{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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_kickstart 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": 10,
   "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 = 1_152_000 # EUR/MWth\n",
    "    hp_opex = 0.04 # in % of CAPEX\n",
    "    hp_devex = 0.005 # in % of CAPEX\n",
    "    \n",
    "    gb_power = 5 # MW\n",
    "    gb_efficiency = 0.9\n",
    "    \n",
    "    storage_power = 1\n",
    "    # storage_volume = 1000 *3/50\n",
    "    # storage_cap_per_volume = 50 * 1e-3\n",
    "    rated_capacity = 3\n",
    "    storage_lifetime = 25\n",
    "    storage_temperature = 144\n",
    "    # storage_min_level = storage_volume * storage_cap_per_volume * 0.05\n",
    "    storage_min_level = rated_capacity * 0.00\n",
    "    # storage_min_level = rated_capacity\n",
    "    storage_capex_per_MW = 0\n",
    "    storage_capex_per_MWh = 100_000\n",
    "    storage_opex_perc_of_capex = 0.03\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": 11,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "class Store():\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Model set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = Store()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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_21988\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def setup_model(c):\n",
    "    s = Store()\n",
    "    \n",
    "    s.time_fw = TimeFramework(start=c.start, end=c.end)\n",
    "    # s.minute_ix = s.time_fw.dt_index(freq='1T')\n",
    "    mipf = Mipf(None, start=s.time_fw.start, end=s.time_fw.end, from_database=True, add_days_to_start_end=False).data\n",
    "    s.mipf = mipf\n",
    "    s.baseline = CaseStudy(time_fw=s.time_fw, freq='1T', name='Baseline')\n",
    "    s.hpcase = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump only', data=mipf)\n",
    "    #s.hpcase_sde = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + SDE', data=mipf)\n",
    "    #s.optcase1 = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation', data=mipf)\n",
    "    s.storage_case_PCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_PCM', data=mipf)\n",
    "    # s.storage_case_TCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_TCM', data=mipf)\n",
    "    # s.storage_case_battery = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_battery', data=mipf)\n",
    "    s.cases = list(CaseStudy.instances.values())\n",
    "    s.optcases = [s.hpcase, s.storage_case_PCM]\n",
    "    s.sde_cases = []\n",
    "    \n",
    "    s.demand = load_demand_data(c, s)\n",
    "    return s\n",
    "\n",
    "s = setup_model(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.demand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Load in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "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",
    "    \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",
    "        case.data['DAM_avg'] = case.data.groupby(case.data.index.date)['DAM'].transform('mean')\n",
    "        \n",
    "        case.data['NEG'] = increase_volatility_by_factor(case.data['NEG'], c.e_price_volatility_multiplier)\n",
    "        case.data['ForeNeg'] = increase_volatility_by_factor(case.data['ForeNeg'], c.e_price_volatility_multiplier)\n",
    "\n",
    "\n",
    "    s.demand['Tsource (VDG)'] += c.tsource_delta\n",
    "    s.demand['Tsink (VDG)'] += c.tsink_delta\n",
    "    for case in s.cases:\n",
    "        case.data = pd.concat([case.data, s.demand], axis=1)        \n",
    "\n",
    "    s.eb_ode_g = get_tax_rate('gas', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n",
    "    s.eb_ode_e = get_tax_rate('electricity', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n",
    "    s.grid_fees = get_grid_tariffs_electricity(c.grid_operator, 2020, c.connection_type)\n",
    "    s.grid_fee_per_MWh = s.grid_fees['kWh tarief'] * 1000\n",
    "    return s\n",
    "\n",
    "s = load_data(c, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.08224016788466"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data['CO2 prices (€/ton)'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>DAM_avg</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",
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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",
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       "      <td>70</td>\n",
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       "      <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",
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       "      <td>1.01</td>\n",
       "      <td>18.144167</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",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2023-01-01 00:04:00+01:00</th>\n",
       "      <td>-3.61</td>\n",
       "      <td>-209.40</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>18.144167</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <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",
       "      <td>...</td>\n",
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       "    <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>14.683333</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>14.683333</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>14.683333</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:58:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.74</td>\n",
       "      <td>-7.87</td>\n",
       "      <td>29.927</td>\n",
       "      <td>77.25</td>\n",
       "      <td>14.90</td>\n",
       "      <td>14.683333</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:59:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.70</td>\n",
       "      <td>-7.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.683333</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 × 13 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)    DAM_avg  Tsource (VDG)  \\\n",
       "2023-01-01 00:00:00+01:00                1.01  18.144167             70   \n",
       "2023-01-01 00:01:00+01:00                1.01  18.144167             70   \n",
       "2023-01-01 00:02:00+01:00                1.01  18.144167             70   \n",
       "2023-01-01 00:03:00+01:00                1.01  18.144167             70   \n",
       "2023-01-01 00:04:00+01:00                1.01  18.144167             70   \n",
       "...                                       ...        ...            ...   \n",
       "2023-12-31 23:55:00+01:00               14.90  14.683333             70   \n",
       "2023-12-31 23:56:00+01:00               14.90  14.683333             70   \n",
       "2023-12-31 23:57:00+01:00               14.90  14.683333             70   \n",
       "2023-12-31 23:58:00+01:00               14.90  14.683333             70   \n",
       "2023-12-31 23:59:00+01:00                 NaN  14.683333             70   \n",
       "\n",
       "                           Tsink (VDG)  MW (VDG)  Total demand  \n",
       "2023-01-01 00:00:00+01:00          144         1             1  \n",
       "2023-01-01 00:01:00+01:00          144         1             1  \n",
       "2023-01-01 00:02:00+01:00          144         1             1  \n",
       "2023-01-01 00:03:00+01:00          144         1             1  \n",
       "2023-01-01 00:04:00+01:00          144         1             1  \n",
       "...                                ...       ...           ...  \n",
       "2023-12-31 23:55:00+01:00          144         1             1  \n",
       "2023-12-31 23:56:00+01:00          144         1             1  \n",
       "2023-12-31 23:57:00+01:00          144         1             1  \n",
       "2023-12-31 23:58:00+01:00          144         1             1  \n",
       "2023-12-31 23:59:00+01:00          144         1             1  \n",
       "\n",
       "[525600 rows x 13 columns]"
      ]
     },
     "execution_count": 23,
     "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": 26,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def cop_curve_new(Tsink, Tsource):\n",
    "    Tsink += 273\n",
    "    Tsource += 273\n",
    "    Tlift = Tsink - Tsource\n",
    "\n",
    "    c0 = 0.0005426*Tlift**2 - 0.1178*Tlift + 6.962\n",
    "    c1 = 6.7058 \n",
    "    c2 = -1.79\n",
    "    \n",
    "    return Polynomial([c0, c1, c2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$x \\mapsto \\text{19.2496754} + \\text{6.7058}\\,x - \\text{1.79}\\,x^{2}$"
      ],
      "text/plain": [
       "Polynomial([19.2496754,  6.7058   , -1.79     ], domain=[-1,  1], window=[-1,  1], symbol='x')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sourceT = 63 + 273\n",
    "sinkT = 140 + 273\n",
    "cop_curve_new(sourceT, sinkT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Create and assign assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1152000\n"
     ]
    }
   ],
   "source": [
    "def create_and_assign_assets(c, s):\n",
    "    heatpump_vdg = Heatpump(\n",
    "        name='Heatpump VDG',\n",
    "        max_th_power=c.hp_vdg_e_power,\n",
    "        min_th_power=c.hp_vdg_e_power * c.hp_min_load,\n",
    "        cop_curve=2.9\n",
    "    )\n",
    "\n",
    "    heatpump_pcm = Heatpump(\n",
    "        name='Heatpump PCM',\n",
    "        max_th_power=c.hp_vdg_e_power,\n",
    "        min_th_power=c.hp_vdg_e_power * c.hp_min_load,\n",
    "        cop_curve=2.9\n",
    "    )\n",
    "        \n",
    "    capex_vdg = c.hp_capex * 1\n",
    "    heatpump_vdg.set_financials(capex=capex_vdg, opex=c.hp_opex*capex_vdg, devex=c.hp_devex*capex_vdg, lifetime=25)\n",
    "\n",
    "    print(capex_vdg)\n",
    "    capex_pcm = c.hp_capex * heatpump_vdg.max_th_power\n",
    "    heatpump_pcm.set_financials(capex=capex_pcm, opex=c.hp_opex*capex_pcm, devex=c.hp_devex*capex_pcm, lifetime=25)\n",
    "    # print(capex_pcm)\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_pcm)\n",
    "    s.storage_case_PCM.add_asset(waterstorage)\n",
    "    # s.storage_case_PCM.add_asset(heatpump_vdg)\n",
    "    return s\n",
    "\n",
    "s = create_and_assign_assets(c, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capex_ws = c.storage_capex_per_MW * 1 + c.storage_capex_per_MWh * 3\n",
    "capex_ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=0.0)"
      ]
     },
     "execution_count": 31,
     "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": 34,
   "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": 35,
   "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(5)\n",
    "    return case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hp_storage_opt_enginge(c, ws, hp_vdg, pos, neg, dam, demand_vdg, tsource_vdg, tsink_vdg):\n",
    "    \n",
    "    from_storage_vdg_MW = 0\n",
    "    to_storage_vdg_MW = 0  \n",
    "    \n",
    "    if neg < dam - c.threshold:\n",
    "        # overproduce\n",
    "        if demand_vdg != 0:\n",
    "            storage_charge_power = ws.charging_power_limit\n",
    "            cop = 2.5\n",
    "            if ws.chargelevel == ws.max_chargelevel:\n",
    "                storage_charge_power = 0\n",
    "                cop = 2.9\n",
    "            else:\n",
    "                # Adjust charge power if charge level is not at max\n",
    "                storage_charge_power = min(ws.charging_power_limit, (ws.max_chargelevel - ws.chargelevel) * 60)\n",
    "\n",
    "            desired_hp_load_vdg = min(demand_vdg + storage_charge_power, 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, cop = cop)\n",
    "\n",
    "            to_storage_vdg_MW = desired_hp_load_vdg - demand_vdg\n",
    "            to_storage_vdg_MW = -ws.charge(round(to_storage_vdg_MW, 5))\n",
    "            # #Added lately\n",
    "            # extra_charging_power_constraint = ws.max_power - to_storage_vdg_MW\n",
    "            # desired_hp_load_new = min(demand_vdg + min(storage_charge_power, extra_charging_power_constraint) - 0.66, hp_vdg.max_th_power)\n",
    "            # e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(desired_hp_load_new, tsink_vdg, tsource_vdg)\n",
    "\n",
    "        else:\n",
    "            e_load_vdg, th_load_vdg = (0,0)\n",
    "            \n",
    "            \n",
    "\n",
    "    elif pos > dam + c.threshold:\n",
    "        # take from storage\n",
    "        if demand_vdg != 0:\n",
    "            from_storage_vdg_MW = demand_vdg\n",
    "            from_storage_vdg_MW = ws.discharge(round(from_storage_vdg_MW, 5))\n",
    "            # from_storage_vdg_MW = min(ws.max_power - from_storage_vdg_MW, ws.discharging_power_limit)\n",
    "            desired_hp_load_vdg = min(demand_vdg - from_storage_vdg_MW, 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, cop = 2.9)\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",
    "            cop = 2.9\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": 37,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def hponly_with_storage(case):\n",
    "    hp_vdg = case.assets['Heatpump PCM']\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_avg'].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(5)\n",
    "    return case\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.storage_case_PCM.data['cop'].fillna(2.5, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>DAM_avg</th>\n",
       "      <th>Tsource (VDG)</th>\n",
       "      <th>...</th>\n",
       "      <th>from_storage_vdg_MW</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>from_storage_MW</th>\n",
       "      <th>storage_level_MWh</th>\n",
       "      <th>hp_output_MWh</th>\n",
       "      <th>hp_input_MWh</th>\n",
       "      <th>to_storage_vdg_MWh</th>\n",
       "      <th>from_storage_vdg_MWh</th>\n",
       "      <th>to_storage_MWh</th>\n",
       "      <th>from_storage_MWh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-12-16 12:57:00+01:00</th>\n",
       "      <td>71.45</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>70.50</td>\n",
       "      <td>51.22</td>\n",
       "      <td>31.164</td>\n",
       "      <td>66.33</td>\n",
       "      <td>12.79</td>\n",
       "      <td>75.55292</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.35833</td>\n",
       "      <td>0.02500</td>\n",
       "      <td>-0.01000</td>\n",
       "      <td>0.00833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00833</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-24 07:40:00+02:00</th>\n",
       "      <td>133.80</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>-23.05</td>\n",
       "      <td>-27.07</td>\n",
       "      <td>40.416</td>\n",
       "      <td>87.26</td>\n",
       "      <td>16.83</td>\n",
       "      <td>104.08833</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0.01667</td>\n",
       "      <td>-0.00667</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM   POS   NEG  ForeNeg  ForePos  \\\n",
       "2023-12-16 12:57:00+01:00   71.45 -40.0 -40.0    70.50    51.22   \n",
       "2023-04-24 07:40:00+02:00  133.80 -25.0 -25.0   -23.05   -27.07   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-12-16 12:57:00+01:00              31.164               66.33   \n",
       "2023-04-24 07:40:00+02:00              40.416               87.26   \n",
       "\n",
       "                           CO2 prices (€/MWh)    DAM_avg  Tsource (VDG)  ...  \\\n",
       "2023-12-16 12:57:00+01:00               12.79   75.55292             70  ...   \n",
       "2023-04-24 07:40:00+02:00               16.83  104.08833             70  ...   \n",
       "\n",
       "                           from_storage_vdg_MW  to_storage_MW  \\\n",
       "2023-12-16 12:57:00+01:00                  0.0            0.5   \n",
       "2023-04-24 07:40:00+02:00                  0.0            0.0   \n",
       "\n",
       "                           from_storage_MW  storage_level_MWh  hp_output_MWh  \\\n",
       "2023-12-16 12:57:00+01:00              0.0            2.35833        0.02500   \n",
       "2023-04-24 07:40:00+02:00              0.0            3.00000        0.01667   \n",
       "\n",
       "                           hp_input_MWh  to_storage_vdg_MWh  \\\n",
       "2023-12-16 12:57:00+01:00      -0.01000             0.00833   \n",
       "2023-04-24 07:40:00+02:00      -0.00667             0.00000   \n",
       "\n",
       "                           from_storage_vdg_MWh  to_storage_MWh  \\\n",
       "2023-12-16 12:57:00+01:00                   0.0         0.00833   \n",
       "2023-04-24 07:40:00+02:00                   0.0         0.00000   \n",
       "\n",
       "                           from_storage_MWh  \n",
       "2023-12-16 12:57:00+01:00               0.0  \n",
       "2023-04-24 07:40:00+02:00               0.0  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1222.5108"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data['to_storage_MWh'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.storage_case_PCM.data.to_csv('storage_data1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.hpcase.data.to_csv('storage_data_hp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
       "      <th>cop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.0</td>\n",
       "      <td>452694.000000</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>1.000300</td>\n",
       "      <td>0.139612</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.708758</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>0.457122</td>\n",
       "      <td>0.224309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.199808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-500.000000</td>\n",
       "      <td>-1549.470000</td>\n",
       "      <td>-1549.470000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.500000</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>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.500000</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>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>122.400000</td>\n",
       "      <td>114.020000</td>\n",
       "      <td>119.592500</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>463.770000</td>\n",
       "      <td>2037.740000</td>\n",
       "      <td>2037.740000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.900000</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       1.000300   \n",
       "std        49.046697     194.977533     195.696551       0.457122   \n",
       "min      -500.000000   -1549.470000   -1549.470000       0.000000   \n",
       "25%        76.935000      47.000000      57.967500       1.000000   \n",
       "50%        99.215000      84.130000      88.570000       1.000000   \n",
       "75%       122.400000     114.020000     119.592500       1.500000   \n",
       "max       463.770000    2037.740000    2037.740000       1.500000   \n",
       "\n",
       "       to_storage_MW  Total demand            cop  \n",
       "count  525600.000000      525600.0  452694.000000  \n",
       "mean        0.139612           1.0       2.708758  \n",
       "std         0.224309           0.0       0.199808  \n",
       "min         0.000000           1.0       2.500000  \n",
       "25%         0.000000           1.0       2.500000  \n",
       "50%         0.000000           1.0       2.900000  \n",
       "75%         0.500000           1.0       2.900000  \n",
       "max         0.500000           1.0       2.900000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'Total demand', 'cop']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>from_storage_MW</th>\n",
       "      <th>Total demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-08-19 14:08:00+02:00</th>\n",
       "      <td>12.59</td>\n",
       "      <td>954.00</td>\n",
       "      <td>954.00</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>2023-08-31 13:49:00+02:00</th>\n",
       "      <td>84.98</td>\n",
       "      <td>-54.00</td>\n",
       "      <td>-54.00</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>2023-11-09 22:51:00+01:00</th>\n",
       "      <td>84.04</td>\n",
       "      <td>-25.88</td>\n",
       "      <td>-25.88</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28 22:48:00+01:00</th>\n",
       "      <td>40.00</td>\n",
       "      <td>-15.62</td>\n",
       "      <td>-15.62</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-19 02:48:00+01:00</th>\n",
       "      <td>99.60</td>\n",
       "      <td>74.34</td>\n",
       "      <td>74.34</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>2023-09-26 02:48:00+02:00</th>\n",
       "      <td>104.93</td>\n",
       "      <td>87.48</td>\n",
       "      <td>87.48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-27 19:13:00+02:00</th>\n",
       "      <td>114.33</td>\n",
       "      <td>66.70</td>\n",
       "      <td>66.70</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>2023-08-13 11:23:00+02:00</th>\n",
       "      <td>13.70</td>\n",
       "      <td>53.60</td>\n",
       "      <td>53.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-18 19:05:00+01:00</th>\n",
       "      <td>123.07</td>\n",
       "      <td>95.81</td>\n",
       "      <td>95.81</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-23 10:05:00+02:00</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-200.11</td>\n",
       "      <td>58.09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-01 01:01:00+02:00</th>\n",
       "      <td>99.50</td>\n",
       "      <td>102.89</td>\n",
       "      <td>102.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-26 13:23:00+01:00</th>\n",
       "      <td>54.63</td>\n",
       "      <td>-94.58</td>\n",
       "      <td>79.34</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-01 12:22:00+01:00</th>\n",
       "      <td>102.63</td>\n",
       "      <td>46.95</td>\n",
       "      <td>46.95</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-25 05:47:00+02:00</th>\n",
       "      <td>103.00</td>\n",
       "      <td>88.12</td>\n",
       "      <td>88.12</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-29 22:05:00+02:00</th>\n",
       "      <td>122.08</td>\n",
       "      <td>111.53</td>\n",
       "      <td>111.53</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>2023-04-07 21:43:00+02:00</th>\n",
       "      <td>146.81</td>\n",
       "      <td>105.02</td>\n",
       "      <td>105.02</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-10 01:15:00+02:00</th>\n",
       "      <td>91.99</td>\n",
       "      <td>82.98</td>\n",
       "      <td>82.98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01 03:37:00+02:00</th>\n",
       "      <td>80.42</td>\n",
       "      <td>95.94</td>\n",
       "      <td>95.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-01 03:16:00+01:00</th>\n",
       "      <td>48.70</td>\n",
       "      <td>99.23</td>\n",
       "      <td>99.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-07 01:46:00+01:00</th>\n",
       "      <td>76.38</td>\n",
       "      <td>-12.07</td>\n",
       "      <td>-12.07</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</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",
       "2023-08-19 14:08:00+02:00   12.59  954.00  954.00           1.0   \n",
       "2023-08-31 13:49:00+02:00   84.98  -54.00  -54.00           1.0   \n",
       "2023-11-09 22:51:00+01:00   84.04  -25.88  -25.88           1.0   \n",
       "2023-12-28 22:48:00+01:00   40.00  -15.62  -15.62           1.5   \n",
       "2023-03-19 02:48:00+01:00   99.60   74.34   74.34           1.0   \n",
       "2023-09-26 02:48:00+02:00  104.93   87.48   87.48           1.0   \n",
       "2023-07-27 19:13:00+02:00  114.33   66.70   66.70           1.0   \n",
       "2023-08-13 11:23:00+02:00   13.70   53.60   53.60           1.0   \n",
       "2023-01-18 19:05:00+01:00  123.07   95.81   95.81           1.5   \n",
       "2023-07-23 10:05:00+02:00    0.00 -200.11   58.09           1.0   \n",
       "2023-07-01 01:01:00+02:00   99.50  102.89  102.89           0.0   \n",
       "2023-02-26 13:23:00+01:00   54.63  -94.58   79.34           1.5   \n",
       "2023-02-01 12:22:00+01:00  102.63   46.95   46.95           1.5   \n",
       "2023-08-25 05:47:00+02:00  103.00   88.12   88.12           1.5   \n",
       "2023-08-29 22:05:00+02:00  122.08  111.53  111.53           1.0   \n",
       "2023-04-07 21:43:00+02:00  146.81  105.02  105.02           1.5   \n",
       "2023-08-10 01:15:00+02:00   91.99   82.98   82.98           1.0   \n",
       "2023-10-01 03:37:00+02:00   80.42   95.94   95.94           0.0   \n",
       "2023-11-01 03:16:00+01:00   48.70   99.23   99.23           0.0   \n",
       "2023-11-07 01:46:00+01:00   76.38  -12.07  -12.07           1.5   \n",
       "\n",
       "                           to_storage_MW  from_storage_MW  Total demand  \n",
       "2023-08-19 14:08:00+02:00            0.0              0.0             1  \n",
       "2023-08-31 13:49:00+02:00            0.0              0.0             1  \n",
       "2023-11-09 22:51:00+01:00            0.0              0.0             1  \n",
       "2023-12-28 22:48:00+01:00            0.5              0.0             1  \n",
       "2023-03-19 02:48:00+01:00            0.0              0.0             1  \n",
       "2023-09-26 02:48:00+02:00            0.0              0.0             1  \n",
       "2023-07-27 19:13:00+02:00            0.0              0.0             1  \n",
       "2023-08-13 11:23:00+02:00            0.0              0.0             1  \n",
       "2023-01-18 19:05:00+01:00            0.5              0.0             1  \n",
       "2023-07-23 10:05:00+02:00            0.0              0.0             1  \n",
       "2023-07-01 01:01:00+02:00            0.0              1.0             1  \n",
       "2023-02-26 13:23:00+01:00            0.5              0.0             1  \n",
       "2023-02-01 12:22:00+01:00            0.5              0.0             1  \n",
       "2023-08-25 05:47:00+02:00            0.5              0.0             1  \n",
       "2023-08-29 22:05:00+02:00            0.0              0.0             1  \n",
       "2023-04-07 21:43:00+02:00            0.5              0.0             1  \n",
       "2023-08-10 01:15:00+02:00            0.0              0.0             1  \n",
       "2023-10-01 03:37:00+02:00            0.0              1.0             1  \n",
       "2023-11-01 03:16:00+01:00            0.0              1.0             1  \n",
       "2023-11-07 01:46:00+01:00            0.5              0.0             1  "
      ]
     },
     "execution_count": 46,
     "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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', '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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>2023-08-01 23:45:00+02:00</th>\n",
       "      <td>92.78</td>\n",
       "      <td>73.79</td>\n",
       "      <td>73.79</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-28 12:47:00+02:00</th>\n",
       "      <td>102.00</td>\n",
       "      <td>166.70</td>\n",
       "      <td>166.70</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-23 04:26:00+02:00</th>\n",
       "      <td>86.64</td>\n",
       "      <td>83.30</td>\n",
       "      <td>83.30</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-13 18:30:00+02:00</th>\n",
       "      <td>128.10</td>\n",
       "      <td>60.03</td>\n",
       "      <td>121.29</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-11 02:50:00+02:00</th>\n",
       "      <td>105.12</td>\n",
       "      <td>80.39</td>\n",
       "      <td>80.39</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-12 20:01:00+01:00</th>\n",
       "      <td>129.97</td>\n",
       "      <td>162.17</td>\n",
       "      <td>162.17</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-14 21:03:00+02:00</th>\n",
       "      <td>151.00</td>\n",
       "      <td>351.00</td>\n",
       "      <td>351.00</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-25 01:22:00+01:00</th>\n",
       "      <td>0.10</td>\n",
       "      <td>70.14</td>\n",
       "      <td>70.14</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-07 13:04:00+01:00</th>\n",
       "      <td>80.30</td>\n",
       "      <td>260.49</td>\n",
       "      <td>260.49</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-25 22:41:00+02:00</th>\n",
       "      <td>126.15</td>\n",
       "      <td>143.74</td>\n",
       "      <td>143.74</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-28 09:39:00+02:00</th>\n",
       "      <td>89.74</td>\n",
       "      <td>94.56</td>\n",
       "      <td>94.56</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-03 04:49:00+02:00</th>\n",
       "      <td>100.43</td>\n",
       "      <td>99.58</td>\n",
       "      <td>99.58</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-13 03:42:00+01:00</th>\n",
       "      <td>87.25</td>\n",
       "      <td>110.81</td>\n",
       "      <td>110.81</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-17 22:07:00+01:00</th>\n",
       "      <td>117.00</td>\n",
       "      <td>76.03</td>\n",
       "      <td>76.03</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.60000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-30 18:23:00+01:00</th>\n",
       "      <td>79.99</td>\n",
       "      <td>53.50</td>\n",
       "      <td>53.50</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-08 15:14:00+02:00</th>\n",
       "      <td>83.39</td>\n",
       "      <td>121.70</td>\n",
       "      <td>121.70</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-13 17:02:00+01:00</th>\n",
       "      <td>141.83</td>\n",
       "      <td>107.75</td>\n",
       "      <td>107.75</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-20 19:46:00+01:00</th>\n",
       "      <td>165.47</td>\n",
       "      <td>137.53</td>\n",
       "      <td>137.53</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-28 09:42:00+01:00</th>\n",
       "      <td>160.14</td>\n",
       "      <td>141.53</td>\n",
       "      <td>141.53</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.34483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-27 17:18:00+02:00</th>\n",
       "      <td>96.90</td>\n",
       "      <td>133.36</td>\n",
       "      <td>133.36</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM     POS     NEG  Total demand  hp_input_MW  \\\n",
       "2023-08-01 23:45:00+02:00   92.78   73.79   73.79             1     -0.34483   \n",
       "2023-04-28 12:47:00+02:00  102.00  166.70  166.70             1     -0.34483   \n",
       "2023-04-23 04:26:00+02:00   86.64   83.30   83.30             1     -0.34483   \n",
       "2023-04-13 18:30:00+02:00  128.10   60.03  121.29             1     -0.34483   \n",
       "2023-07-11 02:50:00+02:00  105.12   80.39   80.39             1     -0.34483   \n",
       "2023-03-12 20:01:00+01:00  129.97  162.17  162.17             1     -0.00000   \n",
       "2023-04-14 21:03:00+02:00  151.00  351.00  351.00             1     -0.00000   \n",
       "2023-03-25 01:22:00+01:00    0.10   70.14   70.14             1     -0.00000   \n",
       "2023-12-07 13:04:00+01:00   80.30  260.49  260.49             1     -0.00000   \n",
       "2023-08-25 22:41:00+02:00  126.15  143.74  143.74             1     -0.34483   \n",
       "2023-10-28 09:39:00+02:00   89.74   94.56   94.56             1     -0.34483   \n",
       "2023-04-03 04:49:00+02:00  100.43   99.58   99.58             1     -0.34483   \n",
       "2023-11-13 03:42:00+01:00   87.25  110.81  110.81             1     -0.34483   \n",
       "2023-02-17 22:07:00+01:00  117.00   76.03   76.03             1     -0.60000   \n",
       "2023-12-30 18:23:00+01:00   79.99   53.50   53.50             1     -0.34483   \n",
       "2023-04-08 15:14:00+02:00   83.39  121.70  121.70             1     -0.34483   \n",
       "2023-01-13 17:02:00+01:00  141.83  107.75  107.75             1     -0.34483   \n",
       "2023-03-20 19:46:00+01:00  165.47  137.53  137.53             1     -0.34483   \n",
       "2023-01-28 09:42:00+01:00  160.14  141.53  141.53             1     -0.34483   \n",
       "2023-04-27 17:18:00+02:00   96.90  133.36  133.36             1     -0.00000   \n",
       "\n",
       "                           hp_output_MW  cop  to_storage_MW  from_storage_MW  \n",
       "2023-08-01 23:45:00+02:00           1.0  2.9            0.0              0.0  \n",
       "2023-04-28 12:47:00+02:00           1.0  2.9            0.0              0.0  \n",
       "2023-04-23 04:26:00+02:00           1.0  2.9            0.0              0.0  \n",
       "2023-04-13 18:30:00+02:00           1.0  2.9            0.0              0.0  \n",
       "2023-07-11 02:50:00+02:00           1.0  2.9            0.0              0.0  \n",
       "2023-03-12 20:01:00+01:00           0.0  NaN            0.0              1.0  \n",
       "2023-04-14 21:03:00+02:00           0.0  NaN            0.0              1.0  \n",
       "2023-03-25 01:22:00+01:00           0.0  NaN            0.0              1.0  \n",
       "2023-12-07 13:04:00+01:00           0.0  NaN            0.0              1.0  \n",
       "2023-08-25 22:41:00+02:00           1.0  2.9            0.0              0.0  \n",
       "2023-10-28 09:39:00+02:00           1.0  2.9            0.0              0.0  \n",
       "2023-04-03 04:49:00+02:00           1.0  2.9            0.0              0.0  \n",
       "2023-11-13 03:42:00+01:00           1.0  2.9            0.0              0.0  \n",
       "2023-02-17 22:07:00+01:00           1.5  2.5            0.5              0.0  \n",
       "2023-12-30 18:23:00+01:00           1.0  2.9            0.0              0.0  \n",
       "2023-04-08 15:14:00+02:00           1.0  2.9            0.0              0.0  \n",
       "2023-01-13 17:02:00+01:00           1.0  2.9            0.0              0.0  \n",
       "2023-03-20 19:46:00+01:00           1.0  2.9            0.0              0.0  \n",
       "2023-01-28 09:42:00+01:00           1.0  2.9            0.0              0.0  \n",
       "2023-04-27 17:18:00+02:00           0.0  NaN            0.0              1.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72906"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "none_count = selection['cop'].isnull().sum()\n",
    "none_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DAM                 95.817313\n",
       "POS                 93.568163\n",
       "NEG                103.438997\n",
       "Total demand         1.000000\n",
       "hp_input_MW         -0.375354\n",
       "hp_output_MW         1.000300\n",
       "cop                  2.708758\n",
       "to_storage_MW        0.139612\n",
       "from_storage_MW      0.139312\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "########################\n",
    "\n",
    "# This is where we left\n",
    "\n",
    "########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 55,
   "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": 57,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def calc_afrr_capacity(case):\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    \n",
    "    capacity = 0\n",
    "    for hp in hp_vdg:\n",
    "        max_th_output = hp.max_th_power\n",
    "        cop = hp.get_cop(max_th_output, Tsink=135, Tsource=60)\n",
    "        e_power = max_th_output / cop\n",
    "        capacity += e_power\n",
    "    return capacity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Run optimisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.899979700142095"
      ]
     },
     "execution_count": 60,
     "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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-181242.64800000025"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data['hp_input_MW'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.34483"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data['hp_input_MW'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-197286.05060999998"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data['hp_input_MW'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data['hp_input_MW'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.664950199846266"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "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": 66,
   "metadata": {},
   "outputs": [
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       "  <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>DAM_avg</th>\n",
       "      <th>Tsource (VDG)</th>\n",
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       "                            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)   DAM_avg  Tsource (VDG)  \\\n",
       "2023-01-01 00:00:00+01:00                1.01  18.14417             70   \n",
       "2023-01-01 00:01:00+01:00                1.01  18.14417             70   \n",
       "2023-01-01 00:02:00+01:00                1.01  18.14417             70   \n",
       "2023-01-01 00:03:00+01:00                1.01  18.14417             70   \n",
       "2023-01-01 00:04:00+01:00                1.01  18.14417             70   \n",
       "\n",
       "                           Tsink (VDG)  MW (VDG)  Total demand  hp_output_MW  \\\n",
       "2023-01-01 00:00:00+01:00          144         1             1             1   \n",
       "2023-01-01 00:01:00+01:00          144         1             1             1   \n",
       "2023-01-01 00:02:00+01:00          144         1             1             1   \n",
       "2023-01-01 00:03:00+01:00          144         1             1             1   \n",
       "2023-01-01 00:04:00+01:00          144         1             1             1   \n",
       "\n",
       "                           hp_input_MW  cop  hp_output_MWh  hp_input_MWh  \n",
       "2023-01-01 00:00:00+01:00     -0.34483  2.9        0.01667      -0.00575  \n",
       "2023-01-01 00:01:00+01:00     -0.34483  2.9        0.01667      -0.00575  \n",
       "2023-01-01 00:02:00+01:00     -0.34483  2.9        0.01667      -0.00575  \n",
       "2023-01-01 00:03:00+01:00     -0.34483  2.9        0.01667      -0.00575  \n",
       "2023-01-01 00:04:00+01:00     -0.34483  2.9        0.01667      -0.00575  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72906"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data['cop'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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",
    "    'hp_input_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": 69,
   "metadata": {},
   "outputs": [
    {
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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",
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       "                           storage_level_MWh  to_storage_MW  from_storage_MW  \\\n",
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0AULrUXY82Pui4hkXjfIqfLwRAoTWjVDnngiMTIDQemRufAqBRgkwHqRR8twXgdELEFqP3pArIFAvAULreklzHwQQaIQAofUo1cd+51oNLL5NA+/4/VFeiY/XW4DQut7i3A+BkQsQWo/cjk8i0AgBQutGqHNPBKojQGhdHUeugkA9BAit66HMPRBAoFEChNajlPf3vayWn/yB4tmXqv/Kv5T8cJRX5OP1EiC0rpc090Fg9AKE1qM35AoI1FOA0Lqe2twLgeoKEFpX15OrIVBLAULrWupybQQQaLQAoXUVOuD3bFPLQx9X0tau0nV/J4WtVbgql6i1AKF1rYW5PgLVEyC0rp4lV0KgHgKE1vVQ5h4I1EaA0Lo2rlwVgVoIEFrXQpVrIoBAVgQIravUCe/Yfhtcm5PWpRu/orRlUpWuzGVqJUBoXStZrotA9QUIratvyhURqKUAoXUtdbk2ArUVILSurS9XR6CaAoTW1dTkWgggkDUBQusqdsSLjqjloU/KO/aGSjd+zZ685pVdAULr7PaGyhA4VYDQmj2BgFsChNZu9YtqEThRgNCa/YCAOwKE1u70ikoRQKByAULrys3O/om4Xy2rPyv/9ZdUuuFLSqYuq/YduF6VBAitqwTJZRCogwChdR2QuQUCVRQgtK4iJpdCoM4ChNZ1Bud2CIxCgNB6FHh8FAEEMi9AaF2TFqVqWvO3atr0Q5Wu+6LimZfU5C5cdHQChNaj8+PTCNRTgNC6ntrcC4HRCxBaj96QKyDQKAFC60bJc18EKhcgtK7cjE8ggIA7AoTWNexV0yv/rKYX/5f6r/isovk31fBOXHokAoTWI1HjMwg0RoDQujHu3BWBkQoQWo9Ujs8h0HgBQuvG94AKEBiuAKH1cKV4HwIIuChAaF3jroVbHlDzU5/TwIX/WQNv+d9rfDcuX4kAoXUlWrwXgcYKEFo31p+7I1CpAKF1pWK8H4HsCBBaZ6cXVILAuQQIrc8lxM8jgIDLAoTWdehesOd5tTz63xR1Xq/+yz4jeX4d7sotziVAaH0uIX4egewIEFpnpxdUgsBwBAith6PEexDIpgChdTb7QlUInE6A0Jp9gQACeRYgtK5Td/2D69Xy0CfsgxlL135eCprrdGducyYBQmv2BgLuCBBau9MrKkXACBBasw8QcFeA0Nrd3lF58QQIrYvXc1aMQJEECK3r2G3vyB61PvC7SsdMU+mGLyltGl/Hu3OrUwUIrdkTCLgjQGjtTq+oFAFCa/YAAm4LEFq73T+qL5YAoXWx+s1qESiaAKF1nTvulQ7ZE9eKSiq966tKx06vcwXc7rgAoTV7AQF3BAit3ekVlSJAaM0eQMBtAUJrt/tH9cUSILQuVr9ZLQJFEyC0bkTHoz61PPrH8g9ttsF1MvH8RlRR+HsSWhd+CwDgkAChtUPNolQEGA/CHkDAaQFCa6fbR/EFEyC0LljDWS4CBRMgtG5Uw9NYzU/8hYKdz6p0w5eVnHdBoyop7H0JrQvbehbuoAChtYNNo+RCCzDTutDtZ/GOCxBaO95Ayi+UAKF1odrNYhEonAChdYNb3vTTb6hp7b+qdPXnFM+9usHVFOv2hNbF6jerdVuA0Nrt/lF98QQIrYvXc1acHwFC6/z0kpXkX4DQOv89ZoUIFFmA0DoD3W9af5+a1vytBlb+kQYW35KBiopRAqF1MfrMKvMhQGidjz6yiuIIEFoXp9esNH8ChNb56ykryq8AoXV+e8vKEEBAIrTOyC4Itq9Wy+rPamDpBzXw9t/LSFX5LoPQOt/9ZXX5EiC0zlc/WU3+BQit899jVphfAULr/PaWleVPgNA6fz1lRQgg8KYAoXWGdoO/72W1PPxJxe3vVP+Vfyn5YYaqy18phNb56ykryq8AoXV+e8vK8ilAaJ3PvrKqYggQWhejz6wyHwKE1vnoI6tAAIHTCxBaZ2xn+N2b1fLgx5VM7FDpur+TwtaMVZifcgit89NLVpJ/AULr/PeYFeZLgNA6X/1kNcUSILQuVr9ZrdsChNZu94/qEUDg7AKE1hncId7R121wrbBFpRu/orRlUgardL8kQmv3e8gKiiNAaF2cXrPSfAgQWuejj6yimAKE1sXsO6t2U4DQ2s2+UTUCCAxPgNB6eE51f5c3cNgG117poEo3fk1JW3vda8j7DQmt895h1pcnAULrPHWTtRRBgNC6CF1mjXkVILTOa2dZVx4FCK3z2FXWhAACxwUIrbO8F+KSWh77jPw3XlXphi8pmbosy9U6VxuhtXMto+ACCxBaF7j5LN1JAUJrJ9tG0QhYAUJrNgIC7ggQWrvTKypFAIHKBQitKzer7yfSRM3P/LXCrQ+pdN0XFc+8pL73z/HdCK1z3FyWljsBQuvctZQF5VyA0DrnDWZ5uRYgtM51e1lczgQIrXPWUJaDAAInCRBaO7Ihmn7x/8r81X/FZxXNv8mRqrNdJqF1tvtDdQicKEBozX5AwC0BQmu3+kW1CJwoQGjNfkDAHQFCa3d6RaUIIFC5AKF15WYN+0S45QE1P/lXGrjo/9DABf+pYXXk5caE1nnpJOsoggChdRG6zBrzJEBonaduspaiCRBaF63jrNdlAUJrl7tH7QggcC4BQutzCWXs54Ndz9g519G8G9V/2Wckz89Yhe6UQ2jtTq+oFAFCa/YAAm4JEFq71S+qReBEAUJr9gMC7ggQWrvTKypFAIHKBQitKzdr+Cf8/a+p5eFPKZm2XKVrPy8FzQ2vycUCCK1d7Bo1F1WA0LqonWfdrgoQWrvaOepGgAcxsgcQcEmA0NqlblErAghUKkBoXalYRt7v9+5Uy4MfUzp2uko3fElp0/iMVOZOGYTW7vSKShEgtGYPIOCWAKG1W/2iWgROFOCkNfsBAXcECK3d6RWVIoBA5QKE1ieY9ZVK+vI3vmn/zSc/9lG1trTYH//ooUf1rW9/z/74bReuOOnnKiev3ie8Y/vV8tDHpSRW6V1ftQE2r+ELEFoP34p3ItBoAULrRneA+yNQmQChdWVevBuBLAkQWmepG9SCwNkFCK3ZIQggkGcBQuvB7h4PrH/+0isnBdMv/fIVfes7d+vP/+RTmjRxgr75rbvsJz76kTszsS+86IiaH/m0/O4uG1wnE8/PRF0uFEFo7UKXqBGBsgChNTsBAbcECK3d6hfVInCiAKE1+wEBdwQIrd3pFZUigEDlAoTWg2YmjG6fPdP+00svvzp0mvr4v7/5xlXlnzslxK6cvAafSCI1P/mXCnY+q9INX1Zy3gU1uEn+Lklonb+esqL8ChBa57e3rCyfAoTW+ewrqyqGAKF1MfrMKvMhQGidjz6yCgQQOL0AobV00ulpMwrkeGhtyMy4kAsvWK7jofW2rh36yje+qU987KPq7JiTqX3VtOaLatr4A5Wu/pziuVdnqrYsFkNoncWuUBMCpxcgtGZnIOCWAKG1W/2iWgROFCC0Zj8g4I4AobU7vaJSBBCoXKDwobUJqXfu2jM07uN0ofXNN1yrC9+ywuqeGlqbP9Rl6dW26W5NfOUfdOitv6/D896bpdIyV0uaSmmayve9zNVGQQi4JhAEvuI4qVnZ6eCIkIBfrzUz5sIIVFMgTlIV8ddrUxjIfFGcFwIuC5hfv+bPx/wJ2eUuNrb2IPQVR7X7c2FjV5etu5uDHZ7nyeMXbLYaQzUIDApMmTgOi1EIFD60NuM/Hn70iV8hNA9c/C//+bf19//4z2c9aW3+I5G1V9j1qFqf+kv1L/lN9V/8sayVl5l6ojjWsb4BtY1rzUxNFIKAqwKp+cOy+UpQjV7mC0zdh49pUtvYGt2ByyKAQDUFDvUc0aS2cSpc6uWbr7BVU5JrIVB/ge7eo5owfowNwnghMBKBWv+5cCQ15fUzPYf7NG5Mk4IgyOsSWRcCTgtwSHJ07St8aH0q34knrVtbWuzoEDPrOtMzrU+zB4K9L6rlkT9U1H65+q/675LHf8ROZWI8yOh+8+DTCNRTgPEg9dTmXgiMXoDxIKM35AoINEqA8SCNkue+CFQuwHiQys34BAIIuCNAaH1Kr04NrU998KIJsc3rox+5M/Nd9g+uV8tDn1AyeZFKq74ghZwoPrFphNaZ38IUiMCQAKE1mwEBtwQIrd3qF9UicKIAoTX7AQF3BAit3ekVlSKAQOUChNbnCK3NT5sg+1vf/p59pxkb8smPfVTmFLYLL+/IHrU89PtSOFalG7+itGWSC2XXpUZC67owcxMEqiJAaF0VRi6CQN0ECK3rRs2NEKi6AKF11Um5IAI1EyC0rhktF0YAgQwIEFpnoAm1LsHr71bLQ5+SVzqo0o1fU9LWXutbOnF9Qmsn2kSRCFgBQms2AgJuCRBau9UvqkXgRAFCa/YDAu4IEFq70ysqRQCBygUIrSs3c/MTcUktj3xa/oH1Kr3rq0omL3ZzHVWsmtC6iphcCoEaCxBa1xiYyyNQZQFC6yqDcjkE6ihAaF1HbG6FwCgFCK1HCcjHEUAg0wKE1pluT5WLS2M1P/k5hTtWq7TqbxTPvKTKN3DrcoTWbvWLaostQGhd7P6zevcECK3d6xkVI3BcgNCavYCAOwKE1u70ikoRQKByAULrys2c/0TTi/9LTa/8s/qv+Kyi+Tc5v56RLoDQeqRyfA6B+gsQWtffnDsiMBoBQuvR6PFZBBorQGjdWH/ujkAlAoTWlWjxXgQQcE2A0Nq1jlWp3nDD/Wp+9gsauOh3NHDBf6rSVd26DKG1W/0aTrV+z1YlE+YN5628xzEBQmvHGka5hRcgtC78FgDAYQFCa4ebR+mFEyC0LlzLWTAChRIgtC5Uu09ebLB9tVoe+xNFi96n/nf+SeEkCK1z0vKoT02b/kPBa3fJ792pZOL5ipbdrmj+u6WwNSeLZBmE1uwBBNwSILR2q19Ui8CJAoTW7AcE3BEgtHanV1SKAAKVCxBaV26Wq0/4+36plof/QMmst6t07edztbZzLYbQ+lxC2f5579h+hWu/p3D9/VKaKFpyq9K2doXr7pV/YJ3ScIyi89+teNkHlUzozPZiqO6cAoTW5yTiDQhkSoDQOlPtoBgEKhIgtK6Iizcj0FABQuuG8nNzBBCosQChdY2BXbi837NNLQ99XOnY6Srd8CWlTeNdKHvUNRJaj5qwIRfwD21S+PK3FW57WMn4WYqX3aGB828+6VS1fc9r31W49SEp6lN83lsUL7lVUed1kt/UkLq56egECK1H58enEai3AKF1vcW5HwLVEyC0rp4lV0Kg1gKE1rUW5voIINBIAULrRupn6N7m1GrLA79nKyq966s2wM77i9DapQ6nCnY+o/CV7yjY+zPFM96maPmdiudcKck740K8gaMKtzygwJy+PrRJafMERQvfo2hx+VQ2L3cECK3d6RWVImAECK3ZBwi4K0Bo7W7vqLx4AoTWxes5K0agSAKE1kXq9jnW6g0cVsvDn5J3eLdKN3099yMVCK0d2PxRn8LNP1L46r/KP7xTUef1ilbcqWTKkoqL9/e9rHD9fQq3PSLFJcUzLi6fvp57teSHFV+PD9RXgNC6vt7cDYHRChBaj1aQzyPQOAFC68bZc2cEKhUgtK5UjPcjgIBLAoTWLnWrHrXG/WpZ/Vn5e19U6fq/U3LeBfW4a0PuQWjdEPZh3bQ8r/puGzLbedWL3q9o+W8qHTNtWJ8/25vMF2fCjf+hYP19MqNx0tYpihb+uqLFtygdN2PU1+cCtREgtK6NK1dFoFYChNa1kuW6CNRegNC69sbcAYFqCRBaV0uS6yCAQBYFCK2z2JWG15Sqac3fqmnjD1S6+nOKzUnUHL4IrbPXVDuL+pXv2FnUZkRNtOw3NbDwvSfNq65m1cHen9vwOux6VEpixbPfqWjJLYrbL5c8v5q34lqjFCC0HiUgH0egzgKE1nUG53YIVFGA0LqKmFwKgRoLEFrXGJjLI4BAQwUIrRvKn+2bN738T2p68e81sPLTGlh8S7aLHUF1hNYjQKvJR47Pq75Lwd6f2tPuN94GAAAgAElEQVT90fIPlcd21Ck49vq7FW78gYL198vv3VkOzBe/X9GiDyhtnVyTVXPRygQIrSvz4t0INFqA0LrRHeD+CIxcgNB65HZ8EoF6CxBa11uc+yGAQD0FCK3rqe3gvcxD7Jqf+pwGln1IA2//mIMrOHPJhNYNbqeZV73lx+V51b3bFXVcq2jFh5VMXdbAwlIFe15QsO4+hdufsHVEc65UbE5fz7rkrA99bGDRhbg1oXUh2swicyRAaJ2jZrKUwgkQWheu5SzYYQFCa4ebR+kIIHBOAULrcxLxhmDP82p59L8par9c/Vf9d8kLcoFCaN2YNnqlQwpf/a7CDfdJ8YCiRe9VtPxOe7o5Sy+v76DCjd9XuP778o7sVdLWrnjxB+z867R5YpZKLUQthNaFaDOLzJEAoXWOmslSCidAaF24lrNghwUIrR1uHqUjgMA5BQitz0nEG4yAf3C9Wh78uJIpi1Va9YWazRiupzahdT21Jb97i8KX/7/yvOoxUxUt/aANrNOm8fUtpNK7pYmCXc8qXHefgl3P2JElUccqxYtvUTzjbZVejfePUIDQeoRwfAyBBgkQWjcIntsiUAUBQusqIHIJBOokQGhdJ2hugwACDREgtG4Iu5s3NbN+Wx76r0qbJ6h041eUtkxycyGDVRNa16d9wc6nFb76L3bsRjJ1uaLldyjqXOXkiX3v2H6F6+5VuPHf5R17Q8mEeXZ0SLTw15SG4+oDWtC7EFoXtPEs21kBQmtnW0fhCIjQmk2AgDsChNbu9IpKEUCgcgFC68rNCv0JM9qh5aFPyOvvUenGr9mRCa6+CK1r2Lm4X+HmH9mw2u/pUjz3KvtwxXj6W2t40zpeOo0V7HiqfPp69xopaFE073pFi29RMm1FHQspzq0IrYvTa1aaDwFC63z0kVUUU4DQuph9Z9VuChBau9k3qkYAgeEJEFoPz4l3nSgQ9anl0T+Wf2C9Su/6qpLJi530IbSuftvsvOrXvqdw/b3y4pIGFrxH0bI7lDr8xY1zKZl51/b09aYfyMzBNr8eInP6ev5NuRijc6711+vnCa3rJc19EKiOAKF1dRy5CgKNECC0boQ690RgZAKE1iNz41MIIOCGAKG1G33KXpVprOYn/kLhzqdVWvU3imdekr0az1ERoXX1WmbnVb/ybYVbHlLaMlHRst+wp44zP6+6egRSEinselzB+vsU7P2Z0nCsovNvUrz0diUT51fzToW8FqF1IdvOoh0WILR2uHmUXngBQuvCbwEAHBIgtHaoWZSKAAIVCxBaV0zGB04UaPrp19X02l3qv+Kz5ZOlDr0IrUffLPNgwvCVu8rzqs0J4xV3Kuq8XvLD0V/c4St4vTvtafNw4w/tKJ1k2nJFS24r2wTNDq+scaUTWjfOnjsjMBIBQuuRqPEZBLIhQGidjT5QBQLDESC0Ho4S70EAAVcFCK1d7VyG6m5af5+a1nxRAxf9nxq44CMZquzspRBaj7BVdl71jxW+epf8nm2K2y9XtOJDimdcPMIL5vhjyYDCbY+UT1+//gulTW2KFt6seMntStrm5Hjh1V8aoXX1TbkiArUUILSupS7XRqC2AoTWtfXl6ghUU4DQupqaXAsBBLImQGidtY44Wk+wfbVaVv+ZPW3df/mfSvIyvxJC68padNK86qhP0fk327Ca8HV4jibgD9beYwN/b+Cw4hkXKV58i6KOawt/Mn04goTWw1HiPQhkR4DQOju9oBIEKhUgtK5UjPcj0DgBQuvG2XNnBBCovQChde2NC3OHYO+Lan7k00pmvl2lqz+X+TEIhNbD25rledXfUbjlQaXNbYqW3mZHXaTNE4Z3Ad51skDUp3DbTxSuu0/+/leVtkxStPC9ipbcqnTcDLTOIEBozdZAwC0BQmu3+kW1CJwoQGjNfkDAHQFCa3d6RaUIIFC5AKF15WZ84iwCfvdmtTz4caXjZ6l0w5cy/SA+Quuzb+Vg17OD86qfVzJpgaLldyqaf6PkN/FroEoC/qFNCtferWDLg/KiY4pnrVS05BbFc66QvKBKd8nHZQit89FHVlEcAULr4vSaleZPgNA6fz1lRfkVILTOb29ZGQIISITW7IKqC3hHX7fBtXmV3vVVpWOnV/0e1bggofVpFM286i0P2LDa79laDlFX3Kl41qXVIOcaZxDwBo4q2Ppg+fT1wQ1Kx0xTtPj9ihZ9QOmYqbhJIrRmGyDglgChtVv9oloEThQgtGY/IOCOAKG1O72iUgQQqFyA0LpyMz4xDAEzs9cE1ybALt30dSUTOofxqfq+hdD6TW87r3rt3QrX3SMToJrZ5NEFH85k3+q7S+p/N/+NVxSuv88+wFFxv+L2K8qnr2evdGJWfK3ECK1rJct1EaiNAKF1bVy5KgL1ECC0rocy90CgOgKE1tVx5CoIIJBNAULrbPYlH1XFJbU89hn5+15W6fq/U3LeBZlaF6G1VJ5XfZc9XZ02jbWzqqOlt9s5y7waK+BFRxRu/A8F6++3fTIjd6LFH1C08H1KWyY2trgG3J3QugHo3BKBUQgQWo8Cj48i0GABQusGN4DbI1CBAKF1BVi8FQEEnBMgtHauZY4VnCZqfuav7UP8zMMZ47lXZ2YBRQ6tg91ryvOqdz9nT1PbedXnvzvzD8/MzOapcyHB679QsO5ehV2PSkoVdVyjePEtimdcXOdKGnc7QuvG2XNnBEYiQGg9EjU+g0A2BAits9EHqkBgOAKE1sNR4j0IIOCqAKG1q51zrO6mX/yjml76Rw2s/LQGFt+SieoLF1onAwo3/1jhq/9iT+7GM95enlfdfnkm+kER5xbwBnoVbviBgvX3ye/dYb/gENvT1+/J9ENPz72yc7+D0PrcRrwDgSwJEFpnqRvUgkBlAoTWlXnxbgQaKUBo3Uh97o0AArUWILSutTDXHxIIN/1AzU//tQZW/JYGLv7dhssUJbT2St0K192tcK2ZV31Y0bwbFa34kJJJCxreAwoYuUCw96cK1t2ncPtqyfMVdV6vaMmtSqatGPlFM/xJQusMN4fSEDiNAKE12wIBdwUIrd3tHZUXT4DQung9Z8UIFEmA0LpI3c7AWoNdz9g519Hca9V/5WclL2hYVXkPrf2ebQpf/rbCrQ8qDVrL85CX/obSMVMbZs6Nqy9gvyix4fv2L+/wbiWTFipecot9mKaZU56XF6F1XjrJOooiQGhdlE6zzjwKEFrnsausKa8ChNZ57SzrQgABI0BozT6ou4C//zW1PPwpJVMWq7TqC1LYWvcazA3zGlqbOdXhK98pz6tum6N42R0aWPBrDXNuSHMLedNUwa41Ctfdp2DnU0qDFsXzb7RfqMjDqXpC60JuahbtsAChtcPNo/TCCxBaF34LAOCQAKG1Q82iVAQQqFiA0LpiMj5QDQG/d6daHvyY0pZJKt34Ffv3er9yFVqbedVbHlD4iplXvVnx9AvtCJB4zpXma1P1puV+DRbwju1XuOF+hRv+Td7RfUqmLldkTl93Xu/sFy8IrRu8qbg9AhUKEFpXCMbbEciQAKF1hppBKQicQ4DQmi2CAAJ5FiC0znN3M742E6y1PPRxedFR9d30P5WOm1nXivMQWpfnVd+jcO3d8vp7y3ONV9ypZMqSulpys4wKpLGCnU8rXHe/gl3PKm0ap2jBzYrN6eu2ORkt+vRlEVo71S6KRUCE1mwCBNwVILR2t3dUXjwBQuvi9ZwVI1AkAULrInU7g2v1oiNqfuTT8g9tsSeuk8mL61aly6G1nVf9ynfs6erUb1a06H2Klt+hdMy0uvlxI7cEvCN7Fa6/T+HGH8jrO2BP48eLzenr6yQ/zPxiCK0z3yIKROAkAUJrNgQC7goQWrvbOyovngChdfF6zooRKJIAoXWRup3VtSaRWh7/vxTseV6lVX+jeOYldanUxdC6PK/6LgW71ygdP0vRsjsULXhPrh64V5fmF/kmSaRw+2oF6+9XsOcFpS0TFS34dUVLb6v7dztU0gZC60q0eC8CjRcgtG58D6gAgZEKEFqPVI7PIVB/AULr+ptzRwQQqJ8AoXX9rLnTWQVSNa35WzVt+L76r/isovk31dzLmdDazqt+UOGr/yL/0CYl512gaPmdiuZeI3l+zZ24QX4FvCN77GiZcNMPZUbNmC8YxWb29dyrJC/I1MIJrTPVDopB4JwChNbnJOINCGRWgNA6s62hMAR+RYDQmk2BAAJ5FiC0znN3HVxb06vfVtNPv66Bi/6LBi7432q6gqyH1nZe9fp7y/OqS92KOq5VtOLDSqYuq6kLFy+ggDl93fWIgnX3KXj9JaVjpipa+D5Fiz+gdOx5mQAhtM5EGygCgWELEFoPm4o3IpA5AULrzLWEghA4owChNZsDAQTyLEBonefuOrq2cNsjan7yLzWw8L0aWPlHkryarCSrofVJ86q9UNGi99qT1enY6TVx4KIInCjg9+5QsPZ7Cjf9WGbmfDz7MkVLblHcflnNfi0OpwOE1sNR4j0IZEeA0Do7vaASBCoVILSuVIz3I9A4AULrxtlzZwQQqL0AoXXtjbnDCASCvS+q5ZE/tOMKSld/TgqaR3CVs38ka6G1melt51XvelbpuBmKln7QBtZp0/iqr50LInBOgbhf4bafKFx3j/w3Xi3vycUfULToA3YOdr1fhNb1Fud+CIxOgNB6dH58GoFGChBaN1KfeyNQmQChdWVevBsBBNwSILR2q1+FqtY/uF4tD31CadsclW74UtXD20yE1nZe9UMKX72rPK966jJFy+9Q1Hld5mYKF2rzsdiTBPzuLQrM7OvND8hL+hXNvdrOvo5nvL1uUoTWdaPmRghURYDQuiqMXASBhggQWjeEnZsiMCIBQusRsfEhBBBwRIDQ2pFGFbVM86C4lod+X1Kg0k1ft7N2q/VqZGj95rzqe+T1HVQ89ypFyz+kePpbq7U8roNA9QWivvJDQdffJ//AOiUT5ipeZE5f/7rSprbq3++EKxJa15SXiyNQdQFC66qTckEE6iZAaF03am6EwKgFCK1HTcgFEEAgwwKE1hluDqWVBbz+brU8+HF5pUMq3fhVJRM6q0LTiNDazqt+9S57YlWep4EF71G07A6lbe1VWRMXQaBeAv7+1xSuu0/htoelNLEPCo0X31qzL7wQWters9wHgeoIEFpXx5GrINAIAULrRqhzTwRGJkBoPTI3PoUAAm4IEFq70SeqjPrU8ugfy9+/VqXr/07JeReM2qSeoXWw54XBedXPKB0zTdGy31C0+JaqjzwZNQoXQKBCAfOwRvPQxmD9/eURNxPPt6NDogXvVhqOq/BqZ347oXXVKLkQAnURILSuCzM3QaAmAoTWNWHlogjURIDQuiasXBQBBDIiQGidkUZQxjAE0ljNT35OYdej9uGM8dyrh/GhM7+l5qF1Einc+pDCV75TDvMmL1a04k5FnddLfjiq2vkwAlkU8Pf9UuG6e+2vUXm+onk3Klr2QSWTFoy6XELrURNyAQTqKkBoXVduboZAVQUIravKycUQqKkAoXVNebk4Agg0WIDQusEN4PaVCzS9+Pdqevmf1H/5nypa8OuVX2DwE7UKrb3+HoXr7lG41syrPqC4/XJFKz6keMbFI66VDyLgkoA30Ktw4w8VrLtffm+XkilLFZnT1/NulMLWES2F0HpEbHwIgYYJEFo3jJ4bIzBqAULrURNyAQTqJkBoXTdqboQAAg0QILRuADq3HL1AuOkHan76/9bAit/WwMW/O6ILVju09nt3KHzl2wo3/9jWE53/bkUrPqykbc6I6uNDCORBINj7MwXr71PY9bjSoFXR+TcpXnp7xbPpCa3zsBtYQ5EECK2L1G3WmjcBQuu8dZT15FmA0DrP3WVtCCBAaM0ecFYg2L5aLas/q6hjlfqv/KzkBRWtpVqhdbD3p+V51TufVto6RdHS2xQtuU1p84SK6uHNCORZwDxQNdzwbwrXf1/e4V2Kz3uL4iW3Kuq8TvKbzrl0QutzEvEGBDIlQGidqXZQDAIVCRBaV8TFmxFoqAChdUP5uTkCCNRYgNC6xsBcvrYC/r6X1fKTP1AydalKq75Q0eiBUYXWQ/Oq75J/aKOd2Rstv0PR/HcNK4CrrQpXRyDLAqmC3c/b2dfBjqeUNo1TtPA9ihbfqrSt/YyFE1pnuafUhsCvChBasysQcFeA0Nrd3lF58QQIrYvXc1aMQJEECK0lffNbd+nhR58Y6vtn/vD3dOFbVth/3ta1Q//ji19Vb+/hoZ+fPWuG/vxPPqVJEzlJm4VfLH7PNrU88HtKx0xV6cavKG2ZNKyyRhJa23nV6+8tz6s+tl/xrEvL86pnXTqse/ImBBB4U8DrO6hww/32BLZ3ZK/iGW9XbGZfm4esnvKwUkJrdg4CbgkQWrvVL6pF4EQBQmv2AwLuCBBau9MrKkUAgcoFCh9aH+ru0d33/1C/dcetam1psSH1V77xTX3iYx9VZ8ecX/nnyon5RD0EvKOvq+XBj8tLSuq76X8qHTfznLetJLQuz6v+jsLNP5LSRNH8mxRd8OGK5/KesyjegEARBdJEwa5nFK67T8HOZ5S2Tla08L2KltyqdOx5VoTQuogbgzW7LEBo7XL3qL3oAoTWRd8BrN8lAUJrl7pFrQggUKlA4UPrU8FMiP1Xn/+SPvKh2+1p61ND7EqBeX/9BLyBw2r+yR/K7+lS6YYvK5my5Kw3H05oXZ5X/S8Kdj5lZ1SbWdXR0tttqMYLAQSqL2BOXJdnX99rkmolUxYrmf5WRbMu1RutCzV14rjq35QrIoBA1QUIratOygURqJsAoXXdqLkRAqMWILQeNSEXQACBDAsQWp/SnJd++Yq+9g/f0p99+uNDJ61PHA/CaJAM7+bB0pof+4zC3WtUuvbzZx3bcbbQ2pyoNg9XtPOqJ8xVtPzDiha9L/uLp0IEciRgTl/7O59VsOtZ+T3blQYtSma+Q0n7OxXNuVLpuBk5Wi1LQSBfAoTW+eonqymWAKF1sfrNat0WILR2u39UjwACZxcgtB70OXF29YkzrU/lM/Ov3zhwQJ/82EftOJGjff3sscwJpBr//BfUuu0B9V7ypyp13nDaCs24gYGBWC0tof15r79XYzZ/X60b7pPft18D512sY0s+qP5Z78zcCikIgawJ+PKUKK1ZWWYEkL/9GY3d/zM1v/5Tme+siMfNVv/MSzQw81INzHi70qC1ZvfnwgggUJnAsb5+jWlplrzKPuf6uwPPV5wmri+D+gsucKxvQK0tTfIK9uu34G2v6vJ9z1OS1u7PhVUt1vGL9ZUiNTcF8n1+wTreSsrPqcDY1uacrqw+yyK0PsX51PEgp7bBhNv/+E//oj/4/d+xD2I8eozQuj5btfK7jF37zxr78j/q6Ft+R0eXfOhXLhAnicxp63EDr6t13XfVuu3H8tJYfXOuU9+SOxRNPL/ym/IJBAoqYP6gbL4QVKtXmqY61j+gsSYESxM1HVyrpj3Pq2nvc2o68Jrk+RqYvEwDsy5V/4xLFE1abP8dLwQQaIyA+aL+mNbmomXWCgJfcUxo3Zhdx12rJXCs1K/WZhNaE4JVy7Ro16n1nwuL5nm29faVBgZDa/7cy75AIIsCY8cQWo+mL4TWp9Ezp6nbZ8/UzTeu+pWfPTW0Hg0+n629QLjlATU/9TkNLHq/Blb+kTlPPXTTdNcLCl7+jlr3PlueV73o/YqWfVDpmKm1L4w7IIBARQJnexCjFx2Rv/sFBWaUyO7n5B3ebX9Nx7MvUTJ7peLZl/HruiJt3ozA6AUYDzJ6Q66AQKMEGA/SKHnui0DlAowHqdyMTyCAgDsChQ+tTQj95LPP68MfvMV27fiYkP/6Ox+xD2J8/IlnNK9zrp1vbV4m0Davj37kTne6XPBKgz3Pq+WRTyuetVL9V/6Fgh2r7cMV/YPrFY1rV7riTg0s+DUpZLRAwbcKy8+wwNlC61PLNvOvTXjt71ojf8/P5EVHlUyYp7h9ZTnEnnGRFLRkeLWUhoD7AoTW7veQFRRXgNC6uL1n5e4JEFq71zMqRgCB4QsUPrTuK5X05W98Uz9/6ZUhtRNnWpsHM/71//P1oZ9724UrhuZZD5+ZdzZawN//qloe/gN5/T22lHj6W1Vacoe6p67UxLYxjS6P+yOAwDkEKgmtT71UsPdFG2AHu5+Xv/+18u8BMy5W0r7SPqw1mbIEfwQQqLIAoXWVQbkcAnUUILSuIza3QmCUAoTWowTk4wggkGmBwofWme4OxVVVwO/dofCX/6Ro6W02pDLzrM1MckLrqjJzMQRqIjCa0PrEgryBXgW7npe/82kFO5+R13dQactEO0IkmXOZDbHNP/NCAIHRCRBaj86PTyPQSAFC60bqc28EKhMgtK7Mi3cjgIBbAoTWbvWLaqsoQGhdRUwuhUCNBaoVWp9apt+9RYEZI7JzjYLXfy7FJSWTF5dPYM9Zqfi8t0p+U41Xx+URyJ8AoXX+esqKiiNAaF2cXrNS9wUIrd3vIStAAIEzCxBaszsKK0BoXdjWs3AHBWoVWp9EEfcr2PdSOcA2QfahTXbWfTz9IiXt71Q8+1IlEzod1KNkBOovQGhdf3PuiEC1BAitqyXJdRCovQChde2NuQMCCDROgNC6cfbcucEChNYNbgC3R6ACgbqE1qfU45UOKdj1rPwdz9oHO5p/TsfNKJ/CNg90nH2p0qbxFayCtyJQHAFC6+L0mpXmT4DQOn89ZUX5FSC0zm9vWRkCCEiE1uyCwgoQWhe29SzcQYFGhNYnM6XyD254c5TIvl9ISaxk2jLFs1aWQ+zzVkhe4KAuJSNQfQFC6+qbckUE6iVAaF0vae6DwOgFCK1Hb8gVEEAguwKE1tntDZXVWIDQusbAXB6BKgo0PrQ+ZTFRn4K9P5O/y4wSeU5+zzZ76jqZebFiE2C3X25PZfNCoKgChNZF7TzrzoMAoXUeusgaiiJAaF2UTrNOBIopQGhdzL6zakmE1mwDBNwRyFxofQqdd2Svgp1Py9/9goLdL8gb6FXS1lGegz3rHYpnXSKFY9wBp1IERilAaD1KwIx83OvvUbB9tfztTykdM0XplEX2YbXJ5AVS0JKRKimj2gKE1tUW5XoI1E6A0Lp2tlwZAQQaL0Bo3fgeUEGDBAitGwTPbREYgUDWQ+tTl+S/8aqC3WvKJ7Ff/4X96Xj6W8tjRMw4kWnLR6DARxBwR4DQ2p1enVqpd+wNhV2Py9/2qP2OkjQcp2TqIvs2/8heeYd32x+bB9MmU5YonbpIyZTF9q+0eaK7C6fyIQFCazYDAu4IEFq70ysqRQCBygUIrSs34xM5ESC0zkkjWUYhBFwLrU9sijdwVP6eF8oB9u7n5PfuVNo8QfHsSwYf6HiZ0jFTC9FHFlkcAUJrt3ptguiw6xEF2x6V+aJb2jpZ8dyrFXeuUjzjYskPhxbkDRyWf2CjnfPv7V8r/+BG+d1bpCRSOva88klsE2JPNSeyFykdP9stDKoVoTWbAAF3BAit3ekVlSKAQOUChNaVm/GJnAgQWuekkSyjEAIuh9anNsiE1ja83rVG/u6fyouOKJkwT3H74AMdZ1zEt90XYlfne5GE1tnvr39ok4KuxxRse0zmx2YOfzT3aiWd19nvDJG84S8iGZB/aLMNsL396+QfXC//wCb7+5ud9z9lYfkk9pQlSiYvVDJx/klB+PBvxDvrIUBoXQ9l7oFAdQQIravjyFUQQCCbAoTW2ewLVdVBgNC6DsjcAoEqCeQptD6JJI0V7HtF/s5ny0H2/tdskBNPv1BJ+0r7UMdk0sIqKXIZBOonQGhdP+tK7uS/8YqCbY8r2P6Y/N4dStrmKO5YpbjzGiVTqz22KJXfu0v+gXXyDmyQf2C9DbO9Y/vt73PJxPOVTFmk1I4WWaJkygI7ioRX4wUIrRvfAypAYLgChNbDleJ9CCDgogChtYtdo+aqCBBaV4WRiyBQF4Hchtan6JkHOAY7y6ewTYjtHX1daesU+yDHpP2dime/U2kLM2Prsum4yagECK1HxVe9D5svjL3+kvxtjync/ri8o/uUTFqguPNaxR3X2h/X++WVDpUD7ONh9v718nu3S0qVtLXb8SLp4GgRezp7zLR6l1j4+xFaF34LAOCQAKG1Q82iVAQQqFiA0LpiMj6QFwFC67x0knUUQaAoofWpvfR7tr4ZYr/+ohT12UAnnnWpkjkrFZ/3VslvKsIWYI2OCRBaN7BhyYCC3c/bsR/BjifklbrtKWpzmjruuM4Gw5l7RX12tEhwYL28A+vkm5PZ3ZuluF9py6TyjGwbZg8+9LFtruT5mVtGXgoitM5LJ1lHEQQIrYvQZdaIQHEFCK2L2/vCr5zQuvBbAACHBIoaWp/UIhNE7fuF/B3mFPYa+xA0ha2Kp180eAr7UiUTOh3qKqXmWYDQus7djY4p3PmM/K7HFex4Wl501M6lNvOpo85Vbp5WTmP53VvfnJNtx4tslNffY3/vsyfGj8/JNjOzzanxoKXO8Pm8HaF1PvvKqvIpQGidz76yKgQQKAsQWrMTCitAaF3Y1rNwBwUIrX+1aeb05PEHOgY7nrKnKc2D1MwIkWTmO+yDHc0D0Hgh0AgBQuvaq3sDhwcfpPi4gp1P2Rua78KIzYMUO67J7Sgh78iewfnYG+Sbhz6a09lHX7frt3OyJy8sjxeZUj6VnTYzUqnS3UhoXakY70egPgL+vpcVrrtXShOlEzuUts1RdzhDrdMXKmjhmQD16QJ3QQCBegoQWtdTm3tlSoDQOlPtoBgEzipAaH3uDWLmwwZ7npe/8zkFe14oBzjTViieaeZhr7QPd+SFQL0ECK1rI+31HVSw/QkFW39if73boLr9CsXzb1A854rCfqHKnL42333i718vzwTZhzbZv8wrHXte+UGPkxcpGZyVnY6fXZsG5eSqhNY5aSTLyIdAXFK49WGFr33PPsz2TC/zDJTEBiOQ6yIAACAASURBVNlzbaCdTJyn1DwnYMK8fDiwCgQQKKQAoXUh286ijQChNfsAAXcECK0r7FVcUrD3xfIDHXeukZmNbU5dJzMvVjx7peL2y+2pbF4I1EqA0Lp6st6RvQq3PSK/6zEF+15WGo5RPOcyJR2rFLVfJoVjqnezPF0p7i+H1wc22DnZZl62DbKjvvLvh/Yk9iKlJtA2PzbBjh/mSWDEayG0HjEdH0SgagLmu0rCtd9TuPGHdixSMnG+4iW3aWDBzeXf95NI/uFdOrp3k8b171bQu0Nez3b7YNvj331ii/F8JeNn2TA7mTBXmtCpZOJcJW0dSsdNN2+oWs1cCAEEEKi2AKF1tUW5njMChNbOtIpCERCh9eg2gXdsv4Jdz8rf9ayCXc+X/+enrb38QMf2dyqe+Q6Cr9ER8+lTBAitR7cl/O4tQ6M/zMm6tHmCPUkdd66yX3jiAawj9U3l93SVg+z9Zkb2+vJ4kdIha5pMMuNFFpXHi0w2fy1U2jR2pDdz9nOE1s62jsKdF0gV7HxG4dq77Z/bzBfSorlX27A6nnHRaVd32pnW5ot2vdvl92yXZ0Ls7i77d/vPfQffvE7QoqRtjg2zyye0O8s/ntCR2xFTzm8RFoBAwQQIrQvWcJb7pgChNbsBAXcECK2r2Ks0kR0lYh7muHONgjdesRePpy0vB9gmyJ6y1J7M4YXASAUIrSuX8/e/pmDbYwq6Hpff2yXzrd6xCSs6r1U88+2SF1R+UT4xLAHv2BuDc7LNeJEN5TC7d6f9rPkCnxkvkg7OyDZhdjpm6rCu6+qbCK1d7Rx1uyrg9Xcr3PgDO6/aO7zbPjw3WvwBRYtvUdo6+azLqvhBjNEx+T3bygG2/atLfk/5lLY30Dt0r7SpTak9kT3HhtnHT2rbh36Hra5SUzcCCDgmQGjtWMMot3oChNbVs+RKCNRagNC6hsLRMTsD244S2bXGBjX2VOfsS5SYUSKzL8t9QFND3cJemtB6GK1PEwWv/0J+16MKt6+WGQNixvZEHdco6bhO8fS38G3bw2Cs1Vu86Ij8AxvLJ7EPmAc+bpTfvdl+S74JkeyM7KEw24wX6chNvwita7WruC4CJwuYL5CFr/6rwm0/keJ+xTMuVrz0NkVzrhr2uKKKQ+uzNME81HtoxMgJp7PNqW0zWun4y3zhzvyel9pT2cdPZ89VMr592HWzFxBAAIHhCBBaD0eJ9+RSgNA6l21lUTkVILSuX2NNcBbsekbBzmfl7/mZvIHDdtZr3L6yHGKbb08NWupXEHdyUoDQ+gxtSyL7RaJg26P2gYpmLIX5Vux47rX2RHUydZmT/S5M0WaGbPeWoTnZJtD2D26yv0+ak4fJpAWKpyx+c072pAVS0OwcD6G1cy2jYJcE4n4bUodr75G//1Wl4ThFC262YbU9xVzhq5qh9dlubb8j5aTT2eWT2v7hnVIyUP6oFygZP1Npm3kQ5OD87AnmtPZc5mdX2FfejgACg7+tHDlaSsFAoIgChNZF7DprdlWA0LpBnUtj+W+8agNsO05k/1r7PyTx9AuVtJtT2CuVTFrYoOK4bZYFCK1P6E7Up9DMlDejP3Y+Xf5CkAk3zXxqE1RPPD/LraS2YQh4vTsVHDxhtIg5nX3sjXKAY04hTllYDrLtrOxF9rtZsvwitM5yd6jNVQH7UN219yjc9O8yJ5rNfwcic6p6/k2jeq5IvULrM7un8g7vGZqhLTtupDxL2z+8W0qT8keH5mcPntA287NNmG1Oa7dMdLWt1I0AAjUW4KR1jYG5fHYFCK2z2xsqQ+BUAULrbOwJE7YFu54rjxLZ/Vx5nIGZuzvrkvI87Nnv5H88stGqhldR9NDa/lrZ8VT5RPXu5+y3VSfTltuQOuq4Xun4WQ3vEQXUVsCcoi+fxB4Ms82Pe7okpXYMTHm8yOKhv9JxM2tbUAVXJ7SuAIu3InBWgVTB7ufLD1bc8ZTkB4o6VpUfrDj9rVWxa3xofZZlmO9OObxTXu+OcpBtRo6Y09nm4ZBHXx/6oPlCXjphjpK2DqWTOsontQf/mfnZVdkmXAQBZwUIrZ1tHYWPVoDQerSCfB6B+gkQWtfPupI7mQf5DIXYe39WDuYmLy4/zHHOSsXnvVXymyq5JO/NiUARQ2tzci7oeqwcVJtfD2liQ4mkY5WizlX2wVq8Ci4Q9ck/tEnB0JzsDfIPbZbiksxDzxL7sMdFSo+H2WZUgB/WHY3Quu7k3DBnAuYLl+HGHypYd4/83h1Kx05XtOQDiha+/5wPVqyUItOh9dkWE/cPnc42p7JtoG1OZ5twu+/gm4H2mGknzM82Y0dMqG3mZ89uyO+PlfaH9yOAwOgECK1H58enHRYgtHa4eZReOAFCawdabmb17vuF/J3lU9j+gXV2xms8/aLBU9iXjmhWowMrp8TTCBQltDYnxcJtj9qHKQav/9KeootnvL08+qPjGr7zgF8d5xYwY5h6tg3OyV4vf785nb1BXn+P/aJfMun88kiRqUvKp7MnL1TaNPbc1x3FOwitR4HHRwstYL4oFb72XYVbH7JfyI9nvkPR0tsVz7nCjguqxcvZ0PosGN7AUXm9XfJtkL1Dnh05Yv6+Xd5Ab/mTdn72LKUTzKlsM2akQ+nEjvLIkbHn5ebBuLXYM1wTAZcECK1d6ha1VlWA0LqqnFwMgZoKEFrXlLcmF7enTs0c7MEQ2zu2335LvD2FbR7oOPtSpU3ja3JvLtp4gTyH1uYUWND1iIJtj5W/OBO02PnuZvRHPOdK9nXjt18uKjDjl8xoEf/AhvKYEXM6+8geu7akbY4dK1I+kW2C7MVKx0yt2roJratGyYWKIJBECrc9Yk9VB/t+af8bYB+suOR2G6bW+pXH0PpsZubPl0MjRk44nW1GjpgvFNiXeTDu+DlDp7JtmG1D7U7+G13rDcn1EaiyAKF1lUG5nDsChNbu9IpKESC0dn8PmJOD5RD7OQV7XrAPIksmny+FY5VMnK900jwlE+fxPxTut9quIG+htQmnTUgddD0uv2er/R/iaM5VSjquUdR+uf1nXgjUWsCMHLAB9v518g6sk39wkx03Yl7mQWaJedjjlCWDYfbCEX93C6F1rTvJ9fMg4B3dp3DdPQo3/rsdZ2EeTG1OVUfz31XX/yYULbQ+a6B9dF95XrY5kd2zQ373FpkH5ZrvZjn+svOzTYg9fo7SSZ3lUSNt7fbPoOaL0LwQQCBbAoTW2eoH1dRRgNC6jtjcCoFRChBajxIwax+PSwreeFn+gU3yzP9QdG+Rf2hr+dvhTfgyZqoNspNJ86UJnUqOB9qtU7K2Euo5g0AeQuvg9Zfkdz2usOvR8kNHm8Yr7ri2fKLaBNW8EMiIgL//tfJ4EfPFwf1ry0G2OXFoThtOXqjYBNn2wY8LlUxdfs6qCa3PScQbCixgvvAerL1H4fbHrUI0/92KF3+gag9WrJSW0Hp4Yt7hXfahkH73NskE2Ye2lgPuw7uGLmDGitiHQU6YYw9R2LEjJtSe0DG8m/AuBBCougChddVJuaArAoTWrnSKOhGQCK2LsQu8/m75B8shtnfIBNlb7KlWM1rEhtnmdPakeUondCqdVA61kwnz7NgRXtkScDK0NnPZ9/6s/CDF7U/I6zugtHWKoo5rlJigesbFNZtJmq3uUY37Aql9mJkNsgdHi5hRI/bhZmYOrPli4PEHPtpZ2Qvt76/HX4TW7u8AVlBdAS86onDTj2xYbU7tmj93RItvUbTo/Q1/dgGh9Sh7nUQ2zDazs+0MbXtKu8s+QNM8t8K+/LA8P3swwDYnte0s7aH52aOsgY8jgMAZBQit2RyFFSC0LmzrWbiDAoTWDjatiiXbb4k/uFlezzZ5hzaXT8eYMPvI3vJdzGlCE8KYWYUmyDbjRuwJmc4qVsGlKhFwJrQ2p/53rVGw9REFO56SCSZsGGGC6o7rGnZyrhJr3ovAcAW8Y2/YhzyacTfe/g3lmdm9O+3HzVgR87A489cbYYcmtY2V73vDvTTvQyCXAma8RPjavyrc8mD5wYqzLlW09DbFc67KzHoJrWvbCvP7pX9411CYPRRulw6Vbxw0l787sK3dhtpDD4M0hyx4dkttm8PVCyFAaF2INrPI0wkQWrMvEHBHgNDanV7VtdLomJ1XaL7V0zto/r5FXo/5ds9yCGODmEkLBh++M29obnYyeVFdyyzizbIcWpsvggQ7nizPqN61RopL9n82YxNSd14zrPEJRewpa86ngDdwVP6BtfJ3PKVwmxmFs0dx63mKO65R2nGF4lkr87lwVoXAWQRMSB2sv1fB678oP1hx4XvKD1Zsa8+cG6F1Y1pifu+04+3MKe2eHfK6zenswXnaA4dtUfY7BM2BirbyuBHz98TM0560sDFFc1cEHBQgtHawaZRcHQFC6+o4chUE6iFAaF0P5Xzdw54g7N4q7+BW+d2b5XVvO+lBPPZEjD2dbR4CaU5nm1C7U2k4Ll8QDVpN1kJrM3om6FpdHv2x69nyFzQmLyrPpzanqiee3yApbotAtgTMd7GUNvxE4/c+q2D/K0rDMYpnv1NJx9X2FDYnB7PVL6qpnoAZRRZu+L59uKJ9sOKUJeURIOfflOkH9BFaV28PVOtKXumQPZltxjQNjRsxM7W7t0hxfznQHjvdjhexIXbnKsnzh26f+s1Sy3ilTW1Km8bV9cGe1TLgOghUS4DQulqSXMc5AUJr51pGwQUWILQucPOrvHQTYNuZhYdMkG3mZm+Tf2jjm/+jMGZa+ds8B0eNpMd/3Dq5ypXk+3JZCK3NLMqw63H7MEUzq9oG1dNW2NPU0dxVSjN4Yi7fu4LVuSJwfKZ1EPUo2PlMeXzO7uftdyWY2e7JnCsUzb2aX0OuNJQ6zyoQ7P25grXfVdj1mH1fNP8mRUtvt/+9cOFFaO1Cl96s0Tu6780T2WZ2tgm2zbNb+nvk9R+WV+o+7YLSlklKW9okE2Q3j7d/yfy9qU0y/765HHCb95T/HaG3WzuDas8kQGjN3iisAKF1YVvPwh0UILR2sGkulZwmdqSIHS1iT2ebYNvMzd5mZ1iaV9oysRxkmxPZE8+3D4Q0M7N5COTpG92o0Nr0Meh6RMG2x+Xvf9WeXIqnX2hPMUUd1yodM82lnUmtCDRE4LQPYkwGbHAdbF9dnv9+bL99EG489yrFHVcrmbZcEjOwG9Iwblq5QHRM4eYfK1x3r/xDm5SOn6VoyS2KFr5XafPEyq/XwE8QWjcQv1a3jo7J6+8d/OuwdNKPe8r/XDI/b35uMOzu7y3/++jYr1blN5WD7qEwe/xgAD7471pN0D0Ygpvwu9mE4ObnBsPxoKVWK+W6CJxTgND6nES8Ia8ChNZ57SzryqMAoXUeu+rCmlL7sEc7N/vQ1vJDIM3cbHNS+/i8wnDs0EMf08mDY0YmzCvPvTzhWz1dWG01a6xnaG0eLGfnU5tT1d2bJT9UPPMdijtX2TDNtQCimn3gWgiMROC0ofVJF0rl739NQdcTdj68Df1aJytuv8L+mjMPqzMPJ+OFQNYEzKnWYN3dCjf+h33wrhl9Y05Vx+2XOftFF0LrrO2yBteTxuUT2zbEPn56u1caMP+uRyoN/t3880nB92AInsaE3g1uIbc/WYDQmh1RWAFC68K2noU7KEBo7WDTcl6yd+yNk8JsO2rEPBDy+Ld1+k32AZBDY0ZMkG1OZ7fNLUSYU9vQOpW/7xU7nzrc/ri8w7vsvNF49spyUM3c3Zz/6mN5tRY4d2h96v9Q7lW4fbV8Mzd+30uSFyiedUn5FLYZI9Li1snVWvty/ToLpLH97oBw7d0K9rxgH44XLXiPomW/oXTczDoXU/3bEVpX37TIVzQPmCwH3Kec5C6Z4LtXKp1ysnsoID/DKW+DyUnvIm+pUa+d0HrUhFzAVQFCa1c7R91FFCC0LmLX3VyzefiOf8gE2FslczL7kBkzsrU8r9C8PF/J+Nl2zEj5IZDm751KJ8xT2jTWzUWfpuqqh9YmdNj7onwbVK+2nuahmfGcy5V0Xqto9mU8qCg3u4eFNFqg0tD6xHrNd6GYOdj+9ift372BI3Z0iD2BPedK+/seLwTqIWC+iBxuuF/huvtknnGQTF2maMltiubdkKsvHhNa12M3cY9hCSSR/T3fK/XYgLs8o7scdtuT33298kzwPRiIl0+DnzAG5XSnvAm9h0Wf5zcRWue5u6ztrAKE1mwQBNwRILR2p1dUenoBE+R4g2G2/bv5y4TZR/YOfcA+Sd6E2ZPM3Oz55WDb/Nix+ZpmQVUJreN+Bbufsyeq7Qzd/h57Qs6e3jQnqmddYk/v8EIAgeoKjCa0PqmSJFLw+s/lb39C4fYn5B3ZY0cnxXOuUmJ+HU+/sNBjlKrbNa52XMDf97I9VR12PWL3V9R5vaLlv6lk8uJcIhFa57KthVyUPeVtQuzB0SUnBd9mtMnxWd5mRJ8dfXKkPPLEhOOnm+U99JsCM71d3lCE1i53j9pHJUBoPSo+PoxAXQUIrevKzc3qKRAdK48ZMaNFDpq/byk/ELJ3l3n8o62k/BDIwSB7kpmb3Vk+pT32vHpWWtG9Rhpam/9hCXY+Lb/rsfIpzeiY0jFTFc292j5MMZ5xkR09wAsBBGonULXQ+pQSzezrYMcTCrqetA9KtV+Ear/MBtiRmSkcjqndorhyvgXiksItDypce4/8g+vLXxxZfIuiRe8tP3wuxy9C6xw3l6UNX+DEU94m0B468T0439ue8h4MuM0s76Hg2/z8YelMp7xNBaMab9KWq+/sGH5DqvdOQuvqWXIlxwQIrR1rGOUWWoDQutDtL+zi/QPr5PecEGabUSO9XUMeaTjGnsguz80eHDdiTme3zWm4WSWhtdffrWD7Uwq2/kTBrmfKQf24mYo6rlHceZ2S897S8PVQAAJFEqhVaH2ioRmlZL6Dovzr/ln7U/GslUrmXqlo7lUy33nCC4FzCfi9OxWs/a7CTT+yIVQ8+zI7q9o8YLEoL0LronSaddZSwDyY1T6k0owyMSNNhkaYmIdXnjjGZPChlvYhloPhd9R31tKO/vbTtSw999cmtM59i1ngmQQIrdkbCLgjQGjtTq+otPYCfvdm+d1d8g5ttn/Zudndm0+6cTJpoZJJneUxIxM6yw+BnLSg9sUN3uFcobWZSR12PWZnVAd7f2Y/ZU/Gdd6guPNaJVOW1K1WboQAAicL1CO0PumOUV95FJAJsbset9/ubX4PsHOw2y/n9wM26K8IBNtXK1x3r9035iR1tOj9ipbemosHK1babkLrSsV4PwLVF/D6DpbndptRJXa8Sffg2JKjGlj+4erfsEBXJLQuULNZ6skChNbsCATcESC0dqdXVNoggTSROXHm2VEjW8thdvc2e1Jbx0+AmIdAtrXbhz7acSOT5ysxgfbEeVX/tvzThdbe4d12xqiZUe2/8Wo5qJ68yIbUZkZ1MmFeg/C4LQIInChQ99D6JP5UZiZx0PWEHSVifg8zp66juVcqmXu14hkXS35IwwooYL4rJ9z47+UHKx7ebR/wGS29XVHHdYX+9ntC6wL+YmDJCBRIgNC6QM1mqYTW7AEEXBUgtHa1c9TdeIFU3uE9g3Ozj4fZ5u/bZL8VcvCVjptRDrAnmQdAnvgQyAkjWsLx0Dro3qTAzKfe9pjMPFsbVE9bYUPqqGOV0vGzRnR9PoQAArUTaGxoffK6vMO7FHY9Ln/7agX7fqk0GKO43YwRuUrxnCuUNo2vHQRXzoSAmVEdvvqvCrf9RJKnaN6NipbdntsHK1aKTmhdqRjvRwABlwQIrV3qFrVWVYCT1lXl5GII1FSA0LqmvFy8oALe0X3lMPvQVnndm+V1by3/2DyJ/XiY3TKpPFpk4nzJzMs+Pjt7zLQzqplT1P3rH9D4vU/LP7xT8nzF099mH6QYdVxrH6zICwEEsiuQpdD6RCXze1P5Qa1PKNi1Rl5cUjz9rfYEtpmBb2bh88qJQNxvQ+pw7d3y979mn9UQL7lV0cL35P7BipV2kNC6UjHejwACLgkQWrvULWqtqgChdVU5uRgCNRUgtK4pLxdH4CQBr++AHTHiH9oimREjdtTIFpl5fUNhdji2/PDHCZ1KJ5+vdOxMea+/ZOdUe8fesG8zD1WL51+v2DxUrXkiyggg4IhAVkPrU/mC3Wvkb39S4fbVMl+ESyaeb09fm99zkvMucESbMk/6wsSRPQrX3qtw47/ZL6DGc65StOSWQj1YsdIdQWhdqRjvRwABlwQIrV3qFrVWVYDQuqqcXAyBmgoQWteUl4sjMCwB80R1/+AWeyLbzs4+ZOZnb7Fh0fFXbGbOdq7SvvFv06RpM+R5w7o0b0IAgQwJuBJan0hmTuMG258sn8Q+sE5py0QbeMad1yhuvyJDupRyOoFg17MK196jYOdTtnf2wYqLb5EZXcXr7AKE1uwQBBDIswChdZ67y9rOKkBozQZBwB0BQmt3ekWlxRPwBo7K69lWHiEStlqA0z2IsXgyrBgBNwVcDK1PlPaO7LWnr/0dTynY/Zx9SF8861LFc65UbMaItPCdH1nYmeYLoeGGHyhYf699kLB53oF9sOL8m7JQnjM1EFo70yoKRQCBEQgQWo8AjY/kQ4DQOh99ZBXFECC0LkafWWV+BAit89NLVlI8AddD65M6Fh1TuPMZ+dufULDzGXn9vUqmLVfccXV5jMiEecVrcINXbB7KG772XYVbH7KVlB+s+EElkxY0uDI3b09o7WbfqBoBBIYnQGg9PCfelUMBQuscNpUl5VaA0Dq3rWVhORUgtM5pY1lWIQRyFVqf2LE0UfD6S+UAe8cT5dO9be12jIh5mKN5qKN5cCyvGggkAwq7Hi0/WHHfy0raOgYfrPhrSpvG1+CGxbkkoXVxes1KESiiAKF1EbvOmq0AoTUbAQF3BAit3ekVlSJgBAit2QcIuCuQ29D6lJb4PVsVmAB7+xPy972itLlNcftlSuZepaj9Mikc424TM1K5eTCvmVUdbvj+4IMVr1S09DbFMy/JSIXul0Fo7X4PWQECCJxZgNCa3VFYAULrwraehTsoQGjtYNMoudAChNaFbj+Ld1ygKKH1iW3ySt0KdjypoGu1gj3PS0mseMbFSjquVjT3aqVjpjre1fqWH+x5wZ6qNqb2wYoL369o6a1Kx0yrbyEFuBuhdQGazBIRKLAAobWkb37rLj386BND2+Azf/h7uvAtK4b++UcPPapvfft79p/fduEKffJjH1VrS0uBt00+lk5onY8+sopiCBBaF6PPrDI/AoTW+eklKymeQBFD65O6HPfb4NoG2Duflndsv5IpS+wM7HjulUomLy7ephjGir3oiMKN/6Fg3b3ye7bZcSvxktsUdayS/HAYV+AtIxEgtB6JGp9BAAFXBAofWh/q7tHd9/9Qv3XHrTaI3ta1Q1/5xjf1iY99VJ0dc/TSL1/Rt75zt/78Tz6lSRMn2IDbvD76kTtd6TF1nkGA0JqtgYA7AoTW7vSKShEwAoTW7AME3BUofGh9UutS+W+8qmD7k/bUsHmIYDpuhqI5V9oxIuY0dtEDWb97i8LXvqdgywNWLp5/k6Jlv6Fk4nx3fxE4VDmhtUPNolQEEKhYoPCh9aliJsT+q89/SR/50O32tLUJqdtnz9TNN66ybz01xK5YnA9kRoDQOjOtoBAEzilAaH1OIt6AQKYECK0z1Q6KQaAiAULrM3N5R/Yq7Hpc/vbV9qGOadCquH1lOcCec0VxHiqYRNYhWHe3dUgmdCpeepuiBTcrDcdVtN948+gECK1H58enEUAg2wKE1qf0x4TSX/uHb+nPPv1xzZhxnr78jW/qwguWD4XWp57ENkEKLzcFBqJEfaV+tY1rdXMBVI1AhgTSVPK82hVkfq/tOdKnSW08FKl2ylwZgeoJHOo5qoltY2v6+0L1qq3mlcxvhPzZsJqiXKv+Aod6j2nCuFb5fg3/w17/ZVX9jt7AYYU7n1Gw4wkFu9bIi44pnn6h4jlXaWDuNfZEdt5eZlRK04b71LTx3+T1HbInzgeW3F4+cX7Cq9Z/Lsyb62jW03O4T2NbmxWG/mguw2cRQKBGAvy3dHSwhNaDfiaM/h9f/Kp6ew/r+EzrvlLJhtY333Dt0IzrU0NrcxKBl6sCqZJE/IHc1fZRd6YEQt9TVOMv4sVJosDnD+SZajzFIHAGgaL+em0KA5nv5OKFgMsCSZLI57+3lbUwidR64Jdq3f20Wvc8o/DYXg20zdOxme9U38wr1D95iSR3vwjQsv8ljdvybxq7+ynFLRN1pONmHZn3PsWtU07rFAaeopgv4FW2iUb2bnOwo/zL1d39NbKV8ykE3BCYMpHvPhlNpwitT9E7cTzIksULz3nSejT4fLaxAowHaaw/d0egEgHGg1SixXsRaLwA40Ea3wMqQGCkAowHGancm5/zD21UsP0JOwvb3/+a0tYpdnxI3HG14pmXSEHz6G9S6ytEx9S0+ccK1t6j/7+9u4+1pLzrAP7cF7NQoIsU2JYuUIpQLLZQQhUqC1JpKDG15UUEqW7UlRja2lpjoonxD2NSE1Pf25gGNahoQ7eVNoHVLmEtrAhCaIGCvJSmsLxDlrLsLou99655BuZk7nTOmTn3nnvPM2c+9x9ezjkzz3x+z3Nn5nufec70S98N8+tODfMnxi9WPKfz63ivNP0w27c8yDBa3kuAQNsEhNYVFSuuY21N67Z16ebtFVo3t/JOAuMWEFqPuwL2T2A4AaH1cF7eTSAlAaH1aKsxtW/n6wH2rWHmmbtCmJrOgusswF5/Vti/Zu1od7jMrU3v2hFmHrwuzD7679lyR3NvPz/M//il2brVftITEFqnVxMtIkBgdAKdD63jch/bb78zXHHphZlqvkzIx6/cmC0JUv7ixRhix59NGy8fXRVsaSwCQuuxsNspgSUJCK2XxOZDBMYmILQeG70dE1i2gNB6rxI4FAAAFspJREFU2YT9NzC3L8w8/T9hJn6J4ZO3halXd4WFI04O8/GLHI8+e3zB8P75MBtnhj+4Ocw8e3dYWHtcmH/HxeEHx18QwqzvE1nBHrHsTQutl01oAwQIJCzQ+dA6X7f6W/fc3ytTvqZ1/j+2bN0Wrrn2S9l/nnrKyeFTV20KB6xZk3BZNa2JgNC6iZL3EEhDQGidRh20gkBTAaF1UynvI5CegNB6lWqyfyHMPP/tMB3D4ie2h+ldj4WFQ9ZnAfbC+g1h/sh3Z7OyV/Jnat+LYfaR68Psw9eH+O9zR5+dhdXz696zkru17REKCK1HiGlTBAgkJ9D50Dq5imjQqgkIrVeN2o4ILFtAaL1sQhsgsKoCQutV5bYzAiMVEFqPlLPxxqZ2PxVmH/9GmN5xS5h5/r6w/0cODvNvfV9YOGZDmDvqjJHOeJ5+/r4w++CXw+zjN4f9aw4Ncyd+JMyd8OGw/8A3NW6vN6YhILROow5aQYDAyggIrVfG1VZbICC0bkGRNJHA6wJCa12BQLsEhNbtqpfWEigKCK3H3x+m/m9XmHniv16bhf3UHWFqYS7MrzvttXWwjzkn+2LHoX/m9oXZ720Nsw9uDtMvPvLa9k66OMyt3+CLFYfGTOcDQut0aqElBAiMXkBoPXpTW2yJgNC6JYXSTAIhBKG1bkCgXQJC63bVS2sJCK0T7gMLc9kXOMYAe/aJ7WFq7/Nh4bB3vL4O9oaw8KMnDGz81MtPhtmHNofZ79wQwv79Ye74D4b5ky4Z3/rZCVO3sWlC6zZWTZsJEGgqILRuKuV9EycgtJ64kjqgCRYQWk9wcR3aRAoIrSeyrA6qIwJmWqdd6OmdD4WZOAN7x/Yw/eLDYf9B68Lc+rPCQvwyx3WnvTZrOq6X/eRt2azq+MWPC4ceH+birOrjzh/pMiNpS3WjdULrbtTZURLoqoDQuquVd9xBaK0TEGiPgNC6PbXSUgJRQGitHxBor4DQuj21m9rzbJjdcUuY3rE9zDxzZwizB2TB9fT3Hw3xtbm3nRfmT7wozK87tT0HpaVDCQith+LyZgIEWiYgtG5ZwTR3dAJC69FZ2hKBlRYQWq+0sO0TGK2A0Hq0nrZGYDUFhNarqT26fU39YG+YefqOMP34N8L+Q98W5n7s55e29vXommRLqyAgtF4FZLsgQGBsAkLrsdHb8bgFhNbjroD9E2guILRubuWdBFIQEFqnUAVtILA0AaH10tx8isA4BITW41C3TwIEVktAaL1a0vaTnIDQOrmSaBCBvgJCa52DQLsEhNbtqpfWEigKCK31BwLtERBat6dWWkqAwPACQuvhzXxiQgSE1hNSSIfRCQGhdSfK7CAnSEBoPUHFdCidExBad67kDrjFAkLrFhdP0wkQqBUQWtcSecOkCgitJ7WyjmsSBYTWk1hVxzTJAkLrSa6uY5t0AaH1pFfY8U2SgNB6kqrpWAgQKAsIrfWJzgoIrTtbegfeQgGhdQuLpsmdFhBad7r8Dr7lAkLrlhdQ8zslILTuVLkdLIHOCQitO1dyB5wLCK31BQLtERBat6dWWkogCgit9QMC7RUQWre3dlrePQGhdfdq7ogJdElAaN2lajvWRQJCax2CQHsEhNbtqZWWEhBa6wME2i0gtG53/bS+WwJC627V29ES6JqA0LprFXe8PQGhtc5AoD0CQuv21EpLCQit9QEC7RYQWre7flrfLQGhdbfq7WgJdE1AaN21ijteAgQIECBAgAABAgQIECBAgAABAgQIJCwgtE64OJpGgAABAgQIECBAgAABAgQIECBAgACBrgkIrbtWccdLgAABAgQIECBAgAABAgQIECBAgACBhAWE1gkXR9MIECBAgAABAikIPPb4E+EvP391+ORVm8Kxx6xPoUnaQIAAAQIECBAgQIDABAsIrSe4uG08tO+/tCv80Z/8eTjyyMPDp67aFA5YsyY7jHiz/Md/+lfh+Lcfu+j/L+cY77nv/vCZz36ut4lTTzl50bb3vfpq+IvPXx2+dc/92Xs2XvEL4YIPnLuoPS+/vDv776Pesi784e/9djh07RsrXy9veznt9lkCKQjk4+O5515Y1PfzMRzbWBwTo2hz/nug37gbNGbLr8X2/P7vfCyc8q6TjdlRFMc2Widw9TX/Gm7admvW7uJY6HcgdaF13fiM292ydVu45tovZbsonhfrxmf+e+Wpp5+tPOe2Dl+DCSxBoDzG4ibOO3dD2LTx8iVsrf9H6sZj/GTxGrp8DVz83VK+fo7/XX69ye+fkR6gjRFYYYH8nPXy7j3hD373t3p/6I3nwCefembkY7bunrZ8Dh10/Vsez8XzdtV4XmFKmydAgEAQWusESQnEk+qf/fUXwu7du7OQOA+U4gVuvFg/6OA3jCy0jifho958ZLaP/AL98MMO611IxH3Gn3gzkJ/sN/7SJdn748XBU8881wux43tf2Lmz17bitvML9HxbSYFrDIElCuRjZs/uveF9Z5zeGwux7992+11hz969Iw+t68bdoDEbx/Dm628IH73souyPYXFbf/OFa3o3E8bsEjuCj7VaIB/HF5z3M73zbb8Dqgut68ZnfP2af9nc+71QHq+Dxmd523G83vPtB0Z2PdDqImp8JwTK16HxoOP4/ecvfiVc8pGf602aGAVG3fmy/LugOB7j/ottyoP2j1+5MfsdU9523e+VURyPbRBYbYH8fvaIww8LBx54YO/ecqVC60H3tPl5/pSfeGd2rV4ec4PO3eXxXPV7aLVt7Y8Age4JCK27V/Okjzg/yf/sOT8d7r3/f8Nv/OoV4dlnnw9fu/HrYf1RbwkPPfrd3k1qcabGIYcc3Auf8hPqhjN/Mty4dVt2vMW/cvcDKF5079v3ahae//qvXNb763jxBru8jfLNePl1N9hJdzuNW4JAfhF85umnhf++6+7wm7/2y9lW/vbv/ynE//fVG7/eC6fKszTyGR5V42LQOBs07uJrw4zZugtvY3YJncJHWidQDq3L/T7e3P7dP34xfPoTV4aXXto11PIgVSH1W496c+8PXIPOm3Xjs+6c27pCaDCBGoEmfX7Q00bx3PrKK6+EPa+8kj1BWLxursMvj8dy8DYoeC4HZuV91Y31urZ5nUCKAvn97MUfviB8+atbeveTVWMnPkkcnyAsjsnlXh8XPx/vo/PzeHwiuG5MDvpdU/fZFGuhTQQItF9AaN3+Gk7UEeQn+RgWb912a3jvae/OZjTHGdHxn/nMqhgqb7vltnDhhz6YHX9xpnN8rWqJkTqoYlhWdQE+KMQa9FrVLO66tnidQOoCxbArjs34k4/T+M98RuUBB6wJW/7j5nDB+e/PZjjHsbL15luyQLscNA8746p8UV5eb3fQuGxyk1188iL1emgfgaUIrGRoXZ59GZfbymd6xbYOGoN1vwvKTzct5dh9hkCbBPIZyz91+nsqlxYoX2sWr6fjGvRxzNxx1zcXPV3U9GmF8ngs/3F5UPBcF0qXn3pqU020lUA/geL4e+ChR3pLghRD66qnePMnANeufeOiiRjl8VwnXxyjVSH0oAkiddfOMWTPn5yoa4fXCRAgMAoBofUoFG1jZALFk3LcaDypHnnEm7IZ1zGk7neBXTwhx8/F0DpfyqNJ48on9OLssnyd6n4n8UE31/lscGtaN6mC97RJoBh2HXvM0dnF9RsOOjCbcf3Y4zsWLQNQPK6qx4rz9f2GeWyyvJ1hxuygmSLGbJt6obYuV2ClQuvy+KxahqTfuXPQ+Myf2iivublcB58n0AaBqjWt8yeXqsZTPJ/lTzeUQ6q6PwzlHlXjsbjd+L5BwXS/cKx4LNa0bkPv08ZhBIr3s8UAuhhgl+89y2OtOM7ie7fc9J+NlsQqb7fqs4PGZdUXLhfXxC5+x9MwJt5LgACBpQoIrZcq53MrIlA8ya9bd0T2RYj5Wpvl0Lj8pRP5TeywoXXVLI+mM63La/X1Q7HUwIp0Fxsdo0A5hCpfXBfXri3faBcfgczD5o9duTH8w7XX9cb7oEOrGndNx2zTJx+M2TF2LrteNYGVCK2rxmdV8FU1ZpuOzyZLJawaoh0RGJNAPE995WtbstnT8SdfZqDYnDxgqpodXV5Sq3wY/cZj05nWTZ6IqJuJPSZauyWwLIHyzOh8Ukb8I1I+UaNfmJz/oan4elwnvri8Vr/GVd3TNp1p3eSe1vIgy+oWPkyAwBIFhNZLhPOxlREY9PhTMUR66OHvLPoStaXOtO73WGJVO6pmqTR9RKpqFujKCNoqgdURGPQFbsXxGNfBLY6TfrPB4v9v8kWr/S6qm4zZpoFYFDRmV6cf2ct4BUYdWg+66S3Pzuw3y6zJsjzDPio9XmV7J7AyAsXA99C1axetW1ve47AzrQedL5usad0ksM7bWP7dsDJatkpg9QTK56iqL2asm2ldXBc7zrKOTzLmT/9WHUm/e9ry9Wy/PyI3vacd5qnI1RO3JwIEJllAaD3J1W3hsQ0TWhdncpbXyW2yPEjdTK3iBX55JkjdY5XXXvdv4awz3rvoSxxf2Lmz0WNdLSybJndQYJjQuvioYb9ZIJ/57OdC3SOHdeNu0Jitmx1izHawEzvkH1pXuurLEx948OFsDfq6L2KsG59V244l2LTx8tovhorn+LhW/invOjmrWvGcP+gmXokJTIpAHD933n3vovWsi2Mqfn9EfDqx+EefOCa/99iOcM6GM7Pl9vLxFv85aE3buvNl1TJfxeX7Bm07fnb77XeGKy69MGtPk9mdk1JDx9Edgar72Xx5q/PO3ZCN4/ze8gPvPzv7guKq6+P4mdtuvyu7n4yf6fczzJcnVi2vV7UkSNxXbOPm628IH73soux7acpt7k5FHSkBAuMUEFqPU9++f0igaWgdPxgvzuM3oMefE44/LuzZu7f35W5NQut87dpiI4rLFgz6Fvb8wqN8APm6fOWlS6xprbNPmkDT0DoGSsWxFoOn+PPJqzb1/qjTdFZz3bgbNGar1gKN7chvHozZSeuhjqefwKBxkodZN227Nfv4L170ofDNe+8Pn/7ElbWhdd34jNsrvqd4Xqwbn+XXrWmtf3dNoLimbH7s5XFQHtvFa9ryNe+g69K68Rj3XzxnFttR1c74/nx/5ev3+N/WtO5ab5784626n83HxjtPOrEXQBfHWnG85kJN/6hTd09bHpfFMVd37i5vu26CyeRX1xESILDaAkLr1Ra3PwIECBBYJBAvmONPnGnihwABAgQIEBitwKDZz6Pdk60RIECAAAECBEYnILQenaUtESBAgMCQAtamHRLM2wkQIECAwJACQushwbydAAECBAgQSEJAaJ1EGTSCAAECBAgQIECAAAECBAgQIECAAAECBKKA0Fo/IECAAAECBAgQIECAAAECBAgQIECAAIFkBITWyZRCQwgQIECAAAECBAgQIECAAAECBAgQIEBAaK0PECBAgAABAgQIECBAgAABAgQIECBAgEAyAkLrZEqhIQQIECBAgAABAgQIECBAgAABAgQIECAgtNYHCBAgQIAAAQIECBAgQIAAAQIECBAgQCAZAaF1MqXQEAIECBAgQIAAAQIECBAgQIAAAQIECBAQWusDBAgQIECAAAECBAgQIECAAAECBAgQIJCMgNA6mVJoCAECBAgQIECAAAECBAgQIECAAAECBAgIrfUBAgQIECBAgAABAgQIECBAgAABAgQIEEhGQGidTCk0hAABAgQIECBAgAABAgQIECBAgAABAgSE1voAAQIECBAgQIAAAQIECBAgQIAAAQIECCQjILROphQaQoAAAQIECBAgQIAAAQIECBAgQIAAAQJCa32AAAECBAgQIECAAAECBAgQIECAAAECBJIREFonUwoNIUCAAAECBAgQIECAAAECBAgQIECAAAGhtT5AgAABAgQIECBAgAABAgQIECBAgAABAskICK2TKYWGECBAgAABAgQIECBAgAABAgQIECBAgIDQWh8gQIAAAQIECBAgQIAAAQIECBAgQIAAgWQEhNbJlEJDCBAgQIAAAQIECBAgQIAAAQIECBAgQEBorQ8QIECAAAECBAgQIECAAAECBAgQIECAQDICQutkSqEhBAgQIECAAAECBAgQIECAAAECBAgQICC01gcIECBAgAABAgQIECBAgAABAgQIECBAIBkBoXUypdAQAgQIECBAgAABAgQIECBAgAABAgQIEBBa6wMECBAgQIAAAQIECBAgQIAAAQIECBAgkIyA0DqZUmgIAQIECBAgQIAAAQIECBAgQIAAAQIECAit9QECBAgQIECAAAECBAgQIECAAAECBAgQSEZAaJ1MKTSEAAECBAgQIECAAAECBAgQIECAAAECBITW+gABAgQIECBAgAABAgQIECBAgAABAgQIJCMgtE6mFBpCgAABAgQIECBAgAABAgQIECBAgAABAkJrfYAAAQIECBAgQIAAAQIECBAgQIAAAQIEkhEQWidTCg0hQIAAAQIECBAgQIAAAQIECBAgQIAAAaG1PkCAAAECBAgQIECAAAECBAgQIECAAAECyQgIrZMphYYQIECAAAECBAgQIECAAAECBAgQIECAgNBaHyBAgAABAgQIECBAgAABAgQIECBAgACBZASE1smUQkMIECBAgAABAgQIECBAgAABAgQIECBAQGitDxAgQIAAAQIECBAgQIAAAQIECBAgQIBAMgJC62RKoSEECBAgQIAAAQIECBAgQIAAAQIECBAgILTWBwgQIECAAAECBAgQIECAAAECBAgQIEAgGQGhdTKl0BACBAgQIECAAAECBAgQIECAAAECBAgQEFrrAwQIECBAgAABAgQIECBAgAABAgQIECCQjIDQOplSaAgBAgQIECBAgAABAgQIECBAgAABAgQICK31AQIECBAgQIAAAQIECBAgQIAAAQIECBBIRkBonUwpNIQAAQIECBAgQIAAAQIECBAgQIAAAQIEhNb6AAECBAgQIECAAAECBAgQIECAAAECBAgkIyC0TqYUGkKAAAECBAgQIECAAAECBAgQIECAAAECQmt9gAABAgQIECBAgAABAgQIECBAgAABAgSSERBaJ1MKDSFAgAABAgQIECBAgAABAgQIECBAgAABobU+QIAAAQIECBAgQIAAAQI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   "source": [
    "vis_data['cycles'].resample('1M').sum().iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1222.5108, 1220.6174099999998)"
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     "execution_count": 75,
     "metadata": {},
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   ],
   "source": [
    "total_to_storage = s.storage_case_PCM.data['to_storage_MWh'].sum()\n",
    "total_from_storage = s.storage_case_PCM.data['from_storage_MWh'].sum()\n",
    "total_to_storage, total_from_storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh'],\n",
       "      dtype='object')"
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     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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   "execution_count": 77,
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     "execution_count": 77,
     "metadata": {},
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    "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)"
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  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
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      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_21988\\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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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat([hp_output, demand_and_to_storage], axis=1).resample('H').sum().iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Financials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', '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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def collect_cashflows(c, s):\n",
    "    s.hpcase.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n",
    "    s.storage_case_PCM.data['nomination_MWh'] = s.hpcase.data['hp_input_MWh']\n",
    "    s.storage_case_PCM.generate_electr_market_results(nom_col='nomination_MWh', real_col='hp_input_MWh')\n",
    "\n",
    "    \n",
    "    for case in [s.baseline]: #, s.optcase1, s.afrr_case]:\n",
    "        case.add_gas_costs(gasvolumes_col='gb_input_MWh')\n",
    "        case.add_co2_costs(volume_cols='gb_input_MWh', fuel='gas')\n",
    "        case.add_eb_ode(commodity='gas', tax_bracket=c.tax_bracket_g)\n",
    "        \n",
    "    for case in [s.hpcase, s.storage_case_PCM]: #, s.hpcase_sde, s.optcase1, s.afrr_case]:\n",
    "        \n",
    "        case.add_eb_ode(commodity='electricity', tax_bracket=c.tax_bracket_e, cons_col='hp_input_MWh')\n",
    "        case.add_grid_costs(\n",
    "            power_MW_col='hp_input_MW',\n",
    "            grid_operator=c.grid_operator,\n",
    "            year=2023, \n",
    "            connection_type=c.connection_type\n",
    "        )\n",
    "        \n",
    "    for case in s.sde_cases:\n",
    "        case = calculate_sde_subsidy(c=c, s=s, case=case)\n",
    "        case.add_cashflow('SDE++ subsidy (€)', case.sde_results['corrected_amount'])\n",
    "    return s\n",
    "\n",
    "s = collect_cashflows(c, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "146760\n",
      "73389\n"
     ]
    }
   ],
   "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-197286.05061\n",
      "-181242.64800000002\n"
     ]
    }
   ],
   "source": [
    "charge_times_pcm = print(sum(s.storage_case_PCM.data['hp_input_MW']))\n",
    "charge_times_hp = print(sum(s.hpcase.data['hp_input_MW']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Visualisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_21988\\1396106630.py:1: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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          "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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///3f+z0XfPhZO0we6tz9YMW+YKrbbn3Mm//3Y9W7/6/jz3sKdq7Quh02bhrrELe+OdM3N7vTvVvz+pz6lRYnPvjR57msuwfG/oHN0FrtElq3g8v2/VS/LmLIe4ybOXHNS7+t+s9/+F8PGDcF0qFP8of8AKTuUPfd4+vqP3Q+rXtyedOXzG7fYwmtmzH3fU3b9G8Dvz04jAABAgQIECBAgAABAgQIEBhZQGg9Mqjm9hPYFsK3W9/0QYz79cLZBPYX2BY+738FLRAgQIAAAQIECBAgQIAAAQIE8hQQWudZ12RHJbROtnQ63hLYZR6DI0CAAAECBAgQIECAAAECBAgQuFBAaG1GRCWwS9jnSeuoSqczLQFPWZsOBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaF1uJ0zCRAgQIAAAQIECBAgQIAAAQIECBAgQGBkAaH1yKCaI0CAAAECBAgQIECAAAECBAgQIECAAIFwAaH1FrtTjzxa3X3PvQdHvfKmG6u33H6sOnrkSLi6MwkQIECAAAECBAgQIECAAAECBAgQIECgV0BovWViPPDgyeraa15a3fSKG6tnz5yp3nXf8eolL35xdey215lSBAgQIECAAAECBAgQIECAAAECBAgQIDCygNB6R9A6xD716c942npHN4cTIECAAAECBAgQIECAAAECBAgQIEBgiIDQeohS65jjJz60+i9PWu8I53ACBAgQIECAAAECBAgQIECAAAECBAgMEBBaD0BqDqnfb33igx+t7rrzjuqqK6/Y4UyHEiBAgAABAgQIECBAgAABAgQIECBAgMAQAaH1EKWqqurA+pffe6J6+9veVN1w/XUDz3IYAQKbBJ75+nPV+aqqXnTpJaAIEBhZ4MyZb1TPnT1XXf5CHxw8Mq3mCFTf+ObZqv4eduXll9IgQGBigaeePlNddNHh6uiRiye+kuYJlCdgP1ZezY2YQEoCQusB1RJYD0ByCIEAAYukADSnEBgoILQeCOUwAgECQusANKcQCBQQWgfCOY3AAAH7sQFIDiFAYDEBofUWeq8EWWxuunABAhZJBRTZEBcTEFovRu/CBQgIrQsosiFGIyC0jqYUOpKhgP1YhkU1JAIZCQittxSz/uDFh04+fMFRl19+mdeEZHQTGMpyAhZJy9m7cv4CQuv8a2yEywkIrZezd+XyBITW5dXciOcTsB+bz9qVCBDYXUBovbuZMwgQGEnAImkkSM0Q6BEQWpsWBKYTEFpPZ6tlAl0BobU5QWA6Afux6Wy1TIDA/gJC6/0NtUCAQKCARVIgnNMIDBAQWg9AcgiBQAGhdSCc0wgECAitA9CcQmCggP3YQCiHESCwiIDQehF2FyVAoBawSDIPCEwnILSezlbLBITW5gCB+QSE1vNZu1J5AvZj5dXciAmkJCC0Tqla+kogMwGLpMwKajhRCQitoyqHzmQmILTOrKCGE7WA0Drq8uhc4gL2Y4kXUPcJZC4gtM68wIZHIGYBi6SYq6NvqQsIrVOvoP7HLCC0jrk6+pabgNA6t4oaT0wC9mMxVUNfCBDoCgitzQkCBBYTsEhajN6FCxAQWhdQZENcTEBovRi9CxcoILQusOiGPJuA/dhs1C5EgECAgNA6AM0pBAiMI2CRNI6jVgj0CQitzQsC0wkIraez1TKBroDQ2pwgMJ2A/dh0tlomQGB/AaH1/oZaIEAgUMAiKRDOaQQGCAitByA5hECggNA6EM5pBAIEhNYBaE4hMFDAfmwglMMIEFhEQGi9CLuLEiBQC1gkmQcEphMQWk9nq2UCQmtzgMB8AkLr+axdqTwB+7Hyam7EBFISEFqnVC19JZCZgEVSZgU1nKgEhNZRlUNnMhMQWmdWUMOJWkBoHXV5dC5xAfuxxAuo+wQyFxBaZ15gwyMQs4BFUszV0bfUBYTWqVdQ/2MWEFrHXB19y01AaJ1bRY0nJgH7sZiqoS8ECHQFhNbmBAECiwlYJC1G78IFCAitCyiyIS4mILRejN6FCxQQWhdYdEOeTcB+bDZqFyJAIEBAaB2A5hQCBMYRsEgax1ErBPoEhNbmBYHpBITW09lqmUBXQGhtThCYTsB+bDpbLRMgsL+A0Hp/Qy0QIBAoYJEUCOc0AgMEhNYDkBxCIFBAaB0I5zQCAQJC6wA0pxAYKGA/NhDKYQQILCIgtF6E3UUJEKgFLJLMAwLTCQitp7PVMgGhtTlAYD4BofV81q5UnoD9WHk1N2ICKQkIrVOqlr4SyEzAIimzghpOVAJC66jKoTOZCQitMyuo4UQtILSOujw6l7iA/VjiBdR9ApkLCK0zL7DhEYhZwCIp5uroW+oCQuvUK6j/MQsIrWOujr7lJiC0zq2ixhOTgP1YTNXQFwIEugJCa3OCAIHFBCySFqN34QIEhNYFFNkQFxMQWi9G78IFCgitCyy6Ic8mYD82G7ULESAQICC0DkBzCgEC4whYJI3jqBUCfQJCa/OCwHQCQuvpbLVMoCsgtDYnCEwnYD82na2WCRDYX0Bovb+hFggQCBSwSAqEcxqBAQJC6wFIDiEQKCC0DoRzGoEAAaF1AJpTCAwUsB8bCOUwAgQWERBaL8LuogQI1AIWSeYBgekEhNbT2WqZgNDaHCAwn4DQej5rVypPwH6svJobMYGUBITWKVVLXwlkJmCRlFlBDScqAaF1VOXQmcwEhNaZFdRwohYQWkddHp1LXMB+LPEC6j6BzAWE1pkX2PAIxCxgkRRzdfQtdQGhdeoV1P+YBYTWMVdH33ITEFrnVlHjiUnAfiymaugLAQJdAaG1OUGAwGICFkmL0btwAQJC6wKKbIiLCQitF6N34QIFhNYFFt2QZxOwH5uN2oUIEAgQEFoHoDmFAIFxBCySxnHUCoE+AaG1eUFgOgGh9XS2WibQFRBamxMEphOwH5vOVssECOwvILTe31ALBAgEClgkBcI5jcAAAaH1ACSHEAgUEFoHwjmNQICA0DoAzSkEBgrYjw2EchgBAosICK0XYXdRAgRqAYsk84DAdAJC6+lstUxAaG0OEJhPQGg9n7UrlSdgP1ZezY2YQEoCQuuUqqWvBDITsEjKrKCGE5WA0DqqcuhMZgJC68wKajhRCwitoy6PziUuYD+WeAF1n0DmAkLrzAtseARiFrBIirk6+pa6gNA69Qrqf8wCQuuYq6NvuQkIrXOrqPHEJGA/FlM19IUAga6A0NqcIEBgMQGLpMXoXbgAAaF1AUU2xMUEhNaL0btwgQJC6wKLbsizCdiPzUbtQgQIBAgIrQPQnEKAwDgCFknjOGqFQJ+A0Nq8IDCdgNB6OlstE+gKCK3NCQLTCdiPTWerZQIE9hcQWu9vqAUCBAIFLJIC4ZxGYICA0HoAkkMIBAoIrQPhnEYgQEBoHYDmFAIDBezHBkI5jACBRQSE1ouwuygBArWARZJ5QGA6AaH1dLZaJiC0NgcIzCcgtJ7P2pXKE7AfK6/mRkwgJQGhdUrV0lcCmQlYJGVWUMOJSkBoHVU5dCYzAaF1ZgU1nKgFhNZRl0fnEhewH0u8gLpPIHMBoXXmBTY8AjELWCTFXB19S11AaJ16BfU/ZgGhdczV0bfcBITWuVXUeGISsB+LqRr6QoBAV0BobU4QILCYgEXSYvQuXICA0LqAIhviYgJC68XoXbhAAaF1gUU35NkE7Mdmo3YhAgQCBITWAWhOIUBgHAGLpHEctUKgT0BobV4QmE5AaD2drZYJdAWE1uYEgekE7Mems9UyAQL7Cwit9zfUAgECgQIWSYFwTiMwQEBoPQDJIQQCBYTWgXBOIxAgILQOQHMKgYEC9mMDoRxGgMAiAkLrRdhdlACBWsAiyTwgMJ2A0Ho6Wy0TEFqbAwTmExBaz2ftSuUJ2I+VV3MjJpCSgNA6pWrpK4HMBCySMiuo4UQlILSOqhw6k5mA0DqzghpO1AJC66jLo3OJC9iPJV5A3SeQuYDQOvMCGx6BmAUskmKujr6lLiC0Tr2C+h+zgNA65uroW24CQuvcKmo8MQnYj8VUDX0hQKArILQ2JwgQWEzAImkxehcuQEBoXUCRDXExAaH1YvQuXKCA0LrAohvybAL2Y7NRuxABAgECQusANKcQIDCOgEXSOI5aIdAnILQ2LwhMJyC0ns5WywS6AkJrc4LAdAL2Y9PZapkAgf0FhNb7G2qBAIFAAYukQDinERggILQegOQQAoECQutAOKcRCBAQWgegOYXAQAH7sYFQDiNAYBEBofUi7C5KgEAtYJFkHhCYTkBoPZ2tlgkIrc0BAvMJCK3ns3al8gTsx8qruRETSElAaJ1StfSVQGYCFkmZFdRwohIQWkdVDp3JTEBonVlBDSdqAaF11OXRucQF7McSL6DuE8hcoNjQ+qtPfq36x//kvdXP/N2fqm64/rq1Zf7c579QveOd76muzRlQAAAgAElEQVROn37q4JhrX3Z1ddedd1RXXXlF5tPD8AhMK2CRNK2v1ssWEFqXXX+jn1ZAaD2tr9YJtAWE1uYDgekE7Mems9UyAQL7CxQXWj975kz1rvuOV5869Wh1+eWXVW9/25u2htbvvu949ebbj208bv9SaIFAeQIWSeXV3IjnExBaz2ftSuUJCK3Lq7kRLycgtF7O3pXzF7Afy7/GRkggZYHiQuumWLs8aS20TnmK63vMAhZJMVdH31IXEFqnXkH9j1lAaB1zdfQtNwGhdW4VNZ6YBOzHYqqGvhAg0BUQWu/4ehCvBnETERhPwCJpPEstEegKCK3NCQLTCQitp7PVMoGugNDanCAwnYD92HS2WiZAYH8BofWW0LpLfPzEh6qvPPFE9Zbbj1VHjxypnnn2uf2roAUChQp84xtnVyO/+OIXFCqQz7APV4eqc9X5fAaUwUjOfvNcdfb8+eoS91cG1TSE2ATOnTtf1d/Djhy56IKuveDQ4ers+XOxdVd/CCQt8NxzZ6vDhw9VF110OOlxlNT5w4cOVefOWxemUHP7sRSqpI8pC7zw6CUpd3/xvgutdwyt6w9mfN8HPly99Y1vWH0QY/2TSX8IEAgTqJ9Uq5ezl1wktA4TjOesejNZhzj+xCPwzbNnq7PnzldHLr4wVIunh3pCIF2Bs+fOVfX3sKOXXHxhaP2Cw9XZs0LrdCur5zEKPPeNb34rtH6B9WKM9enrk3VhKpWqVt/L7MfSqZeepifwwkuF1vtUTWi9Z2i9D75zCZQu4NfRSp8Bxj+lgNeDTKmr7dIFvB6k9Blg/HMKeD3InNquVZqA/VhpFTdeAmkJCK07ofUDD56sHvzYx6u77rxj9ST1bz78W9Wfu+HPVjdcf92qsvXrQeo/x257XVqV1lsCEQpYJEVYFF3KRkBonU0pDSRCAaF1hEXRpWwFhNbZltbAIhCwH4ugCLpAgMBageJC62fPnKnedd/x6lOnHj1AeeVNNx68o7obWp965NHq7nvu7T3WvCJAYD8Bi6T9/JxNYJOA0Nr8IDCdgNB6OlstE+gKCK3NCQLTCdiPTWerZQIE9hcoLrTen0wLBAiMJWCRNJakdgg8X0BobVYQmE5AaD2drZYJCK3NAQLzCdiPzWftSgQI7C4gtN7dzBkECIwkYJE0EqRmCPQICK1NCwLTCQitp7PVMgGhtTlAYD4B+7H5rF2JAIHdBYTWu5s5gwCBkQQskkaC1AwBobU5QGBWAaH1rNwuVriA14MUPgEMf1IB+7FJeTVOgMCeAkLrPQGdToBAuIBFUridMwlsE/Ck9TYh/04gXEBoHW7nTAK7CgitdxVzPIHhAvZjw60cSYDA/AJC6/nNXZEAgT8VsEgyFQhMJyC0ns5WywSE1uYAgfkEhNbzWbtSeQL2Y+XV3IgJpCQgtE6pWvpKIDMBi6TMCmo4UQkIraMqh85kJiC0zqyghhO1gNA66vLoXOIC9mOJF1D3CWQuILTOvMCGRyBmAYukmKujb6kLCK1Tr6D+xywgtI65OvqWm4DQOreKGk9MAvZjMVVDXwgQ6AoIrc0JAgQWE7BIWozehQsQEFoXUGRDXExAaL0YvQsXKCC0LrDohjybgP3YbNQuRIBAgIDQOgDNKQQIjCNgkTSOo1YI9AkIrc0LAtMJCK2ns9Uyga6A0NqcIDCdgP3YdLZaJkBgfwGh9f6GWiBAIFDAIikQzmkEBggIrQcgOYRAoIDQOhDOaQQCBITWAWhOITBQwH5sIJTDCBBYREBovQi7ixIgUAtYJJkHBKYTEFpPZ6tlAkJrc4DAfAJC6/msXak8Afux8mpuxARSEhBap1QtfSWQmYBFUmYFNZyoBITWUZVDZzITEFpnVlDDiVpAaB11eXQucQH7scQLqPsEMhcQWmdeYMMjELOARVLM1dG31AWE1qlXUP9jFhBax1wdfctNQGidW0WNJyYB+7GYqqEvBAh0BYTW5gQBAosJWCQtRu/CBQgIrQsosiEuJiC0XozehQsUEFoXWHRDnk3Afmw2ahciQCBAQGgdgOYUAgTGEbBIGsdRKwT6BITW5gWB6QSE1tPZaplAV0BobU4QmE7Afmw6Wy0TILC/gNB6f0MtECAQKGCRFAjnNAIDBITWA5AcQiBQQGgdCOc0AgECQusANKcQGChgPzYQymEECCwiILRehN1FCRCoBSySzAMC0wkIraez1TIBobU5QGA+AaH1fNauVJ6A/Vh5NTdiAikJCK1Tqpa+EshMwCIps4IaTlQCQuuoyqEzmQkIrTMrqOFELSC0jro8Ope4gP1Y4gXUfQKZCwitMy+w4RGIWcAiKebq6FvqAkLr1Cuo/zELCK1jro6+5SYgtM6tosYTk4D9WEzV0BcCBLoCQmtzggCBxQQskhajd+ECBITWBRTZEBcTEFovRu/CBQoIrQssuiHPJmA/Nhu1CxEgECAgtA5AcwoBAuMIWCSN46gVAn0CQmvzgsB0AkLr6Wy1TKArILQ2JwhMJ2A/Np2tlgkQ2F9AaL2/oRYIEAgUsEgKhHMagQECQusBSA4hECggtA6EcxqBAAGhdQCaUwgMFLAfGwjlMAIEFhEQWi/C7qIECNQCFknmAYHpBITW09lqmYDQ2hwgMJ+A0Ho+a1cqT8B+rLyaGzGBlASE1ilVS18JZCZgkZRZQQ0nKgGhdVTl0JnMBITWmRXUcKIWEFpHXR6dS1zAfizxAuo+gcwFhNaZF9jwCMQsYJEUc3X0LXUBoXXqFdT/mAWE1jFXR99yExBa51ZR44lJwH4spmroCwECXQGhtTlBgMBiAhZJi9G7cAECQusCimyIiwkIrRejd+ECBYTWBRbdkGcTsB+bjdqFCBAIEBBaB6A5hQCBcQQsksZx1AqBPgGhtXlBYDoBofV0tlom0BUQWpsTBKYTsB+bzlbLBAjsLyC03t9QCwQIBApYJAXCOY3AAAGh9QAkhxAIFBBaB8I5jUCAgNA6AM0pBAYK2I8NhHIYAQKLCAitF2F3UQIEagGLJPOAwHQCQuvpbLVMQGhtDhCYT0BoPZ+1K5UnYD9WXs2NmEBKAkLrlKqlrwQyE7BIyqyghhOVgNA6qnLoTGYCQuvMCmo4UQsIraMuj84lLmA/lngBdZ9A5gJC68wLbHgEYhawSIq5OvqWuoDQOvUK6n/MAkLrmKujb7kJCK1zq6jxxCRgPxZTNfSFAIGugNDanCBAYDEBi6TF6F24AAGhdQFFNsTFBITWi9G7cIECQusCi27IswnYj81G7UIECAQICK0D0JxCgMA4AhZJ4zhqhUCfgNDavCAwnYDQejpbLRPoCgitzQkC0wnYj01nq2UCBPYXEFrvb6gFAgQCBSySAuGcRmCAgNB6AJJDCAQKCK0D4ZxGIEBAaB2A5hQCAwXsxwZCOYwAgUUEhNaLsLsoAQK1gEWSeUBgOgGh9XS2WiYgtDYHCMwnILSez9qVyhOwHyuv5kZMICUBoXVK1dJXApkJWCRlVlDDiUpAaB1VOXQmMwGhdWYFNZyoBYTWUZdH5xIXsB9LvIC6TyBzAaF15gU2PAIxC1gkxVwdfUtdQGidegX1P2YBoXXM1dG33ASE1rlV1HhiErAfi6ka+kKAQFdAaG1OECCwmIBF0mL0LlyAgNC6gCIb4mICQuvF6F24QAGhdYFFN+TZBOzHZqN2IQIEAgSE1gFoTiFAYBwBi6RxHLVCoE9AaG1eEJhOQGg9na2WCXQFhNbmBIHpBOzHprPVMgEC+wssElp/9cmvVb/wi7+06v1dd95RXXXlFfuPRAsECCQnYJGUXMl0OCEBoXVCxdLV5ASE1smVTIcTFhBaJ1w8XY9ewH4s+hLpIIGiBRYJrWvxU488Wt19z70X4F/7squF2EVPR4MvTcAiqbSKG++cAkLrObVdqzQBoXVpFTfeJQWE1kvqu3buAvZjuVfY+AikLbBYaN1la56+fuyLjx/8kxA77cml9wS2CVgkbRPy7wTCBYTW4XbOJLBNQGi9Tci/ExhPQGg9nqWWCHQF7MfMCQIEYhaIJrRuIz3w4MnqxP0fqYTWMU8dfSOwv4BF0v6GWiCwTkBobW4QmE5AaD2drZYJdAWE1uYEgekE7Mems9UyAQL7C0QRWh8/8aHqoZMPH4zmlTfdWL3l9mPV0SNH9h+hFggQiFbAIina0uhYBgJC6wyKaAjRCgitoy2NjmUoILTOsKiGFI2A/Vg0pdARAgR6BBYJrbuvAhFSm5sEyhSwSCqz7kY9j4DQeh5nVylTQGhdZt2NehkBofUy7q5ahoD9WBl1NkoCqQpEEVq/9uZXV8due12qhvpNgECggEVSIJzTCAwQEFoPQHIIgUABoXUgnNMIBAgIrQPQnEJgoID92EAohxEgsIjAIqF1d6SnHnm0uvueew/+OsYnrz/3+S9U7/vAh6u3vvEN1VVXXrFIsVyUQG4CFkm5VdR4YhIQWsdUDX3JTUBonVtFjSdmAaF1zNXRt9QF7MdSr6D+E8hbIIrQel2IHcMHMbZfZRJDf/KejkZXmoBFUmkVN945BYTWc2q7VmkCQuvSKm68SwoIrZfUd+3cBezHcq+w8RFIWyCa0Lr7tHXNGtMT1560Tnui632cAhZJcdZFr/IQEFrnUUejiFNAaB1nXfQqTwGhdZ51Nao4BOzH4qiDXhAg0C+wWGh9/MSHqodOPnxBr2IKqbtcQmu3EIHxBSySxjfVIoFGQGhtLhCYTkBoPZ2tlgl0BYTW5gSB6QTsx6az1TIBAvsLLBJaN6/cqLt/1513JPGO6HWh9T/8zX+2fxW0QKBQgfPnzq9GfujwoUIFDJvAdALnz5+vzp+vqsPur+mQtVysQH1v1fdY9/6qv5t96zubPwQIjCVw7tz56tChqjpU/x9/CBAYVcB+bFROjRF4nsA/+qt/j8oeAouE1nv0d7FT14bW/15ovVhRXDh5gVWoVlXVYZuQ5GtZSWqiq2F9d61Ca/dXdLXRofQF1t1f9e1W33f+ECAwnsC5838aWq8WG/4kIWBdmESZ6k7ajyVTKh1NVOAf/aDQep/SCa0H6nk9yEAohxHYQcCvo+2A5VACOwp4PciOYA4nsIOA14PsgOVQAnsKeD3InoBOJ7BBwH7M9CBAIGaBRULr5vUgj33x8Y02177s6mheHyK0jnka61uqAhZJqVZOv1MQEFqnUCV9TFVAaJ1q5fQ7RQGhdYpV0+dUBOzHUqmUfhIoU2DR0Pr0U09Xb3/bm6obrr8uWv2+gP21N7+6Onbb66Lts44RSEXAIimVSulnigJC6xSrps+pCAitU6mUfuYgILTOoYrGEKuA/VisldEvAgRqgUVC64b+gQdPVifu/0j1ypturN5y+7Hq6JEjqkKAQEECFkkFFdtQZxcQWs9O7oIFCQitCyq2oS4uILRevAQ6kLGA/VjGxTU0AhkILBpa137PnjlTveu+49WnTj1a3fb6n6xuveXmDFgNgQCBIQIWSUOUHEMgTEBoHebmLAJDBITWQ5QcQ2AcAaH1OI5aIdAnYD9mXhAgELPA4qF1g1O/M/od73zP6j9jf2VIzAXVNwIpCVgkpVQtfU1NQGidWsX0NyUBoXVK1dLX1AWE1qlXUP9jFrAfi7k6+kaAQDSh9alHHq3uvufeVUV+/ud+trrpFTeqDgECmQtYJGVeYMNbVEBovSi/i2cuILTOvMCGF5WA0DqqcuhMZgL2Y5kV1HAIZCaweGjd/qBDH3CY2ewyHAJbBCySTBEC0wkIraez1TIBobU5QGA+AaH1fNauVJ6A/Vh5NTdiAikJLBpaHz/xoeqhkw/7IMaUZoy+EhhRwCJpRExNEegICK1NCQLTCQitp7PVMoGugNDanCAwnYD92HS2WiZAYH+BRULr5unq00897f3V+9dQCwSSFbBISrZ0Op6AgNA6gSLpYrICQutkS6fjCQoIrRMsmi4nI2A/lkypdJRAkQKLhtaPffHxjejXvuzq6q4776iuuvKKIotj0ARyF7BIyr3CxrekgNB6SX3Xzl1AaJ17hY0vJgGhdUzV0JfcBOzHcquo8RDIS2CR0DovQqMhQCBUwCIpVM55BLYLCK23GzmCQKiA0DpUznkEdhcQWu9u5gwCQwXsx4ZKOY4AgSUEhNZLqLsmAQIrAYskE4HAdAJC6+lstUxAaG0OEJhPQGg9n7UrlSdgP1ZezY2YQEoCQuuUqqWvBDITsEjKrKCGE5WA0DqqcuhMZgJC68wKajhRCwitoy6PziUuYD+WeAF1n0DmAkLrzAtseARiFrBIirk6+pa6gNA69Qrqf8wCQuuYq6NvuQkIrXOrqPHEJGA/FlM19IUAga6A0NqcIEBgMQGLpMXoXbgAAaF1AUU2xMUEhNaL0btwgQJC6wKLbsizCdiPzUbtQgQIBAgIrQPQnEKAwDgCFknjOGqFQJ+A0Nq8IDCdgNB6OlstE+gKCK3NCQLTCdiPTWerZQIE9hcQWu9vqAUCBAIFLJIC4ZxGYICA0HoAkkMIBAoIrQPhnEYgQEBoHYDmFAIDBezHBkI5jACBRQSE1ouwuygBArWARZJ5QGA6AaH1dLZaJiC0NgcIzCcgtJ7P2pXKE7AfK6/mRkwgJQGhdUrV0lcCmQlYJGVWUMOJSkBoHVU5dCYzAaF1ZgU1nKgFhNZRl0fnEhewH0u8gLpPIHMBoXXmBTY8AjELWCTFXB19S11AaJ16BfU/ZgGhdczV0bfcBITWuVXUeGISsB+LqRr6QoBAV0BobU4QILCYgEXSYvQuXICA0LqAIhviYgJC68XoXbhAAaF1gUU35NkE7Mdmo3YhAgQCBITWAWhOIUBgHAGLpHEctUKgT0BobV4QmE5AaD2drZYJdAWE1uYEgekE7Mems9UyAQL7Cwit9zfUAgECgQIWSYFwTiMwQEBoPQDJIQQCBYTWgXBOIxAgILQOQHMKgYEC9mMDoRxGgMAiAkLrRdhdlACBWsAiyTwgMJ2A0Ho6Wy0TEFqbAwTmExBaz2ftSuUJ2I+VV3MjJpCSgNA6pWrpK4HMBCySMiuo4UQlILSOqhw6k5mA0DqzghpO1AJC66jLo3OJC9iPJV5A3SeQuYDQOvMCGx6BmAUskmKujr6lLiC0Tr2C+h+zgNA65uroW24CQuvcKmo8MQnYj8VUDX0hQKArILQ2JwgQWEzAImkxehcuQEBoXUCRDXExAaH1YvQuXKCA0LrAohvybAL2Y7NRuxABAgECQusANKcQIDCOgEXSOI5aIdAnILQ2LwhMJyC0ns5WywS6AkJrc4LAdAL2Y9PZapkAgf0FhNb7G2qBAIFAAYukQDinERggILQegOQQAoECQutAOKcRCBAQWgegOYXAQAH7sYFQDiNAYBEBofUi7C5KgEAtYJFkHhCYTkBoPZ2tlgkIrc0BAvMJCK3ns3al8gTsx8qruRETSElAaJ1StfSVQGYCFkmZFdRwohIQWkdVDp3JTEBonVlBDSdqAaF11OXRucQF7McSL6DuE8hcQGideYENj0DMAhZJMVdH31IXEFqnXkH9j1lAaB1zdfQtNwGhdW4VNZ6YBOzHYqqGvhAg0BUQWpsTBAgsJmCRtBi9CxcgILQuoMiGuJiA0HoxehcuUEBoXWDRDXk2Afux2ahdiACBAAGhdQCaUwgQGEfAImkcR60Q6BMQWpsXBKYTEFpPZ6tlAl0BobU5QWA6Afux6Wy1TIDA/gJC6/0NtUCAQKCARVIgnNMIDBAQWg9AcgiBQAGhdSCc0wgECAitA9CcQmCggP3YQCiHESCwiIDQehF2FyVAoBawSDIPCEwnILSezlbLBITW5gCB+QSE1vNZu1J5AvZj5dXciAmkJCC0Tqla+kogMwGLpMwKajhRCQitoyqHzmQmILTOrKCGE7WA0Drq8uhc4gL2Y4kXUPcJZC4gtM68wIZHIGYBi6SYq6NvqQsIrVOvoP7HLCC0jrk6+pabgNA6t4oaT0wC9mMxVUNfCBDoCgitzQkCBBYTsEhajN6FCxAQWhdQZENcTEBovRi9CxcoILQusOiGPJuA/dhs1C5EgECAgNA6AM0pBAiMI2CRNI6jVgj0CQitzQsC0wkIraez1TKBroDQ2pwgMJ2A/dh0tlomQGB/AaH1/oZaIEAgUMAiKRDOaQQGCAitByA5hECggNA6EM5pBAIEhNYBaE4hMFDAfmwglMMIEFhEQGi9CLuLEiBQC1gkmQcEphMQWk9nq2UCQmtzgMB8AkLr+axdqTwB+7Hyam7EBFISEFqnVC19JZCZgEVSZgU1nKgEhNZRlUNnMhMQWmdWUMOJWkBoHXV5dC5xAfuxxAuo+wQyFxBaZ15gwyMQs4BFUszV0bfUBYTWqVdQ/2MWEFrHXB19y01AaJ1bRY0nJgH7sZiqoS8ECHQFhNbmBAECiwlYJC1G78IFCAitCyiyIS4mILRejN6FCxQQWhdYdEOeTcB+bDZqFyJAIEBAaB2A5hQCBMYRsEgax1ErBPoEhNbmBYHpBITW09lqmUBXQGhtThCYTsB+bDpbLRMgsL+A0Hp/Qy0QIBAoYJEUCOc0AgMEhNYDkBxCIFBAaB0I5zQCAQJC6wA0pxAYKGA/NhDKYQQILCIgtF6E3UUJEKgFLJLMAwLTCQitp7PVMgGhtTlAYD4BofV81q5UnoD9WHk1N2ICKQkUGVqfeuTR6u577l3V6dqXXV3ddecd1VVXXtFbt899/gvVO975nur06acO/n3bOSlNAH0lsKSARdKS+q6du4DQOvcKG9+SAkLrJfVduzQBoXVpFTfeOQXsx+bUdi0CBHYVKC60rkPod993vHrz7ceqG66/rnrgwZPVqU9/pnrL7ceqo0eOPM+ve/yuwI4nQGC9gEWS2UFgOgGh9XS2WiYgtDYHCMwnILSez9qVyhOwHyuv5kZMICWB4kLrOqT+48e+VB277XWrOm0Lpbf9e0rF1lcCsQlYJMVWEf3JSUBonVM1jSU2AaF1bBXRn5wFhNY5V9fYlhawH1u6Aq5PgMAmgeJC6+MnPrTyaELrrz75teoXfvGXqtt++ieqm15x4/Osuq8H8WoQNxSB8QQsksaz1BKBroDQ2pwgMJ2A0Ho6Wy0T6AoIrc0JAtMJ2I9NZ6tlAgT2FygytP72a6+pbr3l5pXettC6S1yH3l954omD14k88+xz+1dBCwQKFfjGN86uRn7xxS8oVCCfYR+uDlXnqvP5DCiDkZz95rnq7Pnz1SXurwyqaQixCZw7d76qv4cdOXLRBV17waHD1dnz52Lrrv4QSFrguefOVocPH6ouuuhw0uMoqfOHDx2qzp23Lkyh5vZjKVRJH1MWeOHRS1Lu/uJ9LzK0rtWHPmndrVD95PX7PvDh6q1vfMPqwxvrn0z6Q4BAmED9pFq9nL3kIqF1mGA8Z9WbyTrE8ScegW+ePVudPXe+OnLxhaFaPD3UEwLpCpw9d66qv4cdveTiC0PrFxyuzp4VWqdbWT2PUeC5b3zzW6H1C6wXY6xPX5+sC1OpVLX6XmY/lk699DQ9gRdeKrTep2rFhda7vtN6W2i9D75zCZQu4NfRSp8Bxj+lgNeDTKmr7dIFvB6k9Blg/HMKeD3InNquVZqA/VhpFTdeAmkJFBdadz9YsQ6xT336Mwev+6j/+8GPfby66847Vk9S/+bDv1X9uRv+bHXD9detKtt9J3Za5dZbAnEJWCTFVQ+9yUtAaJ1XPY0mLgGhdVz10Ju8BYTWedfX6JYVsB9b1t/VCRDYLFBcaF1znHrk0erue+5dyXQ/WLEbWrePrY9/5U03HgTcJhcBAvsJWCTt5+dsApsEhNbmB4HpBITW09lqmUBXQGhtThCYTsB+bDpbLRMgsL9AkaH1/mxaIEBgDAGLpDEUtUGgX0BobWYQmE5AaD2drZYJCK3NAQLzCdiPzWftSgQI7C4gtN7dzBkECIwkYJE0EqRmCPQICK1NCwLTCQitp7PVMgGhtTlAYD4B+7H5rF2JAIHdBYTWu5s5gwCBkQQskkaC1AwBobU5QGBWAaH1rNwuVriA14MUPgEMf1IB+7FJeTVOgMCeAkLrPQGdToBAuIBFUridMwlsE/Ck9TYh/04gXEBoHW7nTAK7CgitdxVzPIHhAvZjw60cSYDA/AJC6/nNXZEAgT8VsEgyFQhMJyC0ns5WywSE1uYAgfkEhNbzWbtSeQL2Y+XV3IgJpCQgtE6pWvpKIDMBi6TMCmo4UQkIraMqh85kJiC0zqyghhO1gNA66vLoXOIC9mOJF1D3CWQuILTOvMCGRyBmAYukmKujb6kLCK1Tr6D+xywgtI65OvqWm4DQOreKGk9MAvZjMVVDXwgQ6AoIrc0JAgQWE7BIWozehQsQEFoXUGRDXExAaL0YvQsXKCC0LrDohjybgP3YbNQuRIBAgIDQOgDNKQQIjCNgkTSOo1YI9AkIrc0LAtMJCK2ns9Uyga6A0NqcIDCdgP3YdLZaJkBgfwGh9f6GWiBAIFDAIikQzmkEBggIrQcgOYRAoIDQOhDOaQQCBITWAWhOITBQwH5sIJTDCBBYREBovQi7ixIgUAtYJJkHBKYTEFpPZ6tlAkJrc4DAfAJC6/msXak8Afux8mpuxARSEhBap1QtfSWQmYBFUmYFNZyoBITWUZVDZzITEFpnVlDDiVpAaB11eXQucQH7scQLqPsEMhcQWmdeYMMjELOARVLM1dG31AWE1qlXUP9jFhBax1wdfctNQGidW0WNJyYB+7GYqqEvBAh0BYTW5gQBAosJWCQtRu/CBQgIrQsosiEuJiC0XozehQsUEFoXWHRDnk3Afmw2ahciQCBAQGgdgOYUAgTGEbBIGsdRKwT6BITW5gWB6QSE1tPZaplAV0BobU4QmE7Afmw6Wy0TILC/gNB6f0MtECAQKGCRFAjnNAIDBITWA5AcQiBQQGgdCOc0AgECQusANKcQGChgPzYQymEECCwiILRehN1FCea109cAABnPSURBVBCoBSySzAMC0wkIraez1TIBobU5QGA+AaH1fNauVJ6A/Vh5NTdiAikJCK1Tqpa+EshMwCIps4IaTlQCQuuoyqEzmQkIrTMrqOFELSC0jro8Ope4gP1Y4gXUfQKZCwitMy+w4RGIWcAiKebq6FvqAkLr1Cuo/zELCK1jro6+5SYgtM6tosYTk4D9WEzV0BcCBLoCQmtzggCBxQQskhajd+ECBITWBRTZEBcTEFovRu/CBQoIrQssuiHPJmA/Nhu1CxEgECAgtA5AcwoBAuMIWCSN46gVAn0CQmvzgsB0AkLr6Wy1TKArILQ2JwhMJ2A/Np2tlgkQ2F9AaL2/oRYIEAgUsEgKhHMagQECQusBSA4hECggtA6EcxqBAAGhdQCaUwgMFLAfGwjlMAIEFhEQWi/C7qIECNQCFknmAYHpBITW09lqmYDQ2hwgMJ+A0Ho+a1cqT8B+rLyaGzGBlASE1ilVS18JZCZgkZRZQQ0nKgGhdVTl0JnMBITWmRXUcKIWEFpHXR6dS1zAfizxAuo+gcwFhNaZF9jwCMQsYJEUc3X0LXUBoXXqFdT/mAWE1jFXR99yExBa51ZR44lJwH4spmroCwECXQGhtTlBgMBiAhZJi9G7cAECQusCimyIiwkIrRejd+ECBYTWBRbdkGcTsB+bjdqFCBAIEBBaB6A5hQCBcQQsksZx1AqBPgGhtXlBYDoBofV0tlom0BUQWpsTBKYTsB+bzlbLBAjsLyC03t9QCwQIBApYJAXCOY3AAAGh9QAkhxAIFBBaB8I5jUCAgNA6AM0pBAYK2I8NhHIYAQKLCAitF2F3UQIEagGLJPOAwHQCQuvpbLVMQGhtDhCYT0BoPZ+1K5UnYD9WXs2NmEBKAkLrlKqlrwQyE7BIyqyghhOVgNA6qnLoTGYCQuvMCmo4UQsIraMuj84lLmA/lngBdZ9A5gJC68wLbHgEYhawSIq5OvqWuoDQOvUK6n/MAkLrmKujb7kJCK1zq6jxxCRgPxZTNfSFAIGugNDanCBAYDEBi6TF6F24AAGhdQFFNsTFBITWi9G7cIECQusCi27IswnYj81G7UIECAQICK0D0JxCgMA4AhZJ4zhqhUCfgNDavCAwnYDQejpbLRPoCgitzQkC0wnYj01nq2UCBPYXEFrvb6gFAgQCBSySAuGcRmCAgNB6AJJDCAQKCK0D4ZxGIEBAaB2A5hQCAwXsxwZCOYwAgUUEhNaLsLsoAQK1gEWSeUBgOgGh9XS2WiYgtDYHCMwnILSez9qVyhOwHyuv5kZMICUBoXVK1dJXApkJWCRlVlDDiUpAaB1VOXQmMwGhdWYFNZyoBYTWUZdH5xIXsB9LvIC6TyBzAaF15gU2PAIxC1gkxVwdfUtdQGidegX1P2YBoXXM1dG33ASE1rlV1HhiErAfi6ka+kKAQFdAaG1OECCwmIBF0mL0LlyAgNC6gCIb4mICQuvF6F24QAGhdYFFN+TZBOzHZqN2IQIEAgSE1gFoTiFAYBwBi6RxHLVCoE9AaG1eEJhOQGg9na2WCXQFhNbmBIHpBOzHprPVMgEC+wsIrfc31AIBAoECFkmBcE4jMEBAaD0AySEEAgWE1oFwTiMQICC0DkBzCoGBAvZjA6EcRoDAIgJC60XYXZQAgVrAIsk8IDCdgNB6OlstExBamwME5hMQWs9n7UrlCdiPlVdzIyaQkoDQOqVq6SuBzAQskjIrqOFEJSC0jqocOpOZgNA6s4IaTtQCQuuoy6NziQvYjyVeQN0nkLmA0DrzAhsegZgFLJJiro6+pS4gtE69gvofs4DQOubq6FtuAkLr3CpqPDEJ2I/FVA19IUCgKyC0NicIEFhMwCJpMXoXLkBAaF1AkQ1xMQGh9WL0LlyggNC6wKIb8mwC9mOzUbsQAQIBAkLrADSnECAwjoBF0jiOWiHQJyC0Ni8ITCcgtJ7OVssEugJCa3OCwHQC9mPT2WqZAIH9BYTW+xtqgQCBQAGLpEA4pxEYICC0HoDkEAKBAkLrQDinEQgQEFoHoDmFwEAB+7GBUA4jQGARAaH1APYHHjxZnbj/I6sjX3nTjdVbbj9WHT1yZMCZDiFAYJOARZL5QWA6AaH1dLZaJiC0NgcIzCcgtJ7P2pXKE7AfK6/mRkwgJQGh9ZZqnXrk0erEBz9a3XXnHdVVV15RHT/xodUZx257XUp11lcCUQpYJEVZFp3KREBonUkhDSNKAaF1lGXRqUwFhNaZFtawohCwH4uiDDpBgMAaAaH1lqlRh9Tffu011a233Lw6shtim1kECIQLWCSF2zmTwDYBofU2If9OIFxAaB1u50wCuwoIrXcVczyB4QL2Y8OtHEmAwPwCQusN5s+eOVO9677j1U1/8eUHofXnPv+F6t33Ha/efPux6obrr5u/Yq5IICMBi6SMimko0QkIraMriQ5lJCC0zqiYhhK9gNA6+hLpYMIC9mMJF0/XCRQgILQeEFrf+tofrG56xY2rI7uh9Vf+21MFTBNDJECAAAECBAgQIECAAAECBAgQIEBgqMBL/sxlQw91XI+A0HpAaL3pSWuhtfuKAAECBAgQIECAAAECBAgQIECAAIG2gNB6v/kgtN7i553W+00wZxMgQIAAAQIECBAgQIAAAQIECBAgQGAXAaH1Fq3uBy/WIXb959htr9vF2bEECBAgQIAAAQIECBAgQIAAAQIECBAgMEBAaD0A6YEHT1Yn7v/I6shX3nRj9Zbbj1VHjxwZcKZDCBAgQIAAAQIEUhDoPqiQQp/1kQABAgQIECBAgECuAlmG1vWHJb7jne+pTp9+arKQuX7i+qGTDx/Mi9te/5PVrbfcfPDf9cbn7nvuXf33tS+7urrrzjuqq668YvXf7X/rC8K7bf/8z/3swQdB5joRjSsegWZ+dud088Ob7t/v0/Nt99FXn/xa9Qu/+EvVY198fHWZ9r2w7T5q/7CpPnfMfu8zZueWKzDnvdUob7tPNt1j7e+lfd/L3GPlzuUURv7smTPVu+47Xn3q1KPV5ZdfVr39bW+qbrj+uo1d3xZab7uf2tfsft/Zdj9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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",
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      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_21988\\1493830805.py:1: FutureWarning:\n",
      "\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": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', '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": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-01-01 00:00:00+0100', tz='Europe/Amsterdam')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data['tsource_vdg'].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.storage_case_PCM.data.loc['2019-01-04 07:45:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " 0,\n",
       " 29.635,\n",
       " 0,\n",
       " 0,\n",
       " HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=0.0),\n",
       " Heatpump(name='Heatpump PCM', max_thermal_power=1.5, cop_curve=2.9, min_th_power=0.0))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case = s.storage_case_PCM\n",
    "hp_vdg = case.assets['Heatpump PCM']\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": 93,
   "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": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 94,
     "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": 95,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "test_hp = Heatpump(\n",
    "    name='Heatpump for Testing',\n",
    "    max_th_power=1,\n",
    "    min_th_power=0.3,\n",
    "    cop_curve=2.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": 97,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "date = s.baseline.data.index[0].strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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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    "fig_steamboiler = s.baseline.data[['Total demand', 'output_MW_th']].abs().loc[date, :].iplot(\n",
    "    subplots=True, \n",
    "    title=f'Steamboiler only case on {date}',\n",
    "    subplot_titles=['Total demand', 'Steam boiler output (MW)'],\n",
    "    legend=False,\n",
    "    dimensions=(800, 400),\n",
    "    colors=[recoydarkblue, recoygreen], \n",
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     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
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   "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",
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     "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"
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Heatpump only</th>\n",
       "      <th>Heatpump + Storage_PCM</th>\n",
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       "      <th>Total net volume (MWh)</th>\n",
       "      <td>-3,022</td>\n",
       "      <td>-3,289</td>\n",
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       "      <th>Total exported to grid (MWh)</th>\n",
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       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total consumed from grid (MWh)</th>\n",
       "      <td>3,022</td>\n",
       "      <td>3,289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total nominated volume (MWh)</th>\n",
       "      <td>-3,022</td>\n",
       "      <td>-3,022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Absolute imbalance volume (MWh)</th>\n",
       "      <td>-</td>\n",
       "      <td>1,109</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>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total day-ahead result (€)</th>\n",
       "      <td>-289,579</td>\n",
       "      <td>-289,579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total POS result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>114,467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total NEG result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-10,793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total imbalance result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>103,675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total combined result (€)</th>\n",
       "      <td>-289,579</td>\n",
       "      <td>-185,904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Heatpump only Heatpump + Storage_PCM\n",
       "Total net volume (MWh)                 -3,022                 -3,289\n",
       "Total exported to grid (MWh)                -                      -\n",
       "Total consumed from grid (MWh)          3,022                  3,289\n",
       "Total nominated volume (MWh)           -3,022                 -3,022\n",
       "Absolute imbalance volume (MWh)             -                  1,109\n",
       "Mean selling price (€/MWh)                  -                      -\n",
       "Mean buying price (€/MWh)                  96                     57\n",
       "Total day-ahead result (€)           -289,579               -289,579\n",
       "Total POS result (€)                        -                114,467\n",
       "Total NEG result (€)                        -                -10,793\n",
       "Total imbalance result (€)                  -                103,675\n",
       "Total combined result (€)            -289,579               -185,904"
      ]
     },
     "execution_count": 100,
     "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": 101,
   "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>-46,080</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Result on electricity market (€)</th>\n",
       "      <td>-289,579</td>\n",
       "      <td>-185,904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electricity taxes (€)</th>\n",
       "      <td>-2,871</td>\n",
       "      <td>-3,125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grid transport costs (€)</th>\n",
       "      <td>-28,883</td>\n",
       "      <td>-50,256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heatpump PCM OPEX (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-69,120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HotWaterStorage OPEX (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-9,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA (€)</th>\n",
       "      <td>201,192</td>\n",
       "      <td>251,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciation (€)</th>\n",
       "      <td>-46,080</td>\n",
       "      <td>-81,120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA + depr (€)</th>\n",
       "      <td>155,112</td>\n",
       "      <td>170,080</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 (€)                  -46,080                      -\n",
       "Result on electricity market (€)      -289,579               -185,904\n",
       "Electricity taxes (€)                   -2,871                 -3,125\n",
       "Grid transport costs (€)               -28,883                -50,256\n",
       "Heatpump PCM OPEX (€)                        -                -69,120\n",
       "HotWaterStorage OPEX (€)                     -                 -9,000\n",
       "EBITDA (€)                             201,192                251,200\n",
       "Depreciation (€)                       -46,080                -81,120\n",
       "EBITDA + depr (€)                      155,112                170,080"
      ]
     },
     "execution_count": 101,
     "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": 102,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\styling.py:42: FutureWarning:\n",
      "\n",
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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>-1,157,760</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>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</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>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "      <td>201,192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciations (€) -/-</th>\n",
       "      <td></td>\n",
       "      <td>-46,080</td>\n",
       "      <td>-46,080</td>\n",
       "      <td>-46,080</td>\n",
       "      <td>-46,080</td>\n",
       "      <td>-46,080</td>\n",
       "      <td>-46,080</td>\n",
       "      <td>-46,080</td>\n",
       "      <td>-46,080</td>\n",
       "      <td>-46,080</td>\n",
       "      <td>-46,080</td>\n",
       "      <td>-46,080</td>\n",
       "      <td>-46,080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBIT (€)</th>\n",
       "      <td></td>\n",
       "      <td>155,112</td>\n",
       "      <td>155,112</td>\n",
       "      <td>155,112</td>\n",
       "      <td>155,112</td>\n",
       "      <td>155,112</td>\n",
       "      <td>155,112</td>\n",
       "      <td>155,112</td>\n",
       "      <td>155,112</td>\n",
       "      <td>155,112</td>\n",
       "      <td>155,112</td>\n",
       "      <td>155,112</td>\n",
       "      <td>155,112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income tax (Vpb.) (€)</th>\n",
       "      <td></td>\n",
       "      <td>-23,267</td>\n",
       "      <td>-23,267</td>\n",
       "      <td>-23,267</td>\n",
       "      <td>-23,267</td>\n",
       "      <td>-23,267</td>\n",
       "      <td>-23,267</td>\n",
       "      <td>-23,267</td>\n",
       "      <td>-23,267</td>\n",
       "      <td>-23,267</td>\n",
       "      <td>-23,267</td>\n",
       "      <td>-23,267</td>\n",
       "      <td>-23,267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOPLAT (€)</th>\n",
       "      <td></td>\n",
       "      <td>131,845</td>\n",
       "      <td>131,845</td>\n",
       "      <td>131,845</td>\n",
       "      <td>131,845</td>\n",
       "      <td>131,845</td>\n",
       "      <td>131,845</td>\n",
       "      <td>131,845</td>\n",
       "      <td>131,845</td>\n",
       "      <td>131,845</td>\n",
       "      <td>131,845</td>\n",
       "      <td>131,845</td>\n",
       "      <td>131,845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciations (€) +/+</th>\n",
       "      <td></td>\n",
       "      <td>46,080</td>\n",
       "      <td>46,080</td>\n",
       "      <td>46,080</td>\n",
       "      <td>46,080</td>\n",
       "      <td>46,080</td>\n",
       "      <td>46,080</td>\n",
       "      <td>46,080</td>\n",
       "      <td>46,080</td>\n",
       "      <td>46,080</td>\n",
       "      <td>46,080</td>\n",
       "      <td>46,080</td>\n",
       "      <td>46,080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free Cash Flow (€)</th>\n",
       "      <td>-1,157,760</td>\n",
       "      <td>177,925</td>\n",
       "      <td>177,925</td>\n",
       "      <td>177,925</td>\n",
       "      <td>177,925</td>\n",
       "      <td>177,925</td>\n",
       "      <td>177,925</td>\n",
       "      <td>177,925</td>\n",
       "      <td>177,925</td>\n",
       "      <td>177,925</td>\n",
       "      <td>177,925</td>\n",
       "      <td>177,925</td>\n",
       "      <td>177,925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simple Payback Period</th>\n",
       "      <td>6.507001039280803 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>11%</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>49,607</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>192,708</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>49,607</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 (€)                                   -1,157,760                     \n",
       "Regular Earnings (€)                                    201,192  201,192   \n",
       "Irregular Cashflows (€)                                       -        -   \n",
       "EBITDA (€)                                              201,192  201,192   \n",
       "Depreciations (€) -/-                                   -46,080  -46,080   \n",
       "EBIT (€)                                                155,112  155,112   \n",
       "Income tax (Vpb.) (€)                                   -23,267  -23,267   \n",
       "NOPLAT (€)                                              131,845  131,845   \n",
       "Depreciations (€) +/+                                    46,080   46,080   \n",
       "Free Cash Flow (€)                          -1,157,760  177,925  177,925   \n",
       "Simple Payback Period          6.507001039280803 years                     \n",
       "IRR (%)                                            11%                     \n",
       "WACC (%)                                           10%                     \n",
       "NPV of explicit period (€)                      49,607                     \n",
       "Discounted residual value (€)                  192,708                     \n",
       "NPV (€)                                         49,607                     \n",
       "\n",
       "                                Year 3   Year 4   Year 5   Year 6   Year 7  \\\n",
       "CAPEX (€)                                                                    \n",
       "Regular Earnings (€)           201,192  201,192  201,192  201,192  201,192   \n",
       "Irregular Cashflows (€)              -        -        -        -        -   \n",
       "EBITDA (€)                     201,192  201,192  201,192  201,192  201,192   \n",
       "Depreciations (€) -/-          -46,080  -46,080  -46,080  -46,080  -46,080   \n",
       "EBIT (€)                       155,112  155,112  155,112  155,112  155,112   \n",
       "Income tax (Vpb.) (€)          -23,267  -23,267  -23,267  -23,267  -23,267   \n",
       "NOPLAT (€)                     131,845  131,845  131,845  131,845  131,845   \n",
       "Depreciations (€) +/+           46,080   46,080   46,080   46,080   46,080   \n",
       "Free Cash Flow (€)             177,925  177,925  177,925  177,925  177,925   \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 (€)           201,192  201,192  201,192  201,192  201,192  \n",
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       "EBITDA (€)                     201,192  201,192  201,192  201,192  201,192  \n",
       "Depreciations (€) -/-          -46,080  -46,080  -46,080  -46,080  -46,080  \n",
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       "Income tax (Vpb.) (€)          -23,267  -23,267  -23,267  -23,267  -23,267  \n",
       "NOPLAT (€)                     131,845  131,845  131,845  131,845  131,845  \n",
       "Depreciations (€) +/+           46,080   46,080   46,080   46,080   46,080  \n",
       "Free Cash Flow (€)             177,925  177,925  177,925  177,925  177,925  \n",
       "Simple Payback Period                                                       \n",
       "IRR (%)                                                                     \n",
       "WACC (%)                                                                    \n",
       "NPV of explicit period (€)                                                  \n",
       "Discounted residual value (€)                                               \n",
       "NPV (€)                                                                     "
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       "                                              Year 0   Year 1   Year 2  \\\n",
       "CAPEX (€)                                 -2,036,640                     \n",
       "Regular Earnings (€)                                  251,200  251,200   \n",
       "Irregular Cashflows (€)                                     -        -   \n",
       "EBITDA (€)                                            251,200  251,200   \n",
       "Depreciations (€) -/-                                 -81,120  -81,120   \n",
       "EBIT (€)                                              170,080  170,080   \n",
       "Income tax (Vpb.) (€)                                 -25,512  -25,512   \n",
       "NOPLAT (€)                                            144,568  144,568   \n",
       "Depreciations (€) +/+                                  81,120   81,120   \n",
       "Free Cash Flow (€)                        -2,036,640  225,688  225,688   \n",
       "Simple Payback Period          9.0241276072978 years                     \n",
       "IRR (%)                                           5%                     \n",
       "WACC (%)                                         10%                     \n",
       "NPV of explicit period (€)                  -453,518                     \n",
       "Discounted residual value (€)                338,768                     \n",
       "NPV (€)                                     -453,518                     \n",
       "\n",
       "                                Year 3   Year 4   Year 5   Year 6   Year 7  \\\n",
       "CAPEX (€)                                                                    \n",
       "Regular Earnings (€)           251,200  251,200  251,200  251,200  251,200   \n",
       "Irregular Cashflows (€)              -        -        -        -        -   \n",
       "EBITDA (€)                     251,200  251,200  251,200  251,200  251,200   \n",
       "Depreciations (€) -/-          -81,120  -81,120  -81,120  -81,120  -81,120   \n",
       "EBIT (€)                       170,080  170,080  170,080  170,080  170,080   \n",
       "Income tax (Vpb.) (€)          -25,512  -25,512  -25,512  -25,512  -25,512   \n",
       "NOPLAT (€)                     144,568  144,568  144,568  144,568  144,568   \n",
       "Depreciations (€) +/+           81,120   81,120   81,120   81,120   81,120   \n",
       "Free Cash Flow (€)             225,688  225,688  225,688  225,688  225,688   \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 (€)           251,200  251,200  251,200  251,200  251,200  \n",
       "Irregular Cashflows (€)              -        -        -        -        -  \n",
       "EBITDA (€)                     251,200  251,200  251,200  251,200  251,200  \n",
       "Depreciations (€) -/-          -81,120  -81,120  -81,120  -81,120  -81,120  \n",
       "EBIT (€)                       170,080  170,080  170,080  170,080  170,080  \n",
       "Income tax (Vpb.) (€)          -25,512  -25,512  -25,512  -25,512  -25,512  \n",
       "NOPLAT (€)                     144,568  144,568  144,568  144,568  144,568  \n",
       "Depreciations (€) +/+           81,120   81,120   81,120   81,120   81,120  \n",
       "Free Cash Flow (€)             225,688  225,688  225,688  225,688  225,688  \n",
       "Simple Payback Period                                                       \n",
       "IRR (%)                                                                     \n",
       "WACC (%)                                                                    \n",
       "NPV of explicit period (€)                                                  \n",
       "Discounted residual value (€)                                               \n",
       "NPV (€)                                                                     "
      ]
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     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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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": 105,
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     "exclude"
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    "# #price chart\n",
    "# from copy import deepcopy\n",
    "\n",
    "# s.optcase1.data['DAM'].resample('D').mean().iplot(dimensions=(800, 300), title='Electricity Prices', colors=recoycolors)\n",
    "\n",
    "# _source_output = s.optcase1.data[['hp_output_MWh', 'gb_output_MWh']].resample('M').sum()\n",
    "# _total_output = _source_output.sum(axis=1)\n",
    "# _data = _source_output.divide(_total_output, axis=0).rename(\n",
    "#     columns={'hp_output_MWh':'Heat pump', 'gb_output_MWh':'Gasboiler'}\n",
    "# ) * 100\n",
    "\n",
    "# production_fig = _data.iplot(\n",
    "#     kind='bar',\n",
    "#     barmode='stack',\n",
    "#     colors=[recoydarkblue, recoygreen],\n",
    "#     title='Hybrid case: Heat production per Month by Source in % share',\n",
    "#     yTitle='Share of production in %',\n",
    "#     dimensions=(600, 400),\n",
    "#     asFigure=True\n",
    "# )\n",
    "\n",
    "# production_fig = production_fig.update_layout(legend_traceorder=\"reversed\")\n",
    "# production_fig"
   ]
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   "execution_count": 106,
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   "source": [
    "report = ComparisonReport(s.cases, kind='capex').report\n",
    "report = report[report.index.str.contains('CAPEX')].T\n",
    "capex_fig = report.iplot(\n",
    "    kind='bar', \n",
    "    barmode='relative',\n",
    "    colors=recoycolors,\n",
    "    title='CAPEX by Casestudy',\n",
    "    yTitle='CAPEX in €',\n",
    "    dimensions=(600, 400),\n",
    "    asFigure=True,\n",
    ")\n",
    "\n",
    "capex_fig = capex_fig.update_layout(legend_traceorder=\"reversed\")\n",
    "capex_fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('4da0d64a-afab-4cae-844f-da3027aedc12');\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": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ebitda_report.show(comparison='relative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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          "heatmap": [
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N66npnEiAAAECBAgQIECAAAECBAgQIECAAAEC0QKa1tGi4hEgQIAAAQIECBAgQIAAAQIECBAgQIDA1AKa1lPTOZEAAQIECBAgQIAAAQIECBAgQIAAAQIEogU0raNFxSNAgAABAgQIECBAgAABAgQIECBAgACBqQU0raemcyIBAgQIECBAgAABAgQIECBAgAABAgQIRAtoWkeLikeAAAECBAgQIECAAAECBAgQIECAAAECUwtoWk9N50QCBAgQIECAAAECBAgQIECAAAECBAgQiBbQtI4WFY8AAQIECBAgQIAAAQIECBAgQIAAAQIEphbQtJ6azokECBAgQIAAAQIECBAgQIAAAQIECBAgEC2gaR0tKh4BAgQIECBAgAABAgQIECBAgAABAgQITC2gaT01nRMJECBAgAABAgQIECBAgAABAgQIECBAIFpA0zpaVDwCBAgQIECAAAECBAgQIECAAAECBAgQmFpA03pqOicSIECAAAECBAgQIECAAAECBAgQIECAQLSApnW0qHgECBAgQIAAAQIECBAgQIAAAQIECBAgMLWApvXUdE4kQIAAAQIECBAgQIAAAQIECBAgQIAAgWgBTetoUfEIECBAgAABAgQIECBAgAABAgQIECBAYGoBTeup6ZxIgAABAgQIECBAgAABAgQIECBAgAABAtECmtbRouIRIECAAAECBAgQIECAAAECBAgQIECAwNQCmtZT0zmRAAECBAgQIECAAAECBAgQIECAAAECBKIFNK2jRcUjQIAAAQIECBAgQIAAAQIECBAgQIAAgakFNK2npnMiAQIECBAgQIAAAQIECBAgQIAAAQIECEQLaFpHi4pHgAABAgQIECBAgAABAgQIECBAgAABAlMLaFpPTedEAgQIECBAgAABAgQIECBAgAABAgQIEIgW0LSOFhWPAAECBAgQIECAAAECBAgQIECAAAECBKYW0LSems6JBAgQIECAAAECBAgQIECAAAECBAgQIBAtoGkdLSoeAQIECBAgQIAAAQIECBAgQIAAAQIECEwtoGk9NZ0TCRAgQIAAAQIECBAgQIAAAQIECBAgQCBaQNM6WlQ8AgQIECBAgAABAgQIECBAgAABAgQIEJhaQNN6ajonEiBAgAABAgQIECBAgAABAgQIECBAgEC0gKZ1tKh4BAgQIECAAAECBAgQIECAAAECBAgQIDC1gKb11HROJECAAAECBAgQIECAAAECBAgQIECAAIFoAU3raFHxCBAgQIAAAQIECBAgQIAAAQIECBAgQGBqAU3rqemcSIAAAQIECBAgQIAAAQIECBAgQIAAAQLRAprW0aLiESBAgAABAgQIECBAgAABAgQIECBAgMDUAprWU9M5kQABAgQIECBAgAABAgQIECBAgAABAgSiBTSto0XFI0CAAAECBAgQIECAAAECBAgQIECAAIGpBTStp6ZzIgECBAgQIECAAAECBAgQIECAAAECBAhEC2haR4uKR4AAAQIECBAgQIAAAQIECBAgQIAAAQJTC2haT03nRAIECBAgQIAAAQIECBAgQIAAAQIECBCIFtC0nkD0iis3pg0XX9o7cv81+6Xjj1mXli9bNsGZDiFAgAABAgQIECBAgAABAgQIECBAgACBxQhoWi+gdc03rk0bPvKxdMrJJ6QdVmyf1m/4aO+MdWtftRhnxxIgQIAAAQIECBAgQIAAAQIECBAgQIDABAKa1gsgFU3qPVbulg495KDekYNN7AmMHUKAAAECBAgQIECAAAECBAgQIECAAAECEwpoWo+Buv+BB9LZF6xPa56871zTetMNN6ZzLlifjjtmXVq9as8JmR1GgAABAgQIECBAgAABAgQIECBAgAABApMIaFpP0LQ+9OAXpjVP2a935GDT+rYf3j2Js2MIECBAgAABAgQIECBAgAABAgQIEOiIwM47btuRmeaZpqb1BE3rcW9aN6Vp/bmbr0mXf/dLaYdHbp3nThKVAAECHRbY8uN702/v9dx04O5P7bBC/NTVrnhTEQkQIFAKqF157gW1K4+rqAQIECgEmla7NK1nu281rRfwa8ue1p/+7r+kK67/57TPjnvMdsc4m0BFAvc/8OO0fNkjKrqayxCYTeC6229Mhz3+OemQvZ4xWyBnzxNQu9wQTRNQu5qWsW6PV+3Kk3+1K4+rqPkE1K58tiLHC6hd8aZ1jqhpvUB2Bj94sWhiF1/r1r6qznl9yNgsnhqVLoNNKVk8uQ2aJGDxlCdbalceV1HzCahd+WxFjhdQu+JNi4hqVx5XUfMJqF35bEWOF1C74k3rHFHTeoLsXHHlxrTh4kt7R+6/Zr90/DHr0vJlyyY4sz6HWDzVJxdGMpmAxdNkTo6qh4DFU548qF15XEXNJ6B25bMVOV5A7Yo31bTOYypqXgG1K6+v6LECalesZ92jaVrXPUNB4/PgHwQpTGUCFk+VUbtQgIDFUwDikBBqVx5XUfMJqF35bEWOF1C74k01rfOYippXQO3K6yt6rIDaFetZ92ia1nXPUND4PPgHQQpTmYDFU2XULhQgYPEUgKhpnQdR1EoF1K5KuV1sRgG1a0bAEad77srjKmo+AbUrn63I8QJqV7xpnSNqWtc5O4Fjs3gKxBSqEgGLp0qYXSRIwOIpCHIgjNqVx1XUfAJqVz5bkeMF1K540yKi2pXHVdR8AmpXPluR4wXUrnjTOkfUtK5zdgLHZvEUiClUJQIWT5Uwu0iQgMVTEKSmdR5IUSsTULsqo3ahAAG1KwBxSAjPXXlcRc0noHblsxU5XkDtijetc0RN6zpnJ3BsFk+BmEJVImDxVAmziwQJWDwFQWpa54EUtTIBtasyahcKEFC7AhA1rfMgilqpgNpVKbeLzSigds0I2LDTNa0blrBph6tpPa2c85ZKwOJpqeRddxoBi6dp1BY+R+1a2MgR9RJQu+qVD6MZL6B25blD1K48rqLmE1C78tmKHC+gdsWb1jmipnWdsxM4NounQEyhKhGweKqE2UWCBCyegiAHwqhdeVxFzSegduWzFTleQO2KNy0iql15XEXNJ6B25bMVOV5A7Yo3rXNETes6ZydwbBZPgZhCVSJg8VQJs4sECVg8BUFqWueBFLUyAbWrMmoXChBQuwIQh4Tw3JXHVdR8AmpXPluR4wXUrnjTOkfUtK5zdgLHZvEUiClUJQIWT5Uwu0iQgMVTEKSmdR5IUSsTULsqo3ahAAG1KwBR0zoPoqiVCqhdlXK72IwCateMgA07XdO6YQmbdria1tPKOW+pBCyelkredacRsHiaRm3hc9SuhY0cUS8Btate+TCa8QJqV547RO3K4ypqPgG1K5+tyPECale8aZ0jalrXOTuBY7N4CsQUqhIBi6dKmF0kSMDiKQhyIIzalcdV1HwCalc+W5HjBdSueNMiotqVx1XUfAJqVz5bkeMF1K540zpH1LSuc3YCx2bxFIgpVCUCFk+VMLtIkIDFUxCkpnUeSFErE1C7KqN2oQABtSsAcUgIz115XEXNJ6B25bMVOV5A7Yo3rXNETes6ZydwbBZPgZhCVSJg8VQJs4sECVg8BUFqWueBFLUyAbWrMmoXChBQuwIQNa3zIIpaqYDaVSm3i80ooHbNCNiw0zWtG5awaYeraT2tnPOWSsDiaankXXcaAYunadQWPkftWtjIEfUSULvqlQ+jGS+gduW5Q9SuPK6i5hNQu/LZihwvoHbFm9Y5oqZ1nbMTODaLp0BMoSoRsHiqhNlFggQsnoIgB8KoXXlcRc0noHblsxU5XkDtijctIqpdeVxFzSegduWzFTleQO2KN61zRE3rOmcncGwWT4GYQlUiYPFUCbOLBAlYPAVBalrngRS1MgG1qzJqFwoQULsCEIeE8NyVx1XUfAJqVz5bkeMF1K540zpH1LSuc3YCx2bxFIgpVCUCFk+VMLtIkIDFUxCkpnUeSFErE1C7KqN2oQABtSsAUdM6D6KolQqoXZVyu9iMAmrXjIANO13TumEJm3a4mtbTyjlvqQQsnpZK3nWnEbB4mkZt4XPUroWNHFEvAbWrXvkwmvECaleeO0TtyuMqaj4BtSufrcjxAmpXvGmdI2pa1zk7gWOzeArEFKoSAYunSphdJEjA4ikIciCM2pXHVdR8AmpXPluR4wXUrnjTIqLalcdV1HwCalc+W5HjBdSueNM6R9S0rnN2Asdm8RSIKVQlAhZPlTC7SJCAxVMQpKZ1HkhRKxNQuyqjdqEAAbUrAHFICM9deVxFzSegduWzFTleQO2KN61zRE3rOmcncGwWT4GYQlUiYPFUCbOLBAlYPAVBalrngRS1MgG1qzJqFwoQULsCEDWt8yCKWqmA2lUpt4vNKKB2zQjYsNM1rRuWsGmHq2k9rZzzlkrA4mmp5F13GgGLp2nUFj5H7VrYyBH1ElC76pUPoxkvoHbluUPUrjyuouYTULvy2YocL6B2xZvWOaKmdZ2zEzg2i6dATKEqEbB4qoTZRYIELJ6CIAfCqF15XEXNJ6B25bMVOV5A7Yo3LSKqXXlcRc0noHblsxU5XkDtijetc0RN6zpnJ3BsFk+BmEJVImDxVAmziwQJWDwFQWpa54EUtTIBtasyahcKEFC7AhCHhPDclcdV1HwCalc+W5HjBdSueNM6R9S0rnN2Asdm8RSIKVQlAhZPlTC7SJCAxVMQpKZ1HkhRKxNQuyqjdqEAAbUrAFHTOg+iqJUKqF2VcrvYjAJq14yADTtd07phCZt2uJrW08o5b6kELJ6WSt51pxGweJpGbeFz1K6FjRxRLwG1q175MJrxAmpXnjtE7crjKmo+AbUrn63I8QJqV7xpnSNqWtc5O4Fjs3gKxBSqEgGLp0qYXSRIwOIpCHIgjNqVx1XUfAJqVz5bkeMF1K540yKi2pXHVdR8AmpXPluR4wXUrnjTOkfUtK5zdgLHZvEUiClUJQIWT5Uwu0iQgMVTEKSmdR5IUSsTULsqo3ahAAG1KwBxSAjPXXlcRc0noHblsxU5XkDtijetc0RN6zpnJ3BsFk+BmEJVImDxVAmziwQJWDwFQWpa54EUtTIBtasyahcKEFC7AhA1rfMgilqpgNpVKbeLzSigds0I2LDTNa0blrBph6tpPa2c85ZKwOJpqeRddxoBi6dp1BY+R+1a2MgR9RJQu+qVD6MZL6B25blD1K48rqLmE1C78tmKHC+gdsWb1jmipnWdsxM4NounQEyhKhGweKqE2UWCBCyegiAHwqhdeVxFzSegduWzFTleQO2KNy0iql15XEXNJ6B25bMVOV5A7Yo3rXNETes6ZydwbBZPgZhCVSJg8VQJs4sECVg8BUFqWueBFLUyAbWrMmoXChBQuwIQh4Tw3JXHVdR8AmpXPluR4wXUrnjTOkfUtK5zdgLHZvEUiClUJQIWT5Uwu0iQgMVTEKSmdR5IUSsTULsqo3ahAAG1KwBR0zoPoqiVCqhdlXK72IwCateMgA07XdO6YQmbdria1tPKOW+pBCyelkredacRsHiaRm3hc9SuhY0cUS8Btate+TCa8QJqV547RO3K4ypqPgG1K5+tyPECale8aZ0jalrXOTuBY7N4CsQUqhIBi6dKmF0kSMDiKQhyIIzalcdV1HwCalc+W5HjBdSueNMiotqVx1XUfAJqVz5bkeMF1K540zpH1LSuc3YCx2bxFIgpVCUCFk+VMLtIkIDFUxCkpnUeSFErE1C7KqN2oQABtSsAcUgIz115XEXNJ6B25bMVOV5A7Yo3rXNETes6ZydwbBZPgZhCVSJg8VQJs4sECVg8BUFqWueBFLUyAbWrMmoXChBQuwIQNa3zIIpaqYDaVSm3i80ooHbNCNiw0zWtG5awaYeraT2tnPOWSsDiaankXXcaAYunadQWPkftWtjIEfUSULvqlQ+jGS+gduW5Q9SuPK6i5hNQu/LZihwvoHbFm9Y5oqZ1nbMTODaLp0BMoSoRsHiqhNlFggQsnoIgB8KoXXlcRc0noHblsxU5XkDtijctIqpdeVxFzSc1y0keAAAgAElEQVSgduWzFTleQO2KN61zRE3rOmcncGwWT4GYQlUiYPFUCbOLBAlYPAVBalrngRS1MgG1qzJqFwoQULsCEIeE8NyVx1XUfAJqVz5bkeMF1K540zpH1LSuc3YCx2bxFIgpVCUCFk+VMLtIkIDFUxCkpnUeSFErE1C7KqN2oQABtSsAUdM6D6KolQqoXZVyu9iMAmrXjIANO13TumEJm3a4mtbTyjlvqQQsnpZK3nWnEbB4mkZt4XPUroWNHFEvAbWrXvkwmvECaleeO0TtyuMqaj4BtSufrcjxAmpXvGmdI2pa1zk7gWOzeArEFKoSAYunSphdJEjA4ikIciCM2pXHVdR8AmpXPluR4wXUrnjTIqLalcdV1HwCalc+W5HjBdSueNM6R9S0rnN2Asdm8RSIKVQlAhZPlTC7SJCAxVMQpKZ1HkhRKxNQuyqjdqEAAbUrAHFICM9deVxFzSegduWzFTleQO2KN61zRE3rOmcncGwWT4GYQlUiYPFUCbOLBAlYPAVBalrngRS1MgG1qzJqFwoQULsCEDWt8yCKWqmA2lUpt4vNKKB2zQjYsNM1rRuWsGmHq2k9rZzzlkrA4mmp5F13GgGLp2nUFj5H7VrYyBH1ElC76pUPoxkvoHbluUPUrjyuouYTULvy2YocL6B2xZvWOaKmdZ2zEzg2i6dATKEqEbB4qoTZRYIELJ6CIAfCqF15XEXNJ6B25bMVOV5A7Yo3LSKqXXlcRc0noHblsxU5XkDtijetc0RN6zpnJ3BsFk+BmEJVImDxVAmziwQJWDwFQWpa54EUtTIBtasyahcKEFC7AhCHhPDclcdV1HwCalc+W5HjBdSueNM6R9S0rnN2Asdm8RSIKVQlAhZPlTC7SJCAxVMQpKZ1HkhRKxNQuyqjdqEAAbUrAFHTOg+iqJUKqF2VcrvYjAJq14yADTtd07phCZt2uJrW08o5b6kELJ6WSt51pxGweJpGbeFz1K6FjRxRLwG1q175MJrxAmpXnjtE7crjKmo+AbUrn63I8QJqV7xpnSNqWtc5O4Fjs3gKxBSqEgGLp0qYXSRIwOIpCHIgjNqVx1XUfAJqVz5bkeMF1K540yKi2pXHVdR8AmpXPluR4wXUrnjTOkfUtK5zdgLHZvEUiClUJQIWT5Uwu0iQgMVTEKSmdR5IUSsTULsqo3ahAAG1KwBxSAjPXXlcRc0noHblsxU5XkDtijetc0RN6zpnJ3BsFk+BmEJVImDxVAmziwQJWDwFQWpa54EUtTIBtasyahcKEFC7AhA1rfMgilqpgNpVKbeLzSigds0I2LDTNa0blrBph6tpPa2c85ZKwOJpqeRddxoBi6dp1BY+R+1a2MgR9RJQu+qVD6MZL6B25blD1K48rqLmE1C78tmKHC+gdsWb1jmipnWdsxM4NounQEyhKhGweKqE2UWCBCyegiAHwqhdeVxFzSegduWzFTleQO2KNy0iql15XEXNJ6B25bMVOV5A7Yo3rXNETesFsnPHljvTqaefldYeeXha85T96pzLsWOzeGps6jo7cIunzqa+kRO3eMqTNrUrj6uo+QTUrny2IscLqF3xpprWeUxFzSugduX1FT1WQO2K9ax7tNY0rddv+Gi6auPVc95rX/2KdOghB839d9l8vnnzLb0/e+tJb5zXhL7iyo1pw8WX9r63/5r90vHHrEvLly1LmtZ1v4WNr60CFk9tzWw752XxlCevmtZ5XEXNJ6B25bMVOV5A7Yo31bTOYypqXgG1K6+v6LECalesZ92jtaJpff8DD6QPX3JZOvylh6UdVmyfNt1wY3rXe85Nb3rD2l5juvj+2ResT2uevG+vkV18/5wL1qfjjlmXVq/aM13zjWvTho98LJ1y8gm984sGePG1bu2rNK3rfgcbX2sFLJ5am9pWTsziKU9aNa3zuIqaT0DtymcrcryA2hVvqmmdx1TUvAJqV15f0WMF1K5Yz7pHa0XTehB5WJP6/Rddkk489g29pvTg94sm9R4rd5t7M7u/iV3E7t8epHzzet8n7d1rajfly4N/UzJlnKWAxZN7oUkCFk95sqV25XEVNZ+A2pXPVuR4AbUr3lTTOo+pqHkF1K68vqLHCqhdsZ51j9bKpvXglh6Db1IXSSnfpv7dI1427y3s4nv9b2KvWLH9XNN6n72f0Dv20INfOLe1yIMPPlj3HPfG9+nvfiV96vp/TnvvtEcjxmuQBB544Mdp2bJHgGiQwFYNGmv0UC2eokV/Hk/TOo+rqPkEPPjnsxU5XkDtijdVu35u2own5Dz5b2JUz11NzFp3x/zt229Mv/n456RDVj+9EQhbbdXlp+TZU9TKpnX/9h4FUdG0vuKqz87tU1382WDTur8RPaxp/cqXvyRtvPqLc1uMlPS3/fDu2bNQQYTP3XxN2njT19Ljt9+tgqu5BAECXRMoinFTfoiXIzf/cefm9Gt7Pj0duPtTc4TvbEy1q7OpN3ECBCoQULvyIKtdKXV9XZjnzhKVAIFCoGm1a+cdt5W4GQRq37QuGs6nnfHeoVPs/8DE8oCiGX3b7bfPa1BHvGldfIDjyt13ndv3egbzJTnV22pLwu6iMwh4W20GPKdWLuBttTzkalceV1HzCahd+WxFjhdQu+JNi4hqVx5XUfMJqF35bEWOF1C74k3rHLH2TevF4A1rWBfnF29Oz7qn9SEven5audsuD3ljezHjW8pjLZ6WUt+1pxGweJpGzTlLJWDxlEde7crjKmo+AbUrn63I8QJqV7yppnUeU1HzCqhdeX1FjxVQu2I96x6tNU3rwS1B+uGHfTDjOResT8cdsy6tXrVnb/uQDR/52Nxb1P2xBvfHvuLKjemab35r3pvcdU+yxVMTMmSMgwIWT+6JJglYPOXJlqZ1HldR8wmoXflsRY4XULviTT135TEVNa+A2pXXV/RYAbUr1rPu0VrRtC4by8UWHv1f/duHDB7z1pPeOPdhisU5RTN6w8WX9k4fdt7aIw+fO37UG911TrYH/zpnx9iGCVg8uS+aJGDxlCdbalceV1HzCahd+WxFjhdQu+JNNa3zmIqaV0DtyusreqyA2hXrWfdoEzetxzVqyzeZd95pp7Ru7avqPudOjs+DfyfT3uhJWzw1On2dG7zFU56Uq115XEXNJ6B25bMVOV5A7Yo31bTOYypqXgG1K6+v6LECalesZ92jTdS0HtwiY9ikhn3YYd0n36XxefDvUrbbMVeLp3bksSuzsHjKk2m1K4+rqPkE1K58tiLHC6hd8aaa1nlMRc0roHbl9RU9VkDtivWse7SJm9Znnve+9LqjjujtAT3sa/DDDus+8a6Nz4N/1zLe/PlaPDU/h12agcVTnmyrXXlcRc0noHblsxU5XkDtijfVtM5jKmpeAbUrr6/osQJqV6xn3aNN3LQ+9fSzUrmvc7H/88rddpm3J7Q3reudag/+9c6P0T1UwOLJXdEkAYunPNlSu/K4ippPQO3KZytyvIDaFW+qaZ3HVNS8AmpXXl/RYwXUrljPukebqGldTKLY07r4KvasLpvW5eTWPGW/ed+v+6S7OD4P/l3MerPnbPHU7Px1bfQWT3kyrnblcRU1n4Dalc9W5HgBtSveVNM6j6moeQXUrry+oscKqF2xnnWPNnHTuvywxWJC+zz+celxj13Vm9v9D/wobbz6i+nWW29Lp5x8QtphxfZ1n3Mnx+fBv5Npb/SkLZ4anb7ODd7iKU/K1a48rqLmE1C78tmKHC+gdsWbalrnMRU1r4DalddX9FgBtSvWs+7RJm5alxMp3ri+auPV8+Z18EEH9t7A9lVfAQ/+9c2NkQ0XsHhyZzRJwOIpT7bUrjyuouYTULvy2YocL6B2xZtqWucxFTWvgNqV11f0WAG1K9az7tEW3bQuJjRse5C6T7Tr4/Pg3/U7oHnzt3hqXs66PGKLpzzZV7vyuIqaT0DtymcrcryA2hVvqmmdx1TUvAJqV15f0WMF1K5Yz7pHm6ppXfdJGd9DBTz4uyuaJmDx1LSMdXu8Fk958q925XEVNZ+A2pXPVuR4AbUr3lTTOo+pqHkF1K68vqLHCqhdsZ51j6ZpXfcMBY3Pg38QpDCVCVg8VUbtQgECFk8BiENCqF15XEXNJ6B25bMVOV5A7Yo31bTOYypqXgG1K6+v6LECalesZ92jaVrXPUNB4/PgHwQpTGUCFk+VUbtQgIDFUwCipnUeRFErFVC7KuV2sRkF1K4ZAUec7rkrj6uo+QTUrny2IscLqF3xpnWOqGld5+wEjs3iKRBTqEoELJ4qYXaRIAGLpyDIgTBqVx5XUfMJqF35bEWOF1C74k2LiGpXHldR8wmoXflsRY4XULviTescUdO6ztkJHJvFUyCmUJUIWDxVwuwiQQIWT0GQmtZ5IEWtTEDtqozahQIE1K4AxCEhPHflcRU1n4Dalc9W5HgBtSvetM4RF920vmPLnenU089KN2++5SHzWrn7rumUk09IO6zYvs5z7uTYLJ46mfZGT9riqdHp69zgLZ7ypFztyuMqaj4BtSufrcjxAmpXvGkRUe3K4ypqPgG1K5+tyPECale8aZ0jLrppvX7DR3vzWbf2VXWel7ENCFg8uSWaJmDx1LSMdXu8Fk958q925XEVNZ+A2pXPVuR4AbUr3lTTOo+pqHkF1K68vqLHCqhdsZ51j7aopnXxlvWZ570vve6oI9LqVXvWfW7G1yfgwd/t0DQBi6emZazb47V4ypN/tSuPq6j5BNSufLYixwuoXfGmmtZ5TEXNK6B25fUVPVZA7Yr1rHs0Teu6ZyhofB78gyCFqUzA4qkyahcKELB4CkAcEkLtyuMqaj4BtSufrcjxAmpXvKmmdR5TUfMKqF15fUWPFVC7Yj3rHm1RTetiMsX2IHus3C0deshBdZ+b8fUJePB3OzRNwOKpaRnr9ngtnvLkX+3K4ypqPgG1K5+tyPECale8qaZ1HlNR8wqoXXl9RY8VULtiPesebdFN60033Jgu/+Rn0utf8+q0fNmyus/P+H4h4MHfrdA0AYunpmWs2+O1eMqTf7Urj6uo+QTUrny2IscLqF3xpprWeUxFzSugduX1FT1WQO2K9ax7tEU1rYs9rU89/ax08+Zbhs5r5e67plNOPiHtsGL7us+7c+Pz4N+5lDd+whZPjU9hpyZg8ZQn3WpXHldR8wmoXflsRY4XULviTTWt85iKmldA7crrK3qsgNoV61n3aItqWtd9MsY3WsCDv7ujaQIWT03LWLfHa/GUJ/9qVx5XUfMJqF35bEWOF1C74k01rfOYippXQO3K6yt6rIDaFetZ92ia1nXPUND4PPgHQQpTmYDFU2XULhQgYPEUgDgkhNqVx1XUfAJqVz5bkeMF1K54U03rPKai5hVQu/L6ih4roHbFetY9mqZ13TMUND4P/kGQwlQmYPFUGbULBQhYPAUgalrnQRS1UgG1q1JuF5tRQO2aEXDE6Z678riKmk9A7cpnK3K8gNoVb1rniBM1rcu9rH/7N389/c0nP2NP6zpn1OKpgdkx5GECFk/uiyYJWDzlyZYH/zyuouYTULvy2YocL6B2xZsWEdWuPK6i5hNQu/LZihwvoHbFm9Y54kRN6zpPwNgmE7B4mszJUfURsHiqTy6MZGEBi6eFjaY5Qu2aRs05Symgdi2lvmsvVkDtWqzYZMerXZM5Oao+AmpXfXJhJAsLqF0LG7XpCE3rNmVzzFwsnjqS6BZN0+KpRcnswFQsnvIkWe3K4ypqPgG1K5+tyPECale8aRFR7crjKmo+AbUrn63I8QJqV7xpnSNqWtc5O4Fjs3gKxBSqEgGLp0qYXSRIwOIpCHIgjNqVx1XUfAJqVz5bkeMF1K54U03rPKai5hVQu/L6ih4roHbFetY9mqZ13TMUND4P/kGQwlQmYPFUGbULBQhYPAUgDgmhduVxFTWfgNqVz1bkeAG1K95U0zqPqah5BdSuvL6ixwqoXbGedY+maV33DAWNz4N/EKQwlQlYPFVG7UIBAhZPAYia1nkQRa1UQO2qlNvFZhRQu2YEHHG65648rqLmE1C78tmKHC+gdsWb1jmipnWdsxM4NounQEyhKhGweKqE2UWCBCyegiAHwqhdeVxFzSegduWzFTleQO2KNy0iql15XEXNJ6B25bMVOV5A7Yo3rXPEkKb1HVvuTKeeflZvnqecfELaYcX2dZ5zJ8dm8dTJtDd60hZPjU5f5wZv8ZQn5WpXHldR8wmoXflsRY4XULviTTWt85iKmldA7crrK3qsgNoV61n3aDM1ra/5xrXptDPe25vjdtttm97+ljen1av2rPucOzk+D/6dTHujJ23x1Oj0dW7wFk95Uq525XEVNZ+A2pXPVuR4AbUr3lTTOo+pqHkF1K68vqLHCqhdsZ51jzZV03r9ho+mqzZePTe3t570xrTmKfvVfa6dHp8H/06nv5GTt3hqZNo6O2iLpzypV7vyuIqaT0DtymcrcryA2hVvWjatL//3f0x7bf/oPBcQlUCwwI9+9JP0yEf+UnBU4QjkEfjullvTS594QDpkr2fkuYCotRKYuGldbgFy8+ZbehMoGtWrVz2mty3I2iMP17SuVVofOhgP/jVPkOE9RMCDv5uiSQIe/PNkS+3K4ypqPgG1K5+tyPECale8aRHxlnt+mP70Sx9JP/nZT/NcQFQCwQI//dnP0sMf9rDgqMIRyCPwiIc/Iv33Xz0i7bLNjnkuIGqtBCZqWo/as7r8c03rWuV06GA8+Nc/R0Y4X8CDvzuiSQIe/PNkS+3K4ypqPgG1K5+tyPECale8qYgEmihw2w/vTjvvuG0Th27MBAi0XGBRTetddtk5HX/MurR82bIei6Z1c+4OD/7NyZWR/lzAg787oUkCHvzzZEvtyuMqaj4BtSufrcjxAmpXvKmIBJoooGndxKwZM4FuCEzUtO5vUBfbg5Qfurhixfa2B2nIfeLBvyGJMsw5AQ/+boYmCXjwz5MttSuPq6j5BNSufLYixwuoXfGmIhJoooCmdROzZswEuiEwcdO6n8MHMTbv5vDg37ycdX3EHvy7fgc0a/4e/PPkS+3K4ypqPgG1K5+tyPECale8qYgEmiigad3ErBkzgW4ITNW0Lmmu+ca16bQz3tv7z/3X7Ddv65Bu8DVnlh78m5MrI/25gAd/d0KTBDz458mW2pXHVdR8AmpXPluR4wXUrnhTEQk0UUDTuolZM2YC3RCYqWldEo36oMZuEDZjlh78m5Eno/xPAQ/+7oYmCXjwz5MttSuPq6j5BNSufLYixwuoXfGmIhJYCoHLP/2F9M6zL+pd+sUvfHZ623FHpa2XP3LoUK6/YXM67h3npu/f+v/PHf/7a1+W9txtp3Tf/T9Kf3bORel7m3+QzvqTN6UdV2zXO+aHW+5KJ/zR+b3/3//ngxcoj7v2uu9MNJalsHJNAgSaJRDStG7WlLs5Wg/+3cx7k2ftwb/J2eve2D3458m52pXHVdR8AmpXPluR4wXUrnhTEQlULfCVr1+XzvvAZXPN5PM+8PHeEI597cuHDqVocO+x+6PTM566T+/7xfH33f/j9H8dc8Rc03rL3femg5/39PSS33he75jinKu+8NV05133jGxal83w1x952Nx5xZ9dv+mmdPCBz6yaxfUIEGiJwKKb1v1bghQG5Ycyrl61Z0tI2jkND/7tzGubZ+XBv83Zbd/cPPjnyanalcdV1HwCalc+W5HjBdSueFMRCVQtUDSdV++x61yjeLCJvdB4iob01V/+Zjr1La/tHVq8af2CA/ZPn/vi19KJR7+y92dnXviXvT/78MevHNm0HhzHQtf1fQIECEwisKimdfEBjN/6t2+nU04+Ie2wYvte/HJrkH2ftHdat/ZVk1zTMUsg4MF/CdBdciYBD/4z8Tm5YgEP/nnA1a48rqLmE1C78tmKHC+gdsWbikigSoFyO49n7f+keW83n3rGB9MpJ70mPW7V7mOHU55fvIjY/6b1Sw89MN20+Qe9c4u3sov/X/xv/xvd/YH7tycp/ny/fR47dhuRKo1ciwCBZgtM3LQu3rDe8JGPzWtYl1MvG9drjzw8rXnKfs0WaenoPfi3NLEtnpYH/xYnt4VT8+CfJ6lqVx5XUfMJqF35bEWOF1C74k1FJFClQNl0LprM5XYfxZYckzStizejL7r00709sAf3tC7iPW71yvTHZ3wwbf+obXpvXF+/6eaRTetiL+vi2OPWvWLBRnmVPq5FgEDzBSZuWhdvWe+xcrd06CEHDZ31FVduTDfd/H1vW9f0nvDgX9PEGNZIAQ/+bo4mCXjwz5MttSuPq6j5BNSufLYixwuoXfGmIhLIJVBs+3H0H54xF/7Cd5+U9t37sb3tPKZ907oINmx7kLIJ3r/lx7htRwbftC7GVjbRc3mIS4BANwQmalrf/8AD6ewL1qdDD37hyDepizexr7jqs+n4Y9al5cuWdUOvQbP04N+gZBlqT8CDvxuhSQIe/PNkS+3K4ypqPgG1K5+tyPECale8qYgEqhaYdU/r4s3s9/z5JenPTv55H6dogve/uV3OZ6G9su1pXXXmXY9ANwQmaloX23+ced770uuOOiKN+sDFTTfcmN5/0SXpxGPfMLffdTcImzFLD/7NyJNR/qeAB393Q5MEPPjnyZbalcdV1HwCalc+W5HjBdSueFMRCVQtMNhMLprHxdexr31573+Lt6Avu+LquT2mP3jJJ9MLDnja3DYexfE33PSDeR/EOE3Tumh+H/eOc9Prjzxs3v7a12+6KR184DOrZnE9AgRaIqBp3ZJELjQND/4LCfl+3QQ8+NctI8YzTsCDf577Q+3K4ypqPgG1K5+tyPECale8qYgElkKgf3uOYo/qtx13VNp6+SOHNq0HtxkZtaf14PYeC71pXVys2Nv6hD86P1173Xd61x4cy1LYuCYBAs0W0LRudv4mHr0H/4mpHFgTAQ/+NUmEYUwk4MF/IqZFH1TUrr+//stp7x1WLvpcJxBYCgG1aynUXXNagW//8Kb0W4//lXTIXs+YNoTzCBBogcBtP7w77bzjti2YiSkQINA2gYmb1qeefla6efMtY+e/cvdd0yknn2B7kBreJZrWNUyKIY0V8ODvBmmSgKZ1nmx9Z8st6ZQvfDDd/+Mf57mAqAQIEOiwwPJfWpbeeeDatNeKXTusYOoECGhauwcIEKirwMRN64X2tK7rBI3r5wKa1u6EpgloWjctY90er6Z1t/Nv9gRKAQ/+7gUCBAgQaJqA2tW0jBkvge4IaFp3JNea1h1JdIumqWndomR2YCqa1h1IsikSmEDAg/8ESA4hQIAAgVoJqF21SofBECDQJ6Bp3ZHbQdO6I4lu0TQ1rVuUzA5MRdO6A0k2RQITCHjwnwDJIQQIECBQKwG1q1bpMBgCBDStu3cPaFp3L+dNn7GmddMz2K3xa1p3K99mS2CUgAd/9wYBAgQINE1A7WpaxoyXQHcEJn7TuqsfxHjHljtTMfe1Rx6e1jxlv8beGZrWjU1dZweuad3Z1Ddy4prWjUybQRMIF/DgH04qIAECBAhkFlC7MgMLT4DA1AITN62b9EGM6zd8NH3r376dTjn5hLTDiu17OGXz+ebNt/T++60nvXFeE/qKKzemDRdf2vve/mv2S8cfsy4tX7Zs7jxN66nvMScSmEpA03oqNictkYCm9RLBuyyBYIHLP/2F9M6zL+pFffELn53edtxRaevlj1zwKl/5+nXp6D88I7377cekFz13/3Tf/T9Kf3bORel7m3+QzvqTN6UdV2zXi/HDLXelE/7o/N7/7//zwQuUx1173XcWPZYFB+sAAgQIECDQJ6Bp7XYgQKCuAq1rWhcN66s2Xp1W7r7rXNP6/gceSGdfsD6tefK+6dBDDkqbbrgxnXPB+nTcMevS6lV7pmu+cW3a8JGPzR1fxCi+1q19laZ1Xe9c42q9gKZ161PcqglqWrcqnSbTUYGi8XzeBy6bayaf94GP9ySOfe3Lx4qUDevioMGm9Za7700HP+/p6SW/8bxejKIpftUXvpruvOuekU3r62/YnI57x7np9UceNnde8WfXb7opHXzgMzuaHdMmQIAAgVwCmta5ZMUlQGBWgVY1rYu3pW+6+fvpWU9/6rwmdNGkfv9Fl6QTj31D783rwSZ20aTeY+VuvYZ28dXfxC7+u397kPKN7X2ftHevqd2UL9uDNCVTxlkKaFq7F5okoGndpGwZK4HhAkWTevUeu841igeb2MPOKprJ56y/NB3/+v+S/uSMD6ajXnHovDetX3DA/ulzX/xaOvHoV/ZOP/PCv0zFn33441eObFoPjkO+CBAgQIBATgFN65y6YhMgMItAa5rWRcP6mm9+q7etx3Xf/vd5TevBN6kLsPJt6t894mXz3sIuvtf/JvaKFdvPNa332fsJvWMPPfiFjdvfWtN6lr8mzl0KAU3rpVB3zWkFNK2nlXMegXoIlNt5PGv/J817u/nUMz6YTjnpNelxq3Z/yECLhnX5/R1XbNvb9mOwaf3SQw9MN23+Qe/cPXZ/dO//F//b/0Z3f+D+7UmKP99vn8eO3UakHnpGQYAAAQJNFtC0bnL2jJ1AuwUmalrXnaBoSl9x1Wfn9qEebFIPfn9Y07q/ET2saf3Kl78kbbz6i3NbjJQm99z7QN15euP7nzf+a7rqhq+mJ6x46ENXIyZgkJ0T+MlPf5Z+6eEP69y8mzrhrbbaKj344INNHf7M4/73O25Ov776memgPfefOZYABAhUL1A0rc/484+m//OQA9L+T35ibwDfvfH76d3nfSj94bG/l/bac7d5g7rjzrvTaed9KP3B2t/pfa/477f+2YXpv77ysPScp/0fvT2ty3h7rdq9d+x222yT3vS6l6fv3rA5/T8f/pt02tuOTjtsv+3YuNVLuCIBAgRmF9jq4Q9LD/70Z7MHEqESgXvv/1HaZoLPb6hkMC5CoGUCj2Xq4IsAACAASURBVNpmWctmVO10at+0LhrOp53x3qEq5Qcmbvz8F+c+RLH/wHJf6003fG/em9fFMYt907r4AMf+fbLL69z3wI+rzdiUV/ufN3w1fWbTV9ITd1g5ZQSnEahWQNO6Wu/Zr7ZVSqm7Tev/746b0m/s9az0osc8bXZKEQgQyCrwr9/8djru7efMXeOcdx2XnvSEvdJ73ntxesaafdJhBx/Q+953v7c5nXbuh9Jb3/x7aa/HzP+hf/G9t5x6QbrlB7c/ZKz98X7rN56bnvbkvdOFF/11eszKXXqxi+tfeNHfpHe/vWha//wDGsuvv7/qi+nd5188b2zF+b4IECDQJIGttkqpw+8yNClVvbHee9+P0jZbL/yhw42bmAETqIHA1sseUYNRNHcIEzWti32cL/zAh9LRr/293p7Qw74mOaYqpsE3rSP2tD7kRc9PK3fbZd4b3VXNJ+I6tgeJUBSjSgHbg1Sp7VqzCtgeZFZB5xNYeoFp9rQuR/3DLXeN3B7kGU/dZ97kFtor257WS38vGAEBAgS6JGB7kC5l21wJNEtg4qZ18WGExdvG476GvYm8FByDTevBD17s3/5j9ao9533wYtGUL9/CLj5osfzgxbVHHt7bx7p/7+zly5rzmr+m9VLcia45i4Cm9Sx6zq1aQNO6anHXIxAvMNhMLprHxdexr31573+L/aYvu+LqoXtMRzati72yj3vHuen1Rx42b3/t6zfdlA4+8JnxExeRAAECBDotoGnd6fSbPIFaC0zctG7ym9ZFBsrmc9l4f+tJb5z3YYpFM3rDxZf2klVuO1I0pQeb1sX3i6b2bbffPreHdq0z/IvBaVo3IUvG2C+gae1+aJKApnWTsmWsBEYL9H8Q4otf+Oz0tuOOSlv/Yp/PqprWxejKJvi1132nN9jBscghAQIECBCIEtC0jpIUhwCBaIGJmtbRFxWvegFN6+rNXXE2AU3r2fycXa2ApnW13q5GoK4CHvzrmhnjIkCAAIFRAmqXe4MAgboKaFrXNTPB49K0DgYVLruApnV2YhcIFNC0DsQUikCDBTz4Nzh5hk6AAIGOCqhdHU28aRNogMBMTetim4yrNl7dm+baV78iHXrIQQ2YcjeHqGndzbw3edaa1k3OXvfGrmndvZybMYFhAh783RcECBAg0DQBtatpGTNeAt0RmLhpXXx44bvec2666667ew3qlbvtkv75q19PxYcVDn7QYXf4mjNTTevm5MpIfy6gae1OaJKApnWTsmWsBPIJePDPZysyAQIECOQRULvyuIpKgMDsAhM1rQeb0uWHFvZ/mOE137g2XXHVZxv14YSz8zUngqZ1c3JlpJrW7oHmCWhaNy9nRkwgh4AH/xyqYhIgQIBATgG1K6eu2AQIzCIwUdP6ji13pjPPe1963VFHpNWr9kyD/10MoHgT+/0XXZJOPPYNaYcV288yJudmENC0zoAqZFYBb1pn5RU8WEDTOhhUOAINFfDg39DEGTYBAgQ6LKB2dTj5pk6g5gKa1jVPUNTwNK2jJMWpSkDTuipp14kQ0LSOUBSDQPMFPPg3P4dmQIAAga4JqF1dy7j5EmiOgKZ1c3I100g1rWfic/ISCGhaLwG6S04toGk9NZ0TCbRKwIN/q9JpMgQIEOiEgNrViTSbJIFGCmhaNzJtix+0pvXizZyxtAKa1kvr7+qLE9C0XpyXowm0VcCDf1sza14ECBBor4Da1d7cmhmBpgtM3LQ+9fSz0s2bbxk735W775pOOfkEe1rX8K7QtK5hUgxprICmtRukSQKa1k3KlrESyCfgwT+frcgECBAgkEdA7crjKioBArMLTNS0nv0yIiy1gKb1UmfA9RcroGm9WDHHL6WApvVS6rs2gfoIePCvTy6MhAABAgQmE1C7JnNyFAEC1QtoWldvviRX1LReEnYXnUFA03oGPKdWLqBpXTm5CxKopYAH/1qmxaAIECBAYIyA2uX2IECgrgITNa3v2HJnOvO896XXHXVEWr1qzzT438XkNt1wY3r/RZekE499g+1BaphtTesaJsWQxgpoWrtBmiSgad2kbBkrgXwCHvzz2YpMgAABAnkE1K48rqISIDC7gKb17IaNiKBp3Yg0GWSfgKa126FJAprWTcqWsRLIJ+DBP5+tyAQIECCQR0DtyuMqKgECswtoWs9u2IgImtaNSJNBalq7BxoqoGnd0MQZNoFgAQ/+waDCESBAgEB2AbUrO7ELECAwpYCm9ZRwTTtN07ppGTNeb1q7B5okoGndpGwZK4F8Ah7889mKTIAAAQJ5BNSuPK6iEiAwu4Cm9eyGjYigad2INBlkn4CmtduhSQKa1k3KlrESyCfgwT+frcgECBAgkEdA7crjKioBArMLaFrPbtiICJrWjUiTQWpauwcaKqBp3dDEGTaBYAEP/sGgwhEgQIBAdgG1KzuxCxAgMKXAxE3rU08/K928+Zaxl1m5+67plJNPSDus2H7K4Tgtl4CmdS5ZcXMJeNM6l6y4OQQ0rXOoikmgeQIe/JuXMyMmQIBA1wXUrq7fAeZPoL4CEzWt6zt8I5tUQNN6UinH1UVA07oumTCOSQQ0rSdRcgyB9gt48G9/js2QAAECbRNQu9qWUfMh0B4BTev25HLsTDStO5LoFk1T07pFyezAVDStO5BkUyQwgYAH/wmQHEKAAAECtRJQu2qVDoMhQKBPQNO6I7eDpnVHEt2iaWpatyiZHZiKpnUHkmyKBCYQ8OA/AZJDCBAgQKBWAmpXrdJhMAQIaFp37x7QtO5ezps+Y03rpmewW+PXtO5Wvs2WwCgBD/7uDQIECBBomoDa1bSMGS+B7gh407ojuda07kiiWzRNTesWJbMDU9G07kCSTZHABAIe/CdAcggBAgQI1EpA7apVOgyGAIE+AU3rjtwOmtYdSXSLpqlp3aJkdmAqmtYdSLIpEphAwIP/BEgOIUCAAIFaCahdtUqHwRAgoGndvXtA07p7OW/6jDWtm57Bbo1f07pb+TZbAqMEPPi7NwgQIECgaQJqV9MyZrwEuiPgTeuO5FrTuiOJbtE0Na1blMwOTEXTugNJNkUCEwh48J8AySEECBAgUCsBtatW6TAYAgT6BDStO3I7aFp3JNEtmqamdYuS2YGpaFp3IMmmSGACAQ/+EyA5hAABAgRqJaB21SodBkOAgKZ19+4BTevu5bzpM9a0bnoGuzV+Tetu5dtsCYwS8ODv3iBAgACBpgmoXU3LmPES6I6AN607kmtN644kukXT1LRuUTI7MBVN6w4k2RQJTCDgwX8CJIcQIECAQK0E1K5apcNgCBDoE9C07sjtoGndkUS3aJqa1i1KZgemomndgSSbIoEJBDz4T4DkEAIECBColYDaVat0GAwBAprW3bsHNK27l/Omz1jTuukZ7Nb4Na27lW+zJTBKwIO/e4MAAQIEmiagdjUtY8ZLoDsC3rTuSK41rTuS6BZNU9O6RcnswFQ0rTuQZFMkMIGAB/8JkBxCgAABArUSULtqlQ6DIUCgT0DTuiO3g6Z1RxLdomlqWrcomR2YiqZ1B5JsigQmEPDgPwGSQwgQIECgVgJqV63SYTAECGhad+8e0LTuXs6bPmNN66ZnsFvj17TuVr7NlsAoAQ/+7g0CBAgQaJqA2tW0jBkvge4IeNO6I7nWtO5Iols0TU3rFiWzA1PRtO5Akk2RwAQCHvwnQHIIAQIECNRKQO2qVToMhgCBPgFN647cDprWHUl0i6apad2iZHZgKprWHUiyKRKYQMCD/wRIDiFAgACBWgmoXbVKh8EQIKBp3b17QNO6ezlv+ow1rZuewW6NX9O6W/k2WwKjBDz4uzcIECBAoGkCalfTMma8BLoj4E3rjuRa07ojiW7RNDWtW5TMDkxF07oDSTZFAhMIePCfAMkhBAgQIFArAbWrVukwGAIE+gQ0rTtyO2hadyTRLZqmpnWLktmBqWhadyDJpkhgAgEP/hMgOYQAAQIEaiWgdtUqHQZDgICmdffuAU3r7uW86TPWtG56Brs1fk3rbuXbbAmMEvDg794gQIAAgaYJqF1Ny5jxEuiOgDetO5JrTeuOJLpF09S0blEyOzAVTesOJNkUCUwg4MF/AiSHECBAgECtBNSuWqXDYAgQ6BPQtO7I7aBp3ZFEt2iamtYtSmYHpqJp3YEkmyKBCQQ8+E+A5BACBAgQqJWA2lWrdBgMAQKa1t27BzStu5fzps9Y07rpGezW+DWtu5VvsyUwSsCDv3uDAAECBJomoHY1LWPGS6A7At607kiuNa07kugWTVPTukXJ7MBUNK07kGRTJDCBgAf/CZAcQoAAAQK1ElC7apUOgyFAoE9A07ojt4OmdUcS3aJpalq3KJkdmIqmdQeSbIoEJhDw4D8BkkMIECBAoFYCalet0mEwBAhoWnfvHtC07l7Omz5jTeumZ7Bb49e07la+zZbAKAEP/u4NAgQIEGiagNrVtIwZL4HuCHjTuiO51rTuSKJbNE1N6xYlswNT0bTuQJJNkcAEAh78J0ByCAECBAjUSkDtqlU6DIYAgT4BTeuO3A6a1h1JdIumqWndomR2YCqa1h1IsikSmEDAg/8ESA4hQIAAgVoJqF21SofBECCgad29e6BoWl/+7/+Y9tr+0d2bvBk3UuBHP/pJeuQjf6mRYzfo7gl8d8ut6aVPPCAdstczujd5MyZAYE7Ag7+bgQABAgSaJqB2NS1jxkugOwLetO5Irm+554fpT7/0kfSTn/20IzM2zaYL/PRnP0sPf9jDmj4N4++IwCMe/oj033/1iLTLNjt2ZMamSYDAMAEP/u4LAgQIEGiagNrVtIwZL4HuCGhadyfXZkqgUQIWT41Kl8ESIECAQEpJ7XIbECBAgEDTBNSupmXMeAl0R0DTuju5NlMCjRKweGpUugyWAAECBDSt3QMECBAg0EABz10NTJohE+iIgKZ1RxJtmgSaJmDx1LSMGS8BAgQIqF3uAQIECBBomoDa1bSMGS+B7ghoWncn12ZKoFECFk+NSpfBEiBAgIA3rd0DBAgQINBAAc9dDUyaIRPoiICm9QKJvmPLnenU089Ka488PK15yn4duS1Mk8DSC1g8LX0OjIAAAQIEFiegdi3Oy9EECBAgsPQCatfS58AICBAYLtCqpvWmG25M73rPuemuu+5O2223bXr7W96cVq/aszfzsvl88+Zbev/91pPeOK8JfcWVG9OGiy/tfW//Nful449Zl5YvWzZ3nqa1v0IEFidw+ae/kN559kW9k178wmentx13VNp6+SNHBrnv/h+lPzvnovSpz365d8zxb3hlevXv/Foq//x7m3+QzvqTN6UdV2zX+/4Pt9yVTvij83v/v//PBy9QHnftdd+ZeCyLm6mjCRAgQIDAzwU8+LsTCBAgQKBpAmpX0zJmvAS6I9CapnXRsD7ngvXpuGPWzTWqyzTe/8AD6ewL1qc1T943HXrIQWnw2Gu+cW3a8JGPpVNOPiHtsGL7tH7DR3unrlv7Kk3r7vxdMNNAga98/bp03gcum2smn/eBj/eiH/valw+9StmYftb+T0ov+Y3nzXvwL7+35e5708HPe/rc94um+FVf+Gq68657Rjatr79hczruHeem1x952Nx5xZ9dv+mmdPCBzwycsVAECBAgQEDT2j1AgAABAs0T0LRuXs6MmEBXBFrRtC6b0oce/MKhW3gUTer3X3RJOvHYN/Sa0oNN7KJJvcfK3XoN7eKrv4ld/Hf/9iDlG9v7PmnvXlPbFwECDxUomtSr99h1rlE82MQePKNoQG+66ZZ5Te1y8VQ2rV9wwP7pc1/8Wjrx6Ff2Tj/zwr9MxZ99+ONXjmxaD45DrggQIECAQE4BD/45dcUmQIAAgRwCalcOVTEJEIgQaEXTenDrjwKmf4uPwTepi++Xb1P/7hEvm/cWdvG9/jexV6zYfq5pvc/eT+gd298cf/DBByPyIMaAANXm3hJFk/m0cz+Unlm8Nf3rz+1N5Ds3bE7vPPN/pHec+F/TY1ft/pDJnf+By9KHPvbpuT/fbZdfTn/8lnXpafs9rrc9SBHvpS9+Xrrx+7f1jtlzt517/7/43/M/+Il05h+/Me3wi21DyiCXf+Z/pT/9xfYkxZ/tt89jhx7XXOl6jXyrB1Pa6mH1GpPRECBAoGqB27fcm3ZasU3Vl3U9AgQI1ErgZz9LKW1VqyEZzBiB2++4J+20w6MYEQgT8Nf/Pym32orGLDdWK5rWo96k3nmnnXpvQxdN6yuu+uzcPtUF2GDTur8RPaxp/cqXvyRtvPqLc1uMlOjFTyV9xQv4ax1vWlXE+x/4UTrnL/4qHfqiX0lP3fcJvcsWb1GfeeFH0olHH9l7A7v/a9jxn/7sP6VP/sOX0qlvWZeWPfKRc/FWP2a39H//+UfTtttsnY5e+9K06XvfT++/5O96x63Ybtt5cbfcdXfv2HWvfslDrlmVRZeu8/CHPyz95Kd+3NSlnJsrAQIPFSieS7zP4M4gQKDrAr/08K3ST39adK59NUGgWMF7/m5CppozRk+F/5mrnXec36doThbrMdLaN62LhvNpZ7x3qFb5NvUtt/xg3vYfxcH9b1dvuuF78/asLr6/2Detiw9wXLn7rnP7XtcjfUZBYGkFim0/jv7DM+YGceG7T0r77v3Y3gcq9u9PXewjfeoZH0ynnPSa9LiBN63L7T9eeuiB6RlP3acXq/jwxGPffk464fWvmItXfr9/y49x2470fxBkEbMYWxl/adVcnQABAgTaKuBXrNuaWfMiQIBAewXUrvbm1swINF2g9k3rSYCL7UHOPO996XVHHTH3IYz9b1cPNrWn2dP6kBc9P63cbZeHvLE9yfgcQ6BrAovd03rw+KJp/bbT16e3/MERafddfrnXBO9vapeeC+2VbU/rrt155kuAAIGlFfDgv7T+rk6AAAECixdQuxZv5gwCBKoRaEXTuqAq3py+7fbbe1uAFF/F3tNrnrxv78MVB5vU/dt/rF6157y3sosPaizfwi62Fin3y1575OG9D3m84sqN6ZpvfmveViPVpMpVCDRHYLCZXDSPi69jX/vy3v8Wb0FfdsXVcx+gWBz/x2f8j3TOO9/cexO7+P7VX/5mOvUtr+0dP23TunjD+7h3nJtef+Rhcx8KWfzZ9ZtuSgcf+MzmgBopAQIECDRCwIN/I9JkkAQIECDQJ6B2uR0IEKirQGua1mVj+mvXXNuzPvigA3v7WZdfgx/W+NaT3thrQpdfRTN6w8WX9v6z/0McB5vWxff7G+TLly2ra26Ni8CSCvRvz/HiFz47ve24o9LWyx/ZG9Ng07r8s3f+4oMTiw9NfMeJr0mPX7Vb74MYp21aF3GLt7ZP+KPz07XXfad37cGxLCmSixMgQIBAqwQ8+LcqnSZDgACBTgioXZ1Is0kSaKRAa5rWjdQ3aAIERgpYPLk5CBAgQKBpAmpX0zJmvAQIECCgdrkHCBCoq4CmdV0zY1wEOi5g8dTxG8D0CRAg0EABtauBSTNkAgQIdFxA7er4DWD6BGosoGld4+QYGoEuC1g8dTn75k6AAIFmCqhdzcybURMgQKDLAmpXl7Nv7gTqLaBpXe/8GB2BzgpYPHU29SZOgACBxgqoXY1NnYETIECgswJqV2dTb+IEai+gaV37FBkggW4KWDx1M+9mTYAAgSYLqF1Nzp6xEyBAoJsCalc3827WBJogoGndhCwZI4EOClg8dTDppkyAAIGGC6hdDU+g4RMgQKCDAmpXB5NuygQaIqBp3ZBEGSaBrglYPHUt4+ZLgACB5guoXc3PoRkQIECgawJqV9cybr4EmiOgad2cXBkpgU4JWDx1Kt0mS4AAgVYIqF2tSKNJECBAoFMCalen0m2yBBoloGndqHQZLIHuCFg8dSfXZkqAAIG2CKhdbcmkeRAgQKA7AmpXd3JtpgSaJqBp3bSMGS+BjghYPHUk0aZJgACBFgmoXS1KpqkQIECgIwJqV0cSbZoEGiigad3ApBkygS4IWDx1IcvmSIAAgXYJqF3tyqfZECBAoAsCalcXsmyOBJopoGndzLwZNYHWC1g8tT7FJkiAAIHWCahdrUupCRE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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": 110,
   "metadata": {
    "tags": [
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   },
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    "_series = SingleFigureComparison(s.optcases, 'npv', label='NPV').report\n",
    "\n",
    "npv_graph = _series.iplot(\n",
    "    kind='bar',\n",
    "    title='NPV by Casestudy in €',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400), \n",
    "    yTitle='NPV in €',\n",
    "    asFigure=True,\n",
    "    yrange=[0, _series.max() * 1.1]\n",
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    "    text=_series.values/1000_000, \n",
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    "    texttemplate=\"%{text:.1f}M €\", \n",
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    "\n",
    "npv_graph"
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  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
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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": 112,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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    "_series = (SingleFigureComparison(s.optcases, 'spp', label='Simple Payback Time').report)\n",
    "spt_fig = _series.iplot(\n",
    "    kind='bar',\n",
    "    title='Simple Payback Time by Casestudy in Years',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400),\n",
    "    yTitle='Years',\n",
    "    asFigure=True,\n",
    "    yrange=[0, _series.max() * 1.2]\n",
    ")\n",
    "\n",
    "spt_fig.update_traces(\n",
    "    text=_series.values, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f} years\", \n",
    ")\n",
    "\n",
    "spt_fig "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_cops = (SingleFigureComparison(s.optcases, 'mean_cop', label='COP').report)\n",
    "cop_fig = mean_cops.iplot(\n",
    "    kind='bar',\n",
    "    title='Mean COP by Casestudy',\n",
    "    colors=recoygreen,\n",
    "    dimensions=(600, 400),\n",
    "    yTitle='COP',\n",
    "    asFigure=True,\n",
    "    yrange=[0, mean_cops.max() * 1.2]\n",
    ")\n",
    "\n",
    "cop_fig.update_traces(\n",
    "    text=mean_cops.values, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f}\", \n",
    ")\n",
    "\n",
    "cop_fig "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Sensitivity analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "We have agreed that for the sensitivity analysis we will vary the following key assumptions in the flex-model:\n",
    "1. Market prices of gas and electricity. We will use the actual prices for 2019, the actual prices for 2020.\n",
    "2. aFRR prices +/- 30%\n",
    "3. CAPEX\n",
    "4. CO2 price\n",
    "5. Tsource +/- 10 degrees Celsius\n",
    "6. Tsink\n",
    "    * Roam off the peak / lower pressure\n",
    "    * Stabalize / running average per hour/ 2 hours\n",
    "7. __A scenario with a constraint on grid capacity and a scenario without grid capacity as a constraint__\n",
    "8. Energy Tax and ODE +/- 30%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "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 = setup_model(c=c)\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"
   ]
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     "text": [
      "testetss\n"
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      "\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",
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      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
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      "\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"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 8.3 s\n",
      "Wall time: 25.9 s\n"
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     "name": "stderr",
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     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_21988\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
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   "source": [
    "%time _s = setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
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      "testetss\n"
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      "\n",
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      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
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      "\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",
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      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
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      "\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_21988\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\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"
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     "name": "stdout",
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     "text": [
      "1152000\n",
      "CPU times: total: 9.48 s\n",
      "Wall time: 30 s\n"
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      "\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": [
       "49607.31309321511"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time result = routine(c, _s)\n",
    "npv = result.hpcase.npv\n",
    "npv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0dec521fd8b841e481644685d957eb71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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_21988\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\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": [
      "1152000\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"
     ]
    },
    {
     "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_21988\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\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": [
      "1152000\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"
     ]
    },
    {
     "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_21988\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\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": [
      "1152000\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"
     ]
    },
    {
     "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_21988\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\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": [
      "1152000\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"
     ]
    }
   ],
   "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": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 376,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'MW (NDG)'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'MW (NDG)'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[376], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m (_s\u001b[38;5;241m.\u001b[39mdemand[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMW (VDG)\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[43m_s\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdemand\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mMW (NDG)\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\u001b[38;5;241m.\u001b[39mto_list()[:\u001b[38;5;241m3\u001b[39m]\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m   3810\u001b[0m     ):\n\u001b[0;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'MW (NDG)'"
     ]
    }
   ],
   "source": [
    "# (_s.demand['MW (VDG)'] + _s.demand['MW (NDG)']).to_list()[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "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_21988\\2484316097.py:3: 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: 11.4 s\n",
      "Wall time: 21.3 s\n"
     ]
    }
   ],
   "source": [
    "%time _s = setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2da0018c8c784c1eafeb17acc2ba97ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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_21988\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\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": [
      "1152000\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"
     ]
    },
    {
     "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_21988\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\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": [
      "1152000\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"
     ]
    },
    {
     "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_21988\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\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": [
      "1152000\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"
     ]
    },
    {
     "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_21988\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\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": [
      "1152000\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"
     ]
    }
   ],
   "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": 457,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Heatpump only', 'Heatpump + Storage_PCM']"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[case.name for case in s.cases][1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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        "linkText": "Export to plot.ly",
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   "source": [
    "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
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   ],
   "source": [
    "output = sens.single_kpi_overview('irr', case_names=[case.name for case in s.cases][1:])\n",
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    "    mode='lines+markers',\n",
    "    symbol='circle-dot',\n",
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    "    xTitle='volume(m3)',\n",
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    "    title='Sensitivity: IRR',\n",
    "    colors=recoycolors,\n",
    "    dimensions=(600, 400),\n",
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"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"font\":{\"color\":\"#4D5663\"},\"text\":\"Sensitivity: payback time\"},\"width\":600,\"xaxis\":{\"gridcolor\":\"#E1E5ED\",\"showgrid\":true,\"tickfont\":{\"color\":\"#4D5663\"},\"title\":{\"font\":{\"color\":\"#4D5663\"},\"text\":\"volume(m3)\"},\"zerolinecolor\":\"#E1E5ED\"},\"yaxis\":{\"gridcolor\":\"#E1E5ED\",\"range\":[7.808401247136963,0],\"showgrid\":true,\"tickfont\":{\"color\":\"#4D5663\"},\"title\":{\"font\":{\"color\":\"#4D5663\"},\"text\":\"years\"},\"zerolinecolor\":\"#E1E5ED\"}},                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}                    ).then(function(){\n",
       "                            \n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 461,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'mean_cop'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[461], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43msens\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msingle_kpi_overview\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmean_cop\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcase_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcase\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcase\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcases\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m output\u001b[38;5;241m.\u001b[39miplot(\n\u001b[0;32m      3\u001b[0m     mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlines+markers\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      4\u001b[0m     symbol\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcircle-dot\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     11\u001b[0m     dimensions\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m600\u001b[39m, \u001b[38;5;241m400\u001b[39m),\n\u001b[0;32m     12\u001b[0m )\n",
      "File \u001b[1;32m~\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\sensitivity.py:63\u001b[0m, in \u001b[0;36mSensitivityAnalysis.single_kpi_overview\u001b[1;34m(self, kpi, case_names)\u001b[0m\n\u001b[0;32m     59\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m case_names:\n\u001b[0;32m     60\u001b[0m     case_names \u001b[38;5;241m=\u001b[39m CaseStudy\u001b[38;5;241m.\u001b[39minstances\u001b[38;5;241m.\u001b[39mkeys()\n\u001b[0;32m     62\u001b[0m kpi_values \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m---> 63\u001b[0m     name: {case: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkpis\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkpi\u001b[49m\u001b[43m]\u001b[49m[case] \u001b[38;5;28;01mfor\u001b[39;00m case \u001b[38;5;129;01min\u001b[39;00m case_names}\n\u001b[0;32m     64\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkpis\u001b[38;5;241m.\u001b[39mkeys()\n\u001b[0;32m     65\u001b[0m }\n\u001b[0;32m     67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mDataFrame(kpi_values)\u001b[38;5;241m.\u001b[39mT\n",
      "\u001b[1;31mKeyError\u001b[0m: 'mean_cop'"
     ]
    }
   ],
   "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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