV3y6c9Dl+++bzv2b16Sfb7pn0vu3FJ8bpnwpHv+5L38rvWftx9IFbzm5c+yyfCmudnnb+ZekF618Vue9X/odtyhwisd96povTJqtLIPqVXA0AgQIECAwfgLKl/HL3I4JECBAoDkCypfmZGVSAgTmRqAV5UtBVVzJcvsdd3RuLVZ8FO/NsuIJB6aVhx2SusuW6m3FirKm+z1hyqtiiluWle8ns+qYI9OKg5anK69al2745rcm3cJsbqKyCgECUwlUr1Qp3+S+KGmKj5Ne8eJtntrr8UX58vFPXZfedPrLO//dKMuX8njlQaYqdXodV3IECBAgQIBAPQLKl3ocHYUAAQIECIxCQPkyClXHJECgyQKtKV/KguVrN9zYyePQQ57Veb+X8qMsUYrbhxUfrz/91Z0ypfwoSpW1l17R+WN5O7P58+ZtU74Un68WPcVjfBAgEEOgWrYUJchpb3p3OukVR3SuVik+is/f9tOfT9wSrPrn4vPFbccOfsJj00tfeMik244NU74UxynKmfdd9q/p/LecnMrnXn3dl9J+y/ae+HMMMVMQIECAAIFmCShfmpWXaQkQIEBgvASUL+OVt90SIDC9QGvKl+m36hEECLRdoPu2X2889bh0+AueObHt7vKl+/HHHnFYOv6YF6YFj5g/q/KlWLB475dX/a9zJtbunqXtWdgfAQIECBAYhYDyZRSqjkmAAAECBOoRUL7U4+goBAi0R0D50p4s7YQAgVkKbH7gV+mBX/6qU774IECAAAECBOIJKF/iZWIiAgQIECBQCihfnAsECBCYLKB8cUYQIEBgq4DyxalAgAABAgRiCyhfYudjOgIECBAYbwHly3jnb/cECGwroHxxVhAgQED54hwgQIAAAQKNEFC+NCImQxIgQIDAmAooX8Y0eNsmQKCvgPLFyUGAAAHli3OAAAECBAg0QkD50oiYDEmAAAECYyqgfBnT4G2bAAHli3OAAAEC0wm47dh0Qj5PgAABAgTyCihf8vpbnQABAgQITCWgfHF+ECBAYLKAK1+cEQQIENgqoHxxKhAgQIAAgdgCypfY+ZiOAAECBMZbQPky3vnbPQEC2wooX5wVBAgQUL44BwgQIECAQCMElC+NiMmQBAgQIDCmAsqXMQ3etgkQ6CugfHFyECBAQPniHCBAgAABAo0QUL40IiZDEiBAgMCYCihfxjR42yZAQPniHCBAgMB0Am47Np2QzxMgQIAAgbwCype8/lYnQIAAAQJTCShfnB8ECBCYLODKF2cEAQIEtgooX5wKBAgQIEAgtoDyJXY+piNAgACB8RZQvox3/nZPgMC2AsoXZwUBAgSUL84BAgQIECDQCAHlSyNiMiQBAgQIjKmA8mVMg7dtAgT6CihfnBwECBBQvjgHCBAgQIBAIwSUL42IyZAECBAgMKYCypcxDd62CRBQvjgHCBAgMJ2A245NJ+TzBAgQIEAgr4DyJa+/1QkQIECAwFQCyhfnBwECBCYLuPLFGUGAAIGtAsoXpwIBAgQIEIgtoHyJnY/pCBAgQGC8BZQv452/3RMgsK2A8sVZQYAAAeWLc4AAAQIECDRCQPnSiJgMSYAAAQJjKqB8GdPgbZsAgb4CyhcnBwECBJQvzgECBAgQINAIAeVLI2IyJAECBAiMqYDyZUyDt20CBJQvzgECBAhMJ+C2Y9MJ+TwBAgQIEMgroHzJ6291AgQIECAwlYDyxflBgACByQKufHFGECBAYKuA8sWpQIAAAQIEYgsoX2LnYzoCBAgQGG8B5ct452/3BAhsK6B8cVYQIEBA+eIcIECAAAECjRBQvjQiJkMSIECAwJgKKF/GNHjbJkCgr4DyxclBgAAB5YtzgAABAgQINEJA+dKImAxJgAABAmMqoHwZ0+BtmwAB5YtzgAABAtMJuO3YdEI+T4AAAQIE8gooX/L6W50AAQIECEwloHxxfhAgQGCygCtfnBEECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRoFlC81YjoUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQED54hwgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNQooHypEdOhCBBon8Att/4onX/RmnTKiavT4sWPSuddtCateMKBaeVhh7Rvs3ZEgAABAgQIDCVQ/T5h2dJ9hnquBxMgQIAAAQIECBAg0G4B5Uu787U7Aq0SuH/z5k758bUbbpzY14IFu6Q3vO7kNKoXQyfO9AAAEldJREFUPJQvrTqFbIYAAQIEKgI3fOPGtPayj6Qzzzgt7bpoYeczd266K5119rlp1TFHphUHLZ+xV13HmfEAc/RE5cscQVuGAAECcygwyq+P4/J1o/w+YP2GjRPJHbxieTr1xNVp/rx5ac3ayzt/v3rV0XOY7OyXKs6Nt5/znilfj5jqMVdetS6tvfSK9PrTXz3p+6zC4+p1123z97Of2BEIEMgtoHzJnYD1CRAYWKAsX6pXnhTfvNzwzW9NfBM38MEGfOC4fHM8IIeHESBAgECLBEb54pLypUUniq0QIEBgzARG+fVxHH6+7PVze3EKXfrhj6Vn/t5TO7842eTypfqLK8W58u6L1078Qmjx+sRHP3HlpF8QLTL/7Oe+mI596Z+m4vNX/ftn0r6PXppe+fJjO0VU8fnC4ye3/TS95oRVs/rllzH7p2q7BBohoHxpREyGJECgEOj1TVz3N8bFNy5vfecF6e677+mgHXrIsyZ+m6b7t2+qv3nT/bnyN1GmuvKlLH4esdNO6f987kud9aq/wdJ9pc6qY1/idmVOZQIECBAIIzDoi0vV3+BcstfiSVfKlL+pWWyqejVq9e+LzxVfAw/c/3GdW3k+6xlPT//w0X/e5ut09wsxxbpXXn1N5xcsNm78aee5xYsV5dfc4mv8U3/3iRO/gVr9mj/d1+heIVRnHvRY1e8TimOWtyotr8jtZRzmBDAIAQIECPQUmO3Xx34/dxaLdd/Jofj5cf1Pbuv8QmGvnyt7/TJD8fVq7yV7dn62LL7e/efnv9zZx83f/d7Ez6Rf/MrXO1dSdP+MWjz3vvvuS7+4777OHSWGuZNEsVbxMd0tuKcrmLqvDCl/Lr///s2dq2/Lq2WqP1t3z118X1F8FFeRlB/9riYpPz/Iz//T/ZPoPjfKn/lXHvrctGzpb0979XBh+OP1P+ksU3wPU1xlXOztcfs9Ov3TJ/9t1lceTze/zxMgMPcCype5N7ciAQIzFOh35UvxzUt5ufJnrvvP9Ohlv935bZqyiCl/e6T6TWoxwsf++VPpkGc/ozNN8U3eYc97ducbyeJ577/kQ+m1J52QNm26q+97vnRfMlz9Rmz+/Hmdb6x33223zmzFN83vuvDidPxxR43sFmkzZPU0AgQIEBhTgUFeXOr1G53l193ia9u6a69Pf/rCP+wIFl9nb7/jjk5ZUr6AUr19Wfl1+elPedLE18bqLc6mK1+KX6444vCVE1+riz+Xx+p+oWeqr9HlLdaqsVevpC3+vvo1fKpjVb9PKH+Tt3xBrN9v/o7p6WbbBAgQaIzAbL8+Fs8vPooX1svypPqzZndRP9XXmfJn1erX0+7ypXqlRfexuu8UUTz381/66qQrNQa9k8Sg5Uu55z322L3vHSq6v+Z3f83s9bN8de7i8Vd++j/Syhc8r3P1SOeKkv+4duIXRHr9udxn+T1Kr5//e32PUD1xu8+NajlWPK77dq7dJ31Zvhx2yLPSJz75b+mlR7ww/fOVV6cXrjw0vePci5QvjfmvhEEJDC6gfBncyiMJEMgs0Os9X4qRqr+dWh2x+g1cUbJUX0iZ6huo6m+v7Lpo0ZTlS/Ub1X6lTfnbr93lT2ZOyxMgQIDAmAt0/+ZplaP87dHuF0eqX+u6X6CoviDR68WiXr8JWz3+dOVL9cWq7t8E7v5z94tNU90Gbbora//zC1+edIvTqb7ed3+u/GWO6V7MGfNT0fYJECAQSqDur4/Vr2+9vhZO9TWr19UU3eVL9WfS7nKg+8+9vq53l0H9whi0fCme3+s9X6p3ghhkjqm+R+iecbr3aq0a33TzdyeVJNWf/6d7v7tuz2rJc8utP5y4YrcohHp9lOVL8Quaxf6KuY980R8NdNVMqH8khiFAYGAB5cvAVB5IgEBugV4vjnT/Xa+Cpvwmr/sbwPLv+31zXbzwNJvypXr7s9LOrcdyn0XWJ0CAAIFSYJDf7O2+fVjx3Oqtx7q/hpafG7R86X4RonheeTVrr9uOnXLi6s4VpMOWL1O9sNLrc9USZZjypXqs4jYy1atznXkECBAg0AyBOr4+dn/9LH9hcJDyZbpbWdVZvkx1h4Z+v/xYptjvlyB7pVx+vzDML3dM9T1CsUb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    "_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": 117,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
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",
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    "_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",
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    "    text=_series.values/1000_000, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f}M €\", \n",
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    "\n",
    "npv_graph"
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  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
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     "exclude"
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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": 119,
   "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",
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    "\n",
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    "    text=_series.values, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f} years\", \n",
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    "\n",
    "spt_fig "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "tags": [
     "exclude"
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   },
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CBAgQIECAAAECBAgQIECAAAECBAgQINCfgPKlP3srEyBAgAABAgQIECBAgAABAgQIECBAgAABAhkKKF8yDNWWCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgf4ElC/92VuZAAECBAgQIECAAAECBAgQIECAAAECBAgQyFBA+ZJhqLZEgAABAgQIECBAgAABAgQIECBAgAABAgQI9CegfOnP3soECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAhgLKlwxDtSUCBAgQIECAAAECBAgQIECAAAECBAgQIECgPwHlS3/2ViZAgAABAgQIECBAgAABAgQIECBAgAABAgQyFFC+ZBiqLREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL9CShf+rO3MgECBAgQIECAAAECBAgQIECAAAECBAgQIJChgPIlw1BtiQABAgQIECBAgAABAgQIECBAgAABAgQIEOhPQPnSn72VCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgQwFlC8ZhmpLBAgQIECAAAECBAgQIECAAAECBAgQIECAQH8Cypf+7K1MgAABAgQIECBAgAABAgQIECBAgAABAgQIZCigfMkwVFsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE+hNQvvRnb2UCBAgQIECAAAECBAgQIECAAAECBAgQIEAgQwHlS4ah2hIBAgQIECBAgAABAgQIECBAgAABAgQIECDQn4DypT97KxMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIZCihfMgzVlggQIECAAAECBAgQIECAAAECBAgQIECAAIH+BJQv/dlbmQABAgQIECBAgAABAgQIECBAgAABAgQIEMhQQPmSYai2RIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPQnoHzpz97KBAgQIECAAAECBAgQIECAAAECBAgQIECAQIYCypcMQ7UlAgQIECBAgAABAgQIECBAgAABAgQIECBAoD8B5Ut/9lYmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEMhRQvmQYqi0RIECAAAECBAgQIECAAAECBAgQIECAAAEC/QkoX/qztzIBAgQIECBAgAABAgQIECBAgAABAgQIECCQoYDyJcNQbYkAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoT0D50p+9lQkQIECAAAECBAgQIECAAAECBAgQIECAAIEMBZQvGYZqSwQIECBAgAABAgQIECBAgAABAgQIECBAgEB/AsqX/uytTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGQooHzJMFRbIkCAAAECBAgQIECAAAECBAgQIECAAAECBPoTUL70Z29lAgQIECBAgAABAgQIECBAgAABAgQIECBAIEMB5UuGodoSAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0J+A8qU/eysTIECAAAECBAgQIECAAAECBAgQIECAAAECGQooXzIM1ZYIECBAgAABAgQIECBAgAABAgQIECBAgACB/gSUL/3ZW5kAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIUED5kmGotkSAAAECBAgQIECAAAECBAgQIECAAAECBAj0J6B8mWJ/+x13hoved3nYteuRwZHHb94Uzjlja1izenV/qVmZAAECBAgQIECAAAECBAgQIECAAAECBAgQiFZA+TIlmmuv2xHWH3FY2HzcpsGR27ZfM/ifW7ecFm2oBiNAgAABAgQIECBAgAABAgQIECBAgAABAgT6E1C+zGhflTE7v3mbu19mdHM4AQIECBAgQIAAAQIECBAgQIAAAQIECBAoRUD5MkPSjz3+ePjAldvCoYcc4s6XGdwcSoAAAQIECBAgQIAAAQIECBAgQIAAAQIEShJQvtRMu3rc2PU7bvSZLzW9HEaAAAECBAgQIECAAAECBAgQIECAAAECBEoVUL7MmLzHjs0I5nACBAhEKPDQrh+Hpz31p8JTVh0Q4XRGIkCAAAECBAgQIJCPwBNP7g6P/vgfwrqDnprPpuyEAAECBAjUEFC+1EAaPeT2O+4MH73qE+G8M08PB69bO+OrHU6AAAECMQgoX2JIwQwECBAgQIAAAQIlCChfSkjZHgkQIEBgkoDyZcp1cfWffia84uUvCRs3HD04snr82H333x/OOWNrWLN6tauKAAECBBIUUL4kGJqRCRAgQIAAAQIEkhRQviQZm6EJECBAoAEB5csUxJ233BouvuSKhaOO37xJ8dLAhecUBAgQ6FNA+dKnvrUJECBAgAABAgRKElC+lJS2vRIgQIDAqIDyxfVAgAABAsUJKF+Ki9yGCRAgQIAAAQIEehJQvvQEb1kCBAgQ6F1A+dJ7BAYgQIAAga4FlC9di1uPAAECBAgQIECgVAHlS6nJ2zcBAgQIKF9cAwQIECBQnIDypbjIbZgAAQIECBAgQKAnAeVLT/CWJUCAAIHeBZQvvUdgAAIECBDoWkD50rW49QgQIECAAAECBEoVUL6Umrx9EyBAgIDyxTVAgAABAsUJKF+Ki9yGCRAgQIAAAQIEehJQvvQEb1kCBAgQ6F1A+dJ7BAYgQIAAga4FlC9di1uPAAECBAgQIECgVAHlS6nJ2zcBAgQIKF9cAwQIECBQnIDypbjIbZgAAQIECBAgQKAnAeVLT/CWJUCAAIHeBZQvvUdgAAIECBDoWkD50rW49QgQIECAAAECBEoVUL6Umrx9EyBAgIDyxTVAgAABAsUJKF+Ki9yGCRAgQIAAAQIEehJQvvQEb1kCBAgQ6F1A+dJ7BAYgQIAAga4FlC9di1uPAAECBAgQIECgVAHlS6nJ2zcBAgQIKF9cAwQIECBQnIDypbjIbZgAAQIECBAgQKAnAeVLT/CWJUCAAIHeBZQvvUdgAAIECBDoWkD50rW49QgQIECAAAECBEoVUL6Umrx9EyBAgIDyxTVAgAABAsUJKF+Ki9yGCRAgQIAAAQIEehJQvvQEb1kCBAgQ6F1A+dJ7BAYgQIAAga4FlC9di1uPAAECBAgQIECgVAHlS6nJ2zcBAgQIKF9cAwQIECBQnIDypbjIbZgAAQIECBAgQKAnAeVLT/CWJUCAAIHeBZQvvUdgAAIECBDoWkD50rW49QgQIECAAAECBEoVUL6Umrx9EyBAgIDyxTVAgAABAsUJKF+Ki9yGCRAgQIAAAQIEehJQvvQEb1kCBAgQ6F1A+dJ7BAYgQIAAga4FlC9di1uPAAECBAgQIECgVAHlS6nJ2zcBAgQIKF9cAwQIECCQlcCPH/uH8G8uuyp87otfWdjXh9/7jvDzL3zewj+Ply8f/KM/C1d98vOLHP7VOW8Jr/m1V2RlYzMECBAgQIAAAQIEmhb4z9/4dnj7v7hk4bS/fuJLw788+y3hqWt+avDvxsuX791xdzj7X10e7rn37xdes+l5zwqX/q9/EJ6x7qCmx3M+AgQIECDQm4DypTd6CxMgQIBAGwIPPLQr/Mmf/VXY+qZXD97wVW8G//CS/ztc9q/PCs/ecORgyUnlS/Xvz/zdf9bGSM5JgAABAgQIECBAIFuBz37+P4ajjvyZwR87Df8Q6rCfecbCz9aTypcLL/lYuOAdb134+TxbHBsjQIAAgaIFlC9Fx2/zBAgQyF+gKmPO/V8+FM783dcu3P2ifMk/dzskQIAAAQIECBDoR6AqY7769W8t3P2ifOknB6sSIECAQP8Cypf+MzABAQIECLQoUD3WYPwv66Y9dswjx1oMxKkJECBAgAABAgSyFqge6Vt9De8qn/bYMY8cy/pysDkCBAgULaB8KTp+mydAgEDeAsPHHrzk+Ocv+vyW8fJlVGH4DOo/fMfvLPqcmLyl7I4AAQIECBAgQIDA/ALVI38/+EefXvT5LePly/gqVVlz7w8fWPQ5MfNP4gwECBAgQKB/AeVL/xmYgAABAgRaEJj0vOnhMsuVL9Ux1RvAjUcdvqiwaWFEpyRAgAABAgQIECCQjcCkz1qsNjetfKn++OmybZ8Mf/iOt4ZnrDsoGw8bIUCAAAECyhfXAAECBAhkJ7Bc8VJtVvmSXeQ2RIAAAQIECBAg0KPAUsWL8qXHUCxNgAABAr0LKF96j8AABAgQINCkwFKPGhuu8cBDu8JZ7748vPafnhB+89RfDtU//9WXvhLe8Jp/Mjhk0mfENDmfcxEgQIAAAQIECBDISWDSo8ZG9/eZa28In/7LG8PlF501uLPl+hu/Fp698ajw7A1HDg4b/4yYnGzshQABAgTKFlC+lJ2/3RMgQCA7geFnttxz798v2ttbXv9rgw/9HC9fhmXN5774lYXjP/zed/i8l+yuDBsiQIAAAQIECBBoQ6AqT6765OcXnfqIw346XPavzxoULOPlS1XWvP1fXLJw/K+f+FKf99JGMM5JgAABAr0LKF96j8AABAgQINC1wLTHjnU9j/UIECBAgAABAgQI5Cow7TNfct23fREgQIAAgeLKl9vvuDNc9L7Lw65djwzSX3/k4eGC888NB69bO/FqGD++zmtcVgQIECAQt4DyJe58TEeAAAECBAgQIJCPgPIlnyzthAABAgRmEyiufNl5y63hrnvuDaeectJAatv2a8J9998fzjlja1izevU+elX5ctmV28LZZ2wNGzccPZuuowkQIEAgSgHlS5SxGIoAAQIECBAgQCBDAeVLhqHaEgECBAjUEiiufBlXqcqY7R//1JJ3vyhfal1HDiJAgEBSAsqXpOIyLAECBAgQIECAQMICypeEwzM6AQIECMwlUHz5cu11O8LOb9627J0vszymbK40vJgAAQIEOhFQvnTCbBECBAgQIECAAAECQfniIiBAgACBUgWKLl/2566W8ceU3f/Qj0q9dmrv+2Pf/lz41v13hBVhRe3XOJAAAQJtCjxjzUHhgcceDsH/XWqT2bkJEJhBYMWKEM5+4T8LRzztkBleVd6hT1l1wOCXeL6WFrj7R38fLvvGn/n/cS4SAgQiEtgbnrH6wPDA435/ElEoRiFQtMDevXvDC376mWHL815ZtEOdzR+y7ul1DnPMEgLFli9V8VLd0fIHp28Jm4/bVPsCqV730as+Ec478/Rw8Lq1Yc+ePbVfW+qBb/zz/y286LD/LhxQ/VbBFwECBCIQ2L0nhJUrVS8RRGEEAgR+InD7wz8MpzzzReHVx76cybICK0MIfv5ejujff/em8IU7/jpsOOhnXEsECBCIQmBvCGH37r1h1QF+JxBFIIYgQCA8uWd3+PoPvxeuedW/pDFFYGX1yxNf+y1QZPmyv8VLpTxevuy3fEEvfMNnLwqvOGpTWLXygIJ2basECMQs8A9P7A6rDlgZVq70BjDmnMxGoCSB79x/Vzhx4/HhNc9RvpSUext7/Xff+XK48e++EZ7zjPVtnN45CRAgMLPAnj17w5O794SfeorfCcyM5wUECLQi8MSeJ8OXv/834ROv+Z9bOb+TEhgKFFe+THvUWPUZMNd94YZwwfnnDu5s+dKNN4VnbjwmbNxw9MCseuxY9bV1y2muopoCypeaUA4jQKAzAeVLZ9QWIkCgpoDypSaUw6YKKF+mEjmAAIGOBZQvHYNbjgCBqQLKl6lEDmhIoLjypSpXtl/9yX343vWO3x88fmy8fNl5y63h4kuuWDj++M2bwjlnbA1rVq9uKIL8T6N8yT9jOySQmoDyJbXEzEsgfwHlS/4Zd7VD5UtX0tYhQKCugPKlrpTjCBDoSkD50pW0dYorX0TevYDypXtzKxIgsLyA8sUVQoBAbALKl9gSSXce5Uu62ZmcQK4Cypdck7UvAukKKF/SzS61yZUvqSWW4LzKlwRDMzKBzAWUL5kHbHsEEhRQviQYWqQjK18iDcZYBAoWUL4UHL6tE4hUQPkSaTAZjqV8yTDU2LakfIktEfMQIKB8cQ0QIBCbgPIltkTSnUf5km52JieQq4DyJddk7YtAugLKl3SzS21y5UtqiSU4r/IlwdCMTCBzAeVL5gHbHoEEBZQvCYYW6cjKl0iDMRaBggWULwV2P0MDAAAgAElEQVSHb+sEIhVQvkQaTIZjKV8yDDW2LSlfYkvEPAQIKF9cAwQIxCagfIktkXTnUb6km53JCeQqoHzJNVn7IpCugPIl3exSm1z5klpiCc6rfEkwNCMTyFxA+ZJ5wLZHIEEB5UuCoUU6svIl0mCMRaBgAeVLweHbOoFIBZQvkQaT4VjKlwxDjW1LypfYEjEPAQLKF9cAAQKxCShfYksk3XmUL+lmZ3ICuQooX3JN1r4IpCugfEk3u9QmV76klliC8ypfEgzNyAQyF1C+ZB6w7RFIUED5kmBokY6sfIk0GGMRKFhA+VJw+LZOIFIB5UukwWQ4lvIlw1Bj25LyJbZEzEOAgPLFNUCAQGwCypfYEkl3HuVLutmZnECuAsqXXJO1LwLpCihf0s0utcmVL6klluC8ypcEQzMygcwFlC+ZB2x7BBIUUL4kGFqkIytfIg3GWAQKFlC+FBy+rROIVED5EmkwGY6lfMkw1Ni2pHyJLRHzECCgfHENECAQm4DyJbZE0p1H+ZJudiYnkKuA8iXXZO2LQLoCypd0s0ttcuVLaoklOK/yJcHQjEwgcwHlS+YB2x6BBAWULwmGFunIypdIgzEWgYIFlC8Fh2/rBCIVUL5EGkyGYylfMgw1ti0pX2JLxDwECChfXAMECMQmoHyJLZF051G+pJudyQnkKqB8yTVZ+yKQroDyJd3sUptc+ZJaYgnOq3xJMDQjE8hcQPmSecC2RyBBAeVLgqFFOrLyJdJgjEWgYAHlS8Hh2zqBSAWUL5EGk+FYypcMQ41tS8qX2BIxDwECyhfXAAECsQkoX2JLJN15lC/pZmdyArkKKF9yTda+CKQroHxJN7vUJle+pJZYgvMqXxIMzcgEMhdQvmQesO0RSFBA+ZJgaJGOrHyJNBhjEShYQPlScPi2TiBSAeVLpMFkOJbyJcNQY9uS8iW2RMxDgIDyxTVAgEBsAsqX2BJJdx7lS7rZmZxArgLKl1yTtS8C6QooX9LNLrXJlS+pJZbgvMqXBEMzMoHMBZQvmQdsewQSFFC+JBhapCMrXyINxlgEChZQvhQcvq0TiFRA+RJpMBmOpXzJMNTYtqR8iS0R8xAgoHxxDRAgEJuA8iW2RNKdR/mSbnYmJ5CrgPIl12Tti0C6AsqXdLNLbXLlS2qJJTiv8iXB0IxMIHMB5UvmAdsegQQFlC8JhhbpyMqXSIMxFoGCBZQvBYdv6wQiFVC+RBpMhmMpXzIMNbYtKV9iS8Q8BAgoX1wDBAjEJqB8iS2RdOdRvqSbnckJ5CqgfMk1WfsikK6A8iXd7FKbXPmSWmIJzqt8STA0IxPIXED5knnAtkcgQQHlS4KhRTqy8iXSYIxFoGAB5UvB4ds6gUgFlC+RBpPhWMqXDEONbUvKl9gSMQ8BAsoX1wABArEJKF9iSyTdeZQv6WZncgK5Cihfck3WvgikK6B8STe71CZXvqSWWILzKl8SDM3IBDIXUL5kHrDtEUhQQPmSYGiRjqx8iTQYYxEoWED5UnD4tk4gUgHlS6TBZDiW8iXDUGPbkvIltkTMQ4CA8sU1QIBAbALKl9gSSXce5Uu62ZmcQK4Cypdck7UvAukKKF/SzS61yZUvqSWW4LzKlwRDMzKBzAWUL5kHbHsEEhRQviQYWqQjK18iDcZYBAoWUL4UHL6tE4hUQPkSaTAZjqV8yTDU2LakfIktEfMQIKB8cQ0QIBCbgPIltkTSnUf5km52JieQq4DyJddk7YtAugLKl3SzS21y5UtqiSU4r/IlwdCMTCBzAeVL5gHbHoEEBZQvCYYW6cjKl0iDMRaBggWULwWHb+sEIhVQvkQaTIZjKV+mhLrzllvDxZdcsXDU8Zs3hXPO2BrWrF6d4eXQzpaUL+24OisBAvsvoHzZfzuvJECgHQHlSzuuJZ5V+VJi6vZMIG4B5Uvc+ZiOQIkCypcSU+9nz8qXKe7XXrcjrD/isLD5uE3hsccfDx+4cls49JBDwtYtp/WTWIKrKl8SDM3IBDIXUL5kHrDtEUhQQPmSYGiRjqx8iTQYYxEoWED5UnD4tk4gUgHlS6TBZDiW8mXGUKsyZuc3b3P3ywxuypcZsBxKgEAnAsqXTpgtQoDADALKlxmwHLqsgPLFBUKAQGwCypfYEjEPAQLKF9dAVwLKlxmlt22/ZvCK4Z0vT+7ePeMZyjv8TX9xcfil9ZvCqpUry9u8HectsDfv7eW8uyee3BNWrVoZVqzIeZf2RoBASgLffeDucOIxLwyvOvblKY3d+awrVqwMe/fu6XzdlBb87HdvCjd+/5Zw7MFHpjS2WQkQyFhg794QnnxyT3jKKr8TyDhmWyOwtECE77uf2LM73HzX34Q/+Y3zJTdFYNUBBzCaQ0D5MgNe9fkv2z/+qXDB+eeGg9etHbzygYceneEMZR76e198f3jp4c8Pq1b4QavMK8CuCcQnMOzNIvwZMD4sExEg0InA9x6+J/zSET8bXrnhxZ2sl+oiT1l1QHjiSX/8tFx+n7vjK+Hme24Lz1p7RKoxm5sAgcwE/OydWaC2QyADgSf37A5f/eG3w4d/5bwMdtPuFp6x7mntLpD52ZUvNQOuipcPfWR7ePc7zwobNxxd81UOqwQ8dsx1QIBAbAIeOxZbIuYhQMBjx1wDTQl47FhTks5DgEBTAh471pSk8xAg0JSAx441Jek80wSUL9OEQgiKlxpIyxyifJnPz6sJEGheQPnSvKkzEiAwn4DyZT4/r/5HAeWLq4EAgdgElC+xJWIeAgSUL66BrgSUL1OkJz1qrKtwcllH+ZJLkvZBIB8B5Us+WdoJgVwElC+5JNn/PpQv/WdgAgIEFgsoX1wRBAjEJqB8iS2RfOdRvkzJdtv2a8L1O25cdNRBBx3o8WMz/DehfJkBy6EECHQioHzphNkiBAjMIKB8mQHLocsKKF9cIAQIxCagfIktEfMQIKB8cQ10JaB86Uq64HWULwWHb+sEIhVQvkQajLEIFCygfCk4/Ia3rnxpGNTpCBCYW0D5MjehExAg0LCA8qVhUKdbUkD54uJoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW9U+dJ7BPkPoHzJP2M7JJCagPIltcTMSyB/AeVL/hl3tUPlS1fS1iFAoK6A8qWulOMIEOhKQPnSlbR1lC+ugdYFlC+tE1uAAIEZBZQvM4I5nACB1gWUL60TF7OA8qWYqG2UQDICypdkojIogWIElC/FRN37RpUvvUeQ/wDKl/wztkMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW0f54hpoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW9U+dJ7BPkPoHzJP2M7JJCagPIltcTMSyB/AeVL/hl3tUPlS1fS1iFAoK6A8qWulOMIEOhKQPnSlbR1lC+ugdYFlC+tE1uAAIEZBZQvM4I5nACB1gWUL60TF7OA8qWYqG2UQDICypdkojIogWIElC/FRN37RpUvvUeQ/wDKl/wztkMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW0f54hpoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW9U+dJ7BPkPoHzJP2M7JJCagPIltcTMSyB/AeVL/hl3tUPlS1fS1iFAoK6A8qWulOMIEOhKQPnSlbR1lC+ugdYFlC+tE1uAAIEZBZQvM4I5nACB1gWUL60TF7OA8qWYqG2UQDICypdkojIogWIElC/FRN37RpUvvUeQ/wDKl/wztkMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW0f54hpoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW9U+dJ7BPkPoHzJP2M7JJCagPIltcTMSyB/AeVL/hl3tUPlS1fS1iFAoK6A8qWulOMIEOhKQPnSlbR1lC+ugdYFlC+tE1uAAIEZBZQvM4I5nACB1gWUL60TF7OA8qWYqG2UQDICypdkojIogWIElC/FRN37RpUvvUeQ/wDKl/wztkMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW0f54hpoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW+01/Jl2/ZrwvU7bhwgnHzSCWHrltN6BzFA8wLKl+ZNnZEAgfkElC/z+Xk1AQLNCyhfmjct9YzKl1KTt28C8QooX+LNxmQEShVQvpSafPf77q18ufa6HWHnN28L55yxNaxZvTpURcxR648Ip55yUvcKVmxVQPnSKq+TEyCwHwLKl/1A8xICBFoVUL60ylvUyZUvRcVtswSSEFC+JBGTIQkUJaB8KSruXjfbS/ny2OOPhw9cuS2cevKJYfNxmwYAt99xZ/joVZ8I5515ejh43dpeUSzerIDypVlPZyNAYH4B5cv8hs5AgECzAsqXZj1LPpvypeT07Z1AnALKlzhzMRWBkgWULyWn3+3eeylfHnzo4fD+D34kvO0tbwwbNxw92PGkf9cthdXaElC+tCXrvAQI7K+A8mV/5byOAIG2BJQvbcmWd17lS3mZ2zGB2AWUL7EnZD4C5QkoX8rLvK8dR1W+XPieS8OWN71u4W6YvlCs26yA8qVZT2cjQGB+AeXL/IbOQIBAswLKl2Y9Sz6b8qXk9O2dQJwCypc4czEVgZIFlC8lp9/t3pUv3XoXuZrypcjYbZpA1ALKl6jjMRyBIgWUL0XG3sqmlS+tsDopAQJzCChf5sDzUgIEWhFQvrTC6qQTBHorX6q7XO66+wfLhrL+yMPDBeef6zNgEr90lS+JB2h8AhkKKF8yDNWWCCQuoHxJPMCIxle+RBSGUQgQGAgoX1wIBAjEJqB8iS2RfOfppXzJl9POJgkoX1wXBAjEJqB8iS0R8xAgoHxxDTQloHxpStJ5CBBoSkD50pSk8xAg0JSA8qUpSeeZJqB8mSbk+3MLKF/mJnQCAgQaFlC+NAzqdAQIzC2gfJmb0Al+IqB8cSkQIBCbgPIltkTMQ4CA8sU10JVA7+XLtu3XhOt33LhovyefdELYuuW0Vg0efOjh8P4PfiS87S1vDBs3HL3kWrffcWe46H2Xh127Hlk4xuPQZotG+TKbl6MJEGhfQPnSvrEVCBCYTUD5MpuXo5cWUL64OggQiE1A+RJbIuYhQED54hroSqC38qUqP6rPfTnssEPDOWdsDWtWrx7s+bHHHw8fuHJbuPfe+1r5vJfh+b++89Zw0EEHhne/86yp5ctlV24LZ5+xddnjugosxXWULymmZmYCeQsoX/LO1+4IpCigfEkxtThnVr7EmYupCJQsoHwpOX17JxCngPIlzlxynKq38qW646X6WuoOl2nfnzeMWe58Ub7Mp618mc/PqwkQaF5A+dK8qTMSIDCfgPJlPj+v/kcB5YurgQCB2ASUL7ElYh4CBJQvroGuBHopX4Z3n5x68olh83GbJu515y23hmuv/+Kiu2KaRJmlfBl97JhHjs2egvJldjOvIECgXQHlS7u+zk6AwOwCypfZzbxisoDyxZVBgEBsAsqX2BIxDwECyhfXQFcCvZQvdYqP6rNWPnrVJ8J5Z54eDl63tnGPOjNMWrS6I+e+++9fKIUefewfGp8ttxP+zl/92/ALR7wgrFq5Mret2Q8BAokK7N69J6xcuSKsWLEi0R0YmwCB3AS+++Dd4ZePOi6c+qyX5ra1RvdzwIqVYffePY2eM7eT/cV/vTn8p7tuDceuOzK3rdkPAQKJCuzduzdUBcwBB/idQKIRGptAdgJP7tkdbr7nW+Fjr3xndntrekNPW/NTTZ+yqPP1Ur6kdOfL+NUwXgo9+mPly7T/Yn7nun8bfuFw5cs0J98nQKA7gd179oQVK1eElUH50p26lQgQWE7guw/9pHx5pvJlOafqF3dVge5raQHli6uDAIHYBPaEvWFvVb74g8zYojEPgWIFBuXLD74VPnaK8mXaRfC0pypfphkt9/1eypdqoGmf6TLt+/Nsunrt/t750vYdOfPuK8bXe+xYjKmYiUDZAh47Vnb+dk8gRgGPHYsxlTRn8tixNHMzNYGcBTx2LOd07Y1AmgIeO5ZmbilO3Vv5UpUfF77n0nDYYYcu+lyX4V0x9957X7jg/HNbeeTYcuXLtdftCNd94YaFtb90403hmRuPCRs3HD3It+1SKMWLaNrMypdpQr5PgEDXAsqXrsWtR4DANAHlyzQh368roHypK+U4AgS6ElC+dCVtHQIE6gooX+pKOW5egd7Kl+HgVdmx/epPLtrHySedELZuOW3evU18/bDc+frOWxe+f/zmTQsF0Hj5svOWW8PFl1wx8dhWBszwpMqXDEO1JQKJCyhfEg/Q+AQyFFC+ZBhqT1tSvvQEb1kCBJYUUL64OAgQiE1A+RJbIvnO03v5ki+tnQ0FlC+uBQIEYhNQvsSWiHkIEFC+uAaaElC+NCXpPAQINCWgfGlK0nkIEGhKQPnSlKTzTBNQvkwT8v25BZQvcxM6AQECDQsoXxoGdToCBOYWUL7MTegEPxFQvrgUCBCITUD5Elsi5iFAQPniGuhKoLfyZfg4r3e94/fD5uM2Ldrvct/rCsY6zQkoX5qzdCYCBJoRUL404+gsBAg0J6B8ac6y9DMpX0q/AuyfQHwCypf4MjERgdIFlC+lXwHd7b+38mXaB9dP+353RFaaV0D5Mq+g1xMg0LSA8qVpUecjQGBeAeXLvIJePxRQvrgWCBCITUD5Elsi5iFAQPniGuhKoJfy5cGHHg4XvufSsOVNr9vnrpfhxqu7X7Z//FPhgvPPDQevW9uVh3VaEFC+tIDqlAQIzCWgfJmLz4sJEGhBQPnSAmqhp1S+FBq8bROIWED5EnE4RiNQqIDypdDge9h2b+XL+z/4kfC2t7wxbNxw9MRt337HneGjV30inHfm6cqXHi6MJpdUvjSp6VwECDQhoHxpQtE5CBBoUkD50qRm2edSvpSdv90TiFFA+RJjKmYiULaA8qXs/LvcfS/ly2OPPx4+cOW2cOrJJy5758u1138xnHPG1rBm9eouTazVsIDypWFQpyNAYG4B5cvchE5AgEDDAsqXhkELPp3ypeDwbZ1ApALKl0iDMRaBggWULwWH3/HWeylfqj1ee92O8P277glbt5w2ccvVZ74ctf6IcOopJ3VMYrmmBZQvTYs6HwEC8wooX+YV9HoCBJoWUL40LVru+ZQv5WZv5wRiFVC+xJqMuQiUK6B8KTf7rnfeW/kyvPul2vDo3S3Df3/vvff5vJeur4aW1lO+tATrtAQI7LeA8mW/6byQAIGWBJQvLcEWeFrlS4Gh2zKByAWUL5EHZDwCBQooXwoMvact91a+DPdb3QGz/epPLtr+lje/3h0vPV0QbSyrfGlD1TkJEJhHQPkyj57XEiDQhoDypQ3VMs+pfCkzd7smELOA8iXmdMxGoEwB5UuZufex697Llz42bc1uBZQv3XpbjQCB6QLKl+lGjiBAoFsB5Uu33jmvpnzJOV17I5CmgPIlzdxMTSBnAeVLzunGtTflS1x5ZDmN8iXLWG2KQNICypek4zM8gSwFlC9ZxtrLppQvvbBblACBZQSULy4PAgRiE1C+xJZIvvMoX/LNNpqdKV+iicIgBAj8RED54lIgQCA2AeVLbImkO4/yJd3sTE4gVwHlS67J2heBdAWUL+lml9rkypfUEktwXuVLgqEZmUDmAsqXzAO2PQIJCihfEgwt0pGVL5EGYywCBQsoXwoO39YJRCqgfIk0mAzHUr5kGGpsW1K+xJaIeQgQUL64BggQiE1A+RJbIunOo3xJNzuTE8hVQPmSa7L2RSBdAeVLutmlNrnyJbXEEpxX+ZJgaEYmkLmA8iXzgG2PQIICypcEQ4t0ZOVLpMEYi0DBAsqXgsO3dQKRCihfIg0mw7GULxmGGtuWlC+xJWIeAgSUL64BAgRiE1C+xJZIuvMoX9LNzuQEchVQvuSarH0RSFdA+ZJudqlNrnxJLbEE51W+JBiakQlkLqB8yTxg2yOQoIDyJcHQIh1Z+RJpMMYiULCA8qXg8G2dQKQCypdIg8lwLOVLhqHGtiXlS2yJmIcAAeWLa4AAgdgElC+xJZLuPMqXdLMzOYFcBZQvuSZrXwTSFVC+pJtdapMrX1JLLMF5lS8JhmZkApkLKF8yD9j2CCQooHxJMLRIR1a+RBqMsQgULKB8KTh8WycQqYDyJdJgMhxL+ZJhqLFtSfkSWyLmIUBA+eIaIEAgNgHlS2yJpDuP8iXd7ExOIFcB5UuuydoXgXQFlC/pZpfa5MqX1BJLcF7lS4KhGZlA5gLKl8wDtj0CCQooXxIMLdKRlS+RBmMsAgULKF8KDt/WCUQqoHyJNJgMx1K+ZBhqbFtSvsSWiHkIEFC+uAYIEIhNQPkSWyLpzqN8STc7kxPIVUD5kmuy9kUgXQHlS7rZpTa58iW1xBKcV/mSYGhGJpC5gPIl84Btj0CCAsqXBEOLdGTlS6TBGItAwQLKl4LDt3UCkQooXyINJsOxlC8ZhhrblpQvsSViHgIElC+uAQIEYhNQvsSWSLrzKF/Szc7kBHIVUL7kmqx9EUhXQPmSbnapTa58SS2xBOdVviQYmpEJZC6gfMk8YNsjkKCA8iXB0CIdWfkSaTDGIlCwgPKl4PBtnUCkAsqXSIPJcCzlS4ahxrYl5UtsiZiHAAHli2uAAIHYBJQvsSWS7jzKl3SzMzmBXAWUL7kma18E0hVQvqSbXWqTK19SSyzBeZUvCYZmZAKZCyhfMg/Y9ggkKKB8STC0SEdWvkQajLEIFCygfCk4fFsnEKmA8iXSYDIcS/lSM9Tb77gzfPSqT4Tzzjw9HLxubc1XOawSUL64DggQiE1A+RJbIuYhQED54hpoSkD50pSk8xAg0JSA8qUpSechQKApAeVLU5LOM01A+TJF6MGHHg4XvufScNfdPwjrjzw8XHD+ucqXaVfV2PeVLzOCOZwAgdYFlC+tE1uAAIEZBZQvM4I5fEkB5YuLgwCB2ASUL7ElYh4CBJQvroGuBJQvNaXd+VITasJhypf9t/NKAgTaEVC+tOPqrAQI7L+A8mX/7bxysYDyxRVBgEBsAsqX2BIxDwECyhfXQFcCypea0sqXmlDKl/2H8koCBDoTUL50Rm0hAgRqCihfakI5bKqA8mUqkQMIEOhYQPnSMbjlCBCYKqB8mUrkgIYElC81IZcqX+5/6Ec1z1DuYW//4qXhJYc/P6xasbJcBDsnQCAygb0/mWdFZHMZhwCBUgX+68P3hF88YlN45YYXl0pQa99PWXVAeOLJ3bWOLfWgz9/x1XDzPbeFZ649olQC+yZAIDoBP3tHF4mBCBQu8OSe3eFrP/x2+D9+5dzCJaZv/5B1T59+kCOWFFC+1Lw4lipf9uzZU/MM5R72xj//N+EV6zeFVSuVL+VeBXZOIC6BJ57cEw44YGVYqXuJKxjTEChY4DsP3B1O3PDC8OpjX16wQp2tVz9P+vl7Oal//92bwo133hKec/CRdUAdQ4AAgdYF9uwNYffuPeEpq/xOoHVsCxAgUEvgiT27w013/U34+KveVev4kg9a6fe5c8WvfKnJ57FjNaEmHOYzX/bfzisJEGhHwGPH2nF1VgIE9l/AY8f2384rFwt47JgrggCB2AQ8diy2RMxDgIDHjrkGuhJQvtSUVr7UhFK+7D+UVxIg0JmA8qUzagsRIFBTQPlSE8phUwWUL1OJHECAQMcCypeOwS1HgMBUAeXLVCIHNCSgfJkC+eBDD4cL33NpuOvuHywcefJJJ4StW05rKIL8T+POl/wztkMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW0f54hpoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW9U+dJ7BPkPoHzJP2M7JJCagPIltcTMSyB/AeVL/hl3tUPlS1fS1iFAoK6A8qWulOMIEOhKQPnSlbR1lC+ugdYFlC+tE1uAAIEZBZQvM4I5nACB1gWUL60TF7OA8qWYqG2UQDICypdkojIogWIElC/FRN37RpUvvUeQ/wDKl/wztkMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW0f54hpoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW9U+dJ7BPkPoHzJP2M7JJCagPIltcTMSyB/AeVL/hl3tUPlS1fS1iFAoK6A8qWulOMIEOhKQPnSlbR1lC+ugdYFlC+tE1uAAIEZBZQvM4I5nACB1gWUL60TF7OA8qWYqG2UQDICypdkojIogWIElC/FRN37RpUvvUeQ/wDKl/wztkMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW0f54hpoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW9U+dJ7BPkPoHzJP2M7JJCagPIltcTMSyB/AeVL/hl3tUPlS1fS1iFAoK6A8qWulOMIEOhKQPnSlbR1lC+ugdYFlC+tE1uAAIEZBZQvM4I5nACB1gWUL60TF7OA8qWYqG2UQDICypdkojIogWIElC/FRN37RpUvvUeQ/wDKl/wztndcaPUAACAASURBVEMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW0f54hpoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW9U+dJ7BPkPoHzJP2M7JJCagPIltcTMSyB/AeVL/hl3tUPlS1fS1iFAoK6A8qWulOMIEOhKQPnSlbR1lC+ugdYFlC+tE1uAAIEZBZQvM4I5nACB1gWUL60TF7OA8qWYqG2UQDICypdkojIogWIElC/FRN37RpUvvUeQ/wDKl/wztkMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW0f54hpoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW9U+dJ7BPkPoHzJP2M7JJCagPIltcTMSyB/AeVL/hl3tUPlS1fS1iFAoK6A8qWulOMIEOhKQPnSlbR1lC+ugdYFlC+tE1uAAIEZBZQvM4I5nACB1gWUL60TF7OA8qWYqG2UQDICypdkojIogWIElC/FRN37RpUvvUeQ/wDKl/wztkMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW0f54hpoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW9U+dJ7BPkPoHzJP2M7JJCagPIltcTMSyB/AeVL/hl3tUPlS1fS1iFAoK6A8qWulOMIEOhKQPnSlbR1lC+ugdYFlC+tE1uAAIEZBZQvM4I5nACB1gWUL60TF7OA8qWYqG2UQDICypdkojIogWIElC/FRN37RpUvvUeQ/wDKl/wztkMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW0f54hpoXUD50jqxBQgQmFFA+TIjmMMJEGhdQPnSOnExCyhfionaRgkkI6B8SSYqgxIoRkD5UkzUvW9U+dJ7BPkPoHzJP2M7JJCagPIltcTMSyB/AeVL/hl3tUPlS1fS1iFAoK6A8qWulOMIEOhKQPnSlbR1lC+ugdYFlC+tE1uAAIEZBZQvM4I5nACB1gWUL60TF7OA8qWYqG2UQDICypdkojIogWIElC/FRN37RpUvvUeQ/wDKl/wztkMCqQkoX1JLzLwE8hdQvuSfcVc7VL50JW0dAgTqCihf6ko5jgCBrgSUL11JW6fI8mXnLbeGiy+5YpD++iMPDxecf244eN3aiVfD7XfcGS563+Vh165HFr4/7TUuq8UCyhdXBAECsQkoX2JLxDwECChfXANNCShfmpJ0HgIEmhJQvjQl6TwECDQloHxpStJ5pgkUV75UZcplV24LZ5+xNWzccHS49rodYec3bwvnnLE1rFm9eh+v8eOngfr+vgLKF1cFAQKxCShfYkvEPAQIKF9cA00JKF+aknQeAgSaElC+NCXpPAQINCWgfGlK0nmmCRRXvlRly/fvuids3XLawGZauTLt+9OAfT8E5YurgACB2ASUL7ElYh4CBJQvroGmBJQvTUk6DwECTQkoX5qSdB4CBJoSUL40Jek80wSKK1+2bb9mYDIsXx586OFw4XsuDVve9Lqw+bhN+3iNP3Zs/JFjT+7ePc24+O+/6S8uDr+0flNYtXJl8RYAMhPYm9l+CtrOE0/uCatWrQwrVhS0aVslQCBqge8+cHc48ZgXhlcd+/Ko5+x7uBUrVoa9e/f0PUbU63/2uzeFG79/Szj24COjntNwBAiUI7B3bwhPPrknPGWV3wmUk7qdEhgRiPB99xN7doeb7/qb8Ce/cb6opgisOuAARnMIFFm+HLX+iHDqKScN2KaVL+O2VXlz3/33Lzym7IGHHp2Dv4yX/t4X3x9eevjzw6oVftAqI3G7JBC/wLA3i/BnwPjxTEiAQCsC33v4nvBLR/xseOWGF7dy/lxO+pRVB4QnnvTHT8vl+bk7vhJuvue28Ky1R+QSu30QIJC4gJ+9Ew/Q+AQyFHhyz+7w1R9+O3z4V87LcHfNbukZ657W7AkLO1uR5UuVcd07X8avh+pOmI9e9Ylw3pmnh4PXrS3sctm/7Xrs2P65eRUBAu0JeOxYe7bOTIDA/gl47Nj+uXnVvgIeO+aqIEAgNgGPHYstEfMQIOCxY66BrgSKK19m/cwX5cv8l6LyZX5DZyBAoFkB5Uuzns5GgMD8AsqX+Q2d4b8JKF9cCQQIxCagfIktEfMQIKB8cQ10JVBc+VLduXLZldvC2WdsDRs3HB2qMmbnN29beIxY9c/XfeGGcMH55w7ubPnSjTeFZ248ZnBs9TX+mTFdBZXyOsqXlNMzO4E8BZQveeZqVwRSFlC+pJxeXLMrX+LKwzQECISgfHEVECAQm4DyJbZE8p2nuPKlinLnLbeGiy+5YpDq+iMPXyhaqn8eL19Gj62+f/zmTQtFTb6XRbM7U7406+lsBAjML6B8md/QGQgQaFZA+dKsZ8lnU76UnL69E4hTQPkSZy6mIlCygPKl5PS73XuR5Uu3xFZTvrgGCBCITUD5Elsi5iFAQPniGmhKQPnSlKTzECDQlIDypSlJ5yFAoCkB5UtTks4zTUD5Mk3I9+cWUL7MTegEBAg0LKB8aRjU6QgQmFtA+TI3oRP8RED54lIgQCA2AeVLbImYhwAB5YtroCsB5UtX0gWvo3wpOHxbJxCpgPIl0mCMRaBgAeVLweE3vHXlS8OgTkeAwNwCype5CZ2AAIGGBZQvDYM63ZICyhcXR+sCypfWiS1AgMCMAsqXGcEcToBA6wLKl9aJi1lA+VJM1DZKIBkB5UsyURmUQDECypdiou59o8qX3iPIfwDlS/4Z2yGB1ASUL6klZl4C+QsoX/LPuKsdKl+6krYOAQJ1BZQvdaUcR4BAVwLKl66kraN8cQ20LqB8aZ3YAgQIzCigfJkRzOEECLQuoHxpnbiYBZQvxURtowSSEVC+JBOVQQkUI6B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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('1aa87810-6484-4f8e-a79c-82f44465f5ff');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "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": 123,
   "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": 124,
   "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 7.73 s\n",
      "Wall time: 27.1 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_17908\\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": 125,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 2.02 s\n",
      "Wall time: 5.68 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": [
       "737153.0128228342"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time result = routine(c, _s)\n",
    "npv = result.hpcase.npv\n",
    "npv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "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_17908\\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_17908\\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_17908\\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_17908\\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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