{
 "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 = 0\n",
    "    storage_capex_per_MWh = 60_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_27812\\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.afrr_case = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation + aFRR', 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.hpcase_sde, s.optcase1, s.afrr_case]\n",
    "    s.optcases = [s.hpcase, s.storage_case_PCM]\n",
    "    #s.sde_cases = [s.hpcase_sde, s.optcase1, s.afrr_case]\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": 21,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def add_afrr_prices(c, case):\n",
    "    try:\n",
    "        aFRR_signal = pd.read_csv(f'data/aFRR_{c.start}.csv', delimiter=';', decimal=',', index_col='datetime')\n",
    "        aFRR_signal.index = case.data.index\n",
    "    except:\n",
    "        data = get_tennet_data('balansdelta2017', pd.to_datetime(c.start), pd.to_datetime(c.end))\n",
    "        data.index = data[[\"datum\", \"tijd\"]].apply(lambda x: \" \".join(x), axis=1)\n",
    "        data.index = pd.to_datetime(data.index, format=\"%d-%m-%Y %H:%M\").tz_localize(\n",
    "            \"Europe/Amsterdam\", ambiguous=True\n",
    "        )\n",
    "        data = data[~data.index.duplicated(keep=\"first\")]\n",
    "        date_ix = pd.date_range(\n",
    "            data.index[0], data.index[-1], freq=\"1T\", tz=\"Europe/Amsterdam\"\n",
    "        )\n",
    "        data = data.reindex(date_ix)\n",
    "        aFRR_signal = data[['Hoogste_prijs_opregelen', 'Mid_prijs_opregelen', 'Laagste_prijs_afregelen']]\n",
    "        aFRR_signal.to_csv(f'data/aFRR_{c.start}.csv', sep=';', decimal=',', index_label='datetime')\n",
    "\n",
    "    try:\n",
    "        aFRR_prices = pd.read_csv(f'data/aFRR_prices_{c.start}.csv', delimiter=';', decimal=',', index_col='datetime')\n",
    "        aFRR_prices.index = case.data.index\n",
    "    except:\n",
    "        data = get_aFRR_prices_nl(pd.to_datetime(c.start), pd.to_datetime(c.end))\n",
    "        data.index = pd.date_range(\n",
    "            start=c.start,\n",
    "            end=pd.to_datetime(c.end) + timedelta(days=1),\n",
    "            tz='Europe/Amsterdam', \n",
    "            freq='15T', \n",
    "            closed='left'\n",
    "        )\n",
    "        aFRR_prices = data.reindex(case.data.index, method='ffill')\n",
    "        aFRR_prices.to_csv(f'data/aFRR_prices_{c.start}.csv', sep=';', decimal=',', index_label='datetime')\n",
    "        \n",
    "    case.data = pd.concat([case.data, aFRR_signal, aFRR_prices], axis=1)\n",
    "    return case"
   ]
  },
  {
   "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,
   "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"
     ]
    }
   ],
   "source": [
    "def load_data(c, s):\n",
    "    if hasattr(s, 'afrr_case'):\n",
    "        s.afrr_case = add_afrr_prices(c, s.afrr_case)\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",
    "    if hasattr(s, 'afrr_case'):\n",
    "        for case in [s.afrr_case]:\n",
    "            case.data['Hoogste_prijs_opregelen'] = multiply_by_factor(case.data['Hoogste_prijs_opregelen'], c.e_price_multiplier)\n",
    "            case.data['Hoogste_prijs_opregelen'] = increase_volatility_by_factor(case.data['Hoogste_prijs_opregelen'], c.e_price_volatility_multiplier)\n",
    "            case.data['aFRR_up'] = multiply_by_factor(case.data['aFRR_up'], c.e_price_multiplier)\n",
    "            case.data['aFRR_up'] = increase_volatility_by_factor(case.data['aFRR_up'], c.e_price_volatility_multiplier)\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2023-01-01 00:01:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.40</td>\n",
       "      <td>-209.40</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:02:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.40</td>\n",
       "      <td>-209.40</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:03:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.40</td>\n",
       "      <td>-209.40</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:04:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.40</td>\n",
       "      <td>-209.40</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:55:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-7.10</td>\n",
       "      <td>-11.16</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 23:56:00+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",
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       "      <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",
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       "      <td>1</td>\n",
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       "    <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": 28,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$x \\mapsto \\text{1.08290551} + \\text{0.86827559}\\,x$"
      ],
      "text/plain": [
       "Polynomial([1.08290551, 0.86827559], domain=[-1,  1], window=[-1,  1], symbol='x')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cop_curve(140, 13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$x \\mapsto \\text{2.05267164} + \\text{1.64583582}\\,x$"
      ],
      "text/plain": [
       "Polynomial([2.05267164, 1.64583582], domain=[-1,  1], window=[-1,  1], symbol='x')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cop_curve(140, 73)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "heatpump = Heatpump(\n",
    "    name='Heatpump',\n",
    "    max_th_power=1,\n",
    "    min_th_power=0,\n",
    "    cop_curve=cop_curve\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#heatpump.get_cop(heat_output=load, Tsink=140, Tsource=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#heatpump.get_cop(heat_output=load, Tsink=140, Tsource=99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "source_Ts = np.arange(25, 75) + 273\n",
    "sink_Ts   = np.arange(80, 170) + 273\n",
    "\n",
    "df = pd.DataFrame(columns=list(sink_Ts), index=list(source_Ts))\n",
    "for sourceT, sinkT in itertools.product(source_Ts, sink_Ts):\n",
    "    df.loc[sourceT, sinkT] = heatpump.get_cop(heat_output=0.5, Tsink=sinkT, Tsource=sourceT)\n",
    "    \n",
    "#df.to_csv('cops_at_50perc_load.csv', sep=';', decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "sourceT = 63 + 273\n",
    "sinkT = 140 + 273\n",
    "loads = np.arange(0, 1, 0.01)\n",
    "\n",
    "cop_series = pd.Series(index=loads, dtype='float')\n",
    "load_series = pd.Series(index=loads, dtype='float')\n",
    "for load in loads:\n",
    "    cop = heatpump.get_cop(heat_output=load, Tsink=sinkT, Tsource=sourceT)\n",
    "    cop_series[load] = cop\n",
    "    load_series[load] = load / cop "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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    "fig_cop_curve = cop_series.iplot(title=f'Heatpump COP curve at Tsource={sourceT} and Tsink={sinkT}', yTitle='COP', xTitle='Thermal load in %', colors=recoygreen, asFigure=True, dimensions=(600,400))\n",
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   "source": [
    "fig_load_curve = load_series.iplot(title=f'Heatpump Load curve at Tsource={sourceT} and Tsink={sinkT}', yTitle='E-load', xTitle='Thermal load in %', colors=recoygreen, asFigure=True, dimensions=(600,400))\n",
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  {
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    "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])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
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     "data": {
      "text/latex": [
       "$x \\mapsto \\text{19.2496754} + \\text{6.7058}\\,x - \\text{1.79}\\,x^{2}$"
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      "text/plain": [
       "Polynomial([19.2496754,  6.7058   , -1.79     ], domain=[-1,  1], window=[-1,  1], symbol='x')"
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   "source": [
    "sourceT = 63 + 273\n",
    "sinkT = 140 + 273\n",
    "cop_curve_new(sourceT, sinkT)"
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  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
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     "exclude"
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   "source": [
    "heatpump = Heatpump(\n",
    "    name='Heatpump',\n",
    "    max_th_power=1,\n",
    "    min_th_power=0,\n",
    "    cop_curve=cop_curve_new\n",
    ")"
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  {
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   "execution_count": 41,
   "metadata": {
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     "exclude"
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   "source": [
    "import itertools\n",
    "\n",
    "source_Ts = np.arange(25, 75) + 273\n",
    "sink_Ts   = np.arange(80, 170) + 273\n",
    "\n",
    "df = pd.DataFrame(columns=list(sink_Ts), index=list(source_Ts))\n",
    "for sourceT, sinkT in itertools.product(source_Ts, sink_Ts):\n",
    "    df.loc[sourceT, sinkT] = heatpump.get_cop(heat_output=0.5, Tsink=sinkT, Tsource=sourceT)\n",
    "    \n",
    "#df.to_csv('cops_at_50perc_load.csv', sep=';', decimal=',')"
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   "execution_count": 42,
   "metadata": {
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   "source": [
    "sourceT = 63 + 273\n",
    "sinkT = 140 + 273\n",
    "loads = np.arange(0, 1, 0.01)\n",
    "\n",
    "cop_series = pd.Series(index=loads, dtype='float')\n",
    "load_series = pd.Series(index=loads, dtype='float')\n",
    "for load in loads:\n",
    "    cop = heatpump.get_cop(heat_output=load, Tsink=sinkT, Tsource=sourceT)\n",
    "    cop_series[load] = cop\n",
    "    load_series[load] = load / cop "
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    "sourceTs = np.arange(50, 100)\n",
    "sinkT = 140 + 273\n",
    "load = 1 \n",
    "cop_series = pd.Series(index=sourceTs, dtype='float')\n",
    "\n",
    "for sourceT in sourceTs:\n",
    "    cop = heatpump.get_cop(heat_output=load, Tsink=sinkT, Tsource=sourceT + 273)\n",
    "    cop_series[sourceT] = cop\n",
    "    \n",
    "cop_series.iplot(yrange=[0, 8], xTitle='Source Temperature', yTitle='COP', dimensions=(800, 400))"
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   "source": [
    "fig_cop_curve = cop_series.iplot(title=f'Heatpump COP curve at Tsource={sourceT} and Tsink={sinkT}', yTitle='COP', xTitle='Thermal load in %', colors=recoygreen, asFigure=True, dimensions=(600,400))\n",
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_load_curve = load_series.iplot(title=f'Heatpump Load curve at Tsource={sourceT} and Tsink={sinkT}', yTitle='E-load', xTitle='Thermal load in %', colors=recoygreen, asFigure=True, dimensions=(600,400))\n",
    "fig_load_curve"
   ]
  },
  {
   "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=cop_curve_new\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",
    "#     s.hpcase_sde.add_asset(heatpump_vdg)\n",
    "#     s.optcase1.add_asset(heatpump_vdg)\n",
    "#     s.optcase1.add_asset(gasboiler)\n",
    "#     s.afrr_case.add_asset(heatpump_vdg)\n",
    "#     s.afrr_case.add_asset(gasboiler)\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": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tref = 0\n",
    "# Cp = 4190  #J/kgK\n",
    "# MWtoJs = 1000_000\n",
    "\n",
    "# def power_to_mass_flow(power_MW, Tsink, Tref, Cp):\n",
    "#     return power_MW * MWtoJs /(Cp*(Tsink - Tref))\n",
    "# def energy_to_storage(hp_heat_output_MW, process_demand_MW):\n",
    "#     return hp_heat_output_MW - process_demand_MW  #MW\n",
    "\n",
    "# def Tsource_calculation(Tstorage, discharge_power, Tsource, process_mass_flow):   \n",
    "#     discharge_mass_flow = power_to_mass_flow(discharge_power, Tstorage, Tref, Cp)\n",
    "    \n",
    "#     combined_mass_flow = (discharge_mass_flow + process_mass_flow)\n",
    "#     if combined_mass_flow == 0:\n",
    "#         return Tsource\n",
    "#     else: \n",
    "#         return (Tstorage * discharge_mass_flow + Tsource * process_mass_flow) / combined_mass_flow"
   ]
  },
  {
   "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": {
      "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>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>2023-09-17 05:03:00+02:00</th>\n",
       "      <td>100.10</td>\n",
       "      <td>64.78</td>\n",
       "      <td>64.78</td>\n",
       "      <td>60.87</td>\n",
       "      <td>55.63</td>\n",
       "      <td>36.763</td>\n",
       "      <td>81.34</td>\n",
       "      <td>15.69</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>0.023</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-07 22:33:00+01:00</th>\n",
       "      <td>127.89</td>\n",
       "      <td>97.11</td>\n",
       "      <td>97.11</td>\n",
       "      <td>103.79</td>\n",
       "      <td>102.22</td>\n",
       "      <td>43.750</td>\n",
       "      <td>89.75</td>\n",
       "      <td>17.31</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>0.023</td>\n",
       "      <td>-0.009</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",
       "2023-09-17 05:03:00+02:00  100.10  64.78  64.78    60.87    55.63   \n",
       "2023-03-07 22:33:00+01:00  127.89  97.11  97.11   103.79   102.22   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-09-17 05:03:00+02:00              36.763               81.34   \n",
       "2023-03-07 22:33:00+01:00              43.750               89.75   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-09-17 05:03:00+02:00               15.69             70          144   \n",
       "2023-03-07 22:33:00+01:00               17.31             70          144   \n",
       "\n",
       "                           ...  from_storage_vdg_MW  to_storage_MW  \\\n",
       "2023-09-17 05:03:00+02:00  ...                  0.0           -0.0   \n",
       "2023-03-07 22:33:00+01:00  ...                  0.0           -0.0   \n",
       "\n",
       "                           from_storage_MW  storage_level_MWh  hp_output_MWh  \\\n",
       "2023-09-17 05:03:00+02:00              0.0                3.0          0.023   \n",
       "2023-03-07 22:33:00+01:00              0.0                3.0          0.023   \n",
       "\n",
       "                           hp_input_MWh  to_storage_vdg_MWh  \\\n",
       "2023-09-17 05:03:00+02:00        -0.009                -0.0   \n",
       "2023-03-07 22:33:00+01:00        -0.009                -0.0   \n",
       "\n",
       "                           from_storage_vdg_MWh  to_storage_MWh  \\\n",
       "2023-09-17 05:03:00+02:00                   0.0            -0.0   \n",
       "2023-03-07 22:33:00+01:00                   0.0            -0.0   \n",
       "\n",
       "                           from_storage_MWh  \n",
       "2023-09-17 05:03:00+02:00               0.0  \n",
       "2023-03-07 22:33:00+01:00               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": [
       "<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>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>1.171826</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>0.198006</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>1.400000</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>1.400000</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       1.171826   \n",
       "std        49.046697     194.977533     195.696551       0.198006   \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       1.400000   \n",
       "max       463.770000    2037.740000    2037.740000       1.400000   \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": [
       "<div>\n",
       "<style scoped>\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>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>2023-01-27 12:26:00+01:00</th>\n",
       "      <td>183.06</td>\n",
       "      <td>289.78</td>\n",
       "      <td>289.78</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>2023-10-23 01:22:00+02:00</th>\n",
       "      <td>101.00</td>\n",
       "      <td>108.41</td>\n",
       "      <td>108.41</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>2023-08-06 16:32:00+02:00</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-22.02</td>\n",
       "      <td>-22.02</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-16 04:20:00+01:00</th>\n",
       "      <td>63.30</td>\n",
       "      <td>-14.46</td>\n",
       "      <td>-14.46</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-30 22:01:00+02:00</th>\n",
       "      <td>86.19</td>\n",
       "      <td>102.28</td>\n",
       "      <td>102.28</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>2023-08-21 03:09:00+02:00</th>\n",
       "      <td>95.12</td>\n",
       "      <td>91.68</td>\n",
       "      <td>91.68</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>2023-08-15 06:24:00+02:00</th>\n",
       "      <td>106.76</td>\n",
       "      <td>68.85</td>\n",
       "      <td>68.85</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-06 18:43:00+01:00</th>\n",
       "      <td>165.28</td>\n",
       "      <td>101.42</td>\n",
       "      <td>101.42</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-15 11:21:00+02:00</th>\n",
       "      <td>-1.01</td>\n",
       "      <td>134.94</td>\n",
       "      <td>134.94</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>2023-08-02 13:23:00+02:00</th>\n",
       "      <td>33.08</td>\n",
       "      <td>389.50</td>\n",
       "      <td>389.50</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>2023-02-23 05:47:00+01:00</th>\n",
       "      <td>129.93</td>\n",
       "      <td>78.68</td>\n",
       "      <td>78.68</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28 09:34:00+01:00</th>\n",
       "      <td>38.79</td>\n",
       "      <td>-12.68</td>\n",
       "      <td>-12.68</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-07 13:57:00+02:00</th>\n",
       "      <td>66.57</td>\n",
       "      <td>35.71</td>\n",
       "      <td>35.71</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-04 12:07:00+01:00</th>\n",
       "      <td>148.84</td>\n",
       "      <td>103.37</td>\n",
       "      <td>103.37</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-22 10:37:00+02:00</th>\n",
       "      <td>74.59</td>\n",
       "      <td>-182.65</td>\n",
       "      <td>-182.65</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-16 05:08:00+01:00</th>\n",
       "      <td>103.20</td>\n",
       "      <td>104.03</td>\n",
       "      <td>104.03</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>2023-11-03 15:09:00+01:00</th>\n",
       "      <td>69.89</td>\n",
       "      <td>91.06</td>\n",
       "      <td>91.06</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>2023-01-12 11:01:00+01:00</th>\n",
       "      <td>111.92</td>\n",
       "      <td>88.83</td>\n",
       "      <td>145.51</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>2023-11-08 05:57:00+01:00</th>\n",
       "      <td>75.01</td>\n",
       "      <td>87.99</td>\n",
       "      <td>87.99</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>2023-04-21 21:45:00+02:00</th>\n",
       "      <td>142.67</td>\n",
       "      <td>101.37</td>\n",
       "      <td>101.37</td>\n",
       "      <td>1.4</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",
       "2023-01-27 12:26:00+01:00  183.06  289.78  289.78           1.0   \n",
       "2023-10-23 01:22:00+02:00  101.00  108.41  108.41           1.0   \n",
       "2023-08-06 16:32:00+02:00    0.00  -22.02  -22.02           1.4   \n",
       "2023-01-16 04:20:00+01:00   63.30  -14.46  -14.46           1.4   \n",
       "2023-07-30 22:01:00+02:00   86.19  102.28  102.28           1.0   \n",
       "2023-08-21 03:09:00+02:00   95.12   91.68   91.68           1.0   \n",
       "2023-08-15 06:24:00+02:00  106.76   68.85   68.85           1.4   \n",
       "2023-12-06 18:43:00+01:00  165.28  101.42  101.42           1.4   \n",
       "2023-10-15 11:21:00+02:00   -1.01  134.94  134.94           1.0   \n",
       "2023-08-02 13:23:00+02:00   33.08  389.50  389.50           1.0   \n",
       "2023-02-23 05:47:00+01:00  129.93   78.68   78.68           1.4   \n",
       "2023-12-28 09:34:00+01:00   38.79  -12.68  -12.68           1.4   \n",
       "2023-07-07 13:57:00+02:00   66.57   35.71   35.71           1.4   \n",
       "2023-02-04 12:07:00+01:00  148.84  103.37  103.37           1.4   \n",
       "2023-07-22 10:37:00+02:00   74.59 -182.65 -182.65           1.4   \n",
       "2023-11-16 05:08:00+01:00  103.20  104.03  104.03           1.0   \n",
       "2023-11-03 15:09:00+01:00   69.89   91.06   91.06           1.0   \n",
       "2023-01-12 11:01:00+01:00  111.92   88.83  145.51           1.0   \n",
       "2023-11-08 05:57:00+01:00   75.01   87.99   87.99           1.0   \n",
       "2023-04-21 21:45:00+02:00  142.67  101.37  101.37           1.4   \n",
       "\n",
       "                           to_storage_MW  from_storage_MW  Total demand  \n",
       "2023-01-27 12:26:00+01:00            0.0              0.0             1  \n",
       "2023-10-23 01:22:00+02:00            0.0              0.0             1  \n",
       "2023-08-06 16:32:00+02:00           -0.0              0.0             1  \n",
       "2023-01-16 04:20:00+01:00           -0.0              0.0             1  \n",
       "2023-07-30 22:01:00+02:00            0.0              0.0             1  \n",
       "2023-08-21 03:09:00+02:00            0.0              0.0             1  \n",
       "2023-08-15 06:24:00+02:00           -0.0              0.0             1  \n",
       "2023-12-06 18:43:00+01:00           -0.0              0.0             1  \n",
       "2023-10-15 11:21:00+02:00            0.0              0.0             1  \n",
       "2023-08-02 13:23:00+02:00            0.0              0.0             1  \n",
       "2023-02-23 05:47:00+01:00           -0.0              0.0             1  \n",
       "2023-12-28 09:34:00+01:00           -0.0              0.0             1  \n",
       "2023-07-07 13:57:00+02:00           -0.0              0.0             1  \n",
       "2023-02-04 12:07:00+01:00           -0.0              0.0             1  \n",
       "2023-07-22 10:37:00+02:00           -0.0              0.0             1  \n",
       "2023-11-16 05:08:00+01:00            0.0              0.0             1  \n",
       "2023-11-03 15:09:00+01:00            0.0              0.0             1  \n",
       "2023-01-12 11:01:00+01:00            0.0              0.0             1  \n",
       "2023-11-08 05:57:00+01:00            0.0              0.0             1  \n",
       "2023-04-21 21:45:00+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>2023-01-15 17:34:00+01:00</th>\n",
       "      <td>139.64</td>\n",
       "      <td>191.21</td>\n",
       "      <td>191.21</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.40</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>2023-12-05 22:54:00+01:00</th>\n",
       "      <td>111.55</td>\n",
       "      <td>78.34</td>\n",
       "      <td>78.34</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>1.4</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 22:25:00+02:00</th>\n",
       "      <td>86.73</td>\n",
       "      <td>72.34</td>\n",
       "      <td>87.05</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.40</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>2023-07-04 06:06:00+02:00</th>\n",
       "      <td>113.74</td>\n",
       "      <td>-38.21</td>\n",
       "      <td>-38.21</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-29 10:30:00+02:00</th>\n",
       "      <td>-2.11</td>\n",
       "      <td>-10.20</td>\n",
       "      <td>-10.20</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.40</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>2023-06-10 22:16:00+02:00</th>\n",
       "      <td>98.00</td>\n",
       "      <td>78.91</td>\n",
       "      <td>78.91</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.40</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>2023-08-13 15:58:00+02:00</th>\n",
       "      <td>6.76</td>\n",
       "      <td>-18.53</td>\n",
       "      <td>-18.53</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-16 04:30:00+01:00</th>\n",
       "      <td>106.79</td>\n",
       "      <td>131.42</td>\n",
       "      <td>131.42</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.40</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>2023-12-27 02:41:00+01:00</th>\n",
       "      <td>57.63</td>\n",
       "      <td>99.69</td>\n",
       "      <td>99.69</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.40</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>2023-10-29 16:12:00+01:00</th>\n",
       "      <td>84.89</td>\n",
       "      <td>-69.76</td>\n",
       "      <td>-69.76</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-27 23:31:00+02:00</th>\n",
       "      <td>94.71</td>\n",
       "      <td>69.55</td>\n",
       "      <td>69.55</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-14 22:31:00+02:00</th>\n",
       "      <td>94.73</td>\n",
       "      <td>61.78</td>\n",
       "      <td>61.78</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-21 10:05:00+02:00</th>\n",
       "      <td>44.83</td>\n",
       "      <td>-36.64</td>\n",
       "      <td>-36.64</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-22 10:21:00+02:00</th>\n",
       "      <td>86.97</td>\n",
       "      <td>83.85</td>\n",
       "      <td>83.85</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.40</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>2023-04-28 15:57:00+02:00</th>\n",
       "      <td>88.26</td>\n",
       "      <td>87.66</td>\n",
       "      <td>87.66</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.40</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>2023-11-28 19:04:00+01:00</th>\n",
       "      <td>161.49</td>\n",
       "      <td>98.74</td>\n",
       "      <td>114.47</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-09 15:54:00+01:00</th>\n",
       "      <td>151.23</td>\n",
       "      <td>158.84</td>\n",
       "      <td>158.84</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.40</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>2023-03-19 20:51:00+01:00</th>\n",
       "      <td>144.71</td>\n",
       "      <td>85.63</td>\n",
       "      <td>85.63</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-10 16:46:00+01:00</th>\n",
       "      <td>140.11</td>\n",
       "      <td>196.40</td>\n",
       "      <td>196.40</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.40</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>2023-07-31 05:16:00+02:00</th>\n",
       "      <td>50.31</td>\n",
       "      <td>-21.33</td>\n",
       "      <td>-21.33</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>1.4</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",
       "2023-01-15 17:34:00+01:00  139.64  191.21  191.21             1        -0.40   \n",
       "2023-12-05 22:54:00+01:00  111.55   78.34   78.34             1        -0.56   \n",
       "2023-03-31 22:25:00+02:00   86.73   72.34   87.05             1        -0.40   \n",
       "2023-07-04 06:06:00+02:00  113.74  -38.21  -38.21             1        -0.56   \n",
       "2023-05-29 10:30:00+02:00   -2.11  -10.20  -10.20             1        -0.40   \n",
       "2023-06-10 22:16:00+02:00   98.00   78.91   78.91             1        -0.40   \n",
       "2023-08-13 15:58:00+02:00    6.76  -18.53  -18.53             1        -0.56   \n",
       "2023-02-16 04:30:00+01:00  106.79  131.42  131.42             1        -0.40   \n",
       "2023-12-27 02:41:00+01:00   57.63   99.69   99.69             1        -0.40   \n",
       "2023-10-29 16:12:00+01:00   84.89  -69.76  -69.76             1        -0.56   \n",
       "2023-05-27 23:31:00+02:00   94.71   69.55   69.55             1        -0.56   \n",
       "2023-07-14 22:31:00+02:00   94.73   61.78   61.78             1        -0.56   \n",
       "2023-10-21 10:05:00+02:00   44.83  -36.64  -36.64             1        -0.56   \n",
       "2023-05-22 10:21:00+02:00   86.97   83.85   83.85             1        -0.40   \n",
       "2023-04-28 15:57:00+02:00   88.26   87.66   87.66             1        -0.40   \n",
       "2023-11-28 19:04:00+01:00  161.49   98.74  114.47             1        -0.56   \n",
       "2023-01-09 15:54:00+01:00  151.23  158.84  158.84             1        -0.40   \n",
       "2023-03-19 20:51:00+01:00  144.71   85.63   85.63             1        -0.56   \n",
       "2023-01-10 16:46:00+01:00  140.11  196.40  196.40             1        -0.40   \n",
       "2023-07-31 05:16:00+02:00   50.31  -21.33  -21.33             1        -0.56   \n",
       "\n",
       "                           hp_output_MW  cop  to_storage_MW  from_storage_MW  \n",
       "2023-01-15 17:34:00+01:00           1.0  2.5            0.0              0.0  \n",
       "2023-12-05 22:54:00+01:00           1.4  2.5           -0.0              0.0  \n",
       "2023-03-31 22:25:00+02:00           1.0  2.5            0.0              0.0  \n",
       "2023-07-04 06:06:00+02:00           1.4  2.5           -0.0              0.0  \n",
       "2023-05-29 10:30:00+02:00           1.0  2.5            0.0              0.0  \n",
       "2023-06-10 22:16:00+02:00           1.0  2.5            0.0              0.0  \n",
       "2023-08-13 15:58:00+02:00           1.4  2.5           -0.0              0.0  \n",
       "2023-02-16 04:30:00+01:00           1.0  2.5            0.0              0.0  \n",
       "2023-12-27 02:41:00+01:00           1.0  2.5            0.0              0.0  \n",
       "2023-10-29 16:12:00+01:00           1.4  2.5           -0.0              0.0  \n",
       "2023-05-27 23:31:00+02:00           1.4  2.5           -0.0              0.0  \n",
       "2023-07-14 22:31:00+02:00           1.4  2.5           -0.0              0.0  \n",
       "2023-10-21 10:05:00+02:00           1.4  2.5           -0.0              0.0  \n",
       "2023-05-22 10:21:00+02:00           1.0  2.5            0.0              0.0  \n",
       "2023-04-28 15:57:00+02:00           1.0  2.5            0.0              0.0  \n",
       "2023-11-28 19:04:00+01:00           1.4  2.5           -0.0              0.0  \n",
       "2023-01-09 15:54:00+01:00           1.0  2.5            0.0              0.0  \n",
       "2023-03-19 20:51:00+01:00           1.4  2.5           -0.0              0.0  \n",
       "2023-01-10 16:46:00+01:00           1.0  2.5            0.0              0.0  \n",
       "2023-07-31 05:16:00+02:00           1.4  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                -500.00\n",
       "POS               -1549.47\n",
       "NEG               -1549.47\n",
       "Total demand          1.00\n",
       "hp_input_MW          -0.56\n",
       "hp_output_MW          1.00\n",
       "cop                   2.50\n",
       "to_storage_MW         0.00\n",
       "from_storage_MW       0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.min()"
   ]
  },
  {
   "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": "code",
   "execution_count": 71,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def aFRR_optimisation(case, s):\n",
    "    s.afrr_case.afrr_capacity = calc_afrr_capacity(s.afrr_case)\n",
    "    gb = case.assets['Gasboiler']\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",
    "    afrr_up = case.data['Hoogste_prijs_opregelen'].fillna(-999).to_list()\n",
    "    gas_prices = case.data['Gas prices (€/MWh)'].to_list()\n",
    "    co2_prices = case.data['CO2 prices (€/MWh)'].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=afrr_up[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=afrr_up[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": [
    "### 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": [
       "2.5"
      ]
     },
     "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": [
       "<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>ForeNeg</th>\n",
       "      <th>ForePos</th>\n",
       "      <th>Gas prices (€/MWh)</th>\n",
       "      <th>CO2 prices (€/ton)</th>\n",
       "      <th>CO2 prices (€/MWh)</th>\n",
       "      <th>Tsource (VDG)</th>\n",
       "      <th>Tsink (VDG)</th>\n",
       "      <th>MW (VDG)</th>\n",
       "      <th>Total demand</th>\n",
       "      <th>hp_output_MW</th>\n",
       "      <th>hp_input_MW</th>\n",
       "      <th>cop</th>\n",
       "      <th>hp_output_MWh</th>\n",
       "      <th>hp_input_MWh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:00:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:01:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:02:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:03:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:04:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>-209.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            DAM    POS    NEG  ForeNeg  ForePos  \\\n",
       "2023-01-01 00:00:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:01:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:02:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:03:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "2023-01-01 00:04:00+01:00 -3.61 -209.4 -209.4      NaN      NaN   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-01-01 00:00:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:01:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:02:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:03:00+01:00              63.998                5.23   \n",
       "2023-01-01 00:04:00+01:00              63.998                5.23   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-01-01 00:00:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:01:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:02:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:03:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:04:00+01:00                1.01             70          144   \n",
       "\n",
       "                           MW (VDG)  Total demand  hp_output_MW  hp_input_MW  \\\n",
       "2023-01-01 00:00:00+01:00         1             1             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",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
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       "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_27812\\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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",
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     "metadata": {},
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   ],
   "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"
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   },
   "source": [
    "## Financials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "tags": [
     "exclude"
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   "source": [
    "def calculate_sde_subsidy(c, s, case):\n",
    "    hp_capacity = abs(case.assets['Heatpump VDG'].max_th_power) + abs(case.assets['Heatpump NDG'].max_th_power)\n",
    "    case.full_load_hours = abs(case.data['hp_output_MWh'].sum() / hp_capacity)\n",
    "\n",
    "    subsidized_hours = min(case.full_load_hours, 8000)\n",
    "    subsidized_MWh = subsidized_hours * hp_capacity\n",
    "\n",
    "    base_amount_per_MWh_th = round(c.sde_base_amount*0.173 + c.longterm_gas_price, 2)\n",
    "    base_subsidy_amount = round(base_amount_per_MWh_th * subsidized_MWh, 2)\n",
    "    long_term_gas_price_LHV = round(EURperHHV_to_EURperLHV(c.longterm_gas_price), 2)\n",
    "    base_amount_gas = (2/3)*long_term_gas_price_LHV*0.9\n",
    "    mean_ttf_price_LHV = round(EURperHHV_to_EURperLHV(case.data['Gas prices (€/MWh)'].mean()), 2)\n",
    "    correction_amount_gas = max(mean_ttf_price_LHV, base_amount_gas)\n",
    "    avoided_gas_consumption = subsidized_MWh/0.9\n",
    "    correction_gas = round(correction_amount_gas * avoided_gas_consumption, 2)\n",
    "\n",
    "    base_amount_co2 = (2/3)*c.longterm_co2_price\n",
    "    avoided_co2_emission = avoided_gas_consumption * 0.226\n",
    "    mean_ets_price = round(EURperHHV_to_EURperLHV(case.data['CO2 prices (€/ton)'].mean()), 2)\n",
    "    correction_amount_co2 = max(base_amount_co2, mean_ets_price)\n",
    "    correction_co2 = round(correction_amount_co2 * avoided_co2_emission, 2)\n",
    "\n",
    "    sde_subsidy_corrected = max(base_subsidy_amount - (correction_gas + correction_co2), 0)\n",
    "    sde_per_MWh_th = sde_subsidy_corrected / subsidized_MWh\n",
    "    sde_per_MWh_e = sde_subsidy_corrected / case.data['hp_input_MWh'].abs().sum()\n",
    "\n",
    "    case.sde_results = {\n",
    "        'base_amount': base_subsidy_amount,\n",
    "        'correction_gas': correction_gas,\n",
    "        'correction_co2': correction_co2,\n",
    "        'corrected_amount': sde_subsidy_corrected,\n",
    "        'sde_per_MWh_th': sde_per_MWh_th,\n",
    "        'sde_per_MWh_e': sde_per_MWh_e\n",
    "    }\n",
    "    return case"
   ]
  },
  {
   "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",
    "#    s.hpcase_sde.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n",
    "    \n",
    "#     s.optcase1.data['DA Nom'] = s.hpcase.data['hp_input_MWh'] * c.day_ahead_buying_perc\n",
    "#     s.optcase1.generate_electr_market_results(nom_col='DA Nom', real_col='hp_input_MWh')\n",
    "    \n",
    "#     s.afrr_case.data['POS'] = s.afrr_case.data['aFRR_up'].fillna(value=s.afrr_case.data['POS'])\n",
    "#     s.afrr_case.data['DA Nom'] = s.hpcase.data['hp_input_MWh']\n",
    "#     s.afrr_case.generate_electr_market_results(nom_col='DA Nom', real_col='hp_input_MWh')\n",
    "#     s.afrr_case.add_cashflow('aFRR capacity fee (€)', c.afrr_capacity_fee * s.afrr_case.afrr_capacity)\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_27812\\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": [
        {
         "line": {
          "color": "rgba(70, 165, 121, 1.0)",
          "dash": "solid",
          "shape": "linear",
          "width": 1.3
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         "mode": "lines",
         "name": "Total demand",
         "text": "",
         "type": "scatter",
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         "text": "Smurfit Kappa: Heat demand by Hour in MW"
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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_27812\\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": [
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     "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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       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_steamboiler = s.baseline.data[['Total demand', 'output_MW_th']].abs().loc[date, :].iplot(\n",
    "    subplots=True, \n",
    "    title=f'Steamboiler only case on {date}',\n",
    "    subplot_titles=['Total demand', 'Steam boiler output (MW)'],\n",
    "    legend=False,\n",
    "    dimensions=(800, 400),\n",
    "    colors=[recoydarkblue, recoygreen], \n",
    "    asFigure=True,\n",
    "    shape = (2, 1)\n",
    ")\n",
    "fig_steamboiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "mode": "lines",
         "name": "Total demand",
         "text": "",
         "type": "scatter",
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       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_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",
       "        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>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>-4,131</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>4,131</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>452</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>88</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>-12,164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total imbalance result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-12,164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total combined result (€)</th>\n",
       "      <td>-352,531</td>\n",
       "      <td>-364,695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Heatpump only Heatpump + Storage_PCM\n",
       "Total net volume (MWh)                 -3,679                 -4,131\n",
       "Total exported to grid (MWh)                -                      -\n",
       "Total consumed from grid (MWh)          3,679                  4,131\n",
       "Total nominated volume (MWh)           -3,679                 -3,679\n",
       "Absolute imbalance volume (MWh)             -                    452\n",
       "Mean selling price (€/MWh)                  -                      -\n",
       "Mean buying price (€/MWh)                  96                     88\n",
       "Total day-ahead result (€)           -352,531               -352,531\n",
       "Total POS result (€)                        -                      -\n",
       "Total NEG result (€)                        -                -12,164\n",
       "Total imbalance result (€)                  -                -12,164\n",
       "Total combined result (€)            -352,531               -364,695"
      ]
     },
     "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",
       "        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>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>-364,695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electricity taxes (€)</th>\n",
       "      <td>-3,495</td>\n",
       "      <td>-3,924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grid transport costs (€)</th>\n",
       "      <td>-19,632</td>\n",
       "      <td>-27,485</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>169,061</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>156,181</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               -364,695\n",
       "Electricity taxes (€)                   -3,495                 -3,924\n",
       "Grid transport costs (€)               -19,632                -27,485\n",
       "HotWaterStorage OPEX (€)                     -                   -440\n",
       "EBITDA (€)                             189,947                169,061\n",
       "Depreciation (€)                       -12,000                -12,880\n",
       "EBITDA + depr (€)                      177,947                156,181"
      ]
     },
     "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",
       "        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>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"
     ]
    },
    {
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        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
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         "name": "Gas prices (€/MWh)",
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     "output_type": "display_data"
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   ],
   "source": [
    "#price chart\n",
    "from copy import deepcopy\n",
    "\n",
    "data = deepcopy(s.baseline.data)\n",
    "data = data[data.columns[:2]]\n",
    "data[data.columns[1]] = MWh_gas_to_tonnes_CO2(data[data.columns[1]])\n",
    "data = data.rename(columns={\"CO2 prices (€/ton)\": \"CO2 prices (€/MWh)\"})\n",
    "\n",
    "data.resample('D').mean().iplot(dimensions=(800, 300), title='Gasprices vs. CO2 prices', colors=recoycolors)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
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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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   "cell_type": "code",
   "execution_count": 113,
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CBAgQIECAAAECBAgQIECAQGIBgVFiYMMTIECAAAECBAgQIECAAAECBAgQIECAAAECBJouIDBqeofMjwABAgQIECBAgAABAgQIECBAgAABAgQIECCQWEBglBjY8AQIECBAgAABAgQIECBAgAABAgQIECBAgACBpgsIjJreIfMjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQWEBglBjY8AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDpAgKjpnfI/AgQIECAAAECBAgQIECAAAECBAgQIECAAAECiQUERomBDU+AAAECBAgQIECAAAECBAgQIECAAAECBAgQaLqAwKjpHTI/AgQIECBAgAABAgQIECBAgAABAgQIECBAgEBiAYFRYmDDEyBAgAABAgQIECBAgAABAgQIECBAgAABAgSaLiAwanqHzI8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkFhAYJQY2PAECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgaYLCIya3iHzI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgkFhAYJQY2PAECBAgQIECAAAECBAgQIECAAAECBAgQIECg6QICo6Z3yPwIECBAgAABAgQIECBAgAABAgQIECBAgAABAokFBEaJgQ1PgAABAgQIECBAgAABAgQIECBAgAABAgQIEGi6gMCo6R0yPwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAYgGBUWJgwxMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEmi4gMGp6h8yPAAECBAgQIECAAAECBAgQIECAAAECBAgQIJBYQGCUGNjwBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGmCwiMmt4h8yNAgAABAgQIECBAgAABAgQIECBAgAABAgQIJBYQGCUGNjwBAgQIECBAgAABAgQIECBAgAABAgQIECBAoOkCAqOmd8j8CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKJBQRGiYENT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBBouoDAqOkdMj8CBAgQIECAAAECBAgQIECAAAECBAgQIECAQGIBgVFiYMMTIECAAAECBAgQIECAAAECBAgQIECAAAECBJouIDBqeofMjwABAgQIECBAgAABAgQIECBAgAABAgQIECCQWEBglBjY8AQIECBAgAABAgQIECBAgAABAgQIECBAgACBpgsIjJreIfMjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQWEBglBjY8AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDpAgKjpnfI/AgQIECAAAECBAgQIECAAAECBAgQIECAAAECiQUERomBDU+AAAECBAgQIECAAAECBAgQIECAAAECBAgQaLqAwKjpHTI/AgQIECBAgAABAgQIECBAgAABAgQIECBAgEBiAYFRYmDDEyBAgAABAgQIECBAgAABAgQIECBAgAABAgSaLiAwanqHzI8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkFhAYJQY2PAECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgaYLCIya3iHzI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgkFhAYJQY2PAECaQT+8eHHwornPy/sscfSNAWMSoAAAQIEGibw+BM/DmFJCHstf27DZmY6BAgQIEAgjcBTTz8THn3sybBqxV5pChiVAAECBAgQmCUgMPJAECDQSgGBUSvbZtIECBAgsAgBgdEi8NxKgAABAq0UEBi1sm0mTYAAAQItFhAYtbh5pk6gzwICoz5339oJECDQTwGBUT/7btUECBDos4DAqM/dt3YCBAgQmIaAwGga6moSILBoAYHRogkNQIAAAQItExAYtaxhpkuAAAECixYQGC2a0AAECBAgQGBBAgKjBXG5mACBpggIjJrSCfMgQIAAgVwCAqNc0uoQIECAQFMEBEZN6YR5ECBAgEBfBARGfem0dRLomIDAqGMNtRwCBAgQmFdAYDQvkQsIECBAoGMCAqOONdRyCBAgQKDxAgKjxrfIBAkQGCYgMPJcECBAgEDfBARGfeu49RIgQICAwMgzQIAAAQIE8goIjPJ6q0aAQE0CAqOaIA1DgAABAq0REBi1plUmSoAAAQI1CQiMaoI0DAECBAgQGFNAYDQmlMsIEGiWgMCoWf0wGwIECBBILyAwSm+sAgECBAg0S0Bg1Kx+mA2Bpgk8et2W8OhVV449raUrV4Z9f+/isOchLxr7HhcS6JuAwKhvHbdeAh0REBh1pJGWQYAAAQJjCwiMxqZyIQECBAh0REBg1JFGWgaBRAL/eN6/C8/++MdhjwMPGqvCT26/Lex9yrvC8n/5+rGudxGB3AKbNl8Tbv/uHeHcc84Oq1aumLP81pu2hRs+tzV85EPvC4cecnBtUxUY1UZpIAIEcgoIjHJqq0WAAAECTRAQGDWhC+ZAoD0Cu77x1fCT7//X9kzYTAkMEXjmmWfDj3/yVFi+7Dl8CLRe4HnrfnXsYKP1i820gJyB0YMPPRzOu+DisOHk48PaI9fMrDD+A3/82LjhpHlXXb02/oP/9u/cHs46fWNYvmxZeHLXrnDJ5ZvC2pcdEdYft24w3p13/TBcedW14QNnnjZngLCQecw10TjOzdtumblkn332ngkkqvOdd8ENuKC6nqPWrpnxjtMrzO+77/5ZIU3x+Vu375hZxbHrjp7pc7TYvOW63Vb44Q+eMfjc5quvHzrefvvuO/JZ2X7bjrD15i/Pml8cq1or1iiewfi1H+28Z6znb9x2CIzGlXIdAQKNEhAYNaodJkOAAAECGQQERhmQlSDQEYFn/uc/hH/4NxvCHi+o76dNO0JjGS0TeDaE8MzTz4Q99ljaspmbLoHZAs8+8URYuudzwk9f/mk0NQq0PTCqhkHx1x/92KXhNb/4ilnBRDlUGsVXR2A0LBCKIcbOe+4bBFhtCoyKwCd6FYFcEb6sPnD/mcAlml/zJ38WHn/sifDW314/8/lqeFf8ugh85rOoBjnzXV+Mv/7YN+wWSFZ3HN34+S+Gdce8bhAgxvv+8D9uCb/1r361tl1GAqMa/yNlKAIE8gkIjPJZq0SAAAECzRAQGDWjD2ZBoA0Cg8DojFPD8l925E4b+mWOowWeefbZ8JOfPB2WPXdPTARaLfD0Pz4Qnr3//vDTH/+DVq+jaZNvYmBUhD6PPPJoKO/OicHL+RdeNkMYd7q86+0nhMv+4P8Op77zxME/9sdrvvN3d4Qf/GhneM+73zEIBGIQ9ILVBw4Cm/LYcaBix8uwsWNIEj/ijqVil8yGU942s3MpjvvEE0+Ex554YvD1k0948+AotPLupnK/q7VXH3TAzA6a8i6e6i6cGHY9/3nPC//5v3xzMN/j3/Qbg51aO+++dzB8dcdPtU68prqjptjZU723PN9oUt3lM+z5jUFO8VHeqTNst1d5B9C2r3x11u6w6tjlAOjQQ14YLvrEFTN9HjaPYTvJxt1dVqyh2JW22N+nAqPFCrqfAIGpCAiMpsKuKAECBAhMUUBgNEV8pQm0TEBg1LKGme5IAYGRh6MrAgKjNJ1sWmBUPbYuBgyfvGLzzJFuw3YBlQOh+P+PXPPS8I1v/u1gx8gBB/zMIPApdp381S1fCy869IWDcKkIVt572obBjpTq2NUdMXFu5dAiXv/1b/7trPffDPtcuXPDdsmUPxevjfMt78KJ4U458Ikm8SPOufA67o3HDIKsUb8ujgGMtW760ldmBVVFmFZ9wsbZcVXenRPvLx/9NywwKq91vsAojlcEPns9/3kjg7hi3tHlr7/17ZmdZYVFEazF68qBX3m9o46ym/R3ncBoUjn3ESAwVQGB0VT5FSdAgACBKQgIjKaAriSBlgoIjFraONPeTUBg5KHoioDAKE0npxEYlf8Bv1hVeadPeVdLNXQYFmIUR5e9/cS3hM9ee8NgB87XvvE3g6GPOPznRr6/aL6xY1jx8cs3hfefvnHmqLJqOBVrVN+9NNc7f8Z551J5Z09cx3zH6ZVNqruCygHc4Ye9ZLf3O80VlJTXOurpi0af+8Jfht991ymDS8rvj6r6VsOsud5hVH7H1XxH0RVzG7ZLaNz3E5XXEd+FtdgPgdFiBd1PgMBUBARGU2FXlAABAgSmKCAwmiK+0gRaJiAwalnDTHekgMDIw9EVAYFRmk5OIzAqdrsUK6oGHltv/vKsd+bMF9IUu1Di+3O+8p+/PggvvnfHfxvsNnnVK18eyuMVIUZxxFycQ7HrpBpGDTvaba7rh3WoCEmOeOlhg2BpVGBUfu9O+Ri1UYFRNZQqB27l9Q4LjMprj3MedSzdODuMqqHSsN1Sw6xj3XGCoCK0i9cf9yuvnzkOcJh1NTCq7jCa6/g9gVGa/74YlQCBlgkIjFrWMNMlQIAAgUULCIwWTWgAAr0REBj1ptWdX6jAqPMt7s0CBUZpWt3EwGihO4yKo+Je8fI1Yfnzls8czfapT38mvPAFq8O+P7Vq8LnqEXPj7DAqH7FW7cA4gUo1GKkewzbqPT+FwbDAKNa9/4EHZkK1he4wKodTcz1V873DaNiRb3G84r1TxXGAo97pNF9gVH6H0aqVK3fb7VWde/VIuvj1cd9hJDBK898XoxIg0DIBgVHLGma6BAgQILBoAYHRogkNQKA3AgKj3rS68wsVGHW+xb1ZoMAoTaubFhhVjy2rvsNoVMhQ7Lgp3vVT3klU/VwRYAw7Iq18/Fs1YCoCiP9x5w/C649+7dB3Hv3hf9wyeHdSfEdS/KgGQsNCmPJ7hZYvX7bbO4yqR9KVA6Jh71k674KLQ7GLK9Y7/8LLZt6BVH2HUbx/6198Kaz/tTeG6lFsxdhxHWedvnHm63GM1QfuP1hfOdwrntBi19G6Y1632xF45ad4vsCoepzcfNePCofifG7/7h0z722Kc7jx818McX6rVq4YTGnYcXaL+R3nSLrF6LmXAIGpCQiMpkavMAECBAhMSUBgNCV4ZQm0UEBg1MKmmfJQAYGRB6MrAgKjNJ1sWmAUV1k+Cq7YrVIEMOVdLeUjxuI/+N/wua3hIx9630xYUw1H4thFgBL/fxx7n+fvNXPU2bCx43XxvTzFsWrl+Yx6n9LmLdfNalZx5F38ZDnIWn3QATMhRvmIueJ4uSLIqAZGVZ+4hiN+/vCZdymV1xiNHn/siXDqO0+c5VKeY3l+w56yUe9kiu+Lih/VdzgV70V6z6nvCJ+68jNhrh1GVas4Xgz44kc1jBoW4JXnW96RVH4HUuFYrlWEiEVPqkHfYn+3CYzmESyflVht1mLx3U+AwOQCAqPJ7dxJgAABAu0UEBi1s29mTWAaAgKjaairmUJAYJRC1ZjTEBAYpVHPGRilWYFR5xIY90i2rigWYVV5R9R8a6vuZJrv+nG+3pnAKOLMlS5WzyUsJ3ERqnx/OeEVGI3zGLmGQH4BgVF+cxUJECBAYLoCAqPp+qtOoE0CAqM2dctc5xIQGHk+uiIgMErTycf+eEt45DObQghLxiqwdOXKsO/vXRz2PORFY13vovwC5R1B1R1a+WeTv+KwI+hGzWLYzrQ6ZtyJwChu2YrbyI5/028Mzu6rhjzV8xZjOvnxyzeF95++cbCdrXr+YnlLnsCojsfMGATqFxAY1W9qRAIECBBotoDAqNn9MTsCTRIQGDWpG+ayGAGB0WL03NskAYFRk7phLgQIzCXQicCousBhAdGVV10bPnDmaYNAqfr14mVW649bNxiqHCDFX5dftlUESEe89LDdzjj0qBEgkE9AYJTPWiUCBAgQaIaAwKgZfTALAm0QEBi1oUvmOI6AwGgcJde0QUBg1IYumSMBAlGgk4FR8fKs9562IcT3DlV3EMWFF7uI3n7iWwYv/yq/wKq8A2nlyhUzgdHhh71kcO36Y98wGNcHAQLTExAYTc9eZQIECBCYjoDAaDruqhJoo4DAqI1dM+dBdyztAAAgAElEQVRhAgIjz0VXBARGXemkdRDovkCnAqPye4o2nPK2UN4xtPXmL4fyC6OqgVE5BBoWGJ3w1t8K22756qxgKT4eP3nq6e4/JdNY4bPTKKpmmwQeefyJsNfyZWGPpUvbNG1z/WeBJYPjhcc7YxgagToEvv8nW8IzP3mqjqGMQWBqAk89809/7txz6R5Tm4PCBOoQWLp8WXjxm06sYyhjjBCIgdED7313eOLnDmNEoNUCzzwbwrPPPhv2WOrvDq1upMmHJY8+Fp739FNh34//Yas0nrOnP3e2qmEmS6AGgU4FRoVH9ci5OnYY7bz73rD6oAPCueecPTjWrvh48OHHa2iDIXYT8GdBD8U8Ak899UzYc4+lMoeWPinxHzuLf/hs6RJMu0UCX/7v3ws//3+dE577tJ9GaFHbTHWoQPEM+4OSB6TdAj/eY0n43v/5++GYFwkzUnXyoUceCt/49/97OPjhR1KVaNW48b+a/hTQqpaZLIHOCSx59tlw65ojw4ln/rtWrW3Vir1aNV+TJUBg8QKdDIwiy9abtoUf7bxn8J6huGNose8wOu6Nx4TVB+4fqjuVFt8CIxAgMImAI+kmUXMPgX4KXPWtr4U/3fGtcOQBL+gngFV3RiD+sETcnDn4gQkfBFos8O17fhSOP/IXwimv+KUWr6LZU7/30YfCb2/+ZPi1n3OUerM7ZXbzCTiSbj4hX2+LwP2PPRrufvThcMM7Tm/LlM2TAIGeCnQiMIpH0V3/p38e4vuIli9bFoqj6WLIE4+lq+44Kh85d+ghB+/2jqPiuLoYNhVjbTj5+MF7i2IQtf07t8863q6nz45lE5iqgMBoqvyKE2iVgMCoVe0y2TkEBEYej64ICIzSd1JglN5YhTwCAqM8zqqkFxAYpTG+4ht/FS655eaxB/+p5+0VNp9wanjJT+8/9j0uJNA3gU4ERrFpMeS5edstM/0rv8MofrL8fqP46w9/8IxBAFR8xCBo85brBr88au2amUCoGhgVte5/4AGhUd9+t1hvowQERo1qh8kQaLSAwKjR7TG5BQgIjBaA5dJGCwiM0rdHYJTeWIU8AgKjPM6qpBcQGKUx/t9u/GzY9dRPwgtX/tRYBf5m513hzNf9Svi1w+zAHQvMRbUIFJtZ4mBnnb5xsOFlro+Yc9z+3Tt2ezVOLZMZY5DOBEZjrNUlBAh0SEBg1KFmWgqBxAICo8TAhs8mIDDKRq1QYgGBUWLgEILAKL2xCnkEBEZ5nFVJLyAwSmOcMzAatqkgrqp8UtViVlnXOIuZwzj3Vk/uKu4pQpH1x75h8KnzL7xs1nDlDRrlL8TxPvqxS8Mjjzw68+lj1x09eM3MqI+i1q3bd4y8p+jX/vvvNyukme/e8qaScv24+WTnPfftdvJY9aSzYXMuvzqn/PVqrfIGl/g8vGD1gYPT03J/CIxyi6tHgEAtAgKjWhgNQqAXAgKjXrS5F4sUGPWizb1YpMAofZsFRumNVcgjIDDK46xKegGBURpjgVEa17lGrb76pbg2Bj9XXnVt+MCZp4U77/pB2Hz19bN2yAzbNRMDkxs+tzV85EPvC/G1McVHvPZVr3z5rNPBiq8VAc0RLz1sVqi05Y9vDL/8S6+aGWf7bTvC1pu/HB5/7Ilw6jtPnPl8df7Fr/fbd9/BePO9jqYa5MwX9MX5XvSJK2bNIa5lmMeNn/9iWHfM68KqlSsGp6V96tOfCe959zsGv875ITDKqa0WAQK1CQiMaqM0EIHOCwiMOt/i3ixQYNSbVnd+oQKj9C0WGKU3ViGPgMAoj7Mq6QUERmmMmxgYzfValFE7SmK4Ud6RE3fjvOvtJ4Tfv+iycPRrXx2+cNO2wQ6c8i6darBRDmyi9nkXXBxiqFK8wiXe++Z//evhP1z6B7uNVewaOvp1rwl/dMPnB82aa5fPsFClvIsmrqcaGBUhSfzfGMyM2rE135MyX6BTDZ3++lvfnrVTZ1jgVYRL8bi4bV/56m67iMpzKgdA8fNFSDYq1CmPXRxFV+7VXGHQXMHZfE6L+brAaDF67iVAYGoCAqOp0StMoHUCAqPWtcyERwgIjDwaXREQGKXvpMAovbEKeQQERnmcVUkvIDBKY9y0wKh6PFk5GFi+fFnY+hdfCut/7Y2Dd9jE4OOmL31lZhdOdadK9Ui1KHjJ5ZvC2pcdMTimbNzAqBzOFMezPfnkrkGgtOHk4we7eIpj4V7zi68YK8ypHktXPo4ujjcqMCp/ftgupPmeklG7m6r3lXfnxDpxp1Hx7qBhY5Qt5wuMYq24jj/5s62Dsm/97fVDd0IVc4pjx4/iaLlqoBi/tuGUtw09em7UUXbzOS326wKjxQq6nwCBqQgIjKbCriiBVgoIjFrZNpMeIiAw8lh0RUBglL6TAqP0xirkERAY5XFWJb2AwCiN8TQCo51337vbYordONWgpBqklG+shi6jAqMi1In3zhVsDNthVNw76hi2Inwa9l6i+Y5aKx/NVt0xMyowqh5bVw5yqu8yKr/Pp3Cby7NsG+vHnUXDdjJVLaoh31zvMIphWPExn0/5uurxeuMGQeV1pPkdNHxUgVFObbUIEKhNQGBUG6WBCHReQGDU+Rb3ZoECo960uvMLFRilb7HAKL2xCnkEBEZ5nFVJLyAwSmM8jcCoHODEVZWDg+rRcsWqi/CjGorss8/eM+/vGScwmuvotLoDo/lCjepc4lqLXTSL2WE011F14+wwGhYqlW2Lr9+6fcfMQ1ne4TPOkXeD9W25Ljzy2OPhvadtmHOHUfVYueoOo/Ixg9XfJQKjNP/dMCoBAh0VEBh1tLGWRSCBgMAoAaohpyIgMJoKu6IJBARGCVArQwqM0hurkEdAYJTHWZX0AgKjNMZNDIzKu2bKqy7CoiJgaPsOo+JdPqec8Obwl//PX4Xf+le/Gg495ODBkhfzDqP53m00X6BTDeWKHqw+6IDB8X/xaMDy0X7VJ3O+8cvvMHrwoYeGvqupPGb1SLr4tXHfYSQwSvPfDaMSINBRAYFRRxtrWQQSCAiMEqAacioCAqOpsCuaQEBglABVYJQeVYWpCAiMpsKuaAIBgVEC1BBC0wKj6vFmRXgS/3fVypXh45dvCu8/feMgWIlhwCev2Dyzw6gaVFSDk+qvq6FM3Mly+3fvGIQi8aP8jqKFHkk37Ii6YR2MNZ944onBl373XacM3s1UrHnz1dfPvJ8pfq48v1UrVwyui2u+4XNbZwzi5+YLjIqvH/HSwwZHzhUfW/74xvDLv/SqcPv3/mvY/p3bZ95ZFL9e3nV0+GEvWVRgVD6Kr1hX/N/yXMpW5Z1Yhc8ojxs//8Ww7pjXhcKnujspze+i3Ud1JF0uaXUIEKhVQGBUK6fBCHRaQGDU6fb2anECo161u9OLFRilb68dRumNVcgjIDDK46xKegGBURrjpgVG5cCjeNdRsbMlhgAxALh52y0DjNUH7j/43yJAKh9VFo8pe9fbTwi/f9FlofzOpPLRaUXoUIx3wlt+M/ztt3eED5x52mDchQZGH/3YpeGRRx6dadSwdwhVu1gcwVed17Cj+UYdvTZsR9B8tYcdKxffI/X2E98yMgwqjtib65q4vrneYbTznvt2C6OGhYRlp/KOpGIHVvH1aq3yuuN9n/r0Z8J73v2OmQApze+i3UcVGOWSVocAgVoFBEa1chqMQKcFBEadbm+vFicw6lW7O71YgVH69gqM0hurkEdAYJTHWZX0AgKjNMY5A6M0Kxg96nw7beqcz7g7iuqs2aex5nsf1DCL6k6mnF4Co5zaahEgUJuAwKg2SgMR6LyAwKjzLe7NAgVGvWl15xcqMErfYoFRemMV8ggIjPI4q5JeQGCUxvgPv/FX4aJbbgpLwpKxCvzU8/YKm084Nbzkp/9pd0+TPwRGTe7OwuZW7IiKd511+saZo/tGjTLs+L6FVVzc1QKjxfm5mwCBKQkIjKYEryyBFgoIjFrYNFMeKiAw8mB0RUBglL6TAqP0xirkERAY5XFWJb2AwCi9sQoECNQjIDCqx9EoBAhkFhAYZQZXjkCLBQRGLW6eqc8SEBh5ILoiIDBK30mBUXpjFfIICIzyOKuSXkBglN5YBQIE6hEQGNXjaBQCBDILCIwygytHoMUCAqMWN8/UBUaegU4KCIzSt1VglN5YhTwCAqM8zqqkFxAYpTdWgQCBegQERvU4GoUAgcwCAqPM4MoRaLGAwKjFzTN1gZFnoJMCAqP0bRUYpTdWIY+AwCiPsyrpBQRG6Y1VIECgHgGBUT2ORiFAILOAwCgzuHIEWiwgMGpx80xdYOQZ6KSAwCh9WwVG6Y1VyCMgMMrjrEp6AYFRemMVCBCoR0BgVI+jUQgQyCwgMMoMrhyBFgsIjFrcPFMXGHkGOikgMErfVoFRemMV8ggIjPI4q5JeQGCU3lgFAgTqERAY1eNoFAIEMgsIjDKDK0egxQICoxY3z9QFRp6BTgoIjNK3VWCU3liFPAICozzOqqQXEBilMb7001vC+ZddOfbg+65aGW644uJw+ItfNPY9LiTQNwGBUd86br0EOiIgMOpIIy2DQAYBgVEGZCWyCDz11DMhLAlhzz2WZqmnCIFUAgKjVLL/a1yBUXpjFfIICIzyOKuSXkBglMb4He//d2HXrh+HQw8+aKwCX7/1tvCh97wr/Oaxrx/r+lwXbb1pW9j+ndvDWadvDMuXLRu77JO7doVLLt8U1r7siLD+uHVh+207wuarrw/nnnN2WLVyxdjjdOnCwiSuaRzPTZuvCbd/945em1X7LzDq0u8IayHQIwGBUY+abakEFikgMFokoNsbIyAwakwrTGSRAgKjRQKOcbvAaAwkl7RCQGDUijaZ5BgCAqMxkCa4ZBqB0Z13/TB89GOXhkceeXRmxseuOzps3HDSBCv4p1uaHhjFUOXmbbfMrO+otWtmhTEPPvRwOO+Ci8P+++836/NFeHPr9h1DreK6N2+5bje3D3/wjLDznvt2C9GKOse98ZhBQDbsI475o5337NaPaq1YY+2RawZDxPW9YPWBI8ecuLEtvVFg1NLGmTaBvgsIjPr+BFg/gfEFBEbjW7my2QICo2b3x+zGFxAYjW816ZUCo0nl3Nc0AYFR0zpiPpMKCIwmlZv7vtyBUQwdbvjc1vCRD70vHHrIwTOTi4HDq1758pkAYqGrnTQwqtape4fRqN06cb6rD9x/Zr2x7tabvxwef+yJcOo7T5yxqe6AKn693777DgKd+dZdDXLir+PHqHAuBkoXfeKKWXMoAqHqLqIbP//FsO6Y1w12YsX7PvXpz4T3vPsdvd2ZVX6WBEYL/R3segIEGiEgMGpEG0yCQCsEBEataJNJjiEgMBoDySWtEBAYpW+TwCi9sQp5BARGeZxVSS8gMEpjnDMwKna3bDj5+DmDoeoOpPLuo1Ffi8HJ177+NwOkO/7b3w/+t7wDpqi98+57Z31tviPpqjt8NpzytsEummK8o1/76vCFm7YNxqyGYPFz4wZQRWD219/69qydOtX5FWPGcCkeF7ftK1+d8yi+cgAU773yqmvDB848bWSoUwRX5aPoovl898WxFxv6pXnCpzOqwGg67qoSILBIAYHRIgHdTqBHAgKjHjW740sVGHW8wT1ansAofbMFRumNVcgjIDDK46xKegGBURrjnIHRuOHJX93ytfCiQ1842GVTBETvPW1DOPywlwzeN7T+2DcMAqcYpmz9iy+F9b/2xkFwUt65VK61fPmyWe8pGjbmsHcYFfcVu3nK4cvKlSuGHiFX7dJ8O3ri9eXdOXfe9YPBTqMisBkWGJV3Fc0XGBUB05/82dbB1N762+vnDOvi2PGjOK6uGrTFrxWhWXWto46yS/PkNntUgVGz+2N2BAiMEBAYeTQIEBhXQGA0rpTrmi4gMGp6h8xvXAGB0bhSk18nMJrczp3NEhAYNasfZjO5gMBocru57swdGJXDkOpuofKOoGLO5cDkta/+hUFIM+z9O9Wj2cq7mVatXBk+fvmm8P7TN84c9VYEOW8/8S2zwqRy0PTQQw8PvS++q6eYy3y7pcZ5t0+sGXcWxWPiqruwqoFR9R1Ec73DqHi/ULQcJ7gqrqseDThuEFReR5qntT2jCoza0yszJUCgJCAw8jgQIDCugMBoXCnXNV1AYNT0DpnfuAICo3GlJr9OYDS5nTubJSAwalY/zGZyAYHR5HZNCow2X319OPecs2cdiTYqJLl1+46ZqRe7WkaFTNXAqAha4m6kGBhVj1QrQpD5AqOPfuzS8Mgjj84ijHNZSGAUbx71zqDyPIuApxzuVI/Ei2OVd/jM9w6jeP0gBNtyXXjkscdD3KlVDpKqz0b1WLnqDqOj1q6Z2f1UvVdg9L9EBEZp/ntlVAIEEgsIjBIDG55AhwQERh1qZs+XIjDq+QPQoeULjNI3U2CU3liFPAICozzOqqQXEBilMc65w2jUO4zKny+OnSuOgRt2JFshUd4N9LVv/M2sd/nUtcNo1Lt7xn0f03zH8FUDsGJtqw86YBCsVY/Tqz4F8wVG5WP0HnzooTAssCuPWT2SLn5t3HcYCYwERmn+K2VUAgSyCQiMslErRKD1AgKj1rfQAv5ZQGDkUeiKgMAofScFRumNVcgjIDDK46xKegGBURrjnIFRXEEMJMrvGoqfGxYYFe8UKh/BFnf1xHf2vPk3f32AMVdgVA5S4rXx3UdFCDXpO4yK8OR/3PmDsPblawbH4813JF0ReMV7i/cSFQ6rD9w/7LznvllBV/xaeddREaAVHtWnYL7AqHok3nxH00XT8rGBRb143+3fvWPW7rAbP//FsO6Y183sFqvuTkrzxLZjVDuM2tEnsyRAoCIgMPJIECAwroDAaFwp1zVdQGDU9A6Z37gCAqNxpSa/TmA0uZ07myUgMGpWP8xmcgGB0eR2c92ZOzCKcxm2q6b8/qIYWpx/4WWDae+zz95hn+fvFY77ldcPwokY/BRH1cWvfeRD7xu8l6j6Lp9ih86qlSsG41SPVivqVXcwVXcEVY+EK2quXLlirMCosI9hys3bbplpRTza7T2nviN86srPhGFh0Kgj86q9nOsdRsPCqOo7kKrjlXckRdfyR7VWuWfxvk99+jPhPe9+x6zjBtM8tc0fVWDU/B6ZIQECQwQERh4LAgTGFRAYjSvluqYLCIya3iHzG1dAYDSu1OTXCYwmt3NnswQERs3qh9lMLiAwmtxurjunERilWYlR6xIowqpR710aVqe6k6muubR1HIFRWztn3gR6LiAw6vkDYPkEFiAgMFoAlksbLSAwanR7TG4BAgKjBWBNeKnAaEI4tzVOQGDUuJaY0IQCAqMJ4ea57dJPbwm/98lNYcmSJWMV2HfVynDDFReHw1/8orGud1H7BEYdozdqJcOOq2vfquudscCoXk+jESCQSUBglAlaGQIdEBAYdaCJljAQEBh5ELoiIDBK30mBUXpjFfIICIzyOKuSXkBglN5YBQIE6hEQGNXjaBQCBDILCIwygytHoMUCAqMWN8/UZwkIjDwQXREQGKXvpMAovbEKeQQERnmcVUkvIDBKb6wCAQL1CAiM6nE0CgECmQUERpnBlSPQYgGBUYubZ+oCI89AJwUERunbKjBKb6xCHgGBUR5nVdILCIzSG6tAgEA9AgKjehyNQoBAZgGBUWZw5Qi0WEBg1OLmmbrAyDPQSQGBUfq2CozSG6uQR0BglMdZlfQCAqP0xioQIFCPgMCoHkejECCQWUBglBlcOQItFhAYtbh5pi4w8gx0UkBglL6tAqP0xirkERAY5XFWJb2AwCi9sQoECNQjIDCqx9EoBAhkFhAYZQZXjkCLBQRGLW6eqQuMPAOdFBAYpW+rwCi9sQp5BARGeZxVSS8gMEpvrAIBAvUICIzqcTQKAQKZBQRGmcGVI9BiAYFRi5tn6gIjz0AnBQRG6dsqMEpvrEIeAYFRHmdV0gsIjNIbq0CAQD0CAqN6HI1CgEBmAYFRZnDlCLRYQGDU4uaZusDIM9BJAYFR+rYKjNIbq5BHQGCUx1mV9AICo/TGKhAgUI+AwKgeR6MQIJBZQGCUGVw5Ai0WEBi1uHmmLjDyDHRSQGCUvq0Co/TGKuQREBjlcVYlvYDAKL2xCgQI1CMgMKrH0SgECGQWEBhlBleOQIsFBEYtbp6pC4w8A50UEBilb6vAKL2xCnkEBEZ5nFVJLyAwSm+sAgEC9QgIjOpxNAoBApkFBEaZwZUj0GIBgVGLm2fqAiPPQCcFBEbp2yowSm+sQh4BgVEeZ1XSCwiM0hurQIBAPQICo3ocjUKAQGYBgVFmcOUItFhAYNTi5pm6wMgz0EkBgVH6tgqM0hurkEdAYJTHWZX0AgKj9MYqECBQj4DAqB5HoxAgkFlAYJQZXDkCLRYQGLW4eaYuMPIMdFJAYJS+rQKj9MYq5BEQGOVxViW9gMAovbEKBAjUI1B7YLRp8zXh/gceCGedvjEsX7Zs1iyf3LUrXHL5prDfvvuGjRtOqmcFRiFAoJcCAqNett2iCUwkIDCaiM1NDRR46qlnQlgSwp57LG3g7EyJwPgCAqPxrSa9UmA0qZz7miYgMGpaR8xnUgGB0aRy7iNAILdArYHRgw89HM674OKw4eTjw9oj1wxdy/bbdoTNV18fzj3n7LBq5Yrc61WPAIGOCAiMOtJIyyCQQUBglAFZiSwCAqMszIpkEBAYpUcWGKU3ViGPgMAoj7Mq6QUERumNVSBAoB6B2gOjiz5xRTj1nSeGQw85eOgM77zrh+HKq64NHzjzNIFRPT00CoFeCgiMetl2iyYwkYDAaCI2NzVQQGDUwKaY0kQCAqOJ2BZ0k8BoQVwubrCAwKjBzTG1BQkIjBbE5WICBKYoUHtgVN5htPWmbWH1gfvP2m1kh9EUu600gQ4JCIw61ExLIZBYQGCUGNjw2QQERtmoFUosIDBKDBxCEBilN1Yhj4DAKI+zKukFBEbpjVUgQKAegVoDozil+A6j+BHfUVQERsVU4zF15a/XswSjECDQRwGBUR+7bs0EJhMQGE3m5q7mCQiMmtcTM5pMQGA0mdtC7hIYLUTLtU0WEBg1uTvmthABgdFCtFxLgMA0BWoPjJ7ctStccvmmwZoOf/HPhp/9F4cM/v+Tu34ctt3y1XDfffd7f9E0O642gY4ICIw60kjLIJBBQGCUAVmJLAICoyzMimQQEBilRxYYpTdWIY+AwCiPsyrpBQRG6Y1VIECgHoHaA6NiWnEn0c3bbpk1y2PXHT3YeeSDAAECixUQGC1W0P0E+iMgMOpPr7u+UoFR1zvcn/UJjNL3WmCU3liFPAICozzOqqQXEBilN1aBAIF6BJIFRnF6w46kq2faRiFAoO8CAqO+PwHWT2B8AYHR+FaubLaAwKjZ/TG78QUERuNbTXqlwGhSOfc1TUBg1LSOmM+kAgKjSeXcR4BAboGkgVHuxahHgEB/BARG/em1lRJYrIDAaLGC7m+KgMCoKZ0wj8UKCIwWKzj//QKj+Y1c0Q4BgVE7+mSW8wsIjOY3cgUBAs0QEBg1ow9mQYDAAgUERgsEczmBHgsIjHrc/I4tXWDUsYb2eDkCo/TNFxilN1Yhj4DAKI+zKukFBEbpjVUgQKAeAYFRPY5GIUAgs4DAKDO4cgRaLCAwanHzTH2WgMDIA9EVAYFR+k4KjNIbq5BHQGCUx1mV9AICo/TGKhAgUI+AwKgeR6MQIJBZQGCUGVw5Ai0WEBi1uHmmLjDyDHRSQGCUvq0Co/TGKuQREBjlcVYlvYDAKL2xCgQI1CMgMJrH8cGHHg7nXXBx2HDy8WHtkWvqUTcKAQKLFhAYLZrQAAR6IyAw6k2rO79QO4w63+LeLFBglL7VAqP0xirkERAY5XFWJb2AwCi9sQoECNQj0JnAaNPma8LN226ZUdlwytvC+uPWzfy6CH523n3v4HMf/uAZswKgrTdtC5u3XDf42lFr14SzTt8Yli9bFgRG9TxoRiFQt4DAqG5R4xHoroDAqLu97dvKBEZ963h31yswSt9bgVF6YxXyCAiM8jirkl5AYJTeWAUCBOoRSBYYVQOa8nRXH3RAOPecs8OqlStqWcWTu3aFz157Qzj+Tb8xGPPOu34YPvqxS8N7T9swCIXi1y+5fFNY+7IjBiFS/PrHL98U3n/6xnDoIQeH7bftCJuvvn5mTjF8ih8bN5wkMKqlQwYhUL+AwKh+UyMS6KqAwKirne3fugRG/et5V1csMErfWYFRemMV8ggIjPI4q5JeQGCU3lgFAgTqEUgWGJVDl3qmOv4owwKiK6+6NnzgzNMGgVL163GuL1h94MyOpHKAFKuWj6QrgrAjXnrYIFDyQYDAdAQERtNxV5VAGwUERm3smjkPExAYeS66IiAwSt9JgVF6YxXyCAiM8jirkl5AYJTeWAUCBOoRSBIYxVDlok9cEU5954mDHTy5P6rHyFV3EMX5FIHW2098y6zdR/Fr5R1IK1eumAmMDj/sJYNr1x/7Bu8zyt1U9QhUBARGHgkCBMYVEBiNK+W6pgsIjJreIfMbV0BgNK7U5NcJjCa3c2ezBARGzeqH2UwuIDCa3M6dBAjkFehkYFTd3RQDo603f3nmvUTDAqNyCDQsMDrhrb8Vtt3y1Zlj7Yo2/fgnT+ftmGoECAwEHnt8V3je8ueGpUuXEGmhwNIlS0L8y58PAjkEttz6X8Ln/u7WsGb/1TnKqUEgmcDTzzwzGHuPpUuT1TAwgRwCt927M7x1zSvDiWtfnaNcL2vc++jD4fgtl4fjXnJEL9dfXXT8G4M/ebbzUYh/ZXj66afDnnvu0c4FmDWBfxaIgdG9jz0c/uik97TK5LnP8XuvVQ0zWQI1CCQJjOK8qse81TDXsYaIde9/4IFZ4VAdO4x23n1vGPbupYcffWKsebmIAIF6BX781FPhOXvsEZYsERjVK5tntD332CM89bTAPY+2Ktfe9o3w5/M/J8YAACAASURBVHd8Oxyxn8DI09BugWf/OWj3va/dfTT7EHbctzP89s8fFd72sl/EkUjgvsceDqdctym88V+8NFGFdg3rh5Xa1a/ybJ8Nz4b4/W/pEj8s0d4umnkU+J+PPxrue+yRcNXxp7YKZMXez2vVfE2WAIHFCyQLjOIunc994S/D777rlLB82bLFz3SMEYaFRfG2OJfFvsPouDceE1YfuP9uO5XGmJZLCBBIIOBIugSohiTQUQFH0nW0sT1cliPpetj0ji7ZkXTpG+tIuvTGKuQRcCRdHmdV0gs4ki69sQoECNQjkCQwKt4hFHflDPsYtlNnscupHkNXHu/JXbtmvaeofORcfMdSdQdSeazq+5C23rQtbP/O7bN2MC127u4nQGDhAgKjhZu5g0BfBQRGfe1899YtMOpeT/u6IoFR+s4LjNIbq5BHQGCUx1mV9AICo/TGKhAgUI9AksConqmNP8qogOqotWtmgp3qNR/+4Blh7ZFrZorEIGjzlusGvx5234aTj5+5ftROpvFn7EoCBBYrIDBarKD7CfRHQGDUn153faUCo653uD/rExil77XAKL2xCnkEBEZ5nFVJLyAwSm+sAgEC9Qh0IjCqh8IoBAi0SUBg1KZumSuB6QoIjKbrr3p9AgKj+iyNNF0BgVF6f4FRemMV8ggIjPI4q5JeQGCU3lgFAgTqERAY1eNoFAIEMgsIjDKDK0egxQICoxY3z9RnCQiMPBBdERAYpe+kwCi9sQp5BARGeZxVSS8gMEpvrAIBAvUI1BoYFce+/fa/+tXwZ1/4y5DzHUb1cBiFAIG2CAiM2tIp8yQwfQGB0fR7YAb1CAiM6nE0yvQFBEbpeyAwSm+sQh4BgVEeZ1XSCwiM0hurQIBAPQK1Bkb1TMkoBAgQmF9AYDS/kSsIEPgnAYGRJ6ErAgKjrnTSOgRG6Z8BgVF6YxXyCAiM8jirkl5AYJTeWAUCBOoREBjV42gUAgQyCwiMMoMrR6DFAgKjFjfP1GcJCIw8EF0REBil76TAKL2xCnkEBEZ5nFVJLyAwSm+sAgEC9QgIjOpxNAoBApkFBEaZwZUj0GIBgVGLm2fqAiPPQCcFBEbp2yowSm+sQh4BgVEeZ1XSCwiM0hurQIBAPQICo3ocjUKAQGYBgVFmcOUItFhAYNTi5pm6wMgz0EkBgVH6tgqM0hurkEdAYJTHWZX0AgKj9MYqECBQj4DAqB5HoxAgkFlAYJQZXDkCLRYQGLW4eaYuMPIMdFJAYJS+rQKj9MYq5BEQGOVxViW9gMAovbEKBAjUIyAwqsfRKAQIZBYQGGUGV45AiwUERi1unqkLjDwDnRQQGKVvq8AovbEKeQQERnmcVUkvIDBKb6wCAQL1CNQaGD340MPh+j/98/D2E98Sli9bNnSGT+7aFT577Q3h+Df9Rli1ckU9qzAKAQK9ExAY9a7lFkxgYgGB0cR0bmyYwFNPPRPCkhD23GNpw2ZmOgQWJiAwWpjXJFcLjCZRc08TBQRGTeyKOU0iIDCaRM09BAhMQ6D2wOi8Cy4OO+++N3z4g2eEtUeumbWm7bftCOdfeFlYfdAB4dxzzhYYTaPjahLoiIDAqCONtAwCGQQERhmQlcgiIDDKwqxIBgGBUXpkgVF6YxXyCAiM8jirkl5AYJTeWAUCBOoRqDUwKqZUBENHrV0Tzjp94+DTl1y+Kdy6fUfYcMrbwvrj1tUze6MQINBbAYFRb1tv4QQWLCAwWjCZGxoqIDBqaGNMa8ECAqMFky34BoHRgsnc0FABgVFDG2NaCxYQGC2YzA0ECExJIElgVKxl0+Zrws3bbhn8sgiPRh1VN6X1K0uAQEsFBEYtbZxpE5iCgMBoCuhKJhEQGCVhNegUBARG6dEFRumNVcgjIDDK46xKegGBUXpjFQgQqEcgWWAU31VU7CqKUz123dFh44aT6pm1UQgQ6L2AwKj3jwAAAmMLCIzGpnJhwwUERg1vkOmNLSAwGptq4gsFRhPTubFhAgKjhjXEdCYWEBhNTOdGAgQyCyQJjLbetC1s3nLdzK6iuKYiPBr2bqPMa1aOAIEOCAiMOtBESyCQSUBglAlameQCAqPkxApkEhAYpYcWGKU3ViGPgMAoj7Mq6QUERumNVSBAoB6BWgOjBx96OJx3wcVh5933hmHBUPXdRo6nq6eJRiHQRwGBUR+7bs0EJhMQGE3m5q7mCQiMmtcTM5pMQGA0mdtC7hIYLUTLtU0WEBg1uTvmthABgdFCtFxLgMA0BWoPjK7/0z8Pbz/xLWFUGBSPqvvstTeE49/0G2HVyhXTXLvaBAi0WEBg1OLmmTqBzAICo8zgyiUTEBglozVwZgGBUXpwgVF6YxXyCAiM8jirkl5AYJTeWAUCBOoRqDUwqmdKRiFAgMD8AgKj+Y1cQYDAPwkIjDwJXREQGHWlk9YhMEr/DAiM0hurkEdAYJTHWZX0AgKj9MYqECBQj4DAqB5HoxAgkFlAYJQZXDkCLRYQGLW4eaY+S0Bg5IHoioDAKH0nBUbpjVXIIyAwyuOsSnoBgVF6YxUIEKhHoNbAKL7D6KJPXBFOfeeJ4dBDDg7VX8cp33nXD8OVV10bPnDmaY6kq6eHRiHQSwGBUS/bbtEEJhIQGE3E5qYGCgiMGtgUU5pIQGA0EduCbhIYLYjLxQ0WEBg1uDmmtiABgdGCuFxMgMAUBQRGU8RXmgCByQUERpPbuZNA3wQERn3reHfXKzDqbm/7tjKBUfqOC4zSG6uQR0BglMdZlfQCAqP0xioQIFCPgMCoHkejECCQWUBglBlcOQItFhAYtbh5pj5LQGDkgeiKgMAofScFRumNVcgjIDDK46xKegGBUXpjFQgQqEdAYFSPo1EIEMgsIDDKDK4cgRYLCIxa3DxTFxh5BjopIDBK31aBUXpjFfIICIzyOKuSXkBglN5YBQIE6hEQGNXjaBQCBDILCIwygytHoMUCAqMWN8/UBUaegU4KCIzSt1VglN5YhTwCAqM8zqqkFxAYpTdWgQCBegQERvU4GoUAgcwCAqPM4MoRaLGAwKjFzTN1gZFnoJMCAqP0bRUYpTdWIY+AwCiPsyrpBQRG6Y1VIECgHoHaA6PzLrg47Lz73jlnt/qgA8K555wdVq1cUc8qjEKAQO8EBEa9a7kFE5hYQGA0MZ0bGybgHUYNa4jpTCwgMJqYbuwbBUZjU7mw4QICo4Y3yPTGFhAYjU3lQgIEpixQa2A05bUoT4BAjwQERj1qtqUSWKSAwGiRgG5vjIDAqDGtMJFFCgiMFgk4xu0CozGQXNIKAYFRK9pkkmMICIzGQHIJAQKNEBAYNaINJkGAwEIFBEYLFXM9gf4KCIz62/uurVxg1LWO9nc9AqP0vRcYpTdWIY+AwCiPsyrpBQRG6Y1VIECgHoEkgdGmzdeEm7fdMpjhseuODhs3nFTPbI1CgACBfxYQGHkUCBAYV0BgNK6U65ouIDBqeofMb1wBgdG4UpNfJzCa3M6dzRIQGDWrH2YzuYDAaHI7dxIgkFeg9sBo603bwvbv3B7OOn1jWL5sWYjh0QtWHxjWH7cu78pUI0Cg0wICo0631+II1CogMKqV02BTFBAYTRFf6VoFBEa1cg4dTGCU3liFPAICozzOqqQXEBilN1aBAIF6BGoNjJ7ctStccvmmsP7YN4S1R64ZzPDOu34Yrrzq2vCBM08Lq1auqGfWRiFAoPcCAqPePwIACIwtIDAam8qFDRcQGDW8QaY3toDAaGyqiS8UGE1M58aGCQiMGtYQ05lYQGA0MZ0bCRDILFBrYPTgQw+Hiz5xRTj1nSeGQw85eLCUYZ/LvEblCBDooIDAqINNtSQCiQQERolgDZtdQGCUnVzBRAICo0SwpWEFRumNVcgjIDDK46xKegGBUXpjFQgQqEcgS2B03gUXhw0nHz+z66ieqRuFAIE+CwiM+tx9ayewMAGB0cK8XN1cAYFRc3tjZgsTEBgtzGuSqwVGk6i5p4kCAqMmdsWcJhEQGE2i5h4CBKYhIDCahrqaBAgsWkBgtGhCAxDojYDAqDet7vxCBUadb3FvFigwSt9qgVF6YxXyCAiM8jirkl5AYJTeWAUCBOoRqD0wiruJdt5975yzW33QAeHcc872TqN6emgUAr0UEBj1su0WTWAiAYHRRGxuaqCAwKiBTTGliQQERhOxLegmgdGCuFzcYAGBUYObY2oLEhAYLYjLxQQITFGg1sBovnXE9xnFQCl+CIzm0/J1AgTmEhAYeT4IEBhXQGA0rpTrmi4gMGp6h8xvXAGB0bhSk18nMJrczp3NEhAYNasfZjO5gMBocjt3EiCQVyBLYLT9th3h/AsvG6xsn332Dh/50PvCoYccnHelqhEg0CkBgVGn2mkxBJIKCIyS8ho8o4DAKCO2UkkFBEZJeQeDC4zSG6uQR0BglMdZlfQCAqP0xioQIFCPQNLAaNPma8LN226ZmemHP3hGWHvkmnpmbhQCBHotIDDqdfstnsCCBARGC+JycYMFBEYNbo6pLUhAYLQgrokuFhhNxOamBgoIjBrYFFOaSEBgNBGbmwgQmIJA7YFRcexc8R6jGBIdesgLB0fRbTj5eIHRFJqsJIEuCgiMuthVayKQRkBglMbVqPkFBEb5zVVMIyAwSuNaHlVglN5YhTwCAqM8zqqkFxAYpTdWgQCBegRqDYxGvaOo+LzAqJ6mGYUAgRAERp4CAgTGFRAYjSvluqYLCIya3iHzG1dAYDSu1OTXCYwmt3NnswQERs3qh9lMLiAwmtzOnQQI5BVIEhjtv/9+4azTN4bly5YNViMwyttU1Qj0QUBg1IcuWyOBegQERvU4GmX6AgKj6ffADOoREBjV4zjXKAKj9MYq5BEQGOVxViW9gMAovbEKBAjUI1BrYFQOh+KRdPvss3f4yIfeF1auXOFIunr6ZRQCBP5ZQGDkUSBAYFwBgdG4Uq5ruoDAqOkdMr9xBQRG40pNfp3AaHI7dzZLQGDUrH6YzeQCAqPJ7dxJgEBegdoDo/L0N22+Jty87ZaZT8X3Ga09ck3eFapGgEAnBQRGnWyrRRFIIiAwSsJq0CkICIymgK5kEgGBURLWWYMKjNIbq5BHQGCUx1mV9AICo/TGKhAgUI9A0sComOL223aE8y+8bPDLo9aumXVcXT3LMAoBAn0TEBj1rePWS2ByAYHR5HbubJaAwKhZ/TCbyQUERpPbjXunwGhcKdc1XUBg1PQOmd+4AgKjcaVcR4DAtAWyBEbFIot3GcVfn3vO2WHVyhXTXr/6BAi0VEBg1NLGmTaBKQgIjKaArmQSAYFRElaDTkFAYJQeXWCU3liFPAICozzOqqQXEBilN1aBAIF6BLIGRvVM2SgECBAIQWDkKSBAYFwBgdG4Uq5ruoDAqOkdMr9xBQRG40pNfp3AaHI7dzZLQGDUrH6YzeQCAqPJ7dxJgEBegdoDo+L4uWHvK5rra3mXrRoBAm0XEBi1vYPmTyCfgMAon7VKaQUERml9jZ5PQGCU3lpglN5YhTwCAqM8zqqkFxAYpTdWgQCBegRqD4w2bb5mMLONG04aOsP5vl7PsoxCgEDXBQRGXe+w9RGoT0BgVJ+lkaYrIDCarr/q9QkIjOqzHDWSwCi9sQp5BARGeZxVSS8gMEpvrAIBAvUI1BoYFe8o2nDy8WHtkWuGzjDuMtp89fXeYVRP/4xCoLcCAqPett7CCSxYQGC0YDI3NFRAYNTQxpjWggUERgsmW/ANAqMFk7mhoQICo4Y2xrQWLCAwWjCZGwgQmJJA7YHRRZ+4Ipz6zhPDoYccPHRJd971w3DlVdeGD5x5Wli1csWUlq0sAQJtFxAYtb2D5k8gn4DAKJ+1SmkFBEZpfY2eT0BglN5aYJTeWIU8AgKjPM6qpBcQGKU3VoEAgXoEag2Mnty1K1xy+aaw/tg3zLnDaOvNXw5nnb4xLF+2rJ5VGIUAgd4JCIx613ILJjCxgMBoYjo3NkxAYNSwhpjOxAICo4npxr5RYDQ2lQsbLiAwaniDTG9sAYHR2FQuJEBgygK1BkZxLVtv2hZ+tPOeOd9h9ILVB4b1x62b8tKVJ0CgzQICozZ3z9wJ5BUQGOX1Vi2dgMAona2R8woIjNJ7C4zSG6uQR0BglMdZlfQCAqP0xioQIFCPQO2BUbHLKE6vvIuo+Px9993v/UX19M4oBHotIDDqdfstnsCCBARGC+JycYMFBEYNbo6pLUhAYLQgrokuFhhNxOamBgoIjBrYFFOaSEBgNBGbmwgQmIJA7YFRsYa402jzlutmLWnDKW+zs2gKTVaSQBcFBEZd7Ko1EUgjIDBK42rU/AICo/zmKqYREBilcS2PKjBKb6xCHgGBUR5nVdILCIzSG6tAgEA9AskCo3qmZxQCBAgMFxAYeTIIEBhXQGA0rpTrmi4gMGp6h8xvXAGB0bhSk18nMJrczp3NEhAYNasfZjO5gMBocjt3EiCQV0BgNI/3gw89HM674OKw4eTjw9oj1+TtjmoECIwUEBh5OAgQGFdAYDSulOuaLiAwanqHzG9cAYHRuFKTXycwmtzOnc0SEBg1qx9mM7mAwGhyO3cSIJBXoHOBUTwK70c77wkbN5w0S7IIfnbefe/g8x/+4BmzAqDyEXpHrV0z8/4lgVHeB1I1AuMKCIzGlXIdAQICI89AVwQERl3ppHUIjNI/AwKj9MYq5BEQGOVxViW9gMAovbEKBAjUI9CZwGj7bTvC+RdeNlA5dt3RswKjJ3ftCpdcvimsfdkRg3co3XnXD8PHL98U3n/6xnDoIQeHeO/mq68P555zdli1ckXYtPmawTgxdBIY1fOgGYVA3QICo7pFjUeguwICo+72tm8rExj1rePdXa/AKH1vBUbpjVXIIyAwyuOsSnoBgVF6YxUIEKhHoDOBUcExbIdRDIiuvOra8IEzTxsEQtUAKQZEL1h94CBMih/lACn+unwkXREgHfHSw3bbxVRPS4xCgMA4AgKjcZRcQ4BAFBAYeQ66IiAw6konrUNglP4ZEBilN1Yhj4DAKI+zKukFBEbpjVUgQKAegV4ERtUdRJGu2EX09hPfMmv3UfxaeQfSypUrZgKjww97yeDa9ce+wfuM6nn+jEJgYgGB0cR0biTQOwGBUe9a3tkFC4w629reLUxglL7lAqP0xirkERAY5XFWJb2AwCi9sQoECNQj0JvAaOvNX555L9GwwKgcAg0LjE5462+Fbbd8deZYu4L/iSd/XE8njEKAwIIEHn/yx2H5c58Tli5dsqD7XNwMgSWxb882Yy5m0X2Bq7d/PXzu724Na/Zf3f3FWmGnBZ555tkQloSwdInvfZ1udA8W9517d4a3rHllOOHlr+rBaqezxPsefTi87ZpPhWNf/PPTmYCqBGoSePbZZ8PTzzwb9txjaU0jGobAdAT+5+OPhnsffSRcfcLvTmcCE1Z93vLnTnin2wgQaKtAbwKj8juKYrMWusNo5933htUHHTDznqOi4Y8+vqutvTdvAq0W2PXjn4TnPmfPsMQ/mrWyj3ssXTL4i58PAjkErvn2N8Kff297OOJnDspRTg0CyQQGgdH/f1yyH5ZIRmzgTALfue/u8KafPyr8zpECo1Tk9z32cDjpj64Iv/KzL01VolXjxr8zxODBR/sEYteeeeaZsMdSgVH7umfGZYFBYPTYo+Gzb9vYKpi991rWqvmaLAECixfoRWBUxzuMjnvjMWH1gfuH6k6lxbfACAQITCLgSLpJ1NxDoJ8CjqTrZ9+7uGpH0nWxq/1ckyPp0vfdkXTpjVXII+BIujzOqqQXcCRdemMVCBCoR6AXgdGTu3bNek9R+ci5Qw85OFTfcVTsPtq44aTw4EMPz7zDaO2Ra8LWm7aF7d+5fdbxdvW0wigECCxEQGC0EC3XEui3gMCo3/3v0uoFRl3qZr/XIjBK33+BUXpjFfIICIzyOKuSXkBglN5YBQIE6hHoTGAUQ5/zL7xslsqHP3hGiCFP/CiCn3i0XPwofy3+OgZBm7dcN/jaUWvXzARC1cAofj0GSvc/8IDQqJ5n0CgEJhIQGE3E5iYCvRQQGPWy7Z1ctMCok23t5aIERunbLjBKb6xCHgGBUR5nVdILCIzSG6tAgEA9Ap0JjOrhMAoBAm0REBi1pVPmSWD6AgKj6ffADOoREBjV42iU6QsIjNL3QGCU3liFPAICozzOqqQXEBilN1aBAIF6BARG9TgahQCBzAICo8zgyhFosYDAqMXNM/VZAgIjD0RXBARG6TspMEpvrEIeAYFRHmdV0gsIjNIbq0CAQD0CAqN6HI1CgEBmAYFRZnDlCLRYQGDU4uaZusDIM9BJAYFR+rYKjNIbq5BHQGCUx1mV9AICo/TGKhAgUI+AwKgeR6MQIJBZQGCUGVw5Ai0WEBi1uHmmLjDyDHRSQGCUvq0Co/TGKuQREBjlcVYlvYDAKL2xCgQI1CMgMKrH0SgECGQWEBhlBleOQIsFBEYtbp6pC4w8A50UEBilb6vAKL2xCnkEBEZ5nFVJLyAwSm+sAgEC9QgIjOpxNAoBApkFBEaZwZUj0GIBgVGLm2fqAiPPQCcFBEbp2yowSm+sQh4BgVEeZ1XSCwiM0hurQIBAPQICo3ocjUKAQGYBgVFmcOUItFhAYNTi5pm6wMgz0EkBgVH6tgqM0hurkEdAYJTHWZX0AgKj9MYqECBQj4DAqB5HoxAgkFlAYJQZXDkCLRYQGLW4eaYuMPIMdFJAYJS+rQKj9MYq5BEQGOVxViW9gMAovbEKBAjUIyAwqsfRKAQIZBYQGGUGV45AiwUERi1unqkLjDwDnRQQGKVvq8AovbEKeQQERnmcVUkvIDBKb6wCAQL1CAiM6nE0CgECmQUERpnBlSPQYgGBUYubZ+oCI89AJwUERunbKjBKb6xCHgGBUR5nVdILCIzSG6tAgEA9AgKjehyNQoBAZgGBUWZw5Qi0WEBg1OLmmbrAyDPQSQGBUfq2CozSG6uQR0BglMdZlfQCAqP0xioQIFCPgMCoHkejECCQWUBglBlcOQItFhAYtbh5pi4w8gx0UkBglL6tAqP0xirkERAY5XFWJb2AwCi9sQoECNQjIDCqx9EoBAhkFhAYZQZXjkCLBQRGLW6eqQuMPAOdFBAYpW+rwCi9sQp5BARGeZxVSS8gMEpvrAIBAvUICIzqcTQKAQKZBQRGmcGVI9BiAYFRi5tn6gIjz0AnBQRG6dsqMEpvrEIeAYFRHmdV0gsIjNIbq0CAQD0CAqN6HI1CgEBmAYFRZnDlCLRYQGDU4uaZusDIM9BJAYFR+rYKjNIbq5BHQGCUx1mV9AICo/TGKhAgUI+AwKgeR6MQIJBZQGCUGVw5Ai0WEBi1uHmmLjDyDHRSQGCUvq0Co/TGKuQREBjlcVYlvYDAKL2xCgQI1CMgMKrH0SgECGQWEBhlBleOQIsFBEYtbp6pC4w8A50UEBilb6vAKL2xCnkEBEZ5nFVJLyAwSm+sAgEC9QgIjOpxNAoBApkFBEaZwZUj0GIBgVGLm2fqAiPPQCcFBEbp2yowSm+sQh4BgVEeZ1XSCwiM0hurQIBAPQICo3ocjUKAQGYBgVFmcOUItFhAYNTi5pm6wMgz0EkBgVH6tgqM0hurkEdAYJTHWZX0AgKj9MYqECBQj4DAqB5HoxAgkFlAYJQZXDkCLRYQGLW4eaYuMPIMdFJAYJS+rQKj9MYq5BEQGOVxViW9gMAovbEKBAjUIyAwqsfRKAQIZBYQGGUGV45AiwUERi1unqkLjDwDnRQQGKVvq8AovbEKeQQERnmcVUkvIDBKb6wCAQL1CAiM6nE0CgECmQUERpnBlSPQYgGBUYubZ+oCI89AJwUERunbKjBKb6xCHgGBUR5nVdILCIzSG6tAgEA9AgKjehyNQoBAZgGBUWZw5Qi0WEBg1OLmmbrAyDPQSQGBUfq2CozSG6uQR0BglMdZlfQCAqP0xioQIFCPgMCoHkejECCQWUBglBlcOQItFhAYtbh5pi4w8gx0UkBglL6tAqP0xirkERAY5XFWJb2AwCi9sQoECNQjIDCqx9EoBAhkFhAYZQZXjkCLBQRGLW6eqQuMPAOdFBAYpW+rwCi9sQp5BARGeZxVSS8gMEpvrAIBAvUICIzqcTQKAQKZBQRGmcGVI9BiAYFRi5tn6gIjz0AnBQRG6dsqMEpvrEIeAYFRHmdV0gsIjNIbq0CAQD0CAqN6HI1CgEBmAYFRZnDlCLRYQGDU4uaZusDIM9BJAYFR+rYKjNIbq5BHQGCUx1mV9AICo/TGKhAgUI+AwKgeR6MQIJBZQGCUGVw5Ai0WEBi1uHmmLjDyDHRSQGCUvq0Co/TGKuQREBjlcVYlvYDAKL2xCgQI1CMgMKrH0SgECGQWEBhlBleOQIsFBEYtbp6pC4w8A50UEBilb6vAKL2xCnkEBEZ5nFVJLyAwSm+sAgEC9QgIjOpxNAoBApkFBEaZwZUj0GIBgVGLm2fqAiPPQCcFBEbp2yowSm+sQh4BgVEeZ1XSCwiM0hurQIBAPQICo3ocjUKAQGYBgVFmcOUItFhAYNTi5pm6wMgz0EkBgVH6tgqM0hurkEdAYJTHWZX0AgKj9MYqECBQj4DAqB5HoxAgkFlAYJQZXDkCLRYQGLW4eaYuMPIMdFJAYJS+rQKj9MYq5BEQGOVxViW9gMAovbEKBAjUIyAwqsfRKAQIZBYQGGUGV45AiwUERi1unqkLjDwDnRQQGKVvq8AovbEKeQQERnmcVUkvIDBKb6wCAQL1CAiM6nE0CgECmQUERpnBlSPQYgGBUYubZ+oCI89AJwUERunbKjBKb6xCHgGBUR5nVdILCIzSG6tAgEA9AgKjehyNQoBAZgGBUWZw5Qi0WEBg1OLmmbrAyDPQSQGBUfq2CozSG6uQR0BglMdZlfQCAqP0xioQIFCPgMCoHkejECCQWUBglBlcOQItFhAYtbh5pi4wJQDidwAAGhtJREFU8gx0UkBglL6tAqP0xirkERAY5XFWJb2AwCi9sQoECNQjIDCqx9EoBAhkFhAYZQZXjkCLBQRGLW6eqQuMPAOdFBAYpW+rwCi9sQp5BARGeZxVSS8gMEpvrAIBAvUICIzqcTQKAQKZBQRGmcGVI9BiAYFRi5tn6gIjz0AnBQRG6dsqMEpvrEIeAYFRHmdV0gsIjNIbq0CAQD0CAqN6HI1CgEBmAYFRZnDlCLRYQGDU4uaZusDIM9BJAYFR+rYKjNIbq5BHQGCUx1mV9AICo/TGKhAgUI+AwKgeR6MQIJBZQGCUGVw5Ai0WEBi1uHmmLjDyDHRSQGCUvq0Co/TGKuQREBjlcVYlvYDAKL2xCgQI1CMgMKrH0SgECGQWEBhlBleOQIsFBEYtbp6pC4w8A50UEBilb6vAKL2xCnkEBEZ5nFVJLyAwSm+sAgEC9QgIjOpxNAoBApkFBEaZwZUj0GIBgVGLm2fqAiPPQCcFBEbp2yowSm+sQh4BgVEeZ1XSCwiM0hurQIBAPQICo3ocjUKAQGYBgVFmcOUItFhAYNTi5pm6wMgz0EkBgVH6tgqM0hurkEdAYJTHWZX0AgKj9MYqECBQj4DAqB5HoxAgkFlAYJQZXDkCLRYQGLW4eaYuMPIMdFJAYJS+rQKj9MYq5BEQGOVxViW9gMAovbEKBAjUIyAwqsfRKAQIZBYQGGUGV45AiwUERi1unqkLjDwDnRQQGKVvq8AovbEKeQQERnmcVUkvIDBKb6wCAQL1CAiM6nE0CgECmQUERpnBlSPQYgGBUYubZ+oCI89AJwUERunbKjBKb6xCHgGBUR5nVdILCIzSG6tAgEA9AgKjehyNQoBAZgGBUWZw5Qi0WEBg1OLmmbrAyDPQSQGBUfq2CozSG6uQR0BglMdZlfQCAqP0xioQIFCPgMCoHkejECCQWUBglBlcOQItFhAYtbh5pi4w8gx0UkBglL6tAqP0xirkERAY5XFWJb2AwCi9sQoECNQjIDCqx9EoBAhkFhAYZQZXjkCLBQRGLW6eqQuMPAOdFBAYpW+rwCi9sQp5BARGeZxVSS8gMEpvrAIBAvUICIzqcTQKAQKZBQRGmcGVI9BiAYFRi5tn6gIjz0AnBQRG6dsqMEpvrEIeAYFRHmdV0gsIjNIbq0CAQD0CAqN6HI1CgEBmAYFRZnDlCLRYQGDU4uaZusDIM9BJAYFR+rYKjNIbq5BHQGCUx1mV9AICo/TGKhAgUI+AwKgeR6MQIJBZQGCUGVw5Ai0WEBi1uHmmLjDyDHRSQGCUvq0Co/TGKuQREBjlcVYlvYDAKL2xCgQI1CMgMKrH0SgECGQWEBhlBleOQIsFBEYtbp6pC4w8A50UEBilb6vAKL2xCnkEBEZ5nFVJLyAwSm+sAgEC9QgIjOpxNAoBApkFBEaZwZUj0GIBgVGLm2fqAiPPQCcFBEbp2yowSm+sQh4BgVEeZ1XSCwiM0hurQIBAPQICo3ocjUKAQGYBgVFmcOUItFhAYNTi5pm6wMgz0EkBgVH6tgqM0hurkEdAYJTHWZX0AgKj9MYqECBQj4DAqB5HoxAgkFlAYJQZXDkCLRYQGLW4eaYuMPIMdFJAYJS+rQKj9MYq5BEQGOVxViW9gMAovbEKBAjUIyAwGsNx603bwuYt1w2uPGrtmnDW6RvD8mXLxrjTJQQIpBIQGKWSNS6B7gkIjLrX076u6KmnnglhSQh77rG0rwTW3REBgVH6RgqM0hurkEdAYJTHWZX0AgKj9MYqECBQj4DAaB7H7bftCJuvvj6ce87ZYdXKFWHT5msGd2zccFI9HTAKAQITCQiMJmJzE4FeCgiMetn2Ti5aYNTJtvZyUQKj9G0XGKU3ViGPgMAoj7Mq6QUERumNVSBAoB4BgdE8jjEgesHqA8P649YNrqwGSPW0wSgECCxUQGC0UDHXE+ivgMCov73v2soFRl3raH/XIzBK33uBUXpjFfIICIzyOKuSXkBglN5YBQIE6hEQGM3h+OSuXeGSyzeFtS87YiYwuvOuH4aPX74pvP/0jeHQQw6upwtGIUBgwQICowWTuYFAbwUERr1tfecWLjDqXEt7uyCBUfrWC4zSG6uQR0BglMdZlfQCAqP0xioQIFCPgMBojMBo/bFvCGuPXDO4shoY3f+Pj9bTiUyj3HztZ8JP3/IXmaopQ4AAAQLzCfxw3wPCb/77C+e7zNcXIXDdjm+Gy//zzWHPnzyziFHcSoAAAQJ1CTz1nKXhzF/+1fCWI15Z15DGqQj8w2OPhN/5o8vDnk88zYYAAQIEGiDwzJIQ9l21Mlx38hkNmM34U9jvp/Ye/2JXEiDQCQGB0RxtHGeHUdsCo048tRZBgAABAgQIECBAgAABAgQIECBAgEBSAYFRUl6DE2ikgMBonrZ4h1Ejn1uTIkCAAAECBAgQIECAAAECBAgQIECAAAECBGoUEBjNg7n9th1h89XXh3PPOTusWrkixAApfmzccFKNbTAUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGB6AgKjMey33rQtbN5y3eDKo9auCWedvjEsX7ZsjDtdQoBAToFyoFsNe3POQy0CBAgQINAXAT9M1ZdOWycBAgQIECBAgAABAn0QEBj1ocvWSGAKAjGwOf/Cy2ZVPnbd0Ul35wmMptBoJQkQINBSgWHvqoxLiT8otP07ty/6B4TqGqfpvAKjpnfI/AgQaIJA6u85fflvcfmHeYu+fviDZ4S1R64Jd971w/DxyzeF95++MRx6yMFNaPtYcyiejVu375i5vvr35rmuefChh8N5F1wc9t9/v1l/dokeH/3YpeHFP3voov9MM9ZCXESAAAECnREQGHWmlRZCoFkC1R0+xR9kN5x8/OAP9Ck++vIXpRR2xiRAgEDfBFL/453AqG9PlPUSIEBgtEDq7zl9+HvQsBMkYijy//6Xvw6n/M6bWx8YrX3ZEWH9cetC8azst+++gx+2LP4efcRLD5v1w5db/vjG8Mu/9KqwcuWKcNEnrgiPPvpo2HDK22b+rh2fiejz/L33Ehj5jxMBAgQILEhAYLQgLhcTIDCuQPUP9MP+khT/EHvztlsGQ+6zz97hIx9638xPg1V/eqz4ybF47ahjIkftMIr3xJ+6Ovq1rw5fuGlbeOSRR3c7XrK8I2r1QQfMvLds3PW6jgABAgTaJTDuP95Vf6o3/mNM/Aed+FH89G78vhI/ip8Irn6++L5y/Z/+eXjiiSfCY088EeJPEpe/91V/Mrqou/7YNwz+8Sd+j7v/gQfCfffdH3befe/g3n/7vn8TbvxPX9xtrOIfl+b6vlftVnnO5XnNN1b5e2/1HyxHGbfrSTFbAgQILF6gju85o/5+VD3ZIR6j/663nxB+/6LLRv79p/pDDfF7wJVXXRs+cOZpg8XGvzvFgKL4u1oc883/+tfDf7j0D3b7u1Tx/evo170m/NENn5/1/XA+ufg95lOf/kx4z7vfMXhn9Fwfc4Viw3bgFH9/HPV3x+q84/fqs87YGC65bNPg+2z8qL6SoPr9PV4zzt9T51rXsGcj9nTrzV8eBD3bvvLVOXc+R8MYGP3K6/9l+PaOvwu/+65Twr33/kP43Bf+Mhy8+qDwve//vcBovgfR1wkQIEBgloDAyANBgEASgWE7jOIfZE9954mDUCj+wTb+4ffNv/nrg/rFP4TFPxTHP+AWf2GJf3GIfzD/H3f+ILz+6NcOwqKbvvSVmUAn3veC1QcO/vFuvsCo2KYf611y+aZQ/BRXnOsnr9g8E1jFGj/aeU/S4/OSoBuUAAECBMYWGOcf74rvF+Wf8i1/L/urW74WXnToCwff14p/RHrvaRsGAc+wHUbx+9TXv/m3s77fFMffxe995aN0hgVGt3/3jlnf/8pjlb8HVo+nqX7fqyJVdwGXvy/Gn1wuH3VTHatct/wPjvH7t/cJjv04upAAgY4LLPZ7zgEH/EzY+hdfCut/7Y2D9ykP+ztRJIw7UuLHfN8Hxg2Myjtcir9LPfnkrsH3heLkiOL732t+8RWzdsSMc7LEQgKjIvgpBzTlx2bYkXTV70PVv3PGI9uKecex4vXxI34fLwyPe+Mxg79rjvp1sc65/p461+M97Nko+vOeU98RPnXlZ2b+3jpsnCIwin/PvmnbLeFVr3x52HnPfWH1gfsP/reOY3Y7/tvT8ggQIECgIiAw8kgQIJBEYNg7jKq7iMqFy3+Yv/OuH8wKcIrr5vvpq89ee8Pg0vgXm/J48XPlv9TEX48KmuLXqv/glQTIoAQIECAwVYFhP41cTKj4ieJqiFP9/lFeQPV71KjAqPg+VXy/KUKi+Ov5AqPyvdXxy7+u/mNevG+uI/Lm2hX82lf/woK+h1a/vxY/1DHVZitOgACBKQvU/T2nGo5Ud98MOw68/H2gumtl2A6jIgipfn+r/npYUDPuEXkLCYyK72Wbt1w3083yyRDjzKN8TfX77rBHZK535JaNDz/sJbN+IDGOVd4lFEO+UR9Vz3Iwte6Y1w3GLXYbDxujHBgVf07Z/2d+erDTaL7dSVP+baE8AQIECDRUQGDU0MaYFoG2Cwz7qeLq56qhUvkP/OWjA4rPL1++bPAH5vILQaNT8Q97iwmMiuMWCnfH0rX9CTR/AgQIzC0wzk97x8Ao/vRxceRcMWJxLN2wfwAsvjZOYFT9R56UgdFc/3A17GtF8LPQwKgY612n/E647IrNMzuLPY8ECBDos0Ad33Oqx6GVfxhvnMBormPO6g6M5jqxYdgPFhbPxlw/YFh9fgrT+PnilIry99EiPCn/4MJc33eL8cvHpsfPFcfNVr9XDguM/r/27p9FriqMA/CtI2mD2EkwakrRGDCQQixt0sQijSApBBH8EmKhXT6AhSlCIAERLSz8BoLgV1Ds3ODutuFMOMs7hzP3z+yMm+F90iS6O3fveebdOXPnd897Nl2nzgmM4mPb9xm1M0bvdyiOqaxEiwFTlv0UM7+2GDsBAgT2ISAw2oeqYxIg0G1DE9/M/nt0tLaKaKxtTb0AuvfJndE7rKZa0sW2CO6AVqQECBDILTD3w7vYIjWKtZtSW2H0og1SdTj+72TVqq+2R8pdbUZPgEB2gfPOOW3b06wrjNo6iteQR0fP1lbq1sCo/F3noqkVRrFlXQl5lq4wGlsJtOl3YGq/v6nQJ15jl3k3/pl6bPbfS+MnQIAAgb6AwEhlECCwF4GpFUal7dz3Dx+f7cUQez6Xr5U/pXd0+0a/7Q1d3mDXft7brjBq9zAqP/PJj78MpQXA1Oare8FzUAIECBDYu8CcD+/KSZQ7deseRuW/67567994Z639TLu3QW8ebO8A7+07VG9uqHdg170a2scuaUnXa00UgXvnXvf2q3sYjd10ET+MK/+e2mdi70+uH0CAAIGXTOC8c07ZLy+ununtwRr3qmlf93t71cVrsTLH1H3yCl1s5720JV2vNdymp2NJS7pyjmV/nnqN2F4n9ua61qndw6i3IqnOae2NIT3Dr799MNR5euw6dc4Ko02riOrPvf7WtbWbMH549GS4dfO9oczTcX/FaC0wesleCJwOAQIEDkRAYHQgT5TTJHBoAr1WA7HNW9vG542rrw/HJyerAKncHRZbANWWc/WNdmxXV1zqkv1tVxiVY7TnW1sPHJq78yVAgACBeQJzPrwr8047X8V2OXHuKP//8iuXho8+vL3aHDs+rs5/j5/+NMQWqO38Fo/3wc13h+PT07N9C7YJjP76+58zjDpXbtKJrY7iGHsfwI3tA1jn1J9//W3VImjsQ7J5z5TvIkCAwOEL7GLOia3SXnv1ygrly88/W63mrK/V5XW/zC2f3rs7fPPdg2FsHojHu3vn4+H3P/4cvvri/uq4SwOjtn1rDVGmnrklgVHv+rK9ZovXiTHIqfsexXm3F2y1c2GZ16+//eZZUBPPoRzr5Ph0rfXqpuvUMYepFUblsb0WuHXsVhhNVZmvEyBAgMBSAYHRUjHfT4AAAQIECBAgQGALgbmbgG9x6LWHTK0oOu/xpx7fuwt86jG+ToAAAQK7E/g/54ElK4p2N8KLP1Lc90lXiot/PpwBAQIECOxOQGC0O0tHIkCAAAECBAgQILBRIENg5AM0vwAECBC4eAGB0X6eg7gqK67GHftp7aqj+L1zj7Gf0TgqAQIECBDoCwiMVAYBAgQIECBAgAABAgQIECBAgAABAgQIECBAILmAwCh5ARg+AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQEBgpAYIECBAgAABAgQIECBAgAABAgQIECBAgAABAskFBEbJC8DwCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICIzVAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDBKXgCGT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQGKkBAgQIECBAgAABAgQIECBAgAABAgQIECBAgEByAYFR8gIwfAIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAwEgNECBAgAABAgQIECBAgAABAgQIECBAgAABAgSSCwiMkheA4RMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBEZqgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQXEBglLwADJ8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIDBSAwQIECBAgAABAgQIECBAgAABAgQIECBAgACB5AICo+QFYPgECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAYGRGiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJBcQGCUvAMMnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAiM1AABAgQIECBAgAABAgQIECBAgAABAgQIECBAILmAwCh5ARg+AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQEBgpAYIECBAgAABAgQIECBAgAABAgQIECBAgAABAskFBEbJC8DwCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICIzVAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDBKXgCGT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQGKkBAgQIECBAgAABAgQIECB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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",
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    "\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('d800e89e-4fa6-43eb-a701-774fe4a1dd2a');\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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   "source": [
    "_series = SingleFigureComparison(s.cases, 'ebitda', label='EBITDA').report\n",
    "ebitda_graph = _series.iplot(\n",
    "    kind='bar',\n",
    "    title='Yearly EBITDA by Casestudy in €',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400), \n",
    "    yTitle='EBITDA in €',\n",
    "    asFigure=True,\n",
    "    yrange=[_series.min() * 1.2, max(_series.max() * 2, 0)]\n",
    ")\n",
    "\n",
    "ebitda_graph.update_traces(\n",
    "    text=_series.values/1000_000, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f}M €\", \n",
    ")\n",
    "\n",
    "ebitda_graph"
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  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
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AgAABAgQIECBAwMDIGSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBBcwMAp+AIRPgAABAgQIECBAgAABAgQIECBAgAABAgQIEDAwcgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAsEFDIyCHwDhEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQMjJwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBwAQOj4AdA+AQIECBAgAABAgQIECBAgAABAgQIECBAgAABAyNngAABAgQIECBAgAABAgQIECBAgAABAgQIECAQXMDAKPgBED4BAgQIECBAgAABAgQIECBAgAABAgQIECBAwMDIGSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBBcwMAp+AIRPgAABAgQIECBAgAABAgQIECBAgAABAgQIEDAwcgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAsEFDIyCHwDhEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQMjJwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBwAQOj4AdA+AQIECBAgAABAgQIECBAgAABAgQIECBAgAABAyNngAABAgQIECBAgAABAgQIECBAgAABAgQIECAQXMDAKPgBED4BAgQIECBAgAABAgQIECBAgAABAgQIECBAwMDIGSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBBcwMAp+AIRPgAABAgQIECBAgAABAgQIECBAgAABAgQIEDAwcgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAsEFDIyCHwDhEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQMjJwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBwAQOj4AdA+AQIECBAgAABAgQIECBAgAABAgQIECBAgAABAyNngAABAgQIECBAgAABAgQIECBAgAABAgQIECAQXMDAKPgBED4BAgQIECBAgAABAgQIECBAgAABAgQIECBAwMDIGSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBBcwMAp+AIRPgAABAgQIECBAgAABAgQIECBAgAABAgQIEDAwcgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAsEFDIyCHwDhEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQMjJwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBwAQOj4AdA+AQIECBAgAABAgQIECBAgAABAgQIECBAgAABAyNngAABAgQIECBAgAABAgQIECBAgAABAgQIECAQXMDAKPgBED4BAgQIECBAgAABAgQIECBAgAABAgQIECBAwMDIGSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIBBcwMAp+AIRPgAABAgQIECBAgAABAgQIECBAgAABAgQIEDAwcgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAsEFDIxGHICHH3k0XXnVNWnz2Wek9SeuC35chE+AAAECBAgQIECAAAECBAgQIECAAAECBAh0UaAzA6NysLP7/j29PJ264eS0ZfNZczkb/P3ll54/bwC0/Y4dadvNt/U+f9L6deni87akFcuXJwOjLh57MREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL9Ap0YGO3dty9de+PWtP5lJ6RNGzekUX++59770nU3bk0XnbclrV1zTNr1pbvTtltuT1dcdkk6ZNXKtHXbrT2jYuBkYORfGAIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDrAp0YGA0b6vQPfYoB0cdv+mR69wXn9gZCgwOl4rNHrz6yN2wqfvoHSMWf+x9JV+51wkuPm3cHU9cPivgIECAQVeDJp76T0rKUDlzx3KgE4iZAgAABAgQIECDQegF1fetTKAACBAgQyCDQiYFR4VQ+Uq541NzaNS9KH77ho+md55w59A6i4vPlQOltZ54+7+6k4nf9dyCtWrVybmB0/HE/0vvsplNP8T6jDIfTFgQIEGiCgMayCVlwDQQIECBAgAABAgSmE1DXT+fn2wQIECAQQ6AzA6NyyFOkbfc3H5j3DqPijqHtd35+7r1EwwZG/UOgYQOjn3/zaWnHzi/MPfauPB6PP7kvxkkRJQECBIIKPP3093qRP/vZBwQVEDYBAgQIECBAgACB9guo69ufQxEQIEBgHIGDDlw+zsd8ZgGBTgyMisfE9d9RVD5y7rBDD+09Nm7wHUXDBkbl+4+K3w0bGO2+f09afdQRc+85Kj337vuuw0WAAAECHRb4zne/13sk3XMNjDqcZaERIECAAAECBAh0XUBd3/UMi48AAQI/EFix/DkophDoxMBo8B1FhUfxiLpdX/5K766iPXu+NfU7jDa+7rVp9ZGH73en0hT2vkqAAAECLRDw6IoWJMklEiBAgAABAgQIEBghoK53RAgQIECAwGiBTgyMijuMrrzqmlQMdTZt3JAG7zAq/1zeRdR/B9HaNcfsdwdS+X6j4u6kcu3NZ5/Re29R/yBqxXK3t40+Yj5BgACBdgtoLNudP1dPgAABAgQIECBAoBBQ1zsHBAgQIEBgtEAnBkZFmMUQ6AMfuj499tjjvahP3XBy73F05U85+CkeLVf8XH7p+b0BUPlTDIK23Xxb748nrV83976jwYFR8ftioPTgQw/NeyfSaGqfIECAAIGcAp/+079I77/2pt6Wbzjl1elXLjonPW/Fc4dewg2//Z/TTbf96X6/+63fuDQddcQL02Uf/K20dvXh89b4+r33p4ved3066YQfXnTtv/kff5d+8Zevnlv7fRefk057/U/npLAXAQIECBAgQIAAgfACBkbhjwAAAgQIEBhDoDMDozFi9RECBAgQCCJQDGlu+O1PpWv+w7vSD606OBUDoeLngne8eSyBYhh03dbb0q9d+vb01N7vpg9e/zvp8cefSBe8883px19+fG+NYs2//8dvpFUHHbjgwKgYWn3sls+k695/YTp2zVG9792586507Nqj5/481gX5EAECBAgQIECAAAECUwkYGE3F58sECBAgEETAwChIooVJgACBSALFMGft0UfM3ckzOEAaZdH//d17HuoNjN70hp9Kd+36Wrpoyxnp/gf+V/r9T+9IL37Rkenur3596MDo2488ln7t6k+ki7a8xXBoFLjfEyBAgAABAgQIEJixgIHRjIEtT4AAAQKdEDAw6kQaBUGAAAECpcBTe7+TPnjdTelVJ710bmBU3DF05dWfSFdc+vaRw5v+u4uKu5PKgdG7f+Hn0mfu/EL6yVe+LH3j/m+lo496Ye9///sXv7rfwKi8hj/5/F/PJeact7x+7DucZJMAAQIECBAgQIAAgWoFDIyq9bQaAQIECHRTwMCom3kVFQECBMIKlMOaN206ee7xcZMMjAbvTuofGBWo137strT6iMN6dxrd8Wd/PXRgVHyuuKvpD7fvXPT9RmGTJHACBAgQIECAAAECmQUMjDKD244AAQIEWilgYNTKtLloAgQIEFhIYJo7jIY9uq5/YHTU4S/o3b1UDqOKdxSNc4fRuuNfMvc+JZkjQIAAAQIECBAgQCC/gIFRfnM7EiBAgED7BAyM2pczV0yAAAECIwSW8g6jYYOmYpv+gdGxa46at/NCA6PiQ95h5JgSIECAAAECBAgQaI6AgVFzcuFKCBAgQKC5AgZGzc2NKyNAgACBJQoM3ilUDJCKnwve8ebe/xZ/fuBb3573uLhhdxdNMzAqvlsMlD52y2fSde+/cO7dSXfuvCsdu/boke9SWmLovkaAAAECBAgQIECAwBABAyPHggABAgQIjBYwMBpt5BMECBAg0EKBYljz/mtv6l35G0559bzh0ODAqLgb6JJf/c10wTtOn3vvURnyUu8wKr9fDKJ+8ZevnhN838XnpNNe/9MtFHXJBAgQIECAAAECBNorYGDU3ty5cgIECBDIJ2BglM/aTgQIECDQQgGNZQuT5pIJECBAgAABAgQIDAio6x0JAgQIECAwWsDAaLSRTxAgQIBAYAGNZeDkC50AAQIECBAgQKAzAur6zqRSIAQIECAwQwEDoxniWpoAAQIE2i+gsWx/DkVAgAABAgQIECBAQF3vDBAgQIAAgdECBkajjXyCAAECBAILaCwDJ1/oBAgQIECAAAECnRFQ13cmlQIhQIAAgRkKGBjNENfS8wW+vffx9Ln/eRcWAgQItErgu09/r3e9z3n2Aa26bhdLgACB17/kVemQ5c8HQYAAAQIECKSUDIwcAwIECBAgMFrAwGi0kU9UJPArf/7x9O29j6UDn7OiohUtQ4AAgdkLPPPMspTSM+lZzyr+1w8BAgTaIfD4d/emFz5vVfrAyW9vxwW7SgIECBAgMGMBA6MZA1ueAAECBDohYGDUiTS2I4hf/Ytt6YBlz0ovPHBVOy7YVRIgQCCl9PTTz6S0LKVnH/AsHgQIEGiNwJ4nH+5d66/91DmtuWYXSoAAAQIEZilgYDRLXWsTIECAQFcEDIy6kskWxGFg1IIkuUQCBPYTMDByKAgQaKOAgVEbs+aaCRAgQGCWAgZGs9S1NgECBAh0RcDAqCuZbEEcBkYtSJJLJEDAwMgZIECgEwIGRp1IoyAIECBAoEIBA6MKMS1FgAABAp0VMDDqbGqbF5iBUfNy4ooIEBgt4A6j0UY+QYBA8wQMjJqXE1dEgAABAvUKGBjV6293AgQIEGiHgIFRO/LUias0MOpEGgVBIJyAgVG4lAuYQCcEDIw6kUZBECBAgECFAgZGFWJaigABAgQ6K2Bg1NnUNi8wA6Pm5cQVESAwWsDAaLSRTxAg0DwBA6Pm5cQVESBAgEC9AgZG9frbnQABAgTaIWBg1I48deIqDYw6kUZBEAgnYGAULuUCJtAJAQOjTqRREAQIECBQoYCBUYWYliJAgACBzgoYGHU2tc0LzMCoeTlxRQQIjBYwMBpt5BMECDRPwMCoeTlxRQQIECBQr4CBUb3+didAgACBdggYGLUjT524SgOjTqRREATCCRgYhUu5gAl0QsDAqBNpFAQBAgQIVChgYFQhpqUIECBAoLMCBkadTW3zAjMwal5OXBEBAqMFDIxGG/kEAQLNEzAwal5OXBEBAgQI1CtgYFSvv90JECBAoB0CBkbtyFMnrtLAqBNpFASBcAIGRuFSLmACnRAwMOpEGgVBgAABAhUKGBhViGkpAgQIEOisgIFRZ1PbvMAMjJqXE1dEgMBoAQOj0UY+QYBA8wQMjJqXE1dEgAABAvUKGBjV6293AgQIEGiHgIFRO/LUias0MOpEGgVBIJyAgVG4lAuYQCcEDIw6kUZBECBAgECFAgZGFWJaigABAgQ6K2Bg1NnUNi8wA6Pm5cQVESAwWsDAaLSRTxAg0DwBA6Pm5cQVESBAgEC9AgZG9frbnQABAgTaIWBg1I48deIqDYw6kUZBEAgnYGAULuUCJtAJAQOjTqRREAQIECBQoYCBUYWYliJAgACBzgoYGHU2tc0LzMCoeTlxRQQIjBYwMBpt5BMECDRPwMCoeTlxRQQIECBQr4CBUb3+didAgACBdggYGLUjT524SgOjTqRREATCCRgYhUu5gAl0QsDAqBNpFAQBAgQIVChgYFQhpqUIECBAoLMCBkadTW3zAjMwal5OXBEBAqMFDIxGG/kEAQLNEzAwal5OXBEBAgQI1CtgYFSvv90JECBAoB0CBkbtyFMnrtLAqBNpFASBcAIGRuFSLmACnRAwMOpEGgVBgAABAhUKGBhViGkpAgQIEOisgIFRZ1PbvMAMjJqXE1dEgMBoAQOj0UY+QYBA8wQMjJqXE1dEgAABAvUKGBjV6293AgQIEGiHgIFRO/LUias0MOpEGgVBIJyAgVG4lAuYQCcEDIw6kUZBECBAgEBK6dN/+hfp/dfe1LN4wymvTr9y0TnpeSueu6DNU3u/kz543U3pTz7/173PvO/ic9Jpr//ptHvPQ+myD/7S/3RwAAAgAElEQVRWWrv68HlrfP3e+9NF77s+nXTCDy+69t/8j79Lv/jLV8/tW64rSQQIECBAoEsCBkZdymbDYzEwaniCXB4BAkMFDIwcDAIE2ihgYNTGrLlmAgQIEBgUKIY0N/z2p9I1/+Fd6YdWHZxu+O3/3PvIBe9481Csclj0qpNe2hsS9f8UA6MPXv876fHHn0gXvPPN6cdffnzv18Waf/+P30irDjpwwYFRMbT62C2fSde9/8J07Jqjet+7c+dd6di1R8/9WfYIECBAgEAXBAyMupDFlsRgYNSSRLlMAgTmCRgYORAECLRRwMCojVlzzQQIECAwKFAMc9YefcTc8GdwgDT4+WKwc8839gwdKJUDoze94afSXbu+li7acka6/4H/lX7/0zvSi190ZLr7q18fOjD69iOPpV+7+hPpoi1vMRxyRAkQIECg8wIGRp1PcXMCNDBqTi5cCQEC4wsYGI1v5ZMECDRHwMCoOblwJQQIECCwNIFhdwsVj4+78upPpCsuffvQ4U0xYLrptj+d2/DIw18wd1dQOTB69y/8XPrMnV9IP/nKl6Vv3P+tdPRRL+z973//4lf3GxgNPt6uWPict7x+wTuclhapbxEgQIAAgeYIGBg1JxedvxIDo86nWIAEOilgYNTJtAqKQOcFDIw6n2IBEiBAoPMC5bDmTZtOnnt83GIDo2GfL+44+tT2nb1H2j2197u9R9IVA6Pi59qP3ZZWH3FY706jO/7sr4cOjIrPFXc1/eH2nSPfndT5hAiQAAECBEIIGBiFSHMzgjQwakYeXAUBApMJGBhN5uXTBAg0Q8DAqBl5cBUECBAgsHSBSe8wGjYwKh4nd8mv/ma64B2np6OOeOHcwOiow1+QPnjdTakcRhWDpXHuMFp3/Evm3qe09Mh8kwABAgQINFfAwKi5uenclRkYdS6lAiIQQsDAKESaBUmgcwIGRp1LqYAIECAQUmDSdxgNfr7//UMrli+fGxgdu+aoeZ4LDYyKD3mHUcijJ2gCBAiEFTAwCpv6/IEbGOU3tyMBAtMLGBhNb2gFAgTyCxgY5Te3IwECBAhUL1A8Du6G3/7U3F09xUCo+LngHW/u/W/x5we+9e25x8UVn/+1q//T3HuL+gdB337k8SUNjIp9inU+dstn5tYt/tmdO+9Kx649eui7lKqXsCIBAgQIEMgjYGCUx9kuKSUDI8eAAIE2ChgYtTFrrpkAAQMjZ4AAAQIEuiJQDGvef+1NvXDecMqr571LaHBgVA53ys/3P0Ju956HljwwKtYthlG/+MtXz7G+7+Jz0mmv/+muMIuDAAECBAj0BAyMHIRsAgZG2ahtRIBAhQIGRhViWooAgWwCBkbZqG1EgAABAi0RePKp76S0LKUDVzy3JVfsMgkQIECAQH4BA6MR5g8/8mi68qpr0uazz0jrT1yXP0Md2tHAqEPJFAqBQAIGRoGSLVQCHRIwMOpQMoVCgAABApUIGBhVwmgRAgQIEOi4QKcGRvfce1/6wIeuT4899ng6+OCD0nvfc2Fau+aYXgrLwc/u+/f0/nz5pefPGwBtv2NH2nbzbb3fnbR+Xbr4vC2peCGigVF1/wYYGFVnaSUCBPIJGBjls7YTAQLVCRgYVWdpJQIECBDohoCBUTfyKAoCBAgQmK1AZwZGxbDouhu3povO2zI3JCrp9u7bl669cWta/7IT0qaNG9LgZ3d96e607Zbb0xWXXZIOWbUybd12a++rWzafZWBU4fkzMKoQ01IECGQTMDDKRm0jAgQqFDAwqhDTUgQIECDQCQEDo06kURAECBAgMGOBTgyMyoHQplNPGfrYuGJA9PGbPpnefcG5vYHQ4ACpGBAdvfrI3jCp+OkfIBV/7n8kXXnH0QkvPa43UPIzvoCB0fhWPkmAQHMEDIyakwtXQoDA+AIGRuNb+eTkAt/e93jace8XJ/+ibxAgQKBGgaef/l76/vdTes5zDqjxKmxNgACByQX+zZofS6uWP3/yL/oGgSUIdGJgNPi4ucKh/7Fyg3cQFb8v7yJ625mnz7v7qPhd/x1Iq1atnBsYHX/cj/Q+u9Bgagn+ob5iYBQq3YIl0BkBA6POpFIgBEIJGBiFSnf2YN/7F/8pPfjEw+l5z/Hi+Oz4NiRAYMkCzzyzLKX0/fSsZy15CV8kQIBAdoEnv7svHfH8Q9OVP705+942jCnQiYHRQncQHXboob27gIqB0fY7Pz/3XqJhA6P+IdCwgdHPv/m0tGPnF+Yea1cel+985+mYJ2cJUb//r343HbDsWemw561awrd9hQABAvUIfO97z6S0LKUDdJb1JMCuBAgsSeCBJx9Oy5al9O9f87Ylfd+XCCwm8P7/VtT1y9T1jgkBAq0S+N4zz/SuV13fqrS5WALhBYq6vvj59695a3iLcQGWP/fZ437U54YIdHJgVMTZf1fRPff+07x3FBW/n/QOo93370mrjzpi7j1HpeUjjz/lYI0p8B//5pO9gdELVqwc8xs+RoAAgfoFvl88tyKltKz4L69+CBAg0BKBbz31SG/Y/X+94udbcsUus00Cv6Gub1O6XCsBAv8soK53FAgQaKOAun7yrK086HmTf8k35gQ6MTAqHkn34Rs+mt55zplp7ZpjesH131W0Z8+3pn6H0cbXvTatPvLw/e5UcpbGF/BIuvGtfJIAgeYIeCRdc3LhSggQGF/AI+nGt/LJyQXU9ZOb+QYBAvULqOvrz4ErIEBgcgF1/eRmvjGdQCcGRgVBccfQgw891HvsXPFTvGto/ctOSJs2bkh79+2b9+f+R84VA6bBdxyVdx8Vj7Mr34+0+ewz0voT16Xtd+xIu778lXmPt5suBXG+rbGMk2uREuiSgMayS9kUC4E4AhrLOLmuI1J1fR3q9iRAYFoBdf20gr5PgEAdAur6OtRj79mZgVE5FPrirrt7GT11w8m99xeVP+Xgp3i0XPFz+aXn9wZA5U8xCNp28229P560ft3cQGhwYFT8vn84tWL58tgnaILoNZYTYPkoAQKNEdBYNiYVLoQAgQkENJYTYPnoxALq+onJfIEAgQYIqOsbkASXQIDAxALq+onJfGFKgc4MjKZ08PUMAhrLDMi2IECgcgGNZeWkFiRAIIOAxjIDcuAt1PWBky90Ai0WUNe3OHkunUBgAXV94OTXFLqBUU3wEbfVWEbMupgJtF9AY9n+HIqAQEQBjWXErOeLWV2fz9pOBAhUJ6Cur87SSgQI5BNQ1+ezttMPBAyMnIRsAhrLbNQ2IkCgQgGNZYWYliJAIJuAxjIbdciN1PUh0y5oAq0XUNe3PoUCIBBSQF0fMu21Bm1gVCt/rM01lrHyLVoCXRHQWHYlk+IgEEtAYxkr37mjVdfnFrcfAQJVCKjrq1C0BgECuQXU9bnF7Wdg5AxkE9BYZqO2EQECFQpoLCvEtBQBAtkENJbZqENupK4PmXZBE2i9gLq+9SkUAIGQAur6kGmvNWgDo1r5Y22usYyVb9ES6IqAxrIrmRQHgVgCGstY+c4drbo+t7j9CBCoQkBdX4WiNQgQyC2grs8tbj8DI2cgm4DGMhu1jQgQqFBAY1khpqUIEMgmoLHMRh1yI3V9yLQLmkDrBdT1rU+hAAiEFFDXh0x7rUEbGNXKH2tzjWWsfIuWQFcENJZdyaQ4CMQS0FjGynfuaNX1ucXtR4BAFQLq+ioUrUGAQG4BdX1ucfsZGDkD2QQ0ltmobUSAQIUCGssKMS1FgEA2AY1lNuqQG6nrQ6Zd0ARaL6Cub30KBUAgpIC6PmTaaw3awKhW/libayxj5Vu0BLoioLHsSibFQSCWgMYyVr5zR6uuzy1uPwIEqhBQ11ehaA0CBHILqOtzi9vPwMgZyCagscxGbSMCBCoU0FhWiGkpAgSyCWgss1GH3EhdHzLtgibQegF1fetTKAACIQXU9SHTXmvQBka18sfaXGMZK9+iJdAVAY1lVzIpDgKxBDSWsfKdO1p1fW5x+xEgUIWAur4KRWsQIJBbQF2fW9x+BkbOQDYBjWU2ahsRIFChgMayQkxLESCQTUBjmY065Ebq+pBpFzSB1guo61ufQgEQCCmgrg+Z9lqDNjCqlT/W5hrLWPkWLYGuCGgsu5JJcRCIJaCxjJXv3NGq63OL248AgSoE1PVVKFqDAIHcAur63OL2MzByBrIJaCyzUduIAIEKBTSWFWJaigCBbAIay2zUITdS14dMu6AJtF5AXd/6FAqAQEgBdX3ItNcatIFRrfyxNtdYxsq3aAl0RUBj2ZVMioNALAGNZax8545WXZ9b3H4ECFQhoK6vQtEaBAjkFlDX5xa3n4GRM5BNQGOZjdpGBAhUKKCxrBDTUgQIZBPQWGajDrmRuj5k2gVNoPUC6vrWp1AABEIKqOtDpr3WoA2MauWPtbnGMla+RUugKwIay65kUhwEYgloLGPlO3e06vrc4vYjQKAKAXV9FYrWIEAgt4C6Pre4/QyMnIFsAhrLbNQ2IkCgQgGNZYWYliJAIJuAxjIbdciN1PUh0y5oAq0XUNe3PoUCIBBSQF0fMu21Bm1gVCt/rM01lrHyLVoCXRHQWHYlk+IgEEtAYxkr37mjVdfnFrcfAQJVCKjrq1C0BgECuQXU9bnF7Vf5wGjrtlvTgw89lC4+b0tasXz5POG9+/ala2/cmg479NC0ZfNZ9IMJaCyDJVy4BDoioLHsSCKFQSCYgMYyWMIzh6uuzwxuOwIEKhFQ11fCaBECBDILqOszg9suVToweviRR9OVV12TNp99Rlp/4rqhvLu+dHfadsvt6YrLLkmHrFopBYEENJaBki1UAh0S0Fh2KJlCIRBIQGMZKNk1hKqurwHdlgQITC2grp+a0AIECNQgoK6vAT34lpUPjD58w0fTO885M61dc8xQ2nvuvS99/KZPpndfcK6BUbDDp7EMlnDhEuiIgMayI4kUBoFgAhrLYAnPHK66PjO47QgQqERAXV8Jo0UIEMgsoK7PDG672d5htP2OHWn1kYfPu9vIHUZxT53GMm7uRU6gzQIayzZnz7UTiCugsYyb+xyRq+tzKNuDAIGqBdT1VYtajwCBHALq+hzK9ugXqPQOo2Lh4h1GxU/xjqJyYFRuWDymrv/3UhFLQGMZK9+iJdAVAY1lVzIpDgKxBDSWsfKdO1p1fW5x+xEgUIWAur4KRWsQIJBbQF2fW9x+lQ+M9u7bl669cWtP9vgfPjYd+5I1vf97777vpB07v5AeeOBB7y8Keu40lkETL2wCLRfQWLY8gS6fQFABjWXQxGcKW12fCdo2BAhUKqCur5TTYgQIZBJQ12eCts2cQOUDo3Ll4k6iO3fsnEd96oaTe3ce+YkpoLGMmXdRE2i7gMay7Rl0/QRiCmgsY+Y9V9Tq+lzS9iFAoEoBdX2VmtYiQCCXgLo+l7R9SoGZDYyKDYY9kg59XAGNZdzci5xAmwU0lm3OnmsnEFdAYxk39zkiV9fnULYHAQJVC6jrqxa1HgECOQTU9TmU7dEvMNOBEWoC/QIaS+eBAIE2Cmgs25g110yAgMbSGZilgLp+lrrWJkBgVgLq+lnJWpcAgVkKqOtnqWvtYQIGRs5FNgGNZTZqGxEgUKGAxrJCTEsRIJBNQGOZjTrkRur6kGkXNIHWC6jrW59CARAIKaCuD5n2WoM2MKqVP9bmGstY+RYtga4IaCy7kklxEIgloLGMle/c0arrc4vbjwCBKgTU9VUoWoMAgdwC6vrc4vYzMHIGsgloLLNR24gAgQoFNJYVYlqKAIFsAhrLbNQhN1LXh0y7oAm0XkBd3/oUCoBASAF1fci01xq0gVGt/LE211jGyrdoCXRFQGPZlUyKg0AsAY1lrHznjlZdn1vcfgQIVCGgrq9C0RoECOQWUNfnFrefgZEzkE1AY5mN2kYECFQooLGsENNSBAhkE9BYZqMOuZG6PmTaBU2g9QLq+tanUAAEQgqo60OmvdagZzYweviRR9OVV12Tdt+/Z78AVx91RLriskvSIatW1hq8zfMKaCzzetuNAIFqBDSW1ThahQCBvAIay7ze0XZT10fLuHgJdENAXd+NPIqCQDQBdX20jNcf78wGRlu33dqLbsvms+qP0hU0QkBj2Yg0uAgCBCYU0FhOCObjBAg0QkBj2Yg0dPYi1PWdTa3ACHRaQF3f6fQKjkBnBdT1nU1tYwObycCouLvowzd8NL3znDPT2jXHNDZ4F5ZXQGOZ19tuBAhUI6CxrMbRKgQI5BXQWOb1jrabuj5axsVLoBsC6vpu5FEUBKIJqOujZbz+eA2M6s9BmCvQWIZJtUAJdEpAY9mpdAqGQBgBjWWYVNcSqLq+FnabEiAwpYC6fkpAXydAoBYBdX0t7KE3ncnAqBAtHkl39Ooj06aNG0IDC/5fBDSWTgMBAm0U0Fi2MWuumQABjaUzMEsBdf0sda1NgMCsBNT1s5K1LgECsxRQ189S19rDBGY2MLrn3vvSpz/7ufRv3/7WtGL5cvoEksbSISBAoI0CGss2Zs01EyCgsXQGZimgrp+lrrUJEJiVgLp+VrLWJUBglgLq+lnqWjvbwKh4h9GVV12Tdt+/Z6j66qOOSFdcdkk6ZNVKWQkkoLEMlGyhEuiQgMayQ8kUCoFAAhrLQMmuIVR1fQ3otiRAYGoBdf3UhBYgQKAGAXV9DejBt5zZHUbBXYU/REBj6VgQINBGAY1lG7PmmgkQ0Fg6A7MUUNfPUtfaBAjMSkBdPytZ6xIgMEsBdf0sda09TMDAyLnIJqCxzEZtIwIEKhTQWFaIaSkCBLIJaCyzUYfcSF0fMu2CJtB6AXV961MoAAIhBdT1IdNea9AGRrXyx9pcYxkr36Il0BUBjWVXMikOArEENJax8p07WnV9bnH7ESBQhYC6vgpFaxAgkFtAXZ9b3H6VDozKdxf972/8mfRHn/2cdxg5X/MENJYOBAECbRTQWLYxa66ZAAGNpTMwSwF1/Sx1rU2AwKwE1PWzkrUuAQKzFFDXz1LX2sMEKh0YdZG4HIJtPvuMtP7EdV0MMVtMGsts1DYiQKBCAY1lhZiWIkAgm4DGMht1yI3U9SHTLmgCrRdQ17c+hQIgEFJAXR8y7bUG3cmB0dZtt6avfPVr6YrLLkmHrFrZAy4HP7vv39P78+WXnj9vALT9jh1p28239X530vp16eLztqQVy5fPfc/AaPpzqrGc3tAKBAjkF9BY5je3IwEC0wtoLKc3tMLCAup6p4MAgTYKqOvbmDXXTICAut4ZyC3QuYFRMSy6c8fOtPqoI+YGRnv37UvX3rg1rX/ZCWnTxg3pnnvvS9fduDVddN6WtHbNMWnXl+5O2265fe7zxRrFz5bNZxkYVXgiNZYVYlqKAIFsAhrLbNQ2IkCgQgGNZYWYltpPQF3vUBAg0EYBdX0bs+aaCRBQ1zsDuQU6NTAq7hL6xu5vple94uXzBkDFgOjjN30yvfuCc3t3HA0OkIoB0dGrj+wNk4qf/gFS8ecrr7omlXcYlXcqnfDS43oDJT/jC2gsx7fySQIEmiOgsWxOLlwJAQLjC2gsx7fyyckF1PWTm/kGAQL1C6jr68+BKyBAYHIBdf3kZr4xnUBnBkbFsGjXl7/Se5Tc333t/5s3MBq8g6ggK+8ietuZp8+7+6j4Xf8dSKtWrZwbGB1/3I/0Prvp1FO8z2gJ505juQQ0XyFAoHaBp7/3TO8ann3As2q/FhdAgACBcQU0luNK+dxSBNT1S1HzHQIE6hZQ19edAfsTILAUAXX9UtR8ZxqBSgdG5d03xQX1vz9omgsc57vFQGj7nZ+fe+/Q4IBo8PfFmoMDo/4h0LCB0c+/+bS0Y+cX5h5rV17Xg99+fJxL9JmU0tW7fj8dkJalF6z4wXul/BAgQKANAt//54tc1oaLdY0ECBD4Z4EH9z6S0rJl6ZKXv4UJgcoF1PWVk1qQAIEMAur6DMi2IECgcgF1/eSkh/3QQZN/yTfmBCodGBWrlkOj3ffv6W1y+aXnz/xunOLuom0337ZfWsv3GN1z7z/Nu+Oo+OCkdxgV8fS/F8kZmlzA30Sc3Mw3CBCoX8CjK+rPgSsgQGByAX8TcXIz3xhfQF0/vpVPEiDQHAF1fXNy4UoIEBhfQF0/vpVPViNQ+cCo/7KKocydO3b2/tGpG07O9s6fwTuMqniH0cbXvTatPvLweXcyVZOCOKtoLOPkWqQEuiSgsexSNsVCII6AxjJOruuIVF1fh7o9CRCYVkBdP62g7xMgUIeAur4O9dh7znRgVNIWA5xfv/ojvT/muEtncGC0d9++ee8p6n/k3No1x6TBz5d3H23ZfNbcHVObzz6jd6dU/7uSVixfHvv0TBi9xnJCMB8nQKARAhrLRqTBRRAgMKGAxnJCMB+fSEBdPxGXDxMg0BABdX1DEuEyCBCYSEBdPxGXD1cgkGVgVF5nObh54IEHZ/qOo8EBULH/qEfl9T/W7qT16+beh1R+rxwYFWsVA6UHH3po7jMV5CHEEhrLEGkWJIHOCWgsO5dSAREIIaCxDJHm2oJU19dGb2MCBKYQUNdPgeerBAjUJqCur40+7MZZB0bl8KXQvuKyS9Ihq1aGhY8YuMYyYtbFTKD9AhrL9udQBAQiCmgsI2Y9X8zq+nzWdiJAoDoBdX11llYiQCCfgLo+n7WdfiCQZWCU+5F0kttMAY1lM/PiqggQWFxAY+mEECDQRgGNZRuz1p5rVte3J1eulACBfxFQ1zsNBAi0UUBd38astfuaZzYwKh8/98Vdd/eETt1wcireCeQnroDGMm7uRU6gzQIayzZnz7UTiCugsYyb+xyRq+tzKNuDAIGqBdT1VYtajwCBHALq+hzK9ugXqHxgdM+996UPfOj69Nhjj/f2ufzS89P6E9dRJ5A0lg4BAQJtFNBYtjFrrpkAAY2lMzBLAXX9LHWtTYDArATU9bOStS4BArMUUNfPUtfawwQqHRh5R5FDtpiAxtL5IECgjQIayzZmzTUTIKCxdAZmKaCun6WutQkQmJWAun5WstYlQGCWAur6Wepae+YDI8QEDIycAQIEuiagsexaRsVDIIaAxjJGnuuK0sCoLnn7EiAwjYC6fho93yVAoC4BdX1d8nH3rfQOo5Jx67Zb0507dvb+6N1FcQ/XYOQaS2eBAIE2Cmgs25g110yAgMbSGZilgLp+lrrWJkBgVgLq+lnJWpcAgVkKqOtnqWvtYQKVD4y237Ej7fryV9LF521JK5YvT8Xw6OjVR6ZNGzfIQHABjWXwAyB8Ai0V0Fi2NHEum0BwAY1l8AMw4/DV9TMGtjwBAjMRUNfPhNWiBAjMWEBdP2Ngy+8nUOnAaO++fenaG7emTaeektafuK632T333pc+ftMn07svODcdsmqlFAQW0FgGTr7QCbRYQGPZ4uS5dAKBBTSWgZOfIXR1fQZkWxAgULmAur5yUgsSIJBBQF2fAdkW8wQqHRg9/Mij6cM3fDS985wz09o1x/Q2GvbP5CCmgMYyZt5FTaDtAhrLtmfQ9ROIKaCxjJn3XFGr63NJ24cAgSoF1PVValqLAIFcAur6XNL2KQWyDIyuvOqatPnsM+buOsIfU0BjGTPvoibQdgGNZdsz6PoJxBTQWMbMe66o1fW5pO1DgECVAur6KjWtRYBALgF1fS5p+xgYOQPZBTSW2cltSIBABQIaywoQLUGAQHYBjWV28lAbqutDpVuwBDojoK7vTCoFQiCUgLo+VLobEWzldxgVdxPtvn/PosGtPuqIdMVll3inUSOOQL6L0Fjms7YTAQLVCWgsq7O0EgEC+QQ0lvmsI+6kro+YdTETaL+Aur79ORQBgYgC6vqIWa835koHRvWGYvemC2gsm54h10eAwDABjaVzQYBAGwU0lm3MWnuuWV3fnly5UgIE/kVAXe80ECDQRgF1fRuz1u5rNjBqd/5adfUay1aly8USIPDPAhpLR4EAgTYKaCzbmLX2XLO6vj25cqUECBgYOQMECLRbQF3f7vy18eorHRg9/MijySPp2ngM8lyzxjKPs10IEKhWwMCoWk+rESCQR0Bjmcc56i7q+qiZFzeBdguo69udP1dPIKqAuj5q5uuLu9KB0agwtm67Nd25Y2fyDqNRUt38vcaym3kVFYGuC2gsu55h8RHopoDGspt5bUpU6vqmZMJ1ECAwiYC6fhItnyVAoCkC6vqmZCLOdWQZGO360t3p16/+SE/18kvPT+tPXBdHWKRzAhpLh4EAgTYKaCzbmDXXTICAxtIZmKWAun6WutYmQGBWAur6WclalwCBWQqo62epa+1hAjMdGO3dty9de+PW9MVdd6dTN5yctmw+SxYCC2gsAydf6ARaLKCxbHHyXDqBwAIay8DJzxC6uj4Dsi0IEKhcQF1fOakFCRDIIKCuz4Bsi3kCMxsYbb9jR9p2823p4IMPSu99z4Vp7Zpj0AcX0FgGPwDCJ9BSAY1lSxPnsgkEF9BYBj8AMw5fXT9jYMsTIDATAXX9TFgtSoDAjAXU9TMGtvx+ApUPjO659770gQ9dnx577PG0+a1vSZs2bsBOoCegsXQQCBBoo4DGso1Zc80ECGgsnYFZCqjrZ6lrbQIEZiWgrp+VrHUJEJilgLp+lrrWHiZQ6cDo4UceTVdedU06/PDD0sXnbUkrli+nTmBOQGPpMBAg0EYBjWUbs+aaCRDQWDoDsxRQ189S19oECMxKQF0/K1nrEiAwSwF1/Sx1rZ1tYLT7/j2Laq8+6oh0xWWXpENWrZSVQAIay0DJFiqBDgloLDuUTKEQCCSgsQyU7BpCVdfXgG5LAgSmFlDXT01oAQIEahBQ19eAHnzLSu8wCm4p/BECGktHhACBNgpoLNuYNddMgIDG0hmYpYC6fpa61iZAYFYC6vpZyVqXAIFZCqjrZ6lr7WECBkbORTYBjWU2ahsRIFChgMayQkxLESCQTUBjmY065Ebq+pBpFzSB1guo61ufQgEQCCmgrkUC8ggAACAASURBVA+Z9lqDNjCqlT/W5hrLWPkWLYGuCGgsu5JJcRCIJaCxjJXv3NGq63OL248AgSoE1PVVKFqDAIHcAur63OL2MzByBrIJaCyzUduIAIEKBTSWFWJaigCBbAIay2zUITdS14dMu6AJtF5AXd/6FAqAQEgBdX3ItNcatIFRrfyxNtdYxsq3aAl0RUBj2ZVMioNALAGNZax8545WXZ9b3H4ECFQhoK6vQtEaBAjkFlDX5xa3n4GRM5BNQGOZjdpGBAhUKKCxrBDTUgQIZBPQWGajDrmRuj5k2gVNoPUC6vrWp1AABEIKqOtDpr3WoA2MauWPtbnGMla+RUugKwIay65kUhwEYgloLGPlO3e06vrc4vYjQKAKAXV9FYrWIEAgt4C6Pre4/QyMnIFsAhrLbNQ2IkCgQgGNZYWYliJAIJuAxjIbdciN1PUh0y5oAq0XUNe3PoUCIBBSQF0fMu21Bm1gVCt/rM01lrHyLVoCXRHQWHYlk+IgEEtAYxkr37mjVdfnFrcfAQJVCKjrq1C0BgECuQXU9bnF7Wdg5AxkE9BYZqO2EQECFQpoLCvEtBQBAtkENJbZqENupK4PmXZBE2i9gLq+9SkUAIGQAur6kGmvNWgDo1r5Y22usYyVb9ES6IqAxrIrmRQHgVgCGstY+c4drbo+t7j9CBCoQkBdX4WiNQgQyC2grs8tbj8DI2cgm4DGMhu1jQgQqFBAY1khpqUIEMgmoLHMRh1yI3V9yLQLmkDrBdT1rU+hAAiEFFDXh0x7rUEbGNXKH2tzjWWsfIuWQFcENJZdyaQ4CMQS0FjGynfuaNX1ucXtR4BAFQLq+ioUrUGAQG4BdX1ucfsZGDkD2QQ0ltmobUSAQIUCGssKMS1FgEA2AY1lNuqQG6nrQ6Zd0ARaL6Cub30KBUAgpIC6PmTaaw3awKhW/libayxj5Vu0BLoioLHsSibFQSCWgMYyVr5zR6uuzy1uPwIEqhBQ11ehaA0CBHILqOtzi9vPwMgZyCagscxGbSMCBCoU0FhWiGkpAgSyCWgss1GH3EhdHzLtgibQegF1fetTKAACIQXU9SHTXmvQBka18sfaXGMZK9+iJdAVAY1lVzIpDgKxBDSWsfKdO1p1fW5x+xEgUIWAur4KRWsQIJBbQF2fW9x+BkbOQDYBjWU2ahsRIFChgMayQkxLESCQTUBjmY065Ebq+pBpFzSB1guo61ufQgEQCCmgrg+Z9lqDNjCqlT/W5hrLWPkWLYGuCGgsu5JJcRCIJaCxjJXv3NGq63OL248AgSoE1PVVKFqDAIHcAur63OL2MzByBrIJaCyzUduIAIEKBTSWFWJaigCBbAIay2zUITdS14dMu6AJtF5AXd/6FAqAQEgBdX3ItNcatIFRrfyxNtdYxsq3aAl0RUBj2ZVMioNALAGNZax8545WXZ9b3H4ECFQhoK6vQtEaBAjkFlDX5xa3n4HRiDPw8COPpiuvuiZtPvuMtP7EdU7MFAIayynwfJUAgdoENJa10duYAIEpBDSWU+D56kgBdf1IIh8gQKCBAur6BibFJREgMFJAXT+SyAcqFujMwGjrtlvTnTt2zvFsfutb0qaNG+b+XA5+dt+/p/fPLr/0/HkDoO137Ejbbr6t97uT1q9LF5+3Ja1YvjwZGFV34jSW1VlaiQCBfAIay3zWdiJAoDoBjWV1llbaX0Bd71QQINBGAXV9G7PmmgkQUNc7A7kFOjEw2rtvX/rdT34qnfGm/y0dsmpluufe+9IHPnR9ete5m3tDoeL31964Na1/2Qm9IVLx++tu3JouOm9LWrvmmLTrS3enbbfcnq647JLe94vhU/GzZfNZBkYVnkiNZYWYliJAIJuAxjIbtY0IEKhQQGNZIaal9hNQ1zsUBAi0UUBd38asuWYCBNT1zkBugU4MjAbRhg2IPn7TJ9O7Lzi3NxAa/H0xIDp69ZFzdyT1D5CKtfsfSVfecXTCS4/rDZT8jC+gsRzfyicJEGiOgMayOblwJQQIjC+gsRzfyicnF1DXT27mGwQI1C+grq8/B66AAIHJBdT1k5v5xnQCnRwYDT5GbvAOooKsvIvobWeePu/uo+J3/XcgrVq1cm5gdPxxP9L77KZTT/E+oyWcO43lEtB8hQCB2gU0lrWnwAUQILAEAY3lEtB8ZWwBdf3YVD5IgECDBNT1DUqGSyFAYGwBdf3YVD5YkUAnB0b9j5QrnIqB0fY7Pz/3XqJhA6P+IdCwgdHPv/m0tGPnF+Yea1f6P/zokxWlovvL/Me//b10QFqWXrBiZfeDFSEBAp0R+P4/R7KsMxEJhACBCALf2vtIWpaWpV/6sZ+LEK4YMwuo6zOD244AgUoE1PWVMFqEAIHMAur6ycEPWXng5F/yjTmBzg2MimHRgw89NG84VMUdRrvv35NWH3XE3HuOSsHvPv09x2lMgSv/8nfTs5ctS4c9b9WY3/AxAgQI1C/w9DPPpGJYdMCznlX/xbgCAgQIjCnwwJMPp7RsWXrfv3rbmN/wMQLjC/yHv/wddf34XD5JgEBDBL73zDOpGBo9W13fkIy4DAIExhH4QV2f0nvV9eNw9T7znGcfMPZnfXB/gU4NjIYNi4qQizuGpn2H0cbXvTatPvLw/e5UcqjGF/DoivGtfJIAgeYIeHRFc3LhSggQGF/AoyvGt/LJyQXU9ZOb+QYBAvULqOvrz4ErIEBgcgF1/eRmvjGdQGcGRoOPoetn2btv37z3FPU/cm7tmmN6j6zbdsvtc3cP9a81+D6k7XfsSLu+/JV5dzBNl4I439ZYxsm1SAl0SUBj2aVsioVAHAGNZZxc1xGpur4OdXsSIDCtgLp+WkHfJ0CgDgF1fR3qsffsxMCoHOoUj43r/zlp/bq5wc7gZy6/9Py0/sR1cx8vBkHbbr6t9+dh39t89hlzn1/oTqbYR2l09BrL0UY+QYBA8wQ0ls3LiSsiQGC0gMZytJFPLF1AXb90O98kQKA+AXV9ffZ2JkBg6QLq+qXb+ebSBDoxMFpa6L6VW0BjmVvcfgQIVCGgsaxC0RoECOQW0FjmFo+1n7o+Vr5FS6ArAur6rmRSHARiCajrY+W7CdEaGDUhC0GuQWMZJNHCJNAxAY1lxxIqHAJBBDSWQRJdU5jq+prgbUuAwFQC6vqp+HyZAIGaBNT1NcEH3tbAKHDyc4euscwtbj8CBKoQ0FhWoWgNAgRyC2gsc4vH2k9dHyvfoiXQFQF1fVcyKQ4CsQTU9bHy3YRoDYyakIUg16CxDJJoYRLomIDGsmMJFQ6BIAIayyCJrilMdX1N8LYlQGAqAXX9VHy+TIBATQLq+prgA29rYBQ4+blD11jmFrcfAQJVCGgsq1C0BgECuQU0lrnFY+2nro+Vb9ES6IqAur4rmRQHgVgC6vpY+W5CtAZGTchCkGvQWAZJtDAJdExAY9mxhAqHQBABjWWQRNcUprq+JnjbEiAwlYC6fio+XyZAoCYBdX1N8IG3NTAKnPzcoWssc4vbjwCBKgQ0llUoWoMAgdwCGsvc4rH2U9fHyrdoCXRFQF3flUyKg0AsAXV9rHw3IVoDoyZkIcg1aCyDJFqYBDomoLHsWEKFQyCIgMYySKJrClNdXxO8bQkQmEpAXT8Vny8TIFCTgLq+JvjA2xoYBU5+7tA1lrnF7UeAQBUCGssqFK1BgEBuAY1lbvFY+6nrY+VbtAS6IqCu70omxUEgloC6Pla+mxCtgVETshDkGjSWQRItTAIdE9BYdiyhwiEQREBjGSTRNYWprq8J3rYECEwloK6fis+XCRCoSUBdXxN84G0NjAInP3foGsvc4vYjQKAKAY1lFYrWIEAgt4DGMrd4rP3U9bHyLVoCXRFQ13clk+IgEEtAXR8r302I1sCoCVkIcg0ayyCJFiaBjgloLDuWUOEQCCKgsQyS6JrCVNfXBG9bAgSmElDXT8XnywQI1CSgrq8JPvC2BkaBk587dI1lbnH7ESBQhYDGsgpFaxAgkFtAY5lbPNZ+6vpY+RYtga4IqOu7kklxEIgloK6Ple8mRGtg1IQsBLkGjWWQRAuTQMcENJYdS6hwCAQR0FgGSXRNYarra4K3LQECUwmo66fi82UCBGoSUNfXBB94WwOjwMnPHbrGMre4/QgQqEJAY1mFojUIEMgtoLHMLR5rP3V9rHyLlkBXBNT1XcmkOAjEElDXx8p3E6I1MGpCFoJcg8YySKKFSaBjAhrLjiVUOASCCGgsgyS6pjDV9TXB25YAgakE1PVT8fkyAQI1Cajra4IPvK2BUeDk5w5dY5lb3H4ECFQhoLGsQtEaBAjkFtBY5haPtZ+6Pla+RUugKwLq+q5kUhwEYgmo62PluwnRGhg1IQtBrkFjGSTRwiTQMQGNZccSKhwCQQQ0lkESXVOY6vqa4G1LgMBUAur6qfh8mQCBmgTU9TXBB97WwChw8nOHrrHMLW4/AgSqENBYVqFoDQIEcgtoLHOLx9pPXR8r36Il0BUBdX1XMikOArEE1PWx8t2EaA2MmpCFINegsQySaGES6JiAxrJjCRUOgSACGssgia4pTHV9TfC2JUBgKgF1/VR8vkyAQE0C6vqa4ANva2AUOPm5Q9dY5ha3HwECVQhoLKtQtAYBArkFNJa5xWPtp66PlW/REuiKgLq+K5kUB4FYAur6WPluQrQGRk3IQpBr0FgGSbQwCXRMQGPZsYQKh0AQAY1lkETXFKa6viZ42xIgMJWAun4qPl8mQKAmAXV9TfCBtzUwCpz83KFrLHOL248AgSoENJZVKFqDAIHcAhrL3OKx9lPXx8q3aAl0RUBd35VMioNALAF1fax8NyFaA6MmZCHINWgsgyRamAQ6JqCx7FhChUMgiIDGMkiiawpTXV8TvG0JEJhKQF0/FZ8vEyBQk4C6vib4wNsaGAVOfu7QNZa5xe1HgEAVAhrLKhStQYBAbgGNZW7xWPup62PlW7QEuiKgru9KJsVBIJaAuj5WvpsQrYFRE7IQ5Bo0lkESLUwCHRPQWHYsocIhEERAYxkk0TWFqa6vCd62BAhMJaCun4rPlwkQqElAXV8TfOBtDYwCJz936BrL3OL2I0CgCgGNZRWK1iBAILeAxjK3eKz91PWx8i1aAl0RUNd3JZPiIBBLQF0fK99NiNbAqAlZCHINGssgiRYmgY4JaCw7llDhEAgioLEMkuiawlTX1wRvWwIEphJQ10/F58sECNQkoK6vCT7wtgZGgZOfO3SNZW5x+xEgUIWAxrIKRWsQIJBbQGOZWzzWfur6WPkWLYGuCKjru5JJcRCIJaCuj5XvJkRrYNSELAS5Bo1lkEQLk0DHBDSWHUuocAgEEdBYBkl0TWGq62uCty0BAlMJqOun4vNlAgRqElDX1wQfeFsDo8DJzx26xjK3uP0IEKhCQGNZhaI1CBDILaCxzC0eaz91fax8i5ZAVwTU9V3JpDgIxBJQ18fKdxOiNTBqQhaCXIPGMkiihUmgYwIay44lVDgEgghoLIMkuqYw1fU1wduWAIGpBNT1U/H5MgECNQmo62uCD7ytgVHg5OcOXWOZW9x+BAhUIaCxrELRGgQI5BbQWOYWj7Wfuj5WvkVLoCsC6vquZFIcBGIJqOtj5bsJ0RoYNSELQa5BYxkk0cIk0DEBjWXHEiocAkEENJZBEl1TmOr6muBtS4DAVALq+qn4fJkAgZoE1PU1wQfe1sAocPJzh66xzC1uPwIEqhDQWFahaA0CBHILaCxzi8faT10fK9+iJdAVAXV9VzIpDgKxBNT1sfLdhGgNjJqQhSDXoLEMkmhhEuiYgMayYwkVDoEgAhrLIImuKUx1fU3wtiVAYCoBdf1UfL5MgEBNAur6muADb2tgFDj5uUPXWOYWtx8BAlUIaCyrULQGAQK5BTSWucVj7aeuj5Vv0RLoioC6viuZFAeBWALq+lj5bkK0BkZNyEKQa9BYBkm0MAl0TEBj2bGECodAEAGNZZBE1xSmur4meNsSIDCVgLp+Kj5fJkCgJgF1fU3wgbc1MAqc/Nyhayxzi9uPAIEqBDSWVShagwCB3AIay9zisfZT18fKt2gJdEVAXd+VTIqDQCwBdX2sfDchWgOjJmQhyDVoLIMkWpgEOiagsexYQoVDIIiAxjJIomsKU11fE7xtCRCYSkBdPxWfLxMgUJOAur4m+MDbGhgFTn7u0DWWucXtR4BAFQIayyoUrUGAQG4BjWVu8Vj7qetj5Vu0BLoioK7vSibFQSCWgLo+Vr6bEK2BUROyEOQaNJZBEi1MAh0T0Fh2LKHCIRBEQGMZJNE1hamurwnetgQITCWgrp+Kz5cJEKhJQF1fE3zgbQ2MAic/d+gay9zi9iNAoAoBjWUVitYgQCC3gMYyt3is/dT1sfItWgJdEVDXdyWT4iAQS0BdHyvfTYjWwKgJWQhyDRrLIIkWJoGOCWgsO5ZQ4RAIIqCxDJLomsJU19cEb1sCBKYSUNdPxefLBAjUJKCurwk+8LYGRoGTnzt0jWVucfsRIFCFgMayCkVrECCQW0BjmVs81n7q+lj5Fi2Brgio67uSSXEQiCWgro+V7yZEa2A0Rha237Ejbbv5tt4nT1q/Ll183pa0YvnyMb7pI/0CGkvngQCBNgpoLNuYNddMgIDG0hmYpYC6fpa61iZAYFYC6vpZyVqXAIFZCqjrZ6lr7WECBkYjzsWuL92dtt1ye7riskvSIatWpq3bbu19Y8vms5yoCQU0lhOC+TgBAo0Q0Fg2Ig0uggCBCQU0lhOC+fhEAur6ibh8mACBhgio6xuSCJdBgMBEAur6ibh8uAIBA6MRiMWA6OjVR6ZNGzf0Pjk4QKogB2GW0FiGSbVACXRKQGPZqXQKhkAYAY1lmFTXEqi6vhZ2mxIgMKWAun5KQF8nQKAWAXV9LeyhNzUwWiT9e/ftS9feuDWtf9kJcwOje+69L11349Z00Xlb0to1x4Q+PJMGr7GcVMznCRBogoDGsglZcA0ECEwqoLGcVMznJxFQ10+i5bMECDRFQF3flEy4DgIEJhFQ10+i5bNVCBgYjTEw2nTqKWn9iet6nxwcGD347ceryEOINX7jb29Nj37niXTgs73/KUTCBUmgIwLfT99Py9KyjkQjDAIEogg88fTe9EMrDk7vWX9mlJDFmVFAXZ8R21YECFQmoK6vjNJCBAhkFFDXT4592A8dNPmXfGNOwMBojIHRYncYGRj5t4kAAQIECBAgQIAAAQIECBAgQIAAAQIECNQvYGA0XQ4MjEb4eYfRdAfMtwkQIECAAAECBAgQIECAAAECBAgQIECAAIHmCxgYjcjRri/dnbbdcnu64rJL0iGrVqZigFT8bNl8VvOz6woJECBAgAABAgQIECBAgAABAgQIECBAgAABAmMIGBiNgbT9jh1p28239T550vp16eLztqQVy72HZww6HyFAgACBGQj4ywszQLUkAQIECBAgQIAAAQIECBAgQCC4gIFR8AMgfAIECOQU2LtvX7r2xq2p/91wxf7FYH7Xl78y9UC+qnVymixlLwOjpaj5DgECBAgQIECg/QKDT0EpInr4kUfTlVddkzaffUZaf+K6JQdZ1TpLvoCMXyzq6Tt37Jzb8eCDD0rvfc+Fae2aY1r7ZJkyf7vv3zMX1+a3viVt2rhh7s/33Htf+sCHrk+PPfb43D+7/NLze+em/MvS5Z/LD5RWg/88Y7psRYAAAQIZBQyMMmLbigABAtEFDIyqOQEGRtU4WoUAAQIECBAg0DYBA6PpMzbsL5kVrru/+UBvuNLWWntw4Ff+eePrXtuLq4jx16/+SOof/BSfuf0PP5PedubpaceffyHd8f/8WXrJi9ekf/v2t/aerFMMmAqPbz7wrfSuczdPNZCcPnNWIECAAIEcAgZGOZTtQYAAAQI9gXEHRuXnvrjr7t73+v9m3ODfijt1w8m998oN/vPVRx3Re/9c0QA99dRT6YmnnkrFev1/e7D4znU3bk0Xnbel97cJy303nXpKrxkqmqMHH3ooPfDAg6n4m3rFd3/pwl9If/Bf/mS/tcqG7OSfeHX67B07en9rb9RjTPuvuf+6Rq3V38QONrQLGTuCBAgQIECAAAEC7RcYd2BUDgeKiMu6uHgvc/HTf3fN4J01/XfdFDX4Ccf/aK9ePvknX5N+71N/3Pt+WX+XaxX/W77nudh3+52f7z05YM+eb/W+Wwwg/t//dtfcd1/1ipf3BheDa5WDnOc/73lznx/3rpbiu8VP/900w7I9qlbudyu+X9bze/fu693FVd69039dhWd/v1G69d/J029WrNv/6oPBHC3WCy12gofdIVbs843d3+wNhIY96aF/vfKzxT8rclT2Qz967IvTH332c1Pfwdb+f/tEQIAAgRgCBkYx8ixKAgQINEJgnIFRcaFFM3PYoYf2Gs+i8fnwDR9N7zznzN5Q5892/mV68doX9f7vcuBS/m23YX9bsGjg/uquv517xET/Z8omdrGB0Ve++rXe4KlosAfX6h/WlA3a4Ycf1muQyzgGH79XJmKwoSua09/86Lbeda5atbLXkC60Vv++hcHHb/pkevcF5/aucdh/RGhE8l0EAQIECBAgQIDA1ALjDIz668qiZi4HAWVtXdxJ8n/87Bt611L+Bamifi2HIv2Ptivr7de88sfmavP+x98N/uWlwYFRMTQ5/bRNvUHO4FqDf3lr8JFok9S14w6Mypj7+4PBpIz6C1mDPchgj1Cst1jPMpif/jhXrFi+aC+02AEaNjAqY9m44eR5f1Fu2DrlOSk+++nPfi793Ok/m/54+53pZzedmn7jmhsNjKb+t9cCBAgQaIeAgVE78uQqCRAg0AmBwb8t1x9U+bf3Boc4ZVN39Ooj9/sbg4MDqIUGRsUa5d967G9Mi38+6g6j/u8Ort//52EN9mLvVBpsgPtj+YlX//h+z6Evmr3SYLCJHfzdMKtOHCBBECBAgAABAgSCCwzeAdPPUd71MlgrDv4Fo/7v9NekxT8ffBfS4FCnrM3LGnnUwKi/1l7okWnlgGqwdp7knUqTDIzKGPrvpup/MsAwv/44RhkMHtHBnmWY2bZbbu/9JbVHHnl0v8FOf62/2PEf9OofbB2yatW8v2Q2bJ3+wWKxZ/H9M970xrR2zYsqeUdW8H91hU+AAIHWCBgYtSZVLpQAAQLtFxjnDqNiYDT4ItYi8vKxdMOGTuXvxhkY9d+xVKw7y4FR/9+wLJ4BPticl4/rKH9XNoOTDozKfd7+1p9LH/notrm7sdp/YkRAgAABAgQIECAwWEOWw4XyEXODg4L+R86V3+1/LN3g0Kn8XfHZcQZGg4OF4nuLPZKuvJt/0oHR4OOi+x0W+4toxecGHwG32Ckqr+uElx7Xi2OcgdtiBsVei/UsgwOg/qFdMTBarBcaJ47ysXnFZ8sh4rDB3+Ba/TH19zHD/mKcfysJECBAoLsCBkbdza3ICBAg0DiBcQdG/Y9YG9YYlo+rc4fRWT2e0uGJx5/sPaqvbNgbdwBcEAECBAgQIECAwFQC4zySbrE7UhZ7HFpxYeMMjBZ7n+awdxgtdWA0yzuMhg1Ldn35K71HS//uJz/V+/WwJxQUtXbxM847RRfqWUbdYbRQLzTq4CzmNerdTcXa/QOj/r0mycOoa/R7AgQIEGi+gIFR83PkCgkQINAZgXEGRkWw/e8wKv5c/I24/3nPP6XXvPoV817WWjYvG1/32t7j6oY10MMe31Y2gMPeI1S8gHehx3lM8ki6UY3VsGsffIdR//PjF3skXdngbbv5trlr78yhEQgBAgQIECBAgMCcwDgDo8GhUPHlP/jjP0kbXvuT6Z57/yn136FU1Ld3/Nc/7z0OrXx/Tv87OAfvTBn23qFy0FLW8cX/FoOXwUdNT3qH0WKPdx48EuM+kq7oRz72iZvTaW/8md5ftCp+Rv0ltPL35QBo2DuMyv5i2Hqjepaizi/fmzr4DqP+Xuhfn/wTi/6bMKr/KO8sK3udYrHiO7f/4WfS2848PRXvtvrG7m/u95fPRq3rX08CBAgQ6JaAgVG38ikaAgQINFpgnIFR8Xi2wUc4HHzwQem977mw19T1P0Kj+OcHP//AtPHf/OvewKj/e+WjNYoGaKHnkxdY/ev91L96ZXriqafSplNPSetPXLff4yjGGRj1PwKifFTeQkkpm83HHns89ce40AtrF3qHURnH4CPuGn0YXBwBAgQIECBAgMDEAuMMjAZr3OLP5SPaBuvsH/3hl6QnnnyyNzAqHnHXXxsXtewJx//ofo9I6x849K9X1LNv3Lgh/d0/fH3JA6PiL0CVP/2P0RsFNe7AqFin+Gz/PsU/66/by1q8qOvLdxuVj2Ura/1+g8G/oDboP9izFL/vf2xgkZsHH3qoZzaqF1rMYZzBTn//Ua5VxuIOo1GnzO8JECAQQ8DAKEaeRUmAAIGwAsMauFlgjNOgzWLfcs0izle94uW9QZcfAgQIECBAgAABAlUIjPPumyr2KdaY5I6iqvZswjoLDWqacG2ugQABAgTiCRgYxcu5iAkQIBBKIMLAqGjkl/qs81CHQbAECBAgQIAAAQITCRgYTcQ11of772AqvlDexVTcXbTQz+B3Bj9X3kE21gX4EAECBAgQWETAwMjxIECAAAECBAgQIECAAAECBAgQIECAAAECBAgEFzAwCn4AhE+AAAECBAgQIECAAAECBAgQIECAAAECBAgQMDByBggQIECAAAECBAgQIECAAAECBAgQIECAAAECwQUMjIIfAOETIECAAAECBAgQIECAAAECBAgQIECAAAECBAyMnAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQHABA6PgB0D4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAEDI2eAAAECBAgQIECAAAECBAgQIECAAAECBAgQIBBcwMAo+AEQPgECBAgQIECAAAECBAgQIECAAAECBAgQIEDAjpIyfgAABx9JREFUwMgZIECAAAECBAgQIECAAAECBAgQIECAAAECBAgEFzAwCn4AhE+AAAECBAgQIECAAAECBAgQIECAAAECBAgQMDByBggQIECAAAECBAgQIECAAAECBAgQIECAAAECwQUMjIIfAOETIECAAAECBAgQIECAAAECBAgQIECAAAECBAyMnAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQHABA6PgB0D4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAEDI2eAAAECBAgQIECAAAECBAgQIECAAAECBAgQIBBcwMAo+AEQPgECBAgQIECAAAECBAgQIECAAAECBAgQIEDAwMgZIECAAAECBAgQIECAAAECBAgQIECAAAECBAgEFzAwCn4AhE+AAAECBAgQIECAAAECBAgQIECAAAECBAgQMDByBggQIECAAAECBAgQIECAAAECBAgQIECAAAECwQUMjIIfAOETIECAAAECBAgQIECAAAECBAgQIECAAAECBAyMnAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQHABA6PgB0D4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAEDI2eAAAECBAgQIECAAAECBAgQIECAAAECBAgQIBBcwMAo+AEQPgECBAgQIECAAAECBAgQIECAAAECBAgQIEDAwMgZIECAAAECBAgQIECAAAECBAgQIECAAAECBAgEFzAwCn4AhE+AAAECBAgQIECAAAECBAgQIECAAAECBAgQMDByBggQIECAAAECBAgQIECAAAECBAgQIECAAAECwQUMjIIfAOETIECAAAECBAgQIECAAAECBAgQIECAAAECBAyMnAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQHABA6PgB0D4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAEDI2eAAAECBAgQIECAAAECBAgQIECAAAECBAgQIBBcwMAo+AEQPgECBAgQIECAAAECBAgQIECAAAECBAgQIEDAwMgZIECAAAECBAgQIECAAAECBAgQIECAAAECBAgEFzAwCn4AhE+AAAECBAgQIECAAAECBAgQIECAAAECBAgQMDByBggQIECAAAECBAgQIECAAAECBAgQIECAAAECwQUMjIIfAOETIECAAAECBAgQIECAAAECBAgQIECAAAECBAyMnAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQHABA6PgB0D4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAEDI2eAAAECBAgQIECAAAECBAgQIECAAAECBAgQIBBcwMAo+AEQPgECBAgQIECAAAECBAgQIECAAAECBAgQIEDAwMgZIECAAAECBAgQIECAAAECBAgQIECAAAECBAgEFzAwCn4AhE+AAAECBAgQIECAAAECBAgQIECAAAECBAgQMDByBggQIECAAAECBAgQIECAAAECBAgQIECAAAECwQUMjIIfAOETIECAAAECBAgQIECAAAECBAgQIECAAAECBAyMnAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQHABA6PgB0D4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAEDI2eAAAECBAgQIECAAAECBAgQIECAAAECBAgQIBBcwMAo+AEQPgECBAgQIECAAAECBAgQIECAAAECBAgQIEDAwMgZIECAAAECBAgQIECAAAECBAgQIECAAAECBAgEFzAwCn4AhE+AAAECBAgQIECAAAECBAgQIECAAAECBAgQMDByBggQIECAAAECBAgQIECAAAECBAgQIECAAAECwQUMjIIfAOETIECAAAECBAgQIECAAAECBAgQIECAAAECBAyMnAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQHABA6PgB0D4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAEDI2eAAAECBAgQIECAAAECBAgQIECAAAECBAgQIBBcwMAo+AEQPgECBAgQIECAAAECBAgQIECAAAECBAgQIEDAwMgZIECAAAECBAgQIECAAAECBAgQIECAAAECBAgEFzAwCn4AhE+AAAECBAgQIECAAAECBAg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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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    "\n",
    "npv_graph.update_traces(\n",
    "    text=_series.values/1000_000, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f}M €\", \n",
    ")\n",
    "\n",
    "npv_graph"
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  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "tags": [
     "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",
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    "    kind='bar',\n",
    "    title='Simple Payback Time by Casestudy in Years',\n",
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    "    dimensions=(600, 400),\n",
    "    yTitle='Years',\n",
    "    asFigure=True,\n",
    "    yrange=[0, _series.max() * 1.2]\n",
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    "    text=_series.values, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f} years\", \n",
    ")\n",
    "\n",
    "spt_fig "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('bb3d3b8c-46bb-499a-aa77-d5454ca33ad8');\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",
       "}}\n",
       "\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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: 8.33 s\n",
      "Wall time: 17.4 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_27812\\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: 2.08 s\n",
      "Wall time: 5.13 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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       "model_id": "9d623f84db7c48a7a8896bdbece2df59",
       "version_major": 2,
       "version_minor": 0
      },
      "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_27812\\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_27812\\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_27812\\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_27812\\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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