main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "697aa0a4",
   "metadata": {},
   "source": [
    "**Databased analysis of conventional plants in Germany**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79b00df",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfd4fa4d",
   "metadata": {},
   "source": [
    "This Notebook is a step for step handbook for calculating the emission factors of conventional power plants in Germany.\n",
    "\n",
    "Only plants, that are mandated to submit generation data are included. This refers to plants with a total nominal electrical power of least 200 MW or single blocks with a nominal elctrical power of at least 100 MW.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "978cef84",
   "metadata": {},
   "source": [
    "## Preparation of Python environment\n",
    "\n",
    "The following modules and their dependencies are required to run this notebook:\n",
    "* Pandas\n",
    "* Numpy\n",
    "* Matplotlib\n",
    "* Seaborn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "c8c9acc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODULE IMPORTS\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d80b363d",
   "metadata": {},
   "source": [
    "## Download of input files and setup of folder structure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb7f224",
   "metadata": {},
   "source": [
    "* The list of conventional powerplants *blocks.xlsx* needs to be included in the *input* folder. <br><br>\n",
    "* Powerplant generation data needs to be downloaded from https://transparency.entsoe.eu/. Therefore, use any SFTP client and follow the instructions on https://transparency.entsoe.eu/content/static_content/Static%20content/knowledge%20base/SFTP-Transparency_Docs.html to access the ento-e database. Open the folder *ActualGenerationOutputPerGenerationUnit_16.1.A* and download all files for the relevant years. Include the files into the *input/entsoe* folder. <br><br>\n",
    "* Emissions and allocations data needs to be downloaded from https://climate.ec.europa.eu/eu-action/eu-emissions-trading-system-eu-ets/union-registry_en#documentation. Access the link, go to section *Phase IV (2021-2030)* - *Reports* and download the file *Verified Emissions for 2021*. Delete the informational rows marked in yellow. Include the file into the */input/EC* folder."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "709036fc",
   "metadata": {},
   "source": [
    "# Data import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "c24a0a01",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ETS-ID</th>\n",
       "      <th>EIC</th>\n",
       "      <th>BNA-ID</th>\n",
       "      <th>MaStR-Nr</th>\n",
       "      <th>Name BNA</th>\n",
       "      <th>City</th>\n",
       "      <th>Plant name</th>\n",
       "      <th>Block name</th>\n",
       "      <th>CHP</th>\n",
       "      <th>Net electrical power [MW_el]</th>\n",
       "      <th>Heat power [MW_th]</th>\n",
       "      <th>Fuel</th>\n",
       "      <th>Fuel emission factor [t/MWh_th]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>11WD8SCHW5X---19</td>\n",
       "      <td>BNA0894c</td>\n",
       "      <td>SEE954086855965</td>\n",
       "      <td>1MKA</td>\n",
       "      <td>Schwedt</td>\n",
       "      <td>IKS PCK Schwedt</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>117</td>\n",
       "      <td>106.0</td>\n",
       "      <td>crude oil</td>\n",
       "      <td>0.2639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>11WD8SCHW5X---27</td>\n",
       "      <td>BNA0894d</td>\n",
       "      <td>SEE995943794058</td>\n",
       "      <td>2MKA</td>\n",
       "      <td>Schwedt</td>\n",
       "      <td>IKS PCK Schwedt</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>117</td>\n",
       "      <td>106.0</td>\n",
       "      <td>crude oil</td>\n",
       "      <td>0.2639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>662</td>\n",
       "      <td>11WD7LUDW2GGT11E</td>\n",
       "      <td>BNA0614b</td>\n",
       "      <td>SEE920494524915</td>\n",
       "      <td>GuD Mitte GT11</td>\n",
       "      <td>Ludwigshafen</td>\n",
       "      <td>Mitte</td>\n",
       "      <td>GT11</td>\n",
       "      <td>1</td>\n",
       "      <td>206</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>662</td>\n",
       "      <td>11WD7LUDW2GGT12C</td>\n",
       "      <td>BNA0614b</td>\n",
       "      <td>SEE914414538991</td>\n",
       "      <td>GuD Mitte GT12</td>\n",
       "      <td>Ludwigshafen</td>\n",
       "      <td>Mitte</td>\n",
       "      <td>GT12</td>\n",
       "      <td>1</td>\n",
       "      <td>206</td>\n",
       "      <td>270.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>662</td>\n",
       "      <td>11WD7LUDW5GSDG1Q</td>\n",
       "      <td>BNA0615</td>\n",
       "      <td>SEE944122961347</td>\n",
       "      <td>GuD Süd GT1</td>\n",
       "      <td>Ludwigshafen</td>\n",
       "      <td>Sued</td>\n",
       "      <td>GT1</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>217.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>662</td>\n",
       "      <td>11WD7LUDW5GSDG2O</td>\n",
       "      <td>BNA0615</td>\n",
       "      <td>SEE923421872301</td>\n",
       "      <td>GuD Süd GT2</td>\n",
       "      <td>Ludwigshafen</td>\n",
       "      <td>Sued</td>\n",
       "      <td>GT2</td>\n",
       "      <td>1</td>\n",
       "      <td>167</td>\n",
       "      <td>203.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>739</td>\n",
       "      <td>11WD8DRES5X----D</td>\n",
       "      <td>BNA0207</td>\n",
       "      <td>SEE950477241393+SEE967194778880+SEE96825436577...</td>\n",
       "      <td>DT+GT1+GT2+GT3</td>\n",
       "      <td>Dresden</td>\n",
       "      <td>HKW Dresden Nossener Bruecke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>455.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>741</td>\n",
       "      <td>11WD2NOR20001055</td>\n",
       "      <td>BNA0969b</td>\n",
       "      <td>SEE952372080091</td>\n",
       "      <td>SWM HKW Nord 2 T20</td>\n",
       "      <td>Muenchen</td>\n",
       "      <td>Nord</td>\n",
       "      <td>2-T20</td>\n",
       "      <td>1</td>\n",
       "      <td>333</td>\n",
       "      <td>550.0</td>\n",
       "      <td>hard coal</td>\n",
       "      <td>0.3368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>750</td>\n",
       "      <td>11WD2S1G2000097U</td>\n",
       "      <td>BNA0683c</td>\n",
       "      <td>SEE901789931854</td>\n",
       "      <td>SWM HKW Sued GuD1 GT2</td>\n",
       "      <td>Muenchen</td>\n",
       "      <td>Sued</td>\n",
       "      <td>GT2</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>750</td>\n",
       "      <td>11WD2S1G3000098K</td>\n",
       "      <td>BNA0683b</td>\n",
       "      <td>SEE985585573104</td>\n",
       "      <td>SWM HKW Sued GuD1 GT3</td>\n",
       "      <td>Muenchen</td>\n",
       "      <td>Sued</td>\n",
       "      <td>GT3</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   ETS-ID               EIC    BNA-ID  \\\n",
       "0      19  11WD8SCHW5X---19  BNA0894c   \n",
       "1      19  11WD8SCHW5X---27  BNA0894d   \n",
       "2     662  11WD7LUDW2GGT11E  BNA0614b   \n",
       "3     662  11WD7LUDW2GGT12C  BNA0614b   \n",
       "4     662  11WD7LUDW5GSDG1Q   BNA0615   \n",
       "5     662  11WD7LUDW5GSDG2O   BNA0615   \n",
       "6     739  11WD8DRES5X----D   BNA0207   \n",
       "7     741  11WD2NOR20001055  BNA0969b   \n",
       "8     750  11WD2S1G2000097U  BNA0683c   \n",
       "9     750  11WD2S1G3000098K  BNA0683b   \n",
       "\n",
       "                                            MaStR-Nr               Name BNA  \\\n",
       "0                                    SEE954086855965                   1MKA   \n",
       "1                                    SEE995943794058                   2MKA   \n",
       "2                                    SEE920494524915         GuD Mitte GT11   \n",
       "3                                    SEE914414538991         GuD Mitte GT12   \n",
       "4                                    SEE944122961347            GuD Süd GT1   \n",
       "5                                    SEE923421872301            GuD Süd GT2   \n",
       "6  SEE950477241393+SEE967194778880+SEE96825436577...         DT+GT1+GT2+GT3   \n",
       "7                                    SEE952372080091     SWM HKW Nord 2 T20   \n",
       "8                                    SEE901789931854  SWM HKW Sued GuD1 GT2   \n",
       "9                                    SEE985585573104  SWM HKW Sued GuD1 GT3   \n",
       "\n",
       "           City                    Plant name Block name  CHP  \\\n",
       "0       Schwedt               IKS PCK Schwedt          1    1   \n",
       "1       Schwedt               IKS PCK Schwedt          2    1   \n",
       "2  Ludwigshafen                         Mitte       GT11    1   \n",
       "3  Ludwigshafen                         Mitte       GT12    1   \n",
       "4  Ludwigshafen                          Sued        GT1    1   \n",
       "5  Ludwigshafen                          Sued        GT2    1   \n",
       "6       Dresden  HKW Dresden Nossener Bruecke        NaN    1   \n",
       "7      Muenchen                          Nord      2-T20    1   \n",
       "8      Muenchen                          Sued        GT2    1   \n",
       "9      Muenchen                          Sued        GT3    1   \n",
       "\n",
       "   Net electrical power [MW_el]  Heat power [MW_th]       Fuel  \\\n",
       "0                           117               106.0  crude oil   \n",
       "1                           117               106.0  crude oil   \n",
       "2                           206               270.0         NG   \n",
       "3                           206               270.0         NG   \n",
       "4                           178               217.0         NG   \n",
       "5                           167               203.0         NG   \n",
       "6                           260               455.0         NG   \n",
       "7                           333               550.0  hard coal   \n",
       "8                           108                92.0         NG   \n",
       "9                           108                92.0         NG   \n",
       "\n",
       "   Fuel emission factor [t/MWh_th]  \n",
       "0                           0.2639  \n",
       "1                           0.2639  \n",
       "2                           0.2010  \n",
       "3                           0.2010  \n",
       "4                           0.2010  \n",
       "5                           0.2010  \n",
       "6                           0.2010  \n",
       "7                           0.3368  \n",
       "8                           0.2010  \n",
       "9                           0.2010  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# INITIAL SETUP\n",
    "\n",
    "# data is currently provided for the years of 2016 to 2021\n",
    "years = [2016, 2017, 2018, 2019, 2020, 2021]\n",
    "\n",
    "# import main file for German powerplant data including EIC, ETS-ID, electrical and heat power, CHP\n",
    "blocks = pd.read_excel('input/blocks.xlsx')\n",
    "# output\n",
    "blocks.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e95cedd8",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>ResolutionCode</th>\n",
       "      <th>AreaCode</th>\n",
       "      <th>AreaTypeCode</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>MapCode</th>\n",
       "      <th>GenerationUnitEIC</th>\n",
       "      <th>PowerSystemResourceName</th>\n",
       "      <th>ProductionType</th>\n",
       "      <th>ActualGenerationOutput</th>\n",
       "      <th>ActualConsumption</th>\n",
       "      <th>InstalledGenCapacity</th>\n",
       "      <th>UpdateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-06 14:00:00.000</td>\n",
       "      <td>PT60M</td>\n",
       "      <td>10YSK-SEPS-----K</td>\n",
       "      <td>CTA</td>\n",
       "      <td>SK CTA</td>\n",
       "      <td>SK</td>\n",
       "      <td>24WG--EBOG41---7</td>\n",
       "      <td>Bohunice TG41</td>\n",
       "      <td>Nuclear</td>\n",
       "      <td>246.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2017-09-14 00:14:20</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-06 15:00:00.000</td>\n",
       "      <td>PT60M</td>\n",
       "      <td>10YSK-SEPS-----K</td>\n",
       "      <td>CTA</td>\n",
       "      <td>SK CTA</td>\n",
       "      <td>SK</td>\n",
       "      <td>24WG--EBOG41---7</td>\n",
       "      <td>Bohunice TG41</td>\n",
       "      <td>Nuclear</td>\n",
       "      <td>246.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2017-09-14 00:14:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-06 16:00:00.000</td>\n",
       "      <td>PT60M</td>\n",
       "      <td>10YSK-SEPS-----K</td>\n",
       "      <td>CTA</td>\n",
       "      <td>SK CTA</td>\n",
       "      <td>SK</td>\n",
       "      <td>24WG--EBOG41---7</td>\n",
       "      <td>Bohunice TG41</td>\n",
       "      <td>Nuclear</td>\n",
       "      <td>246.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2017-09-14 00:14:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-06 17:00:00.000</td>\n",
       "      <td>PT60M</td>\n",
       "      <td>10YSK-SEPS-----K</td>\n",
       "      <td>CTA</td>\n",
       "      <td>SK CTA</td>\n",
       "      <td>SK</td>\n",
       "      <td>24WG--EBOG41---7</td>\n",
       "      <td>Bohunice TG41</td>\n",
       "      <td>Nuclear</td>\n",
       "      <td>246.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2017-09-14 00:14:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 18:00:00.000</td>\n",
       "      <td>PT60M</td>\n",
       "      <td>10YSK-SEPS-----K</td>\n",
       "      <td>CTA</td>\n",
       "      <td>SK CTA</td>\n",
       "      <td>SK</td>\n",
       "      <td>24WG--EBOG41---7</td>\n",
       "      <td>Bohunice TG41</td>\n",
       "      <td>Nuclear</td>\n",
       "      <td>246.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2017-09-14 00:14:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-01-06 19:00:00.000</td>\n",
       "      <td>PT60M</td>\n",
       "      <td>10YSK-SEPS-----K</td>\n",
       "      <td>CTA</td>\n",
       "      <td>SK CTA</td>\n",
       "      <td>SK</td>\n",
       "      <td>24WG--EBOG41---7</td>\n",
       "      <td>Bohunice TG41</td>\n",
       "      <td>Nuclear</td>\n",
       "      <td>246.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2017-09-14 00:14:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-01-06 20:00:00.000</td>\n",
       "      <td>PT60M</td>\n",
       "      <td>10YSK-SEPS-----K</td>\n",
       "      <td>CTA</td>\n",
       "      <td>SK CTA</td>\n",
       "      <td>SK</td>\n",
       "      <td>24WG--EBOG41---7</td>\n",
       "      <td>Bohunice TG41</td>\n",
       "      <td>Nuclear</td>\n",
       "      <td>245.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2017-09-14 00:14:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-01-06 21:00:00.000</td>\n",
       "      <td>PT60M</td>\n",
       "      <td>10YSK-SEPS-----K</td>\n",
       "      <td>CTA</td>\n",
       "      <td>SK CTA</td>\n",
       "      <td>SK</td>\n",
       "      <td>24WG--EBOG41---7</td>\n",
       "      <td>Bohunice TG41</td>\n",
       "      <td>Nuclear</td>\n",
       "      <td>246.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2017-09-14 00:14:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-01-06 22:00:00.000</td>\n",
       "      <td>PT60M</td>\n",
       "      <td>10YSK-SEPS-----K</td>\n",
       "      <td>CTA</td>\n",
       "      <td>SK CTA</td>\n",
       "      <td>SK</td>\n",
       "      <td>24WG--EBOG41---7</td>\n",
       "      <td>Bohunice TG41</td>\n",
       "      <td>Nuclear</td>\n",
       "      <td>246.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2017-09-14 00:14:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-01-06 23:00:00.000</td>\n",
       "      <td>PT60M</td>\n",
       "      <td>10YSK-SEPS-----K</td>\n",
       "      <td>CTA</td>\n",
       "      <td>SK CTA</td>\n",
       "      <td>SK</td>\n",
       "      <td>24WG--EBOG41---7</td>\n",
       "      <td>Bohunice TG41</td>\n",
       "      <td>Nuclear</td>\n",
       "      <td>246.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2017-09-14 00:14:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DateTime ResolutionCode          AreaCode AreaTypeCode  \\\n",
       "0  2016-01-06 14:00:00.000          PT60M  10YSK-SEPS-----K          CTA   \n",
       "1  2016-01-06 15:00:00.000          PT60M  10YSK-SEPS-----K          CTA   \n",
       "2  2016-01-06 16:00:00.000          PT60M  10YSK-SEPS-----K          CTA   \n",
       "3  2016-01-06 17:00:00.000          PT60M  10YSK-SEPS-----K          CTA   \n",
       "4  2016-01-06 18:00:00.000          PT60M  10YSK-SEPS-----K          CTA   \n",
       "5  2016-01-06 19:00:00.000          PT60M  10YSK-SEPS-----K          CTA   \n",
       "6  2016-01-06 20:00:00.000          PT60M  10YSK-SEPS-----K          CTA   \n",
       "7  2016-01-06 21:00:00.000          PT60M  10YSK-SEPS-----K          CTA   \n",
       "8  2016-01-06 22:00:00.000          PT60M  10YSK-SEPS-----K          CTA   \n",
       "9  2016-01-06 23:00:00.000          PT60M  10YSK-SEPS-----K          CTA   \n",
       "\n",
       "  AreaName MapCode GenerationUnitEIC PowerSystemResourceName ProductionType  \\\n",
       "0   SK CTA      SK  24WG--EBOG41---7           Bohunice TG41        Nuclear   \n",
       "1   SK CTA      SK  24WG--EBOG41---7           Bohunice TG41        Nuclear   \n",
       "2   SK CTA      SK  24WG--EBOG41---7           Bohunice TG41        Nuclear   \n",
       "3   SK CTA      SK  24WG--EBOG41---7           Bohunice TG41        Nuclear   \n",
       "4   SK CTA      SK  24WG--EBOG41---7           Bohunice TG41        Nuclear   \n",
       "5   SK CTA      SK  24WG--EBOG41---7           Bohunice TG41        Nuclear   \n",
       "6   SK CTA      SK  24WG--EBOG41---7           Bohunice TG41        Nuclear   \n",
       "7   SK CTA      SK  24WG--EBOG41---7           Bohunice TG41        Nuclear   \n",
       "8   SK CTA      SK  24WG--EBOG41---7           Bohunice TG41        Nuclear   \n",
       "9   SK CTA      SK  24WG--EBOG41---7           Bohunice TG41        Nuclear   \n",
       "\n",
       "   ActualGenerationOutput  ActualConsumption  InstalledGenCapacity  \\\n",
       "0                  246.44                NaN                 250.0   \n",
       "1                  246.41                NaN                 250.0   \n",
       "2                  246.35                NaN                 250.0   \n",
       "3                  246.42                NaN                 250.0   \n",
       "4                  246.33                NaN                 250.0   \n",
       "5                  246.17                NaN                 250.0   \n",
       "6                  245.99                NaN                 250.0   \n",
       "7                  246.19                NaN                 250.0   \n",
       "8                  246.53                NaN                 250.0   \n",
       "9                  246.55                NaN                 250.0   \n",
       "\n",
       "            UpdateTime  \n",
       "0  2017-09-14 00:14:20  \n",
       "1  2017-09-14 00:14:20  \n",
       "2  2017-09-14 00:14:20  \n",
       "3  2017-09-14 00:14:20  \n",
       "4  2017-09-14 00:14:20  \n",
       "5  2017-09-14 00:14:20  \n",
       "6  2017-09-14 00:14:20  \n",
       "7  2017-09-14 00:14:20  \n",
       "8  2017-09-14 00:14:20  \n",
       "9  2017-09-14 00:14:20  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IMPORT OF GENERATION DATA\n",
    "# RUNTIME: Approx. 5 minutes\n",
    "\n",
    "# import block-wise generation data (source: entso-e)\n",
    "pp_gen_data = dict()\n",
    "months = [\"01\", \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\"]\n",
    "for y in years:\n",
    "    pp_gen_data[y] = []\n",
    "    for m in months:\n",
    "        pp_gen_data[y].append(pd.read_csv(\"input/entsoe/\" + str(y) + \"_\" + m + \"_ActualGenerationOutputPerGenerationUnit_16.1.A.csv\", header=0, delimiter='\\t', parse_dates=True))\n",
    "\n",
    "# output\n",
    "pp_gen_data[2016][0].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "01f772e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>REGISTRY_CODE</th>\n",
       "      <th>IDENTIFIER_IN_REG</th>\n",
       "      <th>INSTALLATION_NAME</th>\n",
       "      <th>INSTALLATION_IDENTIFIER</th>\n",
       "      <th>PERMIT_IDENTIFIER</th>\n",
       "      <th>MAIN_ACTIVITY_TYPE_CODE</th>\n",
       "      <th>ALLOCATION_2021</th>\n",
       "      <th>ALLOCATION_RESERVE_2021</th>\n",
       "      <th>ALLOCATION_TRANSITIONAL_2021</th>\n",
       "      <th>CH_ALLOCATION_2021</th>\n",
       "      <th>...</th>\n",
       "      <th>VERIFIED_EMISSIONS_2012</th>\n",
       "      <th>ALLOCATION_2011</th>\n",
       "      <th>VERIFIED_EMISSIONS_2011</th>\n",
       "      <th>ALLOCATION_2010</th>\n",
       "      <th>VERIFIED_EMISSIONS_2010</th>\n",
       "      <th>ALLOCATION_2009</th>\n",
       "      <th>VERIFIED_EMISSIONS_2009</th>\n",
       "      <th>ALLOCATION2008</th>\n",
       "      <th>VERIFIED_EMISSIONS_2008</th>\n",
       "      <th>ACCOUNT_CLOSURE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>DE</td>\n",
       "      <td>ADM Mainz GmbH</td>\n",
       "      <td>0010 Feuerungsanlage (Kesselhaus)</td>\n",
       "      <td>975</td>\n",
       "      <td>14310-0397</td>\n",
       "      <td>20</td>\n",
       "      <td>32443</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>60208</td>\n",
       "      <td>54405</td>\n",
       "      <td>61396</td>\n",
       "      <td>54405</td>\n",
       "      <td>62035</td>\n",
       "      <td>54405</td>\n",
       "      <td>59673</td>\n",
       "      <td>54405</td>\n",
       "      <td>65131</td>\n",
       "      <td>OPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>DE</td>\n",
       "      <td>Eurowings GmbH</td>\n",
       "      <td>2034</td>\n",
       "      <td>207497</td>\n",
       "      <td>19550-0693</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>17622</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>OPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>DE</td>\n",
       "      <td>Handelskonto der Trevo Aviation Ltd.</td>\n",
       "      <td>37070</td>\n",
       "      <td>200816</td>\n",
       "      <td>19550-0115</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>712</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>OPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>DE</td>\n",
       "      <td>CargoLogic Germany GmbH</td>\n",
       "      <td>46300</td>\n",
       "      <td>215501</td>\n",
       "      <td>19550-0711</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>OPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>DE</td>\n",
       "      <td>Emirates</td>\n",
       "      <td>9807</td>\n",
       "      <td>201598</td>\n",
       "      <td>19550-0733</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>39515</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>OPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>DE</td>\n",
       "      <td>SunExpress ETS holding account</td>\n",
       "      <td>10201</td>\n",
       "      <td>200479</td>\n",
       "      <td>19550-0241</td>\n",
       "      <td>10</td>\n",
       "      <td>1543</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>468225</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>OPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>DE</td>\n",
       "      <td>PEGASUS HAVA TASIMACILIGI A.S.</td>\n",
       "      <td>10690</td>\n",
       "      <td>200541</td>\n",
       "      <td>19550-0237</td>\n",
       "      <td>10</td>\n",
       "      <td>1288</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>2225</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>OPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183</th>\n",
       "      <td>DE</td>\n",
       "      <td>Luxaviation Germany GmbH</td>\n",
       "      <td>114</td>\n",
       "      <td>212160</td>\n",
       "      <td>19550-0712</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>OPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>DE</td>\n",
       "      <td>Private Wings Flugcharter GmbH</td>\n",
       "      <td>12196</td>\n",
       "      <td>206844</td>\n",
       "      <td>19550-0373</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>10497</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>OPEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>DE</td>\n",
       "      <td>Emil Capital Partners, LLC</td>\n",
       "      <td>12213</td>\n",
       "      <td>203172</td>\n",
       "      <td>19550-0260</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>547</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>OPEN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     REGISTRY_CODE                     IDENTIFIER_IN_REG  \\\n",
       "1176            DE                        ADM Mainz GmbH   \n",
       "1177            DE                        Eurowings GmbH   \n",
       "1178            DE  Handelskonto der Trevo Aviation Ltd.   \n",
       "1179            DE               CargoLogic Germany GmbH   \n",
       "1180            DE                              Emirates   \n",
       "1181            DE        SunExpress ETS holding account   \n",
       "1182            DE        PEGASUS HAVA TASIMACILIGI A.S.   \n",
       "1183            DE              Luxaviation Germany GmbH   \n",
       "1184            DE        Private Wings Flugcharter GmbH   \n",
       "1185            DE            Emil Capital Partners, LLC   \n",
       "\n",
       "                      INSTALLATION_NAME  INSTALLATION_IDENTIFIER  \\\n",
       "1176  0010 Feuerungsanlage (Kesselhaus)                      975   \n",
       "1177                               2034                   207497   \n",
       "1178                              37070                   200816   \n",
       "1179                              46300                   215501   \n",
       "1180                               9807                   201598   \n",
       "1181                              10201                   200479   \n",
       "1182                              10690                   200541   \n",
       "1183                                114                   212160   \n",
       "1184                              12196                   206844   \n",
       "1185                              12213                   203172   \n",
       "\n",
       "     PERMIT_IDENTIFIER  MAIN_ACTIVITY_TYPE_CODE  ALLOCATION_2021  \\\n",
       "1176        14310-0397                       20            32443   \n",
       "1177        19550-0693                       10               -1   \n",
       "1178        19550-0115                       10               23   \n",
       "1179        19550-0711                       10               -1   \n",
       "1180        19550-0733                       10               -1   \n",
       "1181        19550-0241                       10             1543   \n",
       "1182        19550-0237                       10             1288   \n",
       "1183        19550-0712                       10               -1   \n",
       "1184        19550-0373                       10               -1   \n",
       "1185        19550-0260                       10               -1   \n",
       "\n",
       "      ALLOCATION_RESERVE_2021  ALLOCATION_TRANSITIONAL_2021  \\\n",
       "1176                        0                             0   \n",
       "1177                       -1                            -1   \n",
       "1178                        0                             0   \n",
       "1179                       -1                            -1   \n",
       "1180                       -1                            -1   \n",
       "1181                        0                             0   \n",
       "1182                        0                             0   \n",
       "1183                       -1                            -1   \n",
       "1184                       -1                            -1   \n",
       "1185                       -1                            -1   \n",
       "\n",
       "      CH_ALLOCATION_2021  ... VERIFIED_EMISSIONS_2012 ALLOCATION_2011  \\\n",
       "1176                  -1  ...                   60208           54405   \n",
       "1177               17622  ...                      -1              -1   \n",
       "1178                  -1  ...                     712              -1   \n",
       "1179                  -1  ...                      -1              -1   \n",
       "1180                  -1  ...                   39515              -1   \n",
       "1181                  -1  ...                  468225              -1   \n",
       "1182                  -1  ...                    2225              -1   \n",
       "1183                  -1  ...                      -1              -1   \n",
       "1184                  -1  ...                   10497              -1   \n",
       "1185                  -1  ...                     547              -1   \n",
       "\n",
       "      VERIFIED_EMISSIONS_2011  ALLOCATION_2010  VERIFIED_EMISSIONS_2010  \\\n",
       "1176                    61396            54405                    62035   \n",
       "1177                       -1               -1                       -1   \n",
       "1178                       -1               -1                       -1   \n",
       "1179                       -1               -1                       -1   \n",
       "1180                       -1               -1                       -1   \n",
       "1181                       -1               -1                       -1   \n",
       "1182                       -1               -1                       -1   \n",
       "1183                       -1               -1                       -1   \n",
       "1184                       -1               -1                       -1   \n",
       "1185                       -1               -1                       -1   \n",
       "\n",
       "      ALLOCATION_2009 VERIFIED_EMISSIONS_2009 ALLOCATION2008  \\\n",
       "1176            54405                   59673          54405   \n",
       "1177               -1                      -1             -1   \n",
       "1178               -1                      -1             -1   \n",
       "1179               -1                      -1             -1   \n",
       "1180               -1                      -1             -1   \n",
       "1181               -1                      -1             -1   \n",
       "1182               -1                      -1             -1   \n",
       "1183               -1                      -1             -1   \n",
       "1184               -1                      -1             -1   \n",
       "1185               -1                      -1             -1   \n",
       "\n",
       "      VERIFIED_EMISSIONS_2008  ACCOUNT_CLOSURE  \n",
       "1176                    65131             OPEN  \n",
       "1177                       -1             OPEN  \n",
       "1178                       -1             OPEN  \n",
       "1179                       -1             OPEN  \n",
       "1180                       -1             OPEN  \n",
       "1181                       -1             OPEN  \n",
       "1182                       -1             OPEN  \n",
       "1183                       -1             OPEN  \n",
       "1184                       -1             OPEN  \n",
       "1185                       -1             OPEN  \n",
       "\n",
       "[10 rows x 57 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IMPORT OF EMISSIONS AND ALLOCATION DATA\n",
    "\n",
    "# import data on verified emissions and free emission allocations (source: EC)\n",
    "EC_data = pd.read_excel('input/EC/verified_emissions_2021_en.xlsx')\n",
    "EC_data = EC_data[EC_data.REGISTRY_CODE == 'DE']\n",
    "\n",
    "# output\n",
    "EC_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a9927800",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GENERATION OF EIC AND ETS-ID LISTS\n",
    "\n",
    "# list of all powerplant block EIC codes\n",
    "eic_list = blocks['EIC'].unique()\n",
    "\n",
    "# list of all powerplant location ETS-ID codes\n",
    "ets_list = blocks['ETS-ID'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f80a31c",
   "metadata": {},
   "source": [
    "# Calculation of yearly generation\n",
    "\n",
    "Generation data is provided by entso-e on an hourly basis. The hourly data is aggregated to the per-year level and adjusted for missing data points. The data is EIC-specific, and thus evaluated on the block level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b91657aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIC</th>\n",
       "      <th>Generation elec. 2016 [MWh_el]</th>\n",
       "      <th>Generation elec. 2017 [MWh_el]</th>\n",
       "      <th>Generation elec. 2018 [MWh_el]</th>\n",
       "      <th>Generation elec. 2019 [MWh_el]</th>\n",
       "      <th>Generation elec. 2020 [MWh_el]</th>\n",
       "      <th>Generation elec. 2021 [MWh_el]</th>\n",
       "      <th>No. data points 2016</th>\n",
       "      <th>No. data points 2017</th>\n",
       "      <th>No. data points 2018</th>\n",
       "      <th>No. data points 2019</th>\n",
       "      <th>No. data points 2020</th>\n",
       "      <th>No. data points 2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11WD8SCHW5X---19</td>\n",
       "      <td>7.931348e+05</td>\n",
       "      <td>7.716718e+05</td>\n",
       "      <td>648314.25</td>\n",
       "      <td>789048.50</td>\n",
       "      <td>6.815136e+05</td>\n",
       "      <td>643387.75</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8754.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11WD8SCHW5X---27</td>\n",
       "      <td>7.294016e+05</td>\n",
       "      <td>8.040417e+05</td>\n",
       "      <td>811025.00</td>\n",
       "      <td>562484.00</td>\n",
       "      <td>7.060202e+05</td>\n",
       "      <td>635963.00</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8754.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11WD7LUDW2GGT11E</td>\n",
       "      <td>1.517039e+06</td>\n",
       "      <td>1.402270e+06</td>\n",
       "      <td>1450318.24</td>\n",
       "      <td>1470420.00</td>\n",
       "      <td>1.477277e+06</td>\n",
       "      <td>1399557.59</td>\n",
       "      <td>8779.0</td>\n",
       "      <td>8754.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11WD7LUDW2GGT12C</td>\n",
       "      <td>1.417652e+06</td>\n",
       "      <td>1.406764e+06</td>\n",
       "      <td>1469136.18</td>\n",
       "      <td>1288124.15</td>\n",
       "      <td>1.327720e+06</td>\n",
       "      <td>1571289.29</td>\n",
       "      <td>8779.0</td>\n",
       "      <td>8754.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11WD7LUDW5GSDG1Q</td>\n",
       "      <td>1.109093e+06</td>\n",
       "      <td>1.277882e+06</td>\n",
       "      <td>1131934.27</td>\n",
       "      <td>1142469.02</td>\n",
       "      <td>1.076560e+06</td>\n",
       "      <td>1272899.93</td>\n",
       "      <td>8779.0</td>\n",
       "      <td>8754.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11WD7LUDW5GSDG2O</td>\n",
       "      <td>1.261691e+06</td>\n",
       "      <td>1.180330e+06</td>\n",
       "      <td>954661.79</td>\n",
       "      <td>976565.21</td>\n",
       "      <td>1.027428e+06</td>\n",
       "      <td>1262035.41</td>\n",
       "      <td>8779.0</td>\n",
       "      <td>8754.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11WD8DRES5X----D</td>\n",
       "      <td>1.474537e+06</td>\n",
       "      <td>1.440658e+06</td>\n",
       "      <td>1370235.07</td>\n",
       "      <td>1410970.59</td>\n",
       "      <td>1.390362e+06</td>\n",
       "      <td>1474498.14</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8754.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11WD2NOR20001055</td>\n",
       "      <td>1.557678e+06</td>\n",
       "      <td>1.428692e+06</td>\n",
       "      <td>1109165.92</td>\n",
       "      <td>1118109.21</td>\n",
       "      <td>5.962193e+05</td>\n",
       "      <td>662376.84</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11WD2S1G2000097U</td>\n",
       "      <td>9.154619e+04</td>\n",
       "      <td>1.243172e+05</td>\n",
       "      <td>104722.86</td>\n",
       "      <td>129811.33</td>\n",
       "      <td>3.042117e+05</td>\n",
       "      <td>261150.59</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11WD2S1G3000098K</td>\n",
       "      <td>1.070943e+05</td>\n",
       "      <td>1.075163e+05</td>\n",
       "      <td>104747.26</td>\n",
       "      <td>136587.14</td>\n",
       "      <td>3.083385e+05</td>\n",
       "      <td>203072.27</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8784.0</td>\n",
       "      <td>8760.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                EIC  Generation elec. 2016 [MWh_el]  \\\n",
       "0  11WD8SCHW5X---19                    7.931348e+05   \n",
       "1  11WD8SCHW5X---27                    7.294016e+05   \n",
       "2  11WD7LUDW2GGT11E                    1.517039e+06   \n",
       "3  11WD7LUDW2GGT12C                    1.417652e+06   \n",
       "4  11WD7LUDW5GSDG1Q                    1.109093e+06   \n",
       "5  11WD7LUDW5GSDG2O                    1.261691e+06   \n",
       "6  11WD8DRES5X----D                    1.474537e+06   \n",
       "7  11WD2NOR20001055                    1.557678e+06   \n",
       "8  11WD2S1G2000097U                    9.154619e+04   \n",
       "9  11WD2S1G3000098K                    1.070943e+05   \n",
       "\n",
       "   Generation elec. 2017 [MWh_el]  Generation elec. 2018 [MWh_el]  \\\n",
       "0                    7.716718e+05                       648314.25   \n",
       "1                    8.040417e+05                       811025.00   \n",
       "2                    1.402270e+06                      1450318.24   \n",
       "3                    1.406764e+06                      1469136.18   \n",
       "4                    1.277882e+06                      1131934.27   \n",
       "5                    1.180330e+06                       954661.79   \n",
       "6                    1.440658e+06                      1370235.07   \n",
       "7                    1.428692e+06                      1109165.92   \n",
       "8                    1.243172e+05                       104722.86   \n",
       "9                    1.075163e+05                       104747.26   \n",
       "\n",
       "   Generation elec. 2019 [MWh_el]  Generation elec. 2020 [MWh_el]  \\\n",
       "0                       789048.50                    6.815136e+05   \n",
       "1                       562484.00                    7.060202e+05   \n",
       "2                      1470420.00                    1.477277e+06   \n",
       "3                      1288124.15                    1.327720e+06   \n",
       "4                      1142469.02                    1.076560e+06   \n",
       "5                       976565.21                    1.027428e+06   \n",
       "6                      1410970.59                    1.390362e+06   \n",
       "7                      1118109.21                    5.962193e+05   \n",
       "8                       129811.33                    3.042117e+05   \n",
       "9                       136587.14                    3.083385e+05   \n",
       "\n",
       "   Generation elec. 2021 [MWh_el]  No. data points 2016  No. data points 2017  \\\n",
       "0                       643387.75                8784.0                8754.0   \n",
       "1                       635963.00                8784.0                8754.0   \n",
       "2                      1399557.59                8779.0                8754.0   \n",
       "3                      1571289.29                8779.0                8754.0   \n",
       "4                      1272899.93                8779.0                8754.0   \n",
       "5                      1262035.41                8779.0                8754.0   \n",
       "6                      1474498.14                8784.0                8754.0   \n",
       "7                       662376.84                8784.0                8760.0   \n",
       "8                       261150.59                8784.0                8760.0   \n",
       "9                       203072.27                8784.0                8760.0   \n",
       "\n",
       "   No. data points 2018  No. data points 2019  No. data points 2020  \\\n",
       "0                8760.0                8760.0                8784.0   \n",
       "1                8760.0                8760.0                8784.0   \n",
       "2                8760.0                8760.0                8784.0   \n",
       "3                8760.0                8760.0                8784.0   \n",
       "4                8760.0                8760.0                8784.0   \n",
       "5                8760.0                8760.0                8784.0   \n",
       "6                8760.0                8760.0                8784.0   \n",
       "7                8760.0                8760.0                8784.0   \n",
       "8                8760.0                8760.0                8784.0   \n",
       "9                8760.0                8760.0                8784.0   \n",
       "\n",
       "   No. data points 2021  \n",
       "0                8760.0  \n",
       "1                8760.0  \n",
       "2                8760.0  \n",
       "3                8760.0  \n",
       "4                8760.0  \n",
       "5                8760.0  \n",
       "6                8760.0  \n",
       "7                8760.0  \n",
       "8                8760.0  \n",
       "9                8760.0  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CALCULATION OF YEARLY GENERATION PER BLOCK\n",
    "# RUNTIME: Approx. 30 min\n",
    "\n",
    "gen = pd.DataFrame(eic_list, columns=['EIC'])\n",
    "# create empty columns for yearly generation amounts and number of data points\n",
    "for y in years:\n",
    "    gen['Generation elec. ' + str(y) + ' [MWh_el]'] = 0\n",
    "    gen['No. data points ' + str(y)] = 0\n",
    "\n",
    "# calculate total generation per block per year and extract the number of data points\n",
    "for y in years:\n",
    "    gen_data_y = pp_gen_data[y]\n",
    "    for m in range(12):\n",
    "        gen_data_m = gen_data_y[m]\n",
    "        for p in eic_list:\n",
    "            # extract powerplant-specific hourly generation data (one month)\n",
    "            gen_data_m_p = gen_data_m[gen_data_m['GenerationUnitEIC']==p]\n",
    "            gen_m_p = gen_data_m_p.sum()['ActualGenerationOutput']\n",
    "            # aggregate monthly generation data to per year level\n",
    "            gen.loc[gen['EIC']==p,'Generation elec. ' + str(y) + ' [MWh_el]'] += gen_m_p\n",
    "\n",
    "            # extract the number of hourly generation data points\n",
    "            dp_m_p = len(gen_data_m_p.index)\n",
    "            # aggregate monthly data points to per year level\n",
    "            gen.loc[gen['EIC']==p, 'No. data points '+str(y)] += dp_m_p\n",
    "\n",
    "# if generation is reported for every hour of the year, 8760 data points are available and no adjustemnts are needed\n",
    "# in case that data is missing, the generation is scaled based on the number of data points available\n",
    "for index, row in gen.iterrows():\n",
    "    for y in years:\n",
    "        if row['No. data points '+str(y)] != 0 & row['No. data points '+str(y)] != 8760:\n",
    "            # calculate correction factor as 8760/number of data points\n",
    "            corr_fac = 8760/row['No. data points '+str(y)]\n",
    "            # adjust generation based on correction factor\n",
    "            gen.loc[index,'Generation elec. '+str(y) + ' [MWh_el]'] = gen.loc[index,'Generation elec. '+str(y) + ' [MWh_el]'] * corr_fac\n",
    "\n",
    "# add generation data to the main powerplant block DataFrame\n",
    "for p in eic_list:\n",
    "    for y in years:\n",
    "        blocks.loc[blocks['EIC']==p, 'Generation elec. '+str(y)+' [MWh_el]'] = gen.loc[gen['EIC']==p, 'Generation elec. '+str(y)+' [MWh_el]'].values[0]\n",
    "        blocks.loc[blocks['EIC']==p, 'No. data points '+str(y)] = int(gen.loc[gen['EIC']==p, 'No. data points '+str(y)])\n",
    "\n",
    "# output\n",
    "columns = ['EIC'] + ['Generation elec. ' + str(y) + \" [MWh_el]\" for y in years] + ['No. data points ' + str(y) for y in years]\n",
    "blocks.loc[:,columns].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "866da481",
   "metadata": {},
   "source": [
    "# Aggregation and accessing allocation/emission data\n",
    "\n",
    "While generation data is provided on powerplant block level (EIC-specific), emission and allocation data is provided on powerplant installation level (ETS ID-specific). Thus, aggregation of multiple blocks within the same installation has to be done before accessing the ETS data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "baf02c05",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ETS-ID</th>\n",
       "      <th>City</th>\n",
       "      <th>Plant name</th>\n",
       "      <th>Net electrical power [MW_el]</th>\n",
       "      <th>Heat power [MW_th]</th>\n",
       "      <th>CHP</th>\n",
       "      <th>Fuel</th>\n",
       "      <th>Thermal fuel emission factor [t/MWh_th]</th>\n",
       "      <th>Generation elec. 2016 [MWh_el]</th>\n",
       "      <th>Generation elec. 2017 [MWh_el]</th>\n",
       "      <th>Generation elec. 2018 [MWh_el]</th>\n",
       "      <th>Generation elec. 2019 [MWh_el]</th>\n",
       "      <th>Generation elec. 2020 [MWh_el]</th>\n",
       "      <th>Generation elec. 2021 [MWh_el]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>Schwedt</td>\n",
       "      <td>IKS PCK Schwedt</td>\n",
       "      <td>234</td>\n",
       "      <td>212.0</td>\n",
       "      <td>True</td>\n",
       "      <td>crude oil</td>\n",
       "      <td>0.2639</td>\n",
       "      <td>1.522536e+06</td>\n",
       "      <td>1.575714e+06</td>\n",
       "      <td>1.459339e+06</td>\n",
       "      <td>1.351532e+06</td>\n",
       "      <td>1.387534e+06</td>\n",
       "      <td>1.279351e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>662</td>\n",
       "      <td>Ludwigshafen</td>\n",
       "      <td>Mitte</td>\n",
       "      <td>757</td>\n",
       "      <td>960.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.2010</td>\n",
       "      <td>5.305475e+06</td>\n",
       "      <td>5.267247e+06</td>\n",
       "      <td>5.006050e+06</td>\n",
       "      <td>4.877578e+06</td>\n",
       "      <td>4.908986e+06</td>\n",
       "      <td>5.505782e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>739</td>\n",
       "      <td>Dresden</td>\n",
       "      <td>HKW Dresden Nossener Bruecke</td>\n",
       "      <td>260</td>\n",
       "      <td>455.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.2010</td>\n",
       "      <td>1.474537e+06</td>\n",
       "      <td>1.440658e+06</td>\n",
       "      <td>1.370235e+06</td>\n",
       "      <td>1.410971e+06</td>\n",
       "      <td>1.390362e+06</td>\n",
       "      <td>1.474498e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741</td>\n",
       "      <td>Muenchen</td>\n",
       "      <td>Nord</td>\n",
       "      <td>333</td>\n",
       "      <td>550.0</td>\n",
       "      <td>True</td>\n",
       "      <td>hard coal</td>\n",
       "      <td>0.3368</td>\n",
       "      <td>1.557678e+06</td>\n",
       "      <td>1.428692e+06</td>\n",
       "      <td>1.109166e+06</td>\n",
       "      <td>1.118109e+06</td>\n",
       "      <td>5.962193e+05</td>\n",
       "      <td>6.623768e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>750</td>\n",
       "      <td>Muenchen</td>\n",
       "      <td>Sued</td>\n",
       "      <td>616</td>\n",
       "      <td>646.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.2010</td>\n",
       "      <td>1.520957e+06</td>\n",
       "      <td>1.950211e+06</td>\n",
       "      <td>1.737138e+06</td>\n",
       "      <td>1.804886e+06</td>\n",
       "      <td>2.006706e+06</td>\n",
       "      <td>1.130618e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>795</td>\n",
       "      <td>Burghausen</td>\n",
       "      <td>Burghausen</td>\n",
       "      <td>120</td>\n",
       "      <td>350.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.2010</td>\n",
       "      <td>5.714265e+05</td>\n",
       "      <td>2.302358e+05</td>\n",
       "      <td>2.310816e+05</td>\n",
       "      <td>1.298679e+05</td>\n",
       "      <td>1.441437e+05</td>\n",
       "      <td>2.061632e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>839</td>\n",
       "      <td>Ensdorf</td>\n",
       "      <td>Ensdorf</td>\n",
       "      <td>389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>hard coal</td>\n",
       "      <td>0.3368</td>\n",
       "      <td>1.049334e+06</td>\n",
       "      <td>1.024550e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>845</td>\n",
       "      <td>Querschied</td>\n",
       "      <td>Weiher</td>\n",
       "      <td>656</td>\n",
       "      <td>30.0</td>\n",
       "      <td>True</td>\n",
       "      <td>hard coal</td>\n",
       "      <td>0.3368</td>\n",
       "      <td>1.389828e+06</td>\n",
       "      <td>4.127089e+05</td>\n",
       "      <td>1.730315e+04</td>\n",
       "      <td>3.775472e+04</td>\n",
       "      <td>6.399022e+04</td>\n",
       "      <td>7.546811e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>852</td>\n",
       "      <td>Bexbach</td>\n",
       "      <td>Bexbach</td>\n",
       "      <td>726</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>hard coal</td>\n",
       "      <td>0.3368</td>\n",
       "      <td>2.030855e+06</td>\n",
       "      <td>5.621157e+05</td>\n",
       "      <td>3.167725e+04</td>\n",
       "      <td>5.833684e+04</td>\n",
       "      <td>1.400030e+05</td>\n",
       "      <td>2.293272e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>889</td>\n",
       "      <td>Voelklingen-Fenne</td>\n",
       "      <td>Voelklingen-Fenne</td>\n",
       "      <td>390</td>\n",
       "      <td>395.0</td>\n",
       "      <td>True</td>\n",
       "      <td>hard coal</td>\n",
       "      <td>0.3368</td>\n",
       "      <td>1.362650e+06</td>\n",
       "      <td>9.654558e+05</td>\n",
       "      <td>9.037352e+05</td>\n",
       "      <td>5.063449e+05</td>\n",
       "      <td>4.798329e+05</td>\n",
       "      <td>6.420249e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ETS-ID               City                    Plant name  \\\n",
       "0      19            Schwedt               IKS PCK Schwedt   \n",
       "1     662       Ludwigshafen                         Mitte   \n",
       "2     739            Dresden  HKW Dresden Nossener Bruecke   \n",
       "3     741           Muenchen                          Nord   \n",
       "4     750           Muenchen                          Sued   \n",
       "5     795         Burghausen                    Burghausen   \n",
       "6     839            Ensdorf                       Ensdorf   \n",
       "7     845         Querschied                        Weiher   \n",
       "8     852            Bexbach                       Bexbach   \n",
       "9     889  Voelklingen-Fenne             Voelklingen-Fenne   \n",
       "\n",
       "   Net electrical power [MW_el]  Heat power [MW_th]    CHP       Fuel  \\\n",
       "0                           234               212.0   True  crude oil   \n",
       "1                           757               960.0   True         NG   \n",
       "2                           260               455.0   True         NG   \n",
       "3                           333               550.0   True  hard coal   \n",
       "4                           616               646.0   True         NG   \n",
       "5                           120               350.0   True         NG   \n",
       "6                           389                 0.0   True  hard coal   \n",
       "7                           656                30.0   True  hard coal   \n",
       "8                           726                 0.0  False  hard coal   \n",
       "9                           390               395.0   True  hard coal   \n",
       "\n",
       "   Thermal fuel emission factor [t/MWh_th]  Generation elec. 2016 [MWh_el]  \\\n",
       "0                                   0.2639                    1.522536e+06   \n",
       "1                                   0.2010                    5.305475e+06   \n",
       "2                                   0.2010                    1.474537e+06   \n",
       "3                                   0.3368                    1.557678e+06   \n",
       "4                                   0.2010                    1.520957e+06   \n",
       "5                                   0.2010                    5.714265e+05   \n",
       "6                                   0.3368                    1.049334e+06   \n",
       "7                                   0.3368                    1.389828e+06   \n",
       "8                                   0.3368                    2.030855e+06   \n",
       "9                                   0.3368                    1.362650e+06   \n",
       "\n",
       "   Generation elec. 2017 [MWh_el]  Generation elec. 2018 [MWh_el]  \\\n",
       "0                    1.575714e+06                    1.459339e+06   \n",
       "1                    5.267247e+06                    5.006050e+06   \n",
       "2                    1.440658e+06                    1.370235e+06   \n",
       "3                    1.428692e+06                    1.109166e+06   \n",
       "4                    1.950211e+06                    1.737138e+06   \n",
       "5                    2.302358e+05                    2.310816e+05   \n",
       "6                    1.024550e+06                    0.000000e+00   \n",
       "7                    4.127089e+05                    1.730315e+04   \n",
       "8                    5.621157e+05                    3.167725e+04   \n",
       "9                    9.654558e+05                    9.037352e+05   \n",
       "\n",
       "   Generation elec. 2019 [MWh_el]  Generation elec. 2020 [MWh_el]  \\\n",
       "0                    1.351532e+06                    1.387534e+06   \n",
       "1                    4.877578e+06                    4.908986e+06   \n",
       "2                    1.410971e+06                    1.390362e+06   \n",
       "3                    1.118109e+06                    5.962193e+05   \n",
       "4                    1.804886e+06                    2.006706e+06   \n",
       "5                    1.298679e+05                    1.441437e+05   \n",
       "6                    0.000000e+00                    0.000000e+00   \n",
       "7                    3.775472e+04                    6.399022e+04   \n",
       "8                    5.833684e+04                    1.400030e+05   \n",
       "9                    5.063449e+05                    4.798329e+05   \n",
       "\n",
       "   Generation elec. 2021 [MWh_el]  \n",
       "0                    1.279351e+06  \n",
       "1                    5.505782e+06  \n",
       "2                    1.474498e+06  \n",
       "3                    6.623768e+05  \n",
       "4                    1.130618e+06  \n",
       "5                    2.061632e+05  \n",
       "6                    0.000000e+00  \n",
       "7                    7.546811e+04  \n",
       "8                    2.293272e+05  \n",
       "9                    6.420249e+05  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# AGGREGATION TO PLANT LEVEL\n",
    "\n",
    "# iterate through ETS-IDs and aggregate the data\n",
    "installations = []\n",
    "for id in ets_list:\n",
    "    # extract raw data\n",
    "    id_data_raw = blocks.loc[blocks['ETS-ID']==id]\n",
    "    id_data = dict()\n",
    "    id_data['ETS-ID'] = id\n",
    "    id_data['City'] = id_data_raw['City'].iloc[0]\n",
    "    id_data['Plant name'] = id_data_raw['Plant name'].iloc[0]\n",
    "    # nominal heat and electricity power of the blocks get summed\n",
    "    id_data['Net electrical power [MW_el]'] = id_data_raw['Net electrical power [MW_el]'].sum()\n",
    "    id_data['Heat power [MW_th]'] = id_data_raw['Heat power [MW_th]'].sum()\n",
    "    # if one block of the plant uses CHP, the whole plant is marked as CHP plant\n",
    "    id_data['CHP'] = id_data_raw['CHP'].any()\n",
    "    # differentiation between single and mixed fuel type plants\n",
    "    fuels = id_data_raw['Fuel'].unique()\n",
    "    fuel = fuels[0] if len(fuels)==1 else 'mixed'\n",
    "    id_data['Fuel'] = fuel\n",
    "    if fuel == 'mixed':\n",
    "        id_data['Thermal fuel emission factor [t/MWh_th]'] = np.NaN\n",
    "    else:\n",
    "        id_data['Thermal fuel emission factor [t/MWh_th]'] = id_data_raw['Fuel emission factor [t/MWh_th]'].iloc[0]\n",
    "\n",
    "    for y in years:\n",
    "        # generation and calculated emissions of the blocks get summed\n",
    "        gen_id_y = id_data_raw['Generation elec. ' + str(y) + ' [MWh_el]'].sum()\n",
    "        id_data['Generation elec. ' + str(y) + ' [MWh_el]'] = gen_id_y\n",
    "    \n",
    "    installations.append(id_data)\n",
    "installations = pd.DataFrame(installations)\n",
    "\n",
    "# output\n",
    "installations.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "c89824ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ETS-ID</th>\n",
       "      <th>Allocation 2016 [t]</th>\n",
       "      <th>Allocation 2017 [t]</th>\n",
       "      <th>Allocation 2018 [t]</th>\n",
       "      <th>Allocation 2019 [t]</th>\n",
       "      <th>Allocation 2020 [t]</th>\n",
       "      <th>Allocation 2021 [t]</th>\n",
       "      <th>Emissions 2016 [t]</th>\n",
       "      <th>Emissions 2017 [t]</th>\n",
       "      <th>Emissions 2018 [t]</th>\n",
       "      <th>Emissions 2019 [t]</th>\n",
       "      <th>Emissions 2020 [t]</th>\n",
       "      <th>Emissions 2021 [t]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>1890540.0</td>\n",
       "      <td>1851833.0</td>\n",
       "      <td>1812890.0</td>\n",
       "      <td>1773607.0</td>\n",
       "      <td>1734316.0</td>\n",
       "      <td>1736265.0</td>\n",
       "      <td>3766493.0</td>\n",
       "      <td>3842978.0</td>\n",
       "      <td>3789945.0</td>\n",
       "      <td>3418710.0</td>\n",
       "      <td>3515850.0</td>\n",
       "      <td>3480163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>662</td>\n",
       "      <td>6569.0</td>\n",
       "      <td>6448.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26730.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>66989.0</td>\n",
       "      <td>58638.0</td>\n",
       "      <td>2532.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>739</td>\n",
       "      <td>214344.0</td>\n",
       "      <td>184758.0</td>\n",
       "      <td>156134.0</td>\n",
       "      <td>128432.0</td>\n",
       "      <td>101727.0</td>\n",
       "      <td>63624.0</td>\n",
       "      <td>752872.0</td>\n",
       "      <td>741516.0</td>\n",
       "      <td>724518.0</td>\n",
       "      <td>725752.0</td>\n",
       "      <td>713976.0</td>\n",
       "      <td>770563.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741</td>\n",
       "      <td>228323.0</td>\n",
       "      <td>196808.0</td>\n",
       "      <td>166315.0</td>\n",
       "      <td>136809.0</td>\n",
       "      <td>108361.0</td>\n",
       "      <td>71291.0</td>\n",
       "      <td>1827049.0</td>\n",
       "      <td>1701489.0</td>\n",
       "      <td>1346205.0</td>\n",
       "      <td>1462508.0</td>\n",
       "      <td>881022.0</td>\n",
       "      <td>971094.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>750</td>\n",
       "      <td>393.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1623.0</td>\n",
       "      <td>2528.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>795</td>\n",
       "      <td>437870.0</td>\n",
       "      <td>429831.0</td>\n",
       "      <td>421793.0</td>\n",
       "      <td>413754.0</td>\n",
       "      <td>405716.0</td>\n",
       "      <td>194858.0</td>\n",
       "      <td>684752.0</td>\n",
       "      <td>688971.0</td>\n",
       "      <td>664276.0</td>\n",
       "      <td>521038.0</td>\n",
       "      <td>560948.0</td>\n",
       "      <td>580468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>839</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63526.0</td>\n",
       "      <td>46654.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>845</td>\n",
       "      <td>2853.0</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1355318.0</td>\n",
       "      <td>408420.0</td>\n",
       "      <td>24624.0</td>\n",
       "      <td>47980.0</td>\n",
       "      <td>73995.0</td>\n",
       "      <td>95403.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1843866.0</td>\n",
       "      <td>508496.0</td>\n",
       "      <td>37768.0</td>\n",
       "      <td>79358.0</td>\n",
       "      <td>167776.0</td>\n",
       "      <td>263604.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>889</td>\n",
       "      <td>69359.0</td>\n",
       "      <td>63044.0</td>\n",
       "      <td>56919.0</td>\n",
       "      <td>38356.0</td>\n",
       "      <td>17616.0</td>\n",
       "      <td>5213.0</td>\n",
       "      <td>870263.0</td>\n",
       "      <td>678991.0</td>\n",
       "      <td>646315.0</td>\n",
       "      <td>377923.0</td>\n",
       "      <td>434753.0</td>\n",
       "      <td>335026.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ETS-ID  Allocation 2016 [t]  Allocation 2017 [t]  Allocation 2018 [t]  \\\n",
       "0      19            1890540.0            1851833.0            1812890.0   \n",
       "1     662               6569.0               6448.0                  0.0   \n",
       "2     739             214344.0             184758.0             156134.0   \n",
       "3     741             228323.0             196808.0             166315.0   \n",
       "4     750                393.0                338.0                286.0   \n",
       "5     795             437870.0             429831.0             421793.0   \n",
       "6     839                233.0                  0.0                  0.0   \n",
       "7     845               2853.0               2460.0               1039.0   \n",
       "8     852                  NaN                  NaN                  NaN   \n",
       "9     889              69359.0              63044.0              56919.0   \n",
       "\n",
       "   Allocation 2019 [t]  Allocation 2020 [t]  Allocation 2021 [t]  \\\n",
       "0            1773607.0            1734316.0            1736265.0   \n",
       "1                  0.0                  0.0                  0.0   \n",
       "2             128432.0             101727.0              63624.0   \n",
       "3             136809.0             108361.0              71291.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "5             413754.0             405716.0             194858.0   \n",
       "6                  0.0                  0.0                  0.0   \n",
       "7                  0.0                  0.0                  0.0   \n",
       "8                  NaN                  NaN                  NaN   \n",
       "9              38356.0              17616.0               5213.0   \n",
       "\n",
       "   Emissions 2016 [t]  Emissions 2017 [t]  Emissions 2018 [t]  \\\n",
       "0           3766493.0           3842978.0           3789945.0   \n",
       "1             26730.0              1860.0                23.0   \n",
       "2            752872.0            741516.0            724518.0   \n",
       "3           1827049.0           1701489.0           1346205.0   \n",
       "4              1623.0              2528.0               538.0   \n",
       "5            684752.0            688971.0            664276.0   \n",
       "6             63526.0             46654.0                 0.0   \n",
       "7           1355318.0            408420.0             24624.0   \n",
       "8           1843866.0            508496.0             37768.0   \n",
       "9            870263.0            678991.0            646315.0   \n",
       "\n",
       "   Emissions 2019 [t]  Emissions 2020 [t]  Emissions 2021 [t]  \n",
       "0           3418710.0           3515850.0           3480163.0  \n",
       "1             66989.0             58638.0              2532.0  \n",
       "2            725752.0            713976.0            770563.0  \n",
       "3           1462508.0            881022.0            971094.0  \n",
       "4                 NaN                 NaN                 NaN  \n",
       "5            521038.0            560948.0            580468.0  \n",
       "6                 0.0                 0.0                 0.0  \n",
       "7             47980.0             73995.0             95403.0  \n",
       "8             79358.0            167776.0            263604.0  \n",
       "9            377923.0            434753.0            335026.0  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ACCESSING VERIFIED EMISSION AND ALLOCATION DATA\n",
    "\n",
    "# iterate through ETS-IDs to access verified emissions and free allocations\n",
    "for y in years:\n",
    "    for id in ets_list:\n",
    "        row = EC_data.loc[EC_data['INSTALLATION_IDENTIFIER']==id]\n",
    "        if row.empty:\n",
    "            allocation = None\n",
    "            emissions = None\n",
    "        else:\n",
    "            # access allocation and emission data\n",
    "            allocation = row['ALLOCATION_'+str(y)].values[0]\n",
    "            emissions = row['VERIFIED_EMISSIONS_'+str(y)].values[0]\n",
    "            # access generation data for reference\n",
    "            generation = installations.loc[installations['ETS-ID']==id, 'Generation elec. ' + str(y) + ' [MWh_el]'].values[0]\n",
    "\n",
    "            # processing of missing entries (-1)\n",
    "            # if generation is zero, the missing values are caused by the plant not being operational, thus missing are set to zero\n",
    "            if emissions==-1 and generation==0:\n",
    "                    emissions = 0\n",
    "            if allocation==-1 and generation==0:\n",
    "                    allocation = 0\n",
    "\n",
    "        installations.loc[installations['ETS-ID']==id, 'Allocation '+str(y)+' [t]'] = allocation\n",
    "        installations.loc[installations['ETS-ID']==id, 'Emissions '+str(y)+' [t]'] = emissions\n",
    "    \n",
    "# processing of missing entries (-1)\n",
    "# if only one missing entry per installation is found, this is most likely caused by the plant being practically no longer operational with generation close to zero\n",
    "# in these cases, missing allocation data is set to zero\n",
    "for id in ets_list:\n",
    "    count_missing = 0\n",
    "    for y in years:\n",
    "        if installations.loc[installations['ETS-ID']==id, 'Allocation '+str(y)+' [t]'].values[0] == -1:\n",
    "            count_missing +=1\n",
    "    if count_missing == 1:\n",
    "        for y in years:\n",
    "            if installations.loc[installations['ETS-ID']==id, 'Allocation '+str(y)+' [t]'].values[0] == -1:\n",
    "                installations.loc[installations['ETS-ID']==id, 'Allocation '+str(y)+' [t]'] = 0\n",
    "\n",
    "# use NaN for missing values instead of -1 for calculation purposes\n",
    "installations.replace(-1, np.NaN, inplace=True)\n",
    "\n",
    "# output\n",
    "columns = ['ETS-ID'] + ['Allocation ' + str(y) + \" [t]\" for y in years] + ['Emissions ' + str(y) + \" [t]\" for y in years]\n",
    "installations.loc[:,columns].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084ac4e0",
   "metadata": {},
   "source": [
    "# Separation of electricity and heat related emissions\n",
    "\n",
    "Based on the regulation of the EU European Trading Scheme, electricity and heat emissions can be separated. Linear reduction factor and carbon leakage exposure factor are calculated. In combination with the ETS verified emissions, the share of carbon leakage related emissions \"sigma\" is be calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "da78ef78",
   "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>ETS-ID</th>\n",
       "      <th>Sigma year</th>\n",
       "      <th>Sigma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.962762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>739</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>750</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>795</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>889</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>0.274850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ETS-ID  Sigma year     Sigma\n",
       "0      19      2019.0  0.962762\n",
       "1     662         NaN       NaN\n",
       "2     739      2019.0  0.000000\n",
       "3     741      2019.0  0.000000\n",
       "4     750      2017.0       NaN\n",
       "5     795      2019.0  1.000000\n",
       "6     839         NaN       NaN\n",
       "7     845         NaN       NaN\n",
       "8     852         NaN       NaN\n",
       "9     889      2017.0  0.274850"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CALCULATION OF SIGMA\n",
    "# define combinations of consecutive years\n",
    "years_cons = [[y, y+1] for y in years][:-1]\n",
    "\n",
    "for id in ets_list:\n",
    "    sigma = np.NaN\n",
    "    sigma_list = dict()\n",
    "    # calculation of sigma for each powerplant and set of consecutive years\n",
    "    for y in years_cons:\n",
    "        # 2013 is defined as the base year in the EU ETS regulations\n",
    "        k_0 = y[0] - 2013\n",
    "        k_1 = y[1] - 2013\n",
    "        # calculation of linear reduction factors\n",
    "        lrf_0 = 1 - 0.0174*k_0 if y[0]<=2020 else 0.8782 - 0.022*k_0\n",
    "        lrf_1 = 1 - 0.0174*k_1 if y[1]<=2020 else 0.8782 - 0.022*k_1\n",
    "        # calculation of carbon leakage exposure factors\n",
    "        clef_0 = 0.8 - (0.5/7)*k_0\n",
    "        clef_1 = 0.8 - (0.5/7)*k_1\n",
    "        # extraction of emission allocations\n",
    "        alloc_0 =  installations.loc[installations['ETS-ID']==id, 'Allocation '+str(y[0])+' [t]'].values[0]\n",
    "        alloc_1 =  installations.loc[installations['ETS-ID']==id, 'Allocation '+str(y[1])+' [t]'].values[0]\n",
    "        # calculation of sigma\n",
    "        if alloc_0 == 0 and alloc_1 == 0:\n",
    "            sigma_y = 0\n",
    "        else:\n",
    "            sigma_y = (alloc_0*lrf_1*clef_1 - alloc_1*lrf_0*clef_0)/(alloc_1*lrf_0*(1-clef_0) - alloc_0*lrf_1*(1-clef_1))\n",
    "        sigma_list[str(y)] = sigma_y\n",
    "        \n",
    "    # special case for zero heat emissions over all years\n",
    "    if all(sigma_y == 0 for sigma_y in sigma_list.values()):\n",
    "        sigma = 0\n",
    "        installations.loc[installations['ETS-ID']==id, 'Sigma'] = sigma\n",
    "        continue\n",
    "\n",
    "    # zeros are removed since they typically occur when the plant is not operational, nan values are removed\n",
    "    sigma_list = {y:sigma_y for y,sigma_y in sigma_list.items() if sigma_y!=0}\n",
    "    sigma_list = {y:sigma_y for y,sigma_y in sigma_list.items() if str(sigma_y)!='nan'}\n",
    "\n",
    "    # iterate through consecutive sigma values and identify occasions with a small deviation\n",
    "    success = False\n",
    "    for y_1, sigma_1 in sorted(list(sigma_list.items()), key=lambda x:x[0].lower(), reverse=True):\n",
    "        # define years and sigmas for consecutive values\n",
    "        y_1_list = str.split(y_1[1:-1], sep=',')\n",
    "        y_1_list = [int(x.strip(' ')) for x in y_1_list]\n",
    "        y_0_list = [x-1 for x in y_1_list]\n",
    "        if str(y_0_list) in sigma_list.keys():\n",
    "            sigma_0 = sigma_list[str(y_0_list)]\n",
    "        else:\n",
    "            continue\n",
    "        \n",
    "        # conditions: max. 10% relative difference or max. 0.01 absolute difference\n",
    "        if abs(1-(sigma_1/sigma_0)) <= 0.1 or abs(sigma_1-sigma_0) <= 0.01:\n",
    "            # calculation of the average sigma\n",
    "            sigma_avg = (sigma_0+sigma_1)/2\n",
    "\n",
    "            # only allow realistic values between 0 and 1\n",
    "            if sigma_avg >= 0 and sigma_avg <= 1:\n",
    "                sigma = sigma_avg\n",
    "                success = True\n",
    "            \n",
    "            # round values slightly below 0 to 0 and values slightly above 1 to 1\n",
    "            elif sigma_avg < 0 and sigma_avg >= -0.1:\n",
    "                sigma = 0\n",
    "                success = True\n",
    "            elif sigma_avg > 1 and sigma_avg <= 1.1:\n",
    "                sigma = 1\n",
    "                success = True\n",
    "\n",
    "        if success:\n",
    "            installations.loc[installations['ETS-ID']==id, 'Sigma year'] = y_0_list[1]\n",
    "            installations.loc[installations['ETS-ID']==id, 'Sigma'] = sigma\n",
    "            break\n",
    "    \n",
    "    installations.loc[installations['ETS-ID']==id, 'Sigma'] = sigma\n",
    "\n",
    "# output\n",
    "columns = ['ETS-ID', 'Sigma year', 'Sigma']\n",
    "installations.loc[:,columns].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "9427a559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>ETS-ID</th>\n",
       "      <th>Sigma</th>\n",
       "      <th>Generation heat 2016 [MWh_th]</th>\n",
       "      <th>Generation heat 2017 [MWh_th]</th>\n",
       "      <th>Generation heat 2018 [MWh_th]</th>\n",
       "      <th>Generation heat 2019 [MWh_th]</th>\n",
       "      <th>Generation heat 2020 [MWh_th]</th>\n",
       "      <th>Generation heat 2021 [MWh_th]</th>\n",
       "      <th>Emissions (heat) 2016 [t]</th>\n",
       "      <th>Emissions (heat) 2017 [t]</th>\n",
       "      <th>Emissions (heat) 2018 [t]</th>\n",
       "      <th>Emissions (heat) 2019 [t]</th>\n",
       "      <th>Emissions (heat) 2020 [t]</th>\n",
       "      <th>Emissions (heat) 2021 [t]</th>\n",
       "      <th>Emissions (elec.) 2016 [t]</th>\n",
       "      <th>Emissions (elec.) 2017 [t]</th>\n",
       "      <th>Emissions (elec.) 2018 [t]</th>\n",
       "      <th>Emissions (elec.) 2019 [t]</th>\n",
       "      <th>Emissions (elec.) 2020 [t]</th>\n",
       "      <th>Emissions (elec.) 2021 [t]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>0.962762</td>\n",
       "      <td>9.032976e+06</td>\n",
       "      <td>9.037923e+06</td>\n",
       "      <td>9.040975e+06</td>\n",
       "      <td>9.041472e+06</td>\n",
       "      <td>9.040972e+06</td>\n",
       "      <td>1.135071e+07</td>\n",
       "      <td>2.025916e+06</td>\n",
       "      <td>2.027025e+06</td>\n",
       "      <td>2.027710e+06</td>\n",
       "      <td>2.027821e+06</td>\n",
       "      <td>2.027709e+06</td>\n",
       "      <td>2.545738e+06</td>\n",
       "      <td>1.740577e+06</td>\n",
       "      <td>1.815953e+06</td>\n",
       "      <td>1.762235e+06</td>\n",
       "      <td>1.390889e+06</td>\n",
       "      <td>1.488141e+06</td>\n",
       "      <td>934424.745281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.721545e+06</td>\n",
       "      <td>1.721625e+06</td>\n",
       "      <td>1.721760e+06</td>\n",
       "      <td>1.721446e+06</td>\n",
       "      <td>1.721595e+06</td>\n",
       "      <td>1.767452e+06</td>\n",
       "      <td>3.861080e+05</td>\n",
       "      <td>3.861260e+05</td>\n",
       "      <td>3.861562e+05</td>\n",
       "      <td>3.860858e+05</td>\n",
       "      <td>3.861193e+05</td>\n",
       "      <td>3.964042e+05</td>\n",
       "      <td>3.667640e+05</td>\n",
       "      <td>3.553900e+05</td>\n",
       "      <td>3.383618e+05</td>\n",
       "      <td>3.396662e+05</td>\n",
       "      <td>3.278567e+05</td>\n",
       "      <td>374158.841641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.833820e+06</td>\n",
       "      <td>1.833910e+06</td>\n",
       "      <td>1.834030e+06</td>\n",
       "      <td>1.833727e+06</td>\n",
       "      <td>1.833867e+06</td>\n",
       "      <td>1.980439e+06</td>\n",
       "      <td>4.112890e+05</td>\n",
       "      <td>4.113094e+05</td>\n",
       "      <td>4.113363e+05</td>\n",
       "      <td>4.112683e+05</td>\n",
       "      <td>4.112996e+05</td>\n",
       "      <td>4.441728e+05</td>\n",
       "      <td>1.415760e+06</td>\n",
       "      <td>1.290180e+06</td>\n",
       "      <td>9.348687e+05</td>\n",
       "      <td>1.051240e+06</td>\n",
       "      <td>4.697224e+05</td>\n",
       "      <td>526921.221589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.059861e+06</td>\n",
       "      <td>2.059859e+06</td>\n",
       "      <td>2.059862e+06</td>\n",
       "      <td>2.059859e+06</td>\n",
       "      <td>2.059862e+06</td>\n",
       "      <td>1.237277e+06</td>\n",
       "      <td>4.619857e+05</td>\n",
       "      <td>4.619852e+05</td>\n",
       "      <td>4.619858e+05</td>\n",
       "      <td>4.619853e+05</td>\n",
       "      <td>4.619859e+05</td>\n",
       "      <td>2.774964e+05</td>\n",
       "      <td>2.227663e+05</td>\n",
       "      <td>2.269858e+05</td>\n",
       "      <td>2.022902e+05</td>\n",
       "      <td>5.905274e+04</td>\n",
       "      <td>9.896212e+04</td>\n",
       "      <td>302971.560239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>889</td>\n",
       "      <td>0.274850</td>\n",
       "      <td>4.663992e+05</td>\n",
       "      <td>4.663942e+05</td>\n",
       "      <td>4.664000e+05</td>\n",
       "      <td>3.508949e+05</td>\n",
       "      <td>1.816393e+05</td>\n",
       "      <td>7.512654e+04</td>\n",
       "      <td>1.046040e+05</td>\n",
       "      <td>1.046029e+05</td>\n",
       "      <td>1.046042e+05</td>\n",
       "      <td>7.869871e+04</td>\n",
       "      <td>4.073806e+04</td>\n",
       "      <td>1.684938e+04</td>\n",
       "      <td>7.656590e+05</td>\n",
       "      <td>5.743881e+05</td>\n",
       "      <td>5.417108e+05</td>\n",
       "      <td>2.992243e+05</td>\n",
       "      <td>3.940149e+05</td>\n",
       "      <td>318176.619954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ETS-ID     Sigma  Generation heat 2016 [MWh_th]  \\\n",
       "0      19  0.962762                   9.032976e+06   \n",
       "1     662       NaN                            NaN   \n",
       "2     739  0.000000                   1.721545e+06   \n",
       "3     741  0.000000                   1.833820e+06   \n",
       "4     750       NaN                            NaN   \n",
       "5     795  1.000000                   2.059861e+06   \n",
       "6     839       NaN                            NaN   \n",
       "7     845       NaN                            NaN   \n",
       "8     852       NaN                            NaN   \n",
       "9     889  0.274850                   4.663992e+05   \n",
       "\n",
       "   Generation heat 2017 [MWh_th]  Generation heat 2018 [MWh_th]  \\\n",
       "0                   9.037923e+06                   9.040975e+06   \n",
       "1                            NaN                            NaN   \n",
       "2                   1.721625e+06                   1.721760e+06   \n",
       "3                   1.833910e+06                   1.834030e+06   \n",
       "4                            NaN                            NaN   \n",
       "5                   2.059859e+06                   2.059862e+06   \n",
       "6                            NaN                            NaN   \n",
       "7                            NaN                            NaN   \n",
       "8                            NaN                            NaN   \n",
       "9                   4.663942e+05                   4.664000e+05   \n",
       "\n",
       "   Generation heat 2019 [MWh_th]  Generation heat 2020 [MWh_th]  \\\n",
       "0                   9.041472e+06                   9.040972e+06   \n",
       "1                            NaN                            NaN   \n",
       "2                   1.721446e+06                   1.721595e+06   \n",
       "3                   1.833727e+06                   1.833867e+06   \n",
       "4                            NaN                            NaN   \n",
       "5                   2.059859e+06                   2.059862e+06   \n",
       "6                            NaN                            NaN   \n",
       "7                            NaN                            NaN   \n",
       "8                            NaN                            NaN   \n",
       "9                   3.508949e+05                   1.816393e+05   \n",
       "\n",
       "   Generation heat 2021 [MWh_th]  Emissions (heat) 2016 [t]  \\\n",
       "0                   1.135071e+07               2.025916e+06   \n",
       "1                            NaN                        NaN   \n",
       "2                   1.767452e+06               3.861080e+05   \n",
       "3                   1.980439e+06               4.112890e+05   \n",
       "4                            NaN                        NaN   \n",
       "5                   1.237277e+06               4.619857e+05   \n",
       "6                            NaN                        NaN   \n",
       "7                            NaN                        NaN   \n",
       "8                            NaN                        NaN   \n",
       "9                   7.512654e+04               1.046040e+05   \n",
       "\n",
       "   Emissions (heat) 2017 [t]  Emissions (heat) 2018 [t]  \\\n",
       "0               2.027025e+06               2.027710e+06   \n",
       "1                        NaN                        NaN   \n",
       "2               3.861260e+05               3.861562e+05   \n",
       "3               4.113094e+05               4.113363e+05   \n",
       "4                        NaN                        NaN   \n",
       "5               4.619852e+05               4.619858e+05   \n",
       "6                        NaN                        NaN   \n",
       "7                        NaN                        NaN   \n",
       "8                        NaN                        NaN   \n",
       "9               1.046029e+05               1.046042e+05   \n",
       "\n",
       "   Emissions (heat) 2019 [t]  Emissions (heat) 2020 [t]  \\\n",
       "0               2.027821e+06               2.027709e+06   \n",
       "1                        NaN                        NaN   \n",
       "2               3.860858e+05               3.861193e+05   \n",
       "3               4.112683e+05               4.112996e+05   \n",
       "4                        NaN                        NaN   \n",
       "5               4.619853e+05               4.619859e+05   \n",
       "6                        NaN                        NaN   \n",
       "7                        NaN                        NaN   \n",
       "8                        NaN                        NaN   \n",
       "9               7.869871e+04               4.073806e+04   \n",
       "\n",
       "   Emissions (heat) 2021 [t]  Emissions (elec.) 2016 [t]  \\\n",
       "0               2.545738e+06                1.740577e+06   \n",
       "1                        NaN                         NaN   \n",
       "2               3.964042e+05                3.667640e+05   \n",
       "3               4.441728e+05                1.415760e+06   \n",
       "4                        NaN                         NaN   \n",
       "5               2.774964e+05                2.227663e+05   \n",
       "6                        NaN                         NaN   \n",
       "7                        NaN                         NaN   \n",
       "8                        NaN                         NaN   \n",
       "9               1.684938e+04                7.656590e+05   \n",
       "\n",
       "   Emissions (elec.) 2017 [t]  Emissions (elec.) 2018 [t]  \\\n",
       "0                1.815953e+06                1.762235e+06   \n",
       "1                         NaN                         NaN   \n",
       "2                3.553900e+05                3.383618e+05   \n",
       "3                1.290180e+06                9.348687e+05   \n",
       "4                         NaN                         NaN   \n",
       "5                2.269858e+05                2.022902e+05   \n",
       "6                         NaN                         NaN   \n",
       "7                         NaN                         NaN   \n",
       "8                         NaN                         NaN   \n",
       "9                5.743881e+05                5.417108e+05   \n",
       "\n",
       "   Emissions (elec.) 2019 [t]  Emissions (elec.) 2020 [t]  \\\n",
       "0                1.390889e+06                1.488141e+06   \n",
       "1                         NaN                         NaN   \n",
       "2                3.396662e+05                3.278567e+05   \n",
       "3                1.051240e+06                4.697224e+05   \n",
       "4                         NaN                         NaN   \n",
       "5                5.905274e+04                9.896212e+04   \n",
       "6                         NaN                         NaN   \n",
       "7                         NaN                         NaN   \n",
       "8                         NaN                         NaN   \n",
       "9                2.992243e+05                3.940149e+05   \n",
       "\n",
       "   Emissions (elec.) 2021 [t]  \n",
       "0               934424.745281  \n",
       "1                         NaN  \n",
       "2               374158.841641  \n",
       "3               526921.221589  \n",
       "4                         NaN  \n",
       "5               302971.560239  \n",
       "6                         NaN  \n",
       "7                         NaN  \n",
       "8                         NaN  \n",
       "9               318176.619954  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CALCULATION OF ANNUAL HEAT GENERATION AND HEAT/ELECTRICITY-RELATED EMISSIONS\n",
    "\n",
    "# definition of heat benchmark\n",
    "hb = 0.22428 # in t/MWh_th, original value 62,3 t/TJ\n",
    "\n",
    "# iterate through years and powerplant locations to calculate the heat generation from the allocation data\n",
    "for id in ets_list:\n",
    "    for y in years:\n",
    "        # preparation of linear reduction factor, carbon leakage exposure factor, emission allocations and sigma\n",
    "        k = y - 2013\n",
    "        lrf = 1 - 0.0174*k if y<=2020 else 0.8782 - 0.022*k\n",
    "        clef = 0.8 - (0.5/7)*k\n",
    "        alloc = installations.loc[installations['ETS-ID']==id, 'Allocation '+str(y)+' [t]'].values[0]\n",
    "        sigma = installations.loc[installations['ETS-ID']==id, 'Sigma'].values[0]\n",
    "\n",
    "        # calculation of annual heat generation\n",
    "        gen_heat = alloc / (hb * lrf * (sigma + (1 - sigma) * clef))\n",
    "\n",
    "        # calculation of annual heat-related emissions\n",
    "        em_heat = gen_heat * hb\n",
    "\n",
    "        # calculation of annual electricity-related emissions\n",
    "        em_total = installations.loc[installations['ETS-ID']==id, 'Emissions '+str(y)+' [t]'].values[0]\n",
    "        em_elec = em_total - em_heat\n",
    "\n",
    "        installations.loc[installations['ETS-ID']==id, 'Generation heat ' + str(y) + ' [MWh_th]'] = gen_heat\n",
    "        installations.loc[installations['ETS-ID']==id, 'Emissions (heat) ' + str(y) + ' [t]'] = em_heat\n",
    "        installations.loc[installations['ETS-ID']==id, 'Emissions (elec.) ' + str(y) + ' [t]'] = em_elec\n",
    "\n",
    "# output\n",
    "columns = ['ETS-ID', 'Sigma'] + ['Generation heat ' + str(y) + \" [MWh_th]\" for y in years] + ['Emissions (heat) ' + str(y) + \" [t]\" for y in years] + ['Emissions (elec.) ' + str(y) + \" [t]\" for y in years]\n",
    "installations.loc[:,columns].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00fc476",
   "metadata": {},
   "source": [
    "# Emission factors\n",
    "\n",
    "The yearly installation-specific emission factor is calculated using electricity-related emissions and generation data. Annual emission factors are plotted grouped by individual installations and by fuel type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f32469d5",
   "metadata": {},
   "source": [
    "## Calculation of annual emission factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "82d8c4bd",
   "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>ETS-ID</th>\n",
       "      <th>Sigma</th>\n",
       "      <th>Emission factor avg. [t/MWh_el]</th>\n",
       "      <th>Emission factor 2016 [t/MWh_el]</th>\n",
       "      <th>Emission factor 2017 [t/MWh_el]</th>\n",
       "      <th>Emission factor 2018 [t/MWh_el]</th>\n",
       "      <th>Emission factor 2019 [t/MWh_el]</th>\n",
       "      <th>Emission factor 2020 [t/MWh_el]</th>\n",
       "      <th>Emission factor 2021 [t/MWh_el]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>0.962762</td>\n",
       "      <td>1.064857</td>\n",
       "      <td>1.143209</td>\n",
       "      <td>1.152464</td>\n",
       "      <td>1.207557</td>\n",
       "      <td>1.029120</td>\n",
       "      <td>1.072508</td>\n",
       "      <td>0.730390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.245548</td>\n",
       "      <td>0.248732</td>\n",
       "      <td>0.246686</td>\n",
       "      <td>0.246937</td>\n",
       "      <td>0.240732</td>\n",
       "      <td>0.235807</td>\n",
       "      <td>0.253753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.878937</td>\n",
       "      <td>0.908891</td>\n",
       "      <td>0.903050</td>\n",
       "      <td>0.842857</td>\n",
       "      <td>0.940194</td>\n",
       "      <td>0.787835</td>\n",
       "      <td>0.795501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.735683</td>\n",
       "      <td>0.389842</td>\n",
       "      <td>0.985884</td>\n",
       "      <td>0.875406</td>\n",
       "      <td>0.454714</td>\n",
       "      <td>0.686552</td>\n",
       "      <td>1.469572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>889</td>\n",
       "      <td>0.274850</td>\n",
       "      <td>0.595298</td>\n",
       "      <td>0.561890</td>\n",
       "      <td>0.594940</td>\n",
       "      <td>0.599413</td>\n",
       "      <td>0.590950</td>\n",
       "      <td>0.821150</td>\n",
       "      <td>0.495583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ETS-ID     Sigma  Emission factor avg. [t/MWh_el]  \\\n",
       "0      19  0.962762                         1.064857   \n",
       "1     662       NaN                              NaN   \n",
       "2     739  0.000000                         0.245548   \n",
       "3     741  0.000000                         0.878937   \n",
       "4     750       NaN                              NaN   \n",
       "5     795  1.000000                         0.735683   \n",
       "6     839       NaN                              NaN   \n",
       "7     845       NaN                              NaN   \n",
       "8     852       NaN                              NaN   \n",
       "9     889  0.274850                         0.595298   \n",
       "\n",
       "   Emission factor 2016 [t/MWh_el]  Emission factor 2017 [t/MWh_el]  \\\n",
       "0                         1.143209                         1.152464   \n",
       "1                              NaN                              NaN   \n",
       "2                         0.248732                         0.246686   \n",
       "3                         0.908891                         0.903050   \n",
       "4                              NaN                              NaN   \n",
       "5                         0.389842                         0.985884   \n",
       "6                              NaN                              NaN   \n",
       "7                              NaN                              NaN   \n",
       "8                              NaN                              NaN   \n",
       "9                         0.561890                         0.594940   \n",
       "\n",
       "   Emission factor 2018 [t/MWh_el]  Emission factor 2019 [t/MWh_el]  \\\n",
       "0                         1.207557                         1.029120   \n",
       "1                              NaN                              NaN   \n",
       "2                         0.246937                         0.240732   \n",
       "3                         0.842857                         0.940194   \n",
       "4                              NaN                              NaN   \n",
       "5                         0.875406                         0.454714   \n",
       "6                              NaN                              NaN   \n",
       "7                              NaN                              NaN   \n",
       "8                              NaN                              NaN   \n",
       "9                         0.599413                         0.590950   \n",
       "\n",
       "   Emission factor 2020 [t/MWh_el]  Emission factor 2021 [t/MWh_el]  \n",
       "0                         1.072508                         0.730390  \n",
       "1                              NaN                              NaN  \n",
       "2                         0.235807                         0.253753  \n",
       "3                         0.787835                         0.795501  \n",
       "4                              NaN                              NaN  \n",
       "5                         0.686552                         1.469572  \n",
       "6                              NaN                              NaN  \n",
       "7                              NaN                              NaN  \n",
       "8                              NaN                              NaN  \n",
       "9                         0.821150                         0.495583  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CALCULATION OF EMISSION FACTOR\n",
    "\n",
    "# iterate through years and installations to calculate the emission factors\n",
    "for id in ets_list:\n",
    "    em_fac_list = []\n",
    "    total_gen = 0\n",
    "    total_em = 0\n",
    "    for y in years:\n",
    "        # access generation and emission data\n",
    "        gen = installations.loc[installations['ETS-ID']==id, 'Generation elec. '+str(y)+' [MWh_el]'].values[0]\n",
    "        em_elec = installations.loc[installations['ETS-ID']==id, 'Emissions (elec.) '+str(y)+' [t]'].values[0]\n",
    "\n",
    "        # calculation of annual emission factor\n",
    "        if em_elec == 0 or gen == 0:\n",
    "            em_fac = np.NaN\n",
    "        else:\n",
    "            em_fac = em_elec / gen\n",
    "        em_fac_list.append(em_fac)\n",
    "        installations.loc[installations['ETS-ID']==id, 'Emission factor ' + str(y) + ' [t/MWh_el]'] = em_fac\n",
    "\n",
    "        # sums for average emission factor\n",
    "        total_gen += gen \n",
    "        total_em += em_elec\n",
    "    # calculation of average emission factor\n",
    "    em_fac_avg = total_em / total_gen\n",
    "    installations.loc[installations['ETS-ID']==id, 'Emission factor avg. [t/MWh_el]'] = em_fac_avg\n",
    "\n",
    "# output\n",
    "columns = ['ETS-ID', 'Sigma', 'Emission factor avg. [t/MWh_el]'] + ['Emission factor ' + str(y) + \" [t/MWh_el]\" for y in years]\n",
    "installations.loc[:,columns].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44dcc965",
   "metadata": {},
   "source": [
    "## Analysis and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "7b8d06a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DATA PREPARATION\n",
    "# define unique fuel types\n",
    "fuel_types = list(installations['Fuel'].unique())\n",
    "fuel_types.remove('mixed')\n",
    "\n",
    "# create long format DataFrame containing ETS-ID, Fuel Type, Year, Emission factors\n",
    "em_data_raw = installations[installations['Emission factor avg. [t/MWh_el]'].notna()]\n",
    "em_data = []\n",
    "for id in em_data_raw['ETS-ID'].unique():\n",
    "    for y in years:\n",
    "        em_data_y = []\n",
    "        em_data_y.append(id)\n",
    "        em_data_y.append(y)\n",
    "        em_data_y.append(em_data_raw.loc[em_data_raw['ETS-ID']==id, 'Fuel'].values[0])\n",
    "        em_data_y.append(em_data_raw.loc[em_data_raw['ETS-ID']==id, 'Emission factor '+str(y)+' [t/MWh_el]'].values[0])\n",
    "        em_data.append(em_data_y)\n",
    "em_data = pd.DataFrame(em_data, columns=['ETSID', 'Year', 'Fuel', 'Emission factor [t/MWh_el]'])\n",
    "em_data = em_data[em_data['Emission factor [t/MWh_el]'].notna()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "639cab1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average emission factors per fuel type:\n",
      "crude oil: 1.06 t/MWh_el\n",
      "NG: 0.36 t/MWh_el\n",
      "hard coal: 0.93 t/MWh_el\n",
      "BFG: 2.68 t/MWh_el\n",
      "lignite: 1.01 t/MWh_el\n",
      "heavy fuel oil: 1.09 t/MWh_el\n",
      "light fuel oil: nan t/MWh_el\n"
     ]
    }
   ],
   "source": [
    "# CALCULATION OF AVERAGE EMISSION FACTOR PER FUEL TYPE\n",
    "\n",
    "em_fac_avg = dict()\n",
    "for f in fuel_types:\n",
    "    total_gen = 0\n",
    "    total_em_elec = 0\n",
    "    for id in ets_list:\n",
    "        # filter by fuel type\n",
    "        if installations.loc[installations['ETS-ID']==id]['Fuel'].values[0] == f:\n",
    "            for y in years:\n",
    "                # access generation and emission data\n",
    "                gen = installations.loc[installations['ETS-ID']==id, 'Generation elec. '+str(y)+' [MWh_el]'].values[0]\n",
    "                em_elec = installations.loc[installations['ETS-ID']==id, 'Emissions (elec.) '+str(y)+' [t]'].values[0]\n",
    "                # add values to fuel type-specific sum\n",
    "                if str(gen)!='nan' and str(em_elec)!='nan':\n",
    "                    total_gen += gen\n",
    "                    total_em_elec += em_elec\n",
    "                    \n",
    "    # calculate average emission factor\n",
    "    if total_gen > 0:\n",
    "        em_fac_avg[f] = total_em_elec / total_gen\n",
    "    else:\n",
    "        em_fac_avg[f] = np.NaN\n",
    "    \n",
    "# output\n",
    "print(\"Average emission factors per fuel type:\")\n",
    "for f in fuel_types:\n",
    "    print(f + \": \" + str(round(em_fac_avg[f],2)) + \" t/MWh_el\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "3c40de44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SETUP FOR PLOTTING\n",
    "sns.set(font_scale = 1.25)\n",
    "rcParams['figure.figsize'] = 15,8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "7f53db98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOTTING OF EMISSION FACTORS\n",
    "\n",
    "# for plotting purposes, mixed fuel type plants are exluded\n",
    "em_data_plot = em_data.loc[em_data['Fuel']!='mixed']\n",
    "# color palette for fuel types\n",
    "colors_ft = {'crude oil': 'red', 'NG': 'blue', 'hard coal': 'black', 'BFG': 'purple', 'lignite': 'brown', 'heavy fuel oil': 'orange', 'light fuel oil': 'green'}\n",
    "\n",
    "# yearly emission factors grouped by fuel type\n",
    "em_plot = sns.stripplot(data=em_data_plot, x='Fuel', y='Emission factor [t/MWh_el]', palette=colors_ft)\n",
    "em_plot.set(title=\"Calculated yearly emission factors per fuel type\", ylim=(0, 4.5), xlabel='Fuel type', ylabel='Emission factor in t/MWh_el')\n",
    "plt.savefig('output/plots/emission_factor-fuel_type.png')\n",
    "plt.show()\n",
    "\n",
    "# yearly emission factors grouped by individual installations\n",
    "em_plot = sns.stripplot(data=em_data_plot, x='ETSID', y='Emission factor [t/MWh_el]', order=em_data_plot.sort_values('Emission factor [t/MWh_el]').ETSID.unique())\n",
    "em_plot.set(title=\"Calculated yearly emission factors per ETS installation (sorted by avg. emission factor)\", ylim=(0, 4.5), xticklabels=[], xlabel='ETS-ID', ylabel='Emission factor in t/MWh_el')\n",
    "plt.savefig('output/plots/emission_factor-ETSID.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18066c09",
   "metadata": {},
   "source": [
    "# Utilization ratio\n",
    "\n",
    "The \"Umweltbundesamt\" provides thermal, fuel-specific/fuel-mix specific emission factors. In combination with the installation-specifc emission factors calculated above, the utilization ratio of each installation can be calculated.\n",
    "<br><br>\n",
    "Overview of emission factors (in t CO2 / MWh):\n",
    "* Natural gas (NG): 0.2010\n",
    "* Hard coal: 0.3368\n",
    "* Lignite: Different values for the German mining areas of \"Rheinland\", \"Lausitz\", \"Mitteldeutschland\" and \"Helmstedt\"\n",
    "* Oil: Different values for crude oil, light fuel oil and heavy fuel oil\n",
    "* Blast furnace gas (BFG): 0.9319\n",
    "\n",
    "<br>\n",
    "Full source: Juhrich, K. & Umweltbundesamt, Fachgebiet Emissionssituation. (2009, June). CO2-Emissionsfaktoren für fossile Brennstoffe - Aktualisierung 2022. Climate Change, 28/2022, Article ISSN 1862-4359. https://www.umweltbundesamt.de/publikationen/co2-emissionsfaktoren-fuer-fossile-brennstoffe-0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb2b6d28",
   "metadata": {},
   "source": [
    "## Calculation of annual utilization ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "143c7f14",
   "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>ETS-ID</th>\n",
       "      <th>Sigma</th>\n",
       "      <th>Emission factor avg. [t/MWh_el]</th>\n",
       "      <th>Utilization ratio avg. [MWh_el/MWh_th]</th>\n",
       "      <th>Utilization ratio 2016 [MWh_el/MWh_th]</th>\n",
       "      <th>Utilization ratio 2017 [MWh_el/MWh_th]</th>\n",
       "      <th>Utilization ratio 2018 [MWh_el/MWh_th]</th>\n",
       "      <th>Utilization ratio 2019 [MWh_el/MWh_th]</th>\n",
       "      <th>Utilization ratio 2020 [MWh_el/MWh_th]</th>\n",
       "      <th>Utilization ratio 2021 [MWh_el/MWh_th]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>0.962762</td>\n",
       "      <td>1.064857</td>\n",
       "      <td>0.257029</td>\n",
       "      <td>0.230841</td>\n",
       "      <td>0.228988</td>\n",
       "      <td>0.218540</td>\n",
       "      <td>0.256433</td>\n",
       "      <td>0.246059</td>\n",
       "      <td>0.361314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.245548</td>\n",
       "      <td>0.819388</td>\n",
       "      <td>0.808100</td>\n",
       "      <td>0.814802</td>\n",
       "      <td>0.813973</td>\n",
       "      <td>0.834952</td>\n",
       "      <td>0.852393</td>\n",
       "      <td>0.792108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>741</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.878937</td>\n",
       "      <td>0.392036</td>\n",
       "      <td>0.370561</td>\n",
       "      <td>0.372958</td>\n",
       "      <td>0.399593</td>\n",
       "      <td>0.358224</td>\n",
       "      <td>0.427501</td>\n",
       "      <td>0.423381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.735683</td>\n",
       "      <td>0.303443</td>\n",
       "      <td>0.515593</td>\n",
       "      <td>0.203878</td>\n",
       "      <td>0.229608</td>\n",
       "      <td>0.442036</td>\n",
       "      <td>0.292767</td>\n",
       "      <td>0.136775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>889</td>\n",
       "      <td>0.274850</td>\n",
       "      <td>0.595298</td>\n",
       "      <td>0.564514</td>\n",
       "      <td>0.599406</td>\n",
       "      <td>0.566108</td>\n",
       "      <td>0.561883</td>\n",
       "      <td>0.569930</td>\n",
       "      <td>0.410156</td>\n",
       "      <td>0.679604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ETS-ID     Sigma  Emission factor avg. [t/MWh_el]  \\\n",
       "0      19  0.962762                         1.064857   \n",
       "1     662       NaN                              NaN   \n",
       "2     739  0.000000                         0.245548   \n",
       "3     741  0.000000                         0.878937   \n",
       "4     750       NaN                              NaN   \n",
       "5     795  1.000000                         0.735683   \n",
       "6     839       NaN                              NaN   \n",
       "7     845       NaN                              NaN   \n",
       "8     852       NaN                              NaN   \n",
       "9     889  0.274850                         0.595298   \n",
       "\n",
       "   Utilization ratio avg. [MWh_el/MWh_th]  \\\n",
       "0                                0.257029   \n",
       "1                                     NaN   \n",
       "2                                0.819388   \n",
       "3                                0.392036   \n",
       "4                                     NaN   \n",
       "5                                0.303443   \n",
       "6                                     NaN   \n",
       "7                                     NaN   \n",
       "8                                     NaN   \n",
       "9                                0.564514   \n",
       "\n",
       "   Utilization ratio 2016 [MWh_el/MWh_th]  \\\n",
       "0                                0.230841   \n",
       "1                                     NaN   \n",
       "2                                0.808100   \n",
       "3                                0.370561   \n",
       "4                                     NaN   \n",
       "5                                0.515593   \n",
       "6                                     NaN   \n",
       "7                                     NaN   \n",
       "8                                     NaN   \n",
       "9                                0.599406   \n",
       "\n",
       "   Utilization ratio 2017 [MWh_el/MWh_th]  \\\n",
       "0                                0.228988   \n",
       "1                                     NaN   \n",
       "2                                0.814802   \n",
       "3                                0.372958   \n",
       "4                                     NaN   \n",
       "5                                0.203878   \n",
       "6                                     NaN   \n",
       "7                                     NaN   \n",
       "8                                     NaN   \n",
       "9                                0.566108   \n",
       "\n",
       "   Utilization ratio 2018 [MWh_el/MWh_th]  \\\n",
       "0                                0.218540   \n",
       "1                                     NaN   \n",
       "2                                0.813973   \n",
       "3                                0.399593   \n",
       "4                                     NaN   \n",
       "5                                0.229608   \n",
       "6                                     NaN   \n",
       "7                                     NaN   \n",
       "8                                     NaN   \n",
       "9                                0.561883   \n",
       "\n",
       "   Utilization ratio 2019 [MWh_el/MWh_th]  \\\n",
       "0                                0.256433   \n",
       "1                                     NaN   \n",
       "2                                0.834952   \n",
       "3                                0.358224   \n",
       "4                                     NaN   \n",
       "5                                0.442036   \n",
       "6                                     NaN   \n",
       "7                                     NaN   \n",
       "8                                     NaN   \n",
       "9                                0.569930   \n",
       "\n",
       "   Utilization ratio 2020 [MWh_el/MWh_th]  \\\n",
       "0                                0.246059   \n",
       "1                                     NaN   \n",
       "2                                0.852393   \n",
       "3                                0.427501   \n",
       "4                                     NaN   \n",
       "5                                0.292767   \n",
       "6                                     NaN   \n",
       "7                                     NaN   \n",
       "8                                     NaN   \n",
       "9                                0.410156   \n",
       "\n",
       "   Utilization ratio 2021 [MWh_el/MWh_th]  \n",
       "0                                0.361314  \n",
       "1                                     NaN  \n",
       "2                                0.792108  \n",
       "3                                0.423381  \n",
       "4                                     NaN  \n",
       "5                                0.136775  \n",
       "6                                     NaN  \n",
       "7                                     NaN  \n",
       "8                                     NaN  \n",
       "9                                0.679604  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CALCULATION OF UTILIZATION FACTOR\n",
    "\n",
    "# iterate through years and locations to calculate the utilization factors\n",
    "for id in ets_list:\n",
    "    utilization_list = []\n",
    "    gen_total = 0\n",
    "    em_total = 0\n",
    "    ff_total = 0\n",
    "    for y in years:\n",
    "        # access data\n",
    "        gen = installations.loc[installations['ETS-ID']==id, 'Generation elec. '+str(y)+' [MWh_el]'].values[0]\n",
    "        em_elec = installations.loc[installations['ETS-ID']==id, 'Emissions (elec.) '+str(y)+' [t]'].values[0]\n",
    "        fuel_fac_th = installations.loc[installations['ETS-ID']==id, 'Thermal fuel emission factor [t/MWh_th]'].values[0]\n",
    "        \n",
    "        # calculation of utilization ratio\n",
    "        utilization = fuel_fac_th / (em_elec / gen)\n",
    "        utilization_list.append(utilization)\n",
    "        installations.loc[installations['ETS-ID']==id, 'Utilization ratio ' + str(y) + ' [MWh_el/MWh_th]'] = utilization\n",
    "\n",
    "        # sums for average utilization ratio\n",
    "        gen_total += gen\n",
    "        em_total += em_elec\n",
    "        ff_total += fuel_fac_th\n",
    "    # calculate average utilization ratio\n",
    "    utilization_avg = ff_total / (em_total / gen_total)\n",
    "    installations.loc[installations['ETS-ID']==id, 'Utilization ratio avg. [MWh_el/MWh_th]'] = utilization_avg\n",
    "\n",
    "# output\n",
    "columns = ['ETS-ID', 'Sigma', 'Emission factor avg. [t/MWh_el]', 'Utilization ratio avg. [MWh_el/MWh_th]'] + ['Utilization ratio ' + str(y) + \" [MWh_el/MWh_th]\" for y in years]\n",
    "installations.loc[:,columns].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e08d4d4",
   "metadata": {},
   "source": [
    "## Analysis and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "bdebc584",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DATA PREPARATION\n",
    "\n",
    "# create long format DataFrame containing ETS-ID, Fuel Type, Year, Utilization rate\n",
    "ut_data_raw = installations[installations['Utilization ratio avg. [MWh_el/MWh_th]'].notna()]\n",
    "ut_data = []\n",
    "for id in ut_data_raw['ETS-ID'].unique():\n",
    "    for y in years:\n",
    "        \n",
    "        ut_data_y = []\n",
    "        ut_data_y.append(id)\n",
    "        ut_data_y.append(y)\n",
    "        ut_data_y.append(ut_data_raw.loc[ut_data_raw['ETS-ID']==id, 'Fuel'].values[0])\n",
    "        ut_data_y.append(ut_data_raw.loc[ut_data_raw['ETS-ID']==id, 'Utilization ratio '+str(y)+' [MWh_el/MWh_th]'].values[0])\n",
    "        ut_data.append(ut_data_y)\n",
    "ut_data = pd.DataFrame(ut_data, columns=['ETSID', 'Year', 'Fuel', 'Utilization ratio [MWh_el/MWh_th]'])\n",
    "ut_data = ut_data[ut_data['Utilization ratio [MWh_el/MWh_th]'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "c89a68b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FF198AQAYM2YM+vbti2+++QYdOnTATz/9hAkTJrx1hde6desiOTkZvXv3xu+//45JkyZh8ODBADIW1lm9ejWio6Pxzjvv4P3330epUqWwevXqTIfJGsrPzw8TJkzAihUr0KVLFwQEBGDixIna8wqFAqtXr4ZCocCwYcMwYMAAqFQq/PnnnyhWrFi297e3t8eQIUPwyy+/6Azn7NatG9LT0zP9epjj60BEVNAIIndiJSKiN8yZMwf379/H0qVLzR2FjGTatGl49uwZ1qxZY+4oJnH79m306tULJ06cQPHixbXtlvZ1ICLKCheLISIirUuXLiEsLAwbN27MdPEOooLuyZMnuHbtGv7880907NhRpwgkIqJXzDI09MGDB2jYsKHOxr1v2rp1K9q0aQMfHx8MHDgQd+/eNWFCIiLLdPToUfz4448YPHgw/P39zR2HKMeio6Mxffp0qNVqTJ8+3dxxiIgKLJMPDT127Bi++OILxMTE4OjRo/Dw8NC75vz58/jwww+xcuVKeHt7Y9GiRTh06BD++eefXK9AR0RERERERBlM2iO4ZMkS/PLLL5g0adJbr9uyZQu6dOmi3fB14sSJiImJQUBAgImSEhERERERFV0mLQR79+6NvXv3onHjxm+97u7duzpLRkulUlSoUAGhoaH5HZGIiIiIiKjIM+liMe7u7gZdl5SUBIVCodOmUCiQkpKSH7GIiIiIiIgsSoFcNdTGxgZpaWk6bampqbC3t8/RfeLikqDRcHcMIiIiIiKyLBKJABcXuyzPF8hC0NPTE/fu3dMeq9VqPHjwQGe4qCE0GpGFIBERERER0RvMsn1Ednr37o3du3cjMDAQ6enpmD9/PhwdHVG/fn1zRyMiIiIiIir0CkQh+OTJE/j6+uLSpUsAAH9/f0ybNg2ff/45GjVqhMDAQCxbtgwymczMSYmIiIiIiAo/k+8jaEoxMYkcGkpERERERBZHIhFQrFjWa6wUiB5BIiIiIiIiMh0WgkRERERERBaGhSAREREREZGFYSFIRERERERkYVgIEhERERERWRgWgkRERERERBaGhSAREREREZGFYSFIRERERERkYVgIEhERERERWRgWgkRERERERBaGhSAREREREZGFYSFIRERERERkYVgIEhERERERWRgWgkRERERERBaGhSARGYVGAyiV5k5BRERERIZgIUhEefb331aoWdMOFSrY4+OPrVkQEhERERVwLASJKE8ePhQwaZIC0dESKJUCNmyQY80ambljEREREdFbsBAkojy5dk0KjUbQaQsKkpopDREREREZgoUgEeVJgwZqyOWiTpu/v9pMaYiIiIjIECwEiShP3N1FrFiRiqpV1SheXIOPPkrDwIGcJEhERERUkAmiKIrZX1Y4xcQkQqMpsm+PiIiIiIgoUxKJgGLF7LM+b8IsREREREREVACwECQiIiIiIrIwLASJiIiIiIgsDAtBIiIiIiIiC8NCkIiIiIiIyMKwECQiIiIiIrIwLASJiIiIiIgsDAtBIiIiIiIiC8NCkIiIiIiIyMJYmTsAERUs9+8LOHDACmXKiOjcWQUr/i1BREREVOTwRzwi0rp4UYI+fWyRmioAADp2VGLt2lQzpyIiIiIiY+PQUCLSWrpUri0CAeDAARlu3eJfE0RERERFDX/CIyItjcawNiIiIiIq3FgIEpHWBx8oIZOJ2uNWrVSoXp2VIBEREVFRI4iiKGZ/WeEUE5MIjabIvj2ifBEaKsG+fVYoU0aDnj1VsLY2dyIiIiIiyimJRECxYvZZnmchSEREREREVMRkVwhyaCgREREREZGFYSFIRERERERkYVgIEhERUY6lxcYg/tZNaFQqc0chIqJc4IbyRERElCPhW/7GnaULIarVsClVGnV/mQ/bMh7mjkVERDnAHkEiIiIyWPqLF7izfDFEtRoAkPL0Ce79udLMqYiIKKdYCBIREZHB0mKiICqVOm0pz56aKQ0REeUWC0EiIiIymH2lKrArX0Gnzb1lG/OEISKiXOM+gkRERJQjqc8jcW/tKqQ8eYwSzVvCo0cfCIJg7lhERPQabijPQpCIiIiIiCwMN5QnIiIiIiIiHSwEiYiIiIiILAwLQSIiIiIiIgvDQpCIiIiIiMjCWJk7ABEVXEolcPKkFIIAtGihhlRq7kREREREZAwsBIkoU4mJQLdutrhxI6P68/VVY+fOZNjYmDkYEREREeUZh4YSUaa2bpVpi0AAuHxZij17+NkRERERUVHAQpCIMpWQoL85dHw8N4wmIiIiKgpYCBJRpnr2VMLOTtQeOzmJ6N5dZcZERERERGQsHOdFZGGSk4GgICkqVdKgdGkxy+vKlRNx8GAy1q6VQRCA995Lh7t71tcTERERUeEhiKJYZH+yi4lJhEZTZN8eUY4FB0vwzjs2iImRQCoV8c03afjgA6W5YxERERGRkUkkAooVs8/6vAmzEJGZzZ5tjZiYjG97tVrA999b4+VLM4ciIiIiIpNjIUhkQZ49013sJSVF4AIwRERERBaIhSCRBenTR3cYaKNGKnh4cPg0ERERkaXhYjFEFmTsWCUcHIBDh6zg6anGhAnp5o5ERERERGbAxWKIiIiIiIiKGC4WQ0RERERERDo4NJTIQj1+LGDJEjmiogS8844SbdqozR2JiIiIiEyEQ0OJLFBaGtCkiR0ePXo1KGDjxmS0bs1ikIiIiKgo4NBQItITECDVKQIBYPNmmZnSEBEREZGpsRAkskBubvo95Zm1ERHll9ToKMQGXYI6JcXcUYiILBILQSILVKOGBkOGvNo6olw5DcaO5VYSRGQaj3Ztx+n+PRE4eTxO9e+B+Fs3zR2JiMjicI4gkQW7eVOC6GgBfn5qyOXmTkNElkCdlooTvTpDnZysbXOtWx/15i40YyoioqInuzmCXDWUyIJVr64xdwQisjCqpCSdIhAA0mKizZSGiMhycWgoERERmYy1azG41mug01aybQczpSEislwcGkpEuXL4sBQHDlihcmUNhg1Tws7O3ImIqLBQvnyJBxv/QuL9MBRv1Bge3XtDEATt+ZTIZ4jYuRXqlFSU7tINjp7VzJiWiKhwym5oKAtBItKxZ48Vfv5ZjuTkjLmDrq4ifH3V6NlThf9+Ttu40QoTJ9poX9OqlQqbNnHlPyLKO2ViIs4O7Y/02BgAgCCTwW/ZGthXqmzmZEREhQvnCBKRwe7fF/DBBwqo1RkV38OHr0aPBwWl49tv0wAAa9fqrixz7JgVHj0SULYsP3ghoryJPntKWwQCgKhU4tJHY1Hvt0VwqOJpxmREREUL5wgSkda5c1JtEfimP/+UQaXK+L2jo27BZ2UlwtY2v9MRkSWwcnDUa1O+TMDNn38wQxoioqKLhSCRBXjyRECfPjZwd7dH27a2uHEj82/9WrWyXkVULgck/3/Z5MlpsLV9VQyOHZuOYsXYG0hEeVe8oR9c69bXa39557YZ0hARFV0sBIkswLRp1jh1ygqiKODaNSnGjlVkel3Nmhp8/XUqHBxESKUigFfF3ZQpadpCsGFDDS5eTMLSpSk4fDgJX37JzeiJyDgEqRR1f5kPR+/qOu2u9Rtk8QoiIsoNky4WEx0djRkzZuDixYtQKBQYMGAAxo8fr3edRqPB77//jh07diApKQmVK1fGJ598goYNG+boeVwshihD9ep2iI7W/dzn3r2XsM9i/rBSCajVQHi4BGfPSuHrq4aPD/ccJCLTSYuNQej8uXhx/RqcatSC18QpsC5WzNyxiIgKjQK1WMykSZPg4eGBM2fO4NGjR/jggw9QoUIFdO3aVee6LVu24MiRI9iyZQvc3Nywdu1ajB49GufOnYO1tbUpIxMVCY0aqbFv36tCsEYNdZZFIADIZBm/qlXToFo1FoBEZHrWrsVQ++vvzR2DiKjIMtnQ0PDwcFy4cAFTpkyBQqGAp6cnhg0bhvXr1+tdGxYWBo1GA1EUIYoiBEGAQqFAEd7pgihfzZ6dhnbtVFAoRDRsqMKyZanmjkREREREZmSyHsE7d+7A2dkZxYsX17ZVqlQJoaGhetf2798fR44cQYsWLSCVSmFtbY0VK1ZAoch8XhMRvZ27u4j167nPHxERERFlMFkhmJSUBBsbG502GxsbpKbq90ykp6ejbt26+OOPP1C6dGmsXLkSEyZMwO7du+Hm5mbwM982JpbI0ogikJwM2NmZOwkRERERmZvJCkFbW1u9oi8lJQX2mUxU+vTTTzFmzBhUqlQJAPDhhx9i165dOHDgAIYMGWLwM7lYDFGGc+ekmDBBgfBwCfz8MoaGlirF7w0iIiKioiq7xWJMNkfQ09MTcXFxiI2N1baFhYWhSpUqetc+efIESqVSp83KygpyuTzfcxIVNWo1MHZsRhEIAOfOWWHmTC66RERERGTJTFYIVqhQAb6+vvjpp5+QnJyMu3fvYu3atejVq5fetW3atMGSJUvw6NEjKJVK/Pnnn4iOjkbLli1NFZeoyIiOFvD4se63+pUrUjOlISIiIqKCwKQbys+fPx+JiYlo1aoVhg8fjn79+qFfv3548uQJfH19cenSJQDAV199hWbNmmHQoEFo0qQJjhw5glWrVsHd3d2UcYmKhBIlRFStqtZpa9ZMZaY0RES6RFFE7OVAPD16CMrERHPHISKyGCbdUN7UOEeQKMPt2xJMn26NkBAJWrVS48cfU+HoaO5URETA1ZnT8PzkcQCAzNkFDRYug51HOfOGIiIqArKbI8hCkIiydPq0FJs2WSEsTAJ7e6BPHyX692dvIhEZR0LoLZwfPVynzaNHb3hP+tQ8gYiIipDsCkGTrRpKRIXLvn1WeO893S1fjh+3giim4N13WQwSUd6pkpP02tTJyWZIQkRkeUw6R5CICo8//5Rl2r59e+btREQ55VLbB3YVKmmPBYkUpbt0N2MiIiLLwR5BIsqUvX3mw6rLlNGYOAkRFVWCVIr6vy/B4z07kBYbg1JtO8Cpek1zxyIisgicI0hEmbp6VYJevWyRmCho28qV02DbtmSUL8/vKyIiIqKCjIvFsBAkyrXoaAHHjkmhUGRsQ1GvnhpWHEdAREREVOCxEGQhSEREREREFia7QpCLxRAREREREVkYFoJEREREREQWJkezfRISEpCamoo3R5O6u7sbNRQREREVTsrERDzetwvpsbEo2aYdHKt6mTsSERFlwqA5gteuXcOUKVMQERGh0y6KIgRBQEhISL4FzAvOESQiIjIdUaPB+dHD8fLObQAZ20PU+20RXGr7mDcYEZEFym6OoEE9gl9//TXc3d3x2WefwdHR0WjhiIiIqOh4cf2atggEAFGtRsTuHSwEiYgKIIMKwTt37mDnzp2oXLlyfuchonyWmAicPi2Fh4eImjW5OTwRGY9UoTCojYiIzM+gxWLKli2LhISE/M5CRPns9m0JGja0w9Chtmjd2g5ffGFt7khEVIQ4VvWCm39z7bGVvQPK9XvXjImIiCgrWc4RjIyM1P7+yJEj2LhxIz7//HNUqFABEolu/VhQF4vhHEEiXePHK7B5s0x7LAgiLlxIQvny/D4hIuMQNRrEXDyPtNgYuDX2h9zZxdyRiIgsUq7nCLZo0QKCIACAdpXQ9957T9v2X3tBXiyGiHTFxgo6x6Io4MULgYUgERmNIJGgeKPG5o5BRETZyLIQXLt2rSlzEJEJDBigxJEjr77ta9ZUo3ZtzhMkIiIisjQGbR+xcOFCjBw5EjY2NjrtiYmJWLBgAaZPn55vAfOCQ0OJ9B05IsXOnTJ4eGjwwQfpcHU1dyIiIiIiMrbshoZmWQjGxsYiNTUVANCmTRts3boVLi664/xv3ryJyZMn49q1a0aMbDwsBImIiIiIyBLleo7gyZMnMW3aNO2cwL59+2Z6Xbt27fIYkYiIiIiIiEzprUNDg4KCoNFoMHjwYCxevBhOTk6vXigIsLOzQ5UqVSCVSk0SNqfYI0hERERERJYo10NDX/f48WOULl1aZ8XQzIwcORI//vgjSpQokfOk+YCFIBERERERWaJcDw19XZkyZQx6WFBQENLS0gxLRkRERERERGYhyf4SIiIiIiIiKkpYCBIRAODpUwFjxijQrJktZsywRnKyuRMRERERUX4xaGgoERV9I0bYIDAwY+Gn0FAplEpgzhwO9SYiIiIqitgjSESIjYW2CPzP4cP8nIiIiIioqGIhSERwcgJKltTotFWtqsniaiIiIiIq7IxaCGa3vQQRFUxSKfDbb6koViyj+KtcWYNvv80YFpqYCKxaJcPPP8tx9y6/x4mIiIiKAqOO/SqoG8sTUfbatFHjypUkPHsmoFw5EYIAqNVAjx62CA7O+N5euFCOvXuTUasWewuJiIiICjODNpQHgLS0NNy7dw+pqal48yV169bNl3B5xQ3lifLm1Ckp+vSx1WkbMiQdv/7KRWSIiIiICjKjbCh//PhxTJkyBcnJyXpFoCAICAkJyVtKIiqQZDLD2iIjBSxcKMfDhwK6d1ehTx9V/ocjIiIiolwzqEewc+fOqFKlCsaOHQtHR0e982XKlMmXcHnFHkGivBFFoFcvG5w9m/GZkaOjiP37k+HpqdG5pmVLW4SEvBoaPn9+Ct59l8UgERERkblk1yNoUCFYq1Yt7Nu3D+XKlTNquPzGQpAo79LTgf37rRAdLaBrVxXc3XW/p4KDJWjTxk6nrVkzFbZtSzFlTCIiIiJ6jVGGhnp6eiIiIqLQFYJElHdyOdCjR9a9e25uIqRSEWr1qxVFS5bkBzBEREREBVmWPYJBQUHa3wcGBmL9+vUYP348KlSoAIlEd9cJLhZDZNnmzJFj7lw5RFFAqVIabN+ejMqV+b1HREREZC65Hhrq5eUFQRD0FofRu0EBXiyGhSCR6Tx4ICAiQoIGDdSwtjZ3GiIiIiLLlutC8PHjxwY/hIvFEBERERERFRy5niO4fPly+Pv7o1GjRnBycsqXcERERERERGR6WRaCV69exZYtWyAIAry9vdGkSRM0adIEdevWhVwuN2VGIiIiIiIiMqK3bh+RmJiIwMBABAUF4dKlSwgODoZEIkG9evW0haG3t7cp8+YIh4YS5b+nTwWsXi3Dy5cCBg5UolYtTfYvIiIiIqJ8ZZR9BP+Tnp6Oa9euISgoCJcvX8b58+ehUChw9uxZo4Q1NhaCRPkrMRFo2tQOT55krCRsbS3in3+SWQwSERERmVl2haAkyzNv0Gg0CA4OxsWLF3Hu3DlcuXIFoiiiRo0aRglKRIXP0aNW2iIQANLSBGzeLDNjIiIiIiIyxFs3lH/8+DFOnz6N06dPIyAgAMnJyahevTr8/f0xevRo1K1bFzIZf+gjslSOjvo97pm1UeH0+HEEihUrDoVCYe4oREREZGRZDg3t2LEjwsPDUaZMGe18wMaNGxeqFUQ5NJQofwQESPH993JERUkgisCDBxm9guXKabBvXzLc3fl9V5g9fhyBoUMHIDj4KlxcXPDLL7+jW7ee5o5FREREOZCnDeVLliyJrl27wt/fH/Xq1St0q4WyECQyvvh4wMfHHklJgrZt7Nh0NGyoRuvWKtjYmDEcGcXYse9j27bN2mNHRydcuxYKW1tbM6YiIiKinMj1PoKHDx/G6dOncebMGWzatAlKpRL16tWDv78//P39Ua1atXwJTEQF24ULUp0iEAAePxbQpYvKTInI2EJDb+kcJyTE49mzJ6hUqYqZEhERZVAmKZGemAY796x/uCUiw2RZCJYtWxYDBgzAgAEDoFarceXKFZw9exaHDh3Cr7/+ChcXFzRp0gRNmzZF9+7dTZmZiMzIy0sDiUSERvOqGKxenauEFiXt2rXH9evXtMeVK1dBxYqVzZiIiAi49sdlnPvuNFTJSng0K4cOq7vB2tHa3LGICq0cbR/xn/DwcKxbtw7bt29HSkoKQkJC8iNbnnFoKFH+WLVKhu+/t0ZiItChgwpLlqTCzs7cqchY0tPTMWfO9zh48B9UruyJr76axd5AIsqzuLuxULjYwKZYzucQvIxIwF/1V0J87ee6epMaodF0f2NGJCpSjLKPYGhoKC5fvozLly8jKCgIERERKFOmDPz8/NC4cWN06dLFqKGNhYUgUf5JTc345exs7iRERFSQpcamYN+gHYgMfAaJTIL6k/1Qf4pfju7x6Hg49ryzTaetYqfK6PRnD2NGJSpScj1HcOHChbh8+TKuXr2KpKQkFC9eHH5+fhgzZgwaN26M0qVL50tgIiocFIqMX0RERG9zZUkgIgOfAQA0Sg0uzDmLpOdJaD67NQRByObVGUrWLwVrZ2ukvUjTtpVvVylf8hJZiix7BBs1aqT95efnh8qVC9/8EPYIEhEREZnXwZF7ELbnjl5728WdULWvt8H3iQx6ivM/nkXy80RU61sdPuPrG1xIElmiXA8NFUWx0H9zsRAkIiIiMq/b20JwZOx+vfbqQ2uj5S9tzZCIyDLkemjozJkzDX7It99+m7NURERERGQRqvbxRvz9F7j4U4BOe8n6pcyUiIiAtxSCW7ZsgUQiQfXq1WHzlh2iC3uvIRERERHlrwafNIadux3O/3gWyqR0VB9aG9XeqW7uWEQWLcuhocuXL8eBAwdw7949tGzZEp07d0bLli0hl8tNnTHXODSUiIiIqGApCtOPiAqDPG8f8ejRI/zzzz/Yv38/Hj16hFatWqFLly5o2rQpZDKZ0QMbEwtBIiIiIiKyREbZR/A/9+/fx/79+3HgwAE8ffoUbdu2RadOndC8eXOjhDU2FoJEGTQaQBAyfr3u0SMBX3xhjWvXpGjWTI3vv0+Fo6N5MhIRERGR8Ri1EPyPSqXCpk2bMHfuXCQnJyMkJCRPIfMLC0GydKIIfPWVNf78UwZra2Dq1DSMGqXUnu/Y0RZBQVLtcb9+SixalGqOqERERERkRLleNfRNSqUSZ8+exYEDB/Dvv/9CpVKhRYsW6Nixo1GCEpHx7dhhhaVLM+b1pqQAX3yhgJ+fGrVqafDyJXSKQAA4flya2W2IiIgKpDvbb+HurlA4lHNC3fENYOtuZ+5IRIXGWwvB9PR0nDx5EgcPHsSxY8eg0WjQqlUrfPfdd2jevDmsra1NlZOIcuHNQg8ALlyQolYtDeztgfLlNQgPl2jPVaqkMWU8IiKiXLu16Sb+nXBAexxxIhz9TwzlQjREBsqyEJw8eTKOHz8OiUSCli1bYvbs2WjevHmhWjWUyNI1bqzG8uW6bevWyXDlihQXLkgB6A6djoiQQKMBJBIQEREVaLe36k5Nir0Vg+jg53Cr7W6mRESFS5ZzBL28vGBlZYXatWtDoVC89SarVq3Kl3B5xTmCRECHDra4fNnwIZ+nTyehalX2DBIRUcF2dPwBhG6+qT0WJAKGXH4f9qUczJiKqODI9RzBnj17smudqAioVUttcCHo4CCiTBkWgUREVPDVm9wIEaceIulpIiAA9T5uyCKQKAey7BF88OABKlSoYOI4xsUeQSLg+nUJuna1RXJyxgc7trai9vcAoFCISE0V4OIi4uefU9G9u8pcUYmIiHJEnabCs4tPYO/hCKcKzuaOQ1Sg5Hr7iKZNm0KhUKB58+Zo1qwZ/Pz8YGNjk29B8wMLQaIMDx4I2L1bhmLFRHh6qvHhhzYID5egbFkNVqxIQenSIlxcRHD9JyIiIqKiIU/7CIaEhODUqVM4efIkbty4AR8fHzRr1gzNmjWDp6dnvgQ2JhaCRJnTaIDISAHu7uJbF4Z5+FDA119bIyREitatVZgxIw2F7PMgIiIiIotktA3lExMTERAQgJMnT+L06dMQRVFbFLZv395ogY2JhSBR3rRubYvr11/NLxw1Kh3ff59mxkREREREZAijFYJvunv3rrYo5KqhRIWHRgO8eAG4ur79ushIAbVq6f7lUbGiBufPJ+VfOCIiIiIyilwXgpGRkQY/xN29YO7XwkKQSNeZM1JMmKBARIQEdeqo8ccfKShfPvPvEaUS8PGxQ1TUq7GjHTsqsXZtqqniEhEREVEu5boQ9PLyMnj7iJCQkOwvMgMWgkSvqNVA/fp2ePzY8MLu6NGMwjE6WgJvbzXWrElBxYr8niIiIiIq6HK9j+DatWvzJRARmceLF4JOEQhAZ/5fZtq0UePq1SRERwsoVYoFIBEREVFRkWUh2LBhw0zbVSoVrKyyfBkRFVDFiomoWVOtU/y1bJn9noEyGVgEEhERERUxb1k4XtfOnTvRsWNH+Pj44NGjR/jqq6+waNGi/MxGREa2alUK2rdXoUwZDQYNSsesWVwBlIiIiMgSGVQI7ty5Ez/88AN69uwJqTSjN8HLywsrVqzAihUr8jUgERlPhQoi/vorBZcvJ+G339Jgn/WwcSIiIiIqwgwqBFetWoUvv/wSY8aMgeT/u08PGDAA3377LTZv3pyvAYmIiIiIiMi4DCoEw8PD4ePjo9fu4+OTo20miIiIiIiIyPwMKgRLlSqFW7du6bUHBASgVKlSBj8sOjoaY8aMQb169eDv74+FCxdmee2RI0fQs2dP1KtXD126dMG///5r8HOIyHji4oB58+T4/HNrBAYaPK2YiIiIiAowg5b/HDFiBL7++mtERUVBFEVcuHAB27dvx5o1azB58mSDHzZp0iR4eHjgzJkzePToET744ANUqFABXbt21bnu5MmT+PTTT7Fo0SL4+fnhwIEDmDhxIo4cOYKSJUvm7B0SUa6p1UCPHra4dStjbvDq1TJs25aCJk3UZk5GBZ1arUZQ0CWUKOGO8uUrmDsOERERvSHLDeXftGHDBixbtkw7FNTd3R1jx47Fu+++a9CDwsPD0b59e5w5cwbFixcHAKxZswYHDx7E33//rXPtqFGjULNmTXz00Ufatps3b6JcuXKwz8HqFtxQnihvzpyRolcvW522Pn2UWLIk603oiZ4+fYLevbsiLOwuBEHAhx9+hJkzZ5k7FhERkUXJbkN5g8d5DRw4ECdOnMDZs2dx6dIlnDhxQq8IXLJkCRISEjJ9/Z07d+Ds7KwtAgGgUqVKCA0N1bv2+vXrsLe3x4gRI9CoUSP069cPL1++zFERSETZe/pUwMiRCtSvb4eJExV48UL3vIOD/gcpmbURvW7hwnkIC7sLABBFEQsXzsO9e3fNnIqIiIhel+Od4V1dXbM8t2LFCnTt2hWOjo5655KSkmBjY6PTZmNjg9RU/Z6F+Ph4rF27FgsWLIC3tzd27tyJMWPGYPfu3ShbtqzBWd9WARMR0LcvcPJkxu8fPpRAEGTYsOHV+TZtgD59gG3bMo6LFwc+/1yOuDg5Nm8G3NyAwYMBOzvTZ6eCKybmuV5bcvILuLk5mCENERERZSbHheDbvG2Uqa2trV7Rl5KSkmkvn7W1Nfr164fatWsDAPr164e//voLJ0+exKBBgwzOw6GhRFlLTQVOntT9wXz/fhFRUYk6bYsXA4MGSfH8uYC2bVV48ECCbt1skZIiAACWLVNj//5kSLiODP1fly49sX37du2xh0dZVK1aG1FRL82YioiIyLJkNzTUqIXg23h6eiIuLg6xsbHaXsWwsDBUqVJF79rKlSsjLS1Np02t5uIURMakUACVK2sQFvaqgqtRQ//7TBCApk1fta9ZI9MWgQBw+bIUAQFS+Pvze5QydOvWE8uXr8bWrZvg7l4KEydOglwuN3csIiIieo3JPsOvUKECfH198dNPPyE5ORl3797F2rVr0atXL71r+/fvj/Xr1+PixYtQq9XYtGkTHj9+jLZt25oqLlGRsm2bFbp2tUG/fjY4e1aqbZ8/PwVly2oAANWqqTF7dlpWt9DKrOdPKtVvI8vWs2cf/PXXZvz66+9cNZSIiKgAMlmPIADMnz8fs2bNQqtWrSCTyTBw4ED069cPT548QZcuXbBixQrUr18f/fr1AwB8/fXXePLkCSpUqIClS5fC3d3dlHGJioRTp6QYO/bV/Nzz56U4dy4JpUuLaNBAg4sXkxAdLaBECcOGUderp8amTSLS0jJ6BZs0UaFRI/YGEhERERUmBm8fYQhfX98cL+iSnzhHkAiYMcMay5frDsubNy8FAweqcnyvX36R46efrLXHw4al4/vv08BRf0REREQFi9G2jzCEIAjZX0REJlWpkiaTtrd/QBIfD0RH634/K5XAwoW6Fd/Jk1YsAomIiIgKIaMWgkbsXCQiI2nVSgWZ7NX3pr29iOrVsx7KOWuWHNWr26NGDTuMHq2AUvnq3Jvf4lzDiYiITEWVqsKF2Wews9dmBHx3CsokZfYvIqIsGbUQXL16NefxERUwu3fLoFS+6t1LTBSwd2/m04PPnZNi4UJrKJUCRFHAjh0ybN4sAwDIZMDIkek6148dm57ZbYiIiIzu1Of/4tLc83hyJgKX51/EscmHzB2JqFAzaLGYhw8fYubMmbh+/XqmG8Bfv34dAODj42PUcESUd1Kpfk99Vqt83r6t/9nQrVuv2mbOTEfDhmpcuSJF06ZqnW0liIiI8tPdnbd1ju/tuQNxqcipSUS5ZFAh+MUXXyAmJgbjx4+Hk5NTfmciIiPq31+FP/7Q4MmTjIKualU1unbNfKGYFi0yhpG+3oPYrp3utR07qtGxIwtAAh48uI+jRw+jShVPNG/ekj+MEVG+cijrgNiQGO2xfRkH/r1DlAcGFYLXrl3Dxo0b4e3tnd95iMiIkpKA2bPlUKkAT081+vZVYtQoJezsMr++fHkR69alYN48OVJSBIwcmY7mzVn0kb6TJ49j4MC+SE/PGB48fPhI/PTTb2ZORURFWdPvWuHgiD1Ii0+DzF6OZj+2MnckokLNoO0jWrdujcWLF8PLy8sUmYyG20eQpfv8c2v88cerZT0rVdIgICAJ/ACV3uavv/7E9u1bULJkKXzyyWeoVKmK3jV9+/bAyZPHtMcSiQTXrt1GiRIlTBmViCyMMkmJmJAouHoVh9yey1YTvY1Rto8YO3Ysvv/+ezx69MhowYgo/508qTsZ8N49CSIiBCiVwJdfWqNGDTu0b2+Ls2czrktOBn7+WY7Bg22weLEMqpxvNUiF3MaN6zF58gScPn0SW7duQu/e3bS9fq9LT0/TOdZoNFCpuIIfEeUvmZ0MJeuXZhFIZARZDg2tUaOGzrhrlUqF9u3bQxAESCS69eN/i8UQUcFSs6YGt2+/KgaLF9egZEkR8+fLsWxZxj+iUVHA0KE2uHIlEZMmKbBrV8YqoYcOWeHZMwlmzUrL9N5UtKxatQKLFy9AVNRznfYnTx4jKOgS/Pya6LSPGjUG588HaLcN6tq1B0qXLmOyvERERJQ3WQ4N3bFjh8E36dWrl9ECGROHhpKle/ZMwPvvK3DhghXKlNFg7txUHD1qhRUrZBBF3fGhW7cm4513bKDRvGp3c9Pgxo0kU8cmEzt79jR69uyc6TmJRIJLl4Lh4VFW79yFC+dx8OA/qFLFE336vAO5nJ/QExERFRTZDQ3NskfQ2toafn5+cHV1zZdgRJT/SpYUsXdvChISAHt74NAhKZYv1/9hXaHI2GS+eHERz58LOq+nou/06ZOZtltZWWHq1OmZFoEA0LBhIzRs2Cg/oxEREVE+ybIQnDJlCgRBQLVq1dC4cWP4+/ujfv36sLa2NmU+IjICR8eM/964ob+BoJ2dBvPmpaF4ceDbb9MwYYIC6ekC7OxEfP11Gp4/FxAbK8DLS2Pi1GQqPj6+em2//DIfHTp0gru7uxkSERERUX7Lcmjoy5cvcenSJQQGBuLSpUu4ceMGBEGAr68vmjRpgiZNmqBmzZoFev8WDg0l0rV8uQwzZiheaxFx8GAyfH1fFXnR0QJu3pTAx0eNFSvk+PVXOVQqATVqqPHxx+moVUuNSpVExMYCv/1mjVu3JGjTRoVRo5RZblRPBd93332NFSuWQBAkGDduAj799HNzRyIiIqI8yG5oqEHbRwBAWloarly5gsDAQAQGBuLatWuQSCRo1KgR5s+fb7TAxsRCkOiV6GgBnTvb4MED3Wrt6NEk1Kql39v34IGARo3s9OYSCoKIyZPTERAgxdmzrwYVfPJJGj79VH91SSo8lEolBEGAlZVBW8wSERFRAZbrOYJvsra2Rt26dWFlZQWJRAI7OzucOnUKZ8+eNUpQIso/9+8LaN7cDmlp+j34CkUmLwDw8KFErwgEAFEU8Ntvcp1FZQBg1y4rFoKFnEwmM3cEIiIiMpFsC8H79+/j9OnTOH36NC5cuAClUolatWqhSZMmGDZsGHx8fEwQk4jyYsECeaZFYI8eSnh6Zj73r2FDNUqU0OD5c/3tRjUaAXK5iPT0V/csU4a970RERESFRZaF4MyZM3H69Gk8ffoU5cqVg7+/P37++Wf4+fnB3j7rLkYiKnhSU/WLwHLl1Fi2LDXL1ygUwPbtKfj1VzmuXZMgLOzVkFJbWxHJya/u6eQk4ssvud8gERERUWGR5RxBLy8vlCpVCqNHj0bPnj2hyGr8WAHGOYJEGYKDJWjXzva14Zwi/vgjFd27qwy+x44dVti71wo2NiI2b9bdgqJLFyVWr866qCQiIiIi08r1YjFbtmzB2bNnERAQgKSkJPj6+qJp06ba1UILAxaCREB4uIDDh60gisCxY1KkpQmYODENLVrkbjuIbdukGDvWVqetb18lFi9mIUhERERUUOR51VBRFBEcHIyzZ8/izJkzuHz5MhwcHNC4cWM0adIETZs2RcmSJY0e3BhYCJKlO3dOin79bLTzA/v1U2LRoswLtgMHpFixQg65HJgwIR1Nmqj1rnnyRECTJnY6w0IVChF79yYjOlpAUJAUjRur4e+v/1oiIiIiMh2jbR/xn+TkZFy8eBHHjx/Hrl27kJqaips3b+Y5aH5gIUiWbsgQGxw8qDsV+NKlRJQrp/t9ERQkQadOttpVQq2tRZw+nYS0tIw9BRs3VsPdXcSaNTJ8+qnuMPERI9Lg4gL8+qu1tu3771MxapQyn94VEREREWXHaNtH3Lt3D5cvX0ZQUBCuXr2Ke/fuwcPDA35+fkYJSkTGl57Jbg6qTKYF7t9vpbNVRFqagJkzrbF/f8Z2AgqFiHXrUuDhoT+ctGpVEd9+a63TtmiRnIUgERERUQGWZSF48eJFBAUF4fLly7hy5Qri4+Ph7u6ORo0aYeTIkWjcuHGBHRJKRBlGjUrHiRNS7SIxbduqUKmSfi95uXL6Bd6//7766yE1VcCcOdbYuzcZvXopsWNHRoHYtKkK/fsr8d13uoWgoL9IKREREREVIFkWgkOGDIGLiwsaNmyIjz76CH5+fqhYsaIpsxFRHrVtq8aBA8nYv98K5ctr0KdP5quERkfr7hWoUIhIe2M3iPh4QCIBli1LxaefpiEtTUD16hkF5Pjx6Zg9+1UxOGECN5YnIiIiKsiynCN469YteHl5mTqPUXGOIJFhevSwQUCA7udCzZurcPLkq7ZvvknF2LFKJCcDL18KcHfX/d46dUqKy5el8PNToWHD3K1ISkRERETGkes5gnfu3MGdO3cMeki3bt1ynoyICgxvbw0CAl4d29iIWLw4BTt2yHDzphStWqnQpIkaPXrY4MIFKdRqAU2aqLB6dQpcXDJe06yZGs2acbVQIiIiosLgrRvKC/+f6PO2hUUFQUBISEj+pMsj9ggSGSY6WsDIkQoEBFjB2VnEDz+kom9f3WGkbdva4to1qU7buHHp+PrrN8aQEhEREZHZ5bpH0N/fHxcuXECdOnXQuXNndOzYEa6urvkSkojMq3hxEbt2pSAqSoCjowhr3bVfEBEh6BWBABAaKtFrIyIiIqKCL8uf4lauXInTp0+jR48eOHr0KFq1aoX33nsPW7ZsQXx8vCkzEpGJuLm9KgL/+EOGTp1sMXKkApGRAiQS/d71tm0zX3yGiIiIiAo2gzeUj42NxeHDh7F//34EBQWhUaNG6Ny5M9q2bQsHB4f8zpkrHBpKlDt//SXD5MmvNo6XSETtFhT/qVlTjSNHkiFhpyARERFRgZPd0FCDC8HXxcbGYteuXVi4cCGUSiWuXbuWp5D5hYUgUe4MGmSDw4ezHDkOAOjeXYk//kg1USIiIiIiyolczxHMzMuXL3H06FEcOHAAZ8+ehZOTEzp06JDnkERUsFSqlP32D716cVgoERERUWGVbY/gixcvcPjwYRw6dAgBAQFwdXVF+/bt0bFjR9SrV0+7smhBxB5BotyJihIwcKANrl6VQhBEiOKr7/OSJTX4+us0VK6sQcWKGjg6mjEoEREREWUq10NDN27ciIMHD+LixYsoXrw42rdvjw4dOqBevXr5FtbYWAgS5c3duxkF4Ny51rh8WYomTVTo21eJDz6wQWSkBLa2IoYNU+LmTQlsbERMnJiO+vW5mXxRJIoiAgLO4OXLl2jRohUUCkX2LyIiIiKzyXUh6OXlBZlMhsaNG8PX1/etPX9jxozJe9J8wEKQyPj69LHBqVOvjyoXAWT8/WBrK+L8+SS4u/P7rqi4efMG5sz5HmfOnERCQgIAoHz5Cti37whKlChh5nRERESUlVwXgq1btzboAYIg4OjRo7lLl89YCBIZX/36dnj4MOulQufNS8HAgZw/WBSkpKSgXr2aiI6O0jv3ySfT8Omnn5shFRERERki14vF/Pvvv/kSiIgKt27dVFi0SJ7l+fLl+eFLUREYeDHTIhAAXryIM3EaIiIiMqYcrRpKRPT552lwcBBx7JgU1appcOeOBAEBVhAEEYMHK+HvrzZ3RDKSihUrQSKRQKPRnfcpk8nQv/9AM6UiIiIiY8jVPoKFBYeGEpnG3bsCbGyAMmX4/VbULF68AD/+OAtpaWlwdy+Jli3bYMSI9+HrW3gWDiMiIrJE+bKhfGHBQpCIKO9evIhDXFwcKlasZO4oREREZCCjbihPRESWx9nZBc7OLuaOQUREREaU9dJ/RGQxkpKAyZOtUauWHfr0scGtW/yrgYiIiKgoM2hoaFRUFObPn48rV64gPT1d7/zBgwfzJVxecWgoERAWJmDNGjnUamDoUCW8vPQ3fJ8+3RorV75aCbRiRQ3OnUvCW7YPJSIiIqICzChDQ2fOnImgoCD06NEDjo6ORgtHRPkrMlJAx452iI/PqOg2bJDh+PEkVKig+wHJqVNSneP79yV49EhAuXL8IIWIiIioKDKoEAwMDMS8efPQpEmT/M5DREa0Z4+VtggEgORkATt3yvDxx7o9+7VqaXD79qti0M1Ng1KlWAQSERERFVUGTQSSy+Vwd3fP7yxEZGTOzvrFnJOTftvXX6fBz08FAPDw0GDx4lTIZPkej4iIiIjMxKA5gosXL0ZoaChmz54NGxsbU+QyCs4RJEuXmgr06mWLwMCM3r4aNdTYsycZ9lkMF3/5ErCzAyRcK4aIiIioUDPKPoLvv/8+zp8/D1EU4ebmBrlcrnOei8UQFVxqNXD6tBRqNdC8uRpW3DSGiIiIqMgzymIxPj4+8PHxMVYmIjIhqRRo0UJt7hhEREREVIAY1CNYWLFHkIiIiIiILFGuewSXLl2K4cOHQ6FQYOnSpVneQBAEjB49Om8piYiIiIiIyGSy7BFs3bo1tm3bBhcXF7Ru3TrrGwgCjh49mm8B84I9gkREREREZImMslhMYcVCkIiIiIiILFF2hSAXiSciIiIiIrIwLASJiIiIiIgsDAtBIiIiIiIiC8NCkIiIiIiIyMIYtKE8AIiiiJMnT+LOnTuwsrJClSpV0LhxY0il0vzMR0REREREREZmUCEYFxeHkSNH4ubNm3BxcYFGo0F8fDyqV6+OVatWwdnZOZ9jEhERERERkbEYNDR09uzZUKvV2LdvHwICAnD+/Hns3bsXoijil19+ye+MRERUgBXhXYiIiIiKLIMKwePHj2PmzJmoXLmytq1KlSr44osvCuxm8kQEpKQAs2bJ0aaNLT7+2BrPnwvmjkRFiEqlwieffIxy5UqgVq2q2LJlo7kjERERkYEMKgRFUYSTk5Neu7OzM1JSUoweioiM45tvrLFwoTWCg6XYsEGO0aMV5o5EhcyjRw8xbNhA1K9fC1OmTERi4kvtuTVr/sDatauQlpaGyMhnmDhxLCIiHpkxLRERERnKoELQx8cHK1asgFqt1rap1WosX74ctWvXzrdwRJQ3+/frTgM+c8YKCQlmCkOF0siRQ7B//148fBiOdevW4Msvp2vPXbp0UedatVqNK1cumzoiERER5YJBi8V88sknGDhwINq1a6ct/K5du4bExESsWrUqXwMSUe5VqaLB06evPu8pWVIDOzszBqJCJS4uVq+w+/ffI9rf+/k1wfbtW7THMpkM9es3MFk+IiIiyj2DegSrVq2KnTt3omPHjkhOToZarUaPHj2wf/9+1KxZM78zElEuzZqVhrJlNQAAZ2cRv/6aCu74QoZycnKGh0dZnbYaNV79nT9kyHCMHTsBzs7OqFSpMpYtW42SJUuZOiYRERHlgiAW4eXeYmISodEU2bdHZBC1Grh3TwIPDw1sbMydhgqbM2dO4cMPP8CTJ49RvXpNrFq1DpUqVc7+hURERGRWEomAYsXsszyfZSE4YsQI/P7773BwcMCIESPe+pCCOjyUhSARUd6p1WrExsbCzc0tz/eKj3+Bbdu2QKVSolevfka5JxEREenLrhDMco6gu7s7BEHQ/p6IiCyTVCo1SsH28mUC2rZtjvDwBwCA+fN/w9Gjp/lvDBERkRkYNDT0yZMnKFmyJCQS3SmFarUaISEhBXaeIHsEiYgKjr///gsffTROp61p0xb488/1cHBwNFMqIiKioim7HkGDFotp06YNXrx4odf+9OlTDBo0KNfhiIiocLp9OxQbN67H/fv38nSf06dP4IMP3jNSKiIiIjJUlj2C27Ztw65duwAAFy5cgK+vL2Qymc41kZGRUKvVOHLkSGa3MDv2CBIRGd+qVSswbdoUAIBEIsHixSvQu3e/bF+XmPgSzZv7Zbrp/K1b9+HqWszoWYmIiCxVrnsE27Zti/Lly6NcuXIAgDJlyqBcuXLaX+XLl0erVq2wePFi46cmIiKzCQ6+ijFjRmDo0AE4duyozrnjx//F9OmfaI81Gg1mz/7OoPva2zugbNlyeu0ODo6wteUGl0RERKZk0BzBhQsXYsSIEbC1tTVFJqNhjyARUc5ERkaiceO6SEx8CSCjx2/fvsOoV68BNBoN6tWricePI3ReU7x4cdy8eQ/XrwfD1tb2rdtLlCzpDI1Go9P288/zMGzY21enJiIiopzJ9aqhrxs/fjxiY2MREhKi/QdcFEWkp6cjODgYY8eONU5aIiIyq6NHD2mLQCCjx2/37p2oV68BEhNf6hWBAPDuu4PRtWt7XLhwDgDQv/9ALFiwNNP716njg8uXg7THPj51WQQSERGZgUGF4M6dOzFz5kykp6dDEASIoqjdWqJcuXIsBImIiojSpcvotR06tB+hoSG4dOkibG1tkZycrD3XtGlzuLmV0BaBALBp0wb07z8QTZs217vX778vwdix7+PGjWDUqlUHixYtz583QkRERG9l0KqhS5cuRc+ePXH48GE4Ojpi+/btWL58OUqVKoXRo0fnd0YiIjKRFi1a6S38EhZ2F//+ewQJCfE6RSAA9O3bH0+e6PcSPnhwP9P7e3l549ixMwgPj8TRo6fg6VnVeOGJiIjIYAYVghEREXjvvfdQtmxZeHl54fnz52jWrBm++OILrF27Nr8zEhGRiQiCgKVLV6JRIz+Drj98+CC6deulHSXynyNHDr31dTY2NnpzBYmIiMh0DCoEbWxstJvJly9fHrdv3wYAeHt7Izw83OCHRUdHY8yYMahXrx78/f2xcOHCbF9z5swZeHt7IyJC/xNnIiLKHz4+9Qy6rnz5CmjYsBGcnJx12vfv34uEhPhMX3Pv3l107twWJUs6o1On1ggLu5PXuFSIaVQqJD16CE16urmjEBFZFIMKQV9fX6xcuRJpaWmoXr06jh07BgC4evUq7OwMX/J70qRJcHFxwZkzZ7BmzRps27YNe/fuzfL6qKgofPbZZ/zUmIjIBJRKJaZOnYQKFUrin3/2wNOzWqbX/ffBYM2atfHee6Owc+d2xMe/0LlGKpVCJpNn+vqPPvoQly5dAAAEBl7CxInjjPcmyCTSYqIRNPUjHGntj/NjRuDlvbu5uk9C6C2cGdgHZ4e8g5Pv9EDM//9cEBFR/jOoEJw8eTKOHj2KdevWoWvXrnj27BkaNWqEqVOnonv37gY9KDw8HBcuXMCUKVOgUCjg6emJYcOGYf369Zler9Fo8Mknn+Cdd94x/N0QEVGurVixFH/+uRLJycl49OghHj58gH/+OQp3d3ftNR4eZREUdAMnTpzDoEFD4Ofniw8+GI43dyJydnZBTEw0goOv4bvvvsYffyxFYmIiACAo6JLOtW8eU8F3a97PiLl4HqJGjYRbNxE8a2bu7jP/V6Q+jwQAKF/EIeTX2caMSUREb2HQqqFubm44cuQIUlJSYG9vj82bN+PQoUNwdXVFp06dDHrQnTt34OzsjOLFi2vbKlWqhNDQ0EyvX7x4MZydnfHOO+9g0aJFBj2DiIhy7+LF8zrHaWlp+OuvNYiMjNS2RUQ8wvXr11CzZm3MmDENarU603tFR0ehbt0a2pWmAWDbts3455+j8PNrglOnTmiv9fNrkg/vhvLTi+vBOsdJD+5BlZQEqzdGCaVEPsOteT8j4dZNuPjUhddHn0Du7KI9n/xId3pJyrOn0KSnQyLPvDeZiIiMx6BCsE+fPliwYAFq1aoFIKMwHDRoUI4elJSUBBsbG502GxsbpKam6l174cIF7Nq1C9u3b0dSUlKOnvO6t22gSEREulq2bIZ9+3brtG3YsE7vuvv3b6NkyWJZFoGve72nMDDwEv799x88f/4MQMbCNA0bNsT69evg5uaQx/RkSjbFXJEeF6s9dihfHqUqlNS77vDkcYgOytg3MvLYUcitJGg2b572fNk2bXBvxw7tcakmTeBeppjOPV6GhyP25k24+frCtqT+M4iIKHcMKgRFUYQ8j5/O2dra6hV9//Uwvi42NhbTpk3D3Llz4eDgkKdCMCYmERqNmP2FRESEgQNHIDg4BJs2rUdaWlqW19Wv3wSVK1eHi4sL4uLicvSMH36YrR0JIooiXr5Mgq2tK6KiXmbzSioo0l+8QPx93e1BZG7uev8PNUolov5fBP7n6dkAnesqfDARKokcsVeC4OTljSqjxumcj9i7EyG/zgFEEYKVFWp/9R1KNGtp/DdFRFQESSTCWzvGDO4RfP/999G7d294eHhAoVDonO/WrVu29/D09ERcXBxiY2Ph6uoKAAgLC0OVKlV0rjt16hRiYmLw/vvvA3j1aXL37t3xzTffGPQsIiLKOZlMhl9+mYeOHTth4MB+mV5jY2MDpVIFOzs7bNq0A9988yVCQ2+heHE3PH8eidjYmCzv37FjF5w8eVyn7ebN69BoNNoFaKjgUybEA2/0BqtTU/Suk8hksKtQCUkP7r1qs7bGid5d4FC5CqpNnAK7suVQbcKkTJ8jajS4u2Ip8P+fA0SVCnf/WMpCkIjISATxzRn+mfDy8sr6BoKAkJAQgx727rvvokKFCpg5cyaePHmCUaNGYdy4cejXL/MfOADg2bNnaNGiBY4ePQoPDw+DnvMf9ggSEeVcSkoKGjasg8jIZ5meb9CgEfbtO6zXrlKpEBp6CzNmfIYzZ05BEAS0b98J7dp1QKlSpdCmTXsMHz4IBw7s076madPm2L4969WjqWA6P/o9JIS++rffa9JUlO3RR++6hNu3cP27r5D0MBwyR6eMIvL/7CpUQpM1G7J8hkalwr8dW0JUqbRtctdiaLF9X5avISKiV4zSI3jr1i2jhJk/fz5mzZqFVq1aQSaTYeDAgejXrx+ePHmCLl26YMWKFahfv75RnkVERLljY2OD3bsPYOHC3/Hs2RMcPnxQ53xk5LP/D+tMgKOjk7bdysoKISE3cObMKQAZIzoOHvwHw4ePQJs27QEAc+cugEJhjYCAs6hbtz5mz/7FdG+MjMZ3zlw82PgXkiMiUKJZC5Tu0DnT6xyreqHJ2k1QJSfh3MghOoVg0oN7SIuNgbVrxpzAh1s34dGubZDa2KLy8Pfh1qQpSnfojMevzVst08WwlcqJiCh7BvUIFlbsESQiyrshQ/rj4MH92uN33nkX58+fQ3j4A9StWw/Ll69BsWLFMWHCGOzbt1tvK4mZM7/F+PEfmTo2FTBXZ07D89eGBlu7lUCzjTsgSKV4fvokrs74VHtOsLKC/7rNsHYrgXtrViD2chDcGvujwqBhEATBDOmJiAofo/QIEhGR5VqyZCUWLpyHGzeC0axZCyxbthiPHj0EAAQFBeKLLz5F7do+2Lt3V6avb926rSnjUgFVbfwkpMXEIP5GMGxKlUaNaV9CkEoBADGXdLcuEVUqxF0Jgjo9Hff/+hMAEH/zOqyLFUfpTl1Nnp2IqChiIUhERFmKi4vFoUMH0KBBQ0ydOh3R0VGYMWOazjXBwdfeupVE3GvbDJDlUpRwR8NFK6BKSoLU1lanZ8+hsqfe9fZVPBE09eNXDaKIsDV/sBAkIjISFoJERAQAeP48EmFhd1Gnji9sbW1x795ddO7cFrGxGYVcmzbt0L17L73X2dra4tixo1ned/PmjfD3b5ZvualweXPTeQBwb90OEXt34WXoLUhkMlQaPhKOntWgeWPbKXWK/uqkRESUO1yvm4jIwomiiBEjBqNmTU/06NEJVauWw6FDB7BixVJtEQgAR48e1i4E87q7d++8tUfw338Po0OHlpluTk9F15MD+3CyX3cc69IWd5Yv1ps7+robP3yDl6EhAERolOnaYtGjZ2+d68r26pufkYmILIpBPYJRUVGYP38+rly5gvT0dL3zBw8ezORVRERUGBw+fAB7975amTE9PR0ffvgBOnXqonetj48vNm/+O0f3j4x8hsjIZ7h8OQglSpRA27Yd8pyZCrZn/x7Gjdnfao8fbFgLQSJBlffH6F2bHheLqDMnddoi9uxC2Z59Uf7dwUiLjUHq06co1aETPLr2zO/oREQWw6BCcObMmQgKCkKPHj3g6OiY35mIiMiEMtsiKD7+BTp06IRt2zZDqVQCALy9q2P48Pdx8uRxHDjwT66etWXLJty8eQMlSrijZ88+UCgUecpOBc+dZYvw4G/93t/769eibK++sC5WXKc9+XEEIAjajeMBQJBKcGvhPDw9+A9ULxMyrnvyGCX8m0Pu4pq/b4CIyEIYtH1Ew4YNMW/ePDRp0sQUmYyG20cQEWXv+vVgtG7tr9c+bNgIDB06An/9tQaxsbHo2/cddOjQGVFRUejfvxeuX78GIGPfwdTUNIiiJttnSaVS7TDSxo39sWvX/mxeQQWRKikJL8PuwL5iZcgcHLTtypcvcaJXJ51N4F9X8/OvUKp9p1fXJyTg9IBeUCUlvbpIIskoCjP58aTqhx+hfL8BxnsjRERFmFG2j5DL5XB3dzdaKCIiKjhq1qwFT89quHMnVKd969ZNCAy8iLCwu0hJScGuXdvx7ruDMH/+EuzefQB161bHixcvkJKDBTxen0sYEHAGly5dQP36DY32Xij/xVw6j6szp0OdnAyJQoHaM7+FQ1WvjN67lGSIb5kvalehos7x5c8/0S0CAUjl1lCnZv5nShC4tAERkbEY9DfqwIEDMX/+/Bz9Y0+Fm3zvbjgOHwT7zyZDEvHI3HGIKJ8tX74aEonuPwlJSUm4fj1Y5+/+jRvX4969MJw+fRIvXrzI83PffCYVfKEL50GdnAwA0KSmIuS3n3F+1DDcXbEED/76ExKrzD9jti1bDg6e1bTHaTExiP9/r/LrrF7rYXydooQ7SrbraIR3QEREgIE9gkFBQTh//jwaNGgANzc3yOVynfNcLKZoke/dDacRg18dHz2C2IBAQCYzYyoypgMHpAgMlMLPT402bbL+9J4sx6ZNG6DRZD+0EwCuXr0Ma2vrPD+zadPmqFu3fp7vQ6aV+jxS5zgtOkpnGKdGqYSidBmkPnmsc13yo4c4P2oY6v66AHInJ6RFPde7t8TaGtUmTsH1WV9Co8xYnM6pRi2Uat8JJVu3hcyB6xQQERmLQYWgj48PfHx88jkKFRSKbZt1jqUPH0B28TyUTZqaKREZ0+zZcsydm/FD/O+/AzNmpGHiRP3VgMlyPHhwH8uWLTL4+nHjRmU6RK9OHR9cvXrFoHv4+TXG339vM/iZVHCUbNMej/fs1B47VvNGwq2bOteokxIhL1Yc6THROu0v797Gw22bUGXEB0h6rD/axLVeA7g3awHHtRsRfe4sbMp4oFiDRjqbzxMRkXEYVAiOHz8+v3NQAaIpWdKgNip8RBFYtky3R3/ZMhkLQQsXHR311j3e6tVrgMDAi9rjjHl+uj3JEokEYWF3YWdnh6Q35nxlZujQEUbpVSTT85owGQq3EngRfBX2lT1hZWeHhNuhgObVnwllfDwarfgTTw8fwMM3thtJjXwGAHCp4wuJTK7t+QOAUv8f+mlTqjT3DCQiymcGT844fPgw+vXrBx8fH9SvXx/vvvsuDh06lJ/ZyEySx38MdfkKr45Hfwh1pSrmC0RGIwj6I3w54pfq1q2PqlWrZXru/ffHYObMWdneQ6PRIDExEUlJSdnO++vduy/69u2fq6xkfhK5HJWGjkCtr75H5PGjCFu5TKcIBAArO3vYlS2PysNHQebsonPOvVVbAICiuBvqfP8TnLxrwLZsOXiOnYCS/z9HRET5z6DtI/bv34/JkyejY8eOqF+/PjQaDS5duoQjR47gt99+Q/v27U2RNce4fUQeqFSQXTwPjbs7i8AiZskSGb766tXebT//nIphw5RmTEQFQWRkJGbM+Ay7dm3XtpUqVRqBgdchlUoxYEAf/PvvEaM869KlYAiCAA+PshzyV4g93rcbN3/+Qa9dYm2Nsj36wHP0hxCkUiSG38eDDeugjH+B0p26wr1FazOkJSKyPNltH2FQIditWzd07doVo0eP1mlfvnw5Dhw4gO3bt2fxSvNiIUiUuYsXJQgKkqJRIzV8fAxbIISKvvT0dNSuXQ2xsTHatjVrNqBz566YOfNzLF26MM/PqF27Dp49e4bnzyNRqVJl/Pnn36hWzSvP9yXTe7x/L27O+U63USIB/r/okHurNqj91fc6pzUqFSJ2b8eL68FwrlkLHt1766wymhYTA5mTU5YrjxIRkeGMUgjWrl0be/bsQfny5XXaw8PD0b17d1y9ejXvSfMBC0EiIsMdPXoYAwb00WmrUsUTZ88GwtOzLOLj43N8T7ncGunpadpjmUwGpfJVD3SzZi2xbdvu3Icms1ElJ+HcqGFIeRwBAHrz/QDAf8M22JYuoz2+Ne8XPNq5VXtctldfeH30CVKePsGVGZ8hMewO5K7FUHP6TBRr0Eh7XeL9e7Cys4OiBPc0JiIyVHaFoEFzBEuVKoXbt2/rtd+6dQsuLi6ZvIKIiAqbVatW6LXduxeGly9f6i0AY+iQTuUbhcHrRSAAhIaG5DAlFRRWtnZotGwNvKdMg9fHn8C5to/+RW9sSfL4wF6d4yf79wHI2JswMewOACA9NgbXZ38LjUoFZWIiLowfhYD3BuJU/564tWBuvrwXIiJLZNDYi759++Krr77CixcvULduXQBAYGAg5s2bh/79OeGfiKgouHw5UK9No9Fgz56dUKlUOu1yuTXS0lJ12mrXroNr116NEKlWrRrc3Nxx+vRJbZuLiyvi4mK1x+3adTBWfDIDmb09PLr1BADYepRF3JVAiOqMhWPcmraArUdZnevlTs5ITX326vXOzgCAxPthOtelx0Tj3p8rIVUoEH89OKNRFPFo22aUatcJTl7e+fOGiIgsiEGF4IgRIxAZGYlvvvkGarUaoihCJpPhvffe49YSRERFRPny5REdHaXTJpfL0bixP2xsbJCSkqJtf70ItLa2xhdffIVhw0Zix46tePr0CZo1a4mGDRshKioKX345DZcuXUSjRn4YP/4j/P77XAQHX0WzZi0wY8Y3Jnt/lL/sK1aGdfESSI18CgCZDuP0HDMe17//GqJKBcHKClXHZPwMUayhHyJ26u4reX/dahRr4Kd3j5Snj1kIEhEZgUFzBP+TmJiIe/fuwdraGhUqVCjwe0BxjiARkeHOnz+HwYP7aecC2tra4uef56Ffv3exadMGfPHFZ0hI0J8n2Lp1O2zcyM3hLd3txfMRvnmDTlvjP/+GffmKOm2p0VFIuBUCRy9vKIq7AQDUKSkI+mwSXly7onNtiRat8PzEMe2xlZ09mm7cDpmDY/68CSKiIiTXi8UEBQWhTp06kEqlCAoKeutD/hsuWtCwECQiypnk5GQEB1+Dm5sbPDzKQi6Xa8+lpKQgJOQGOnVqo7MBvZWVFdzdS2LUqLEYN26Czv3Onj2NmTM/x+PHj9CzZx9MmjQVe/fuhihq0KtXX7i6FjPZeyPjizp3Fvf/Wg1NWhoEqRUSbt3UOV/3l99RrH6jLF6tKz7kBi6MHanTVvvrHwCIiNi7CzJ7B1QcNAwOnlWNFZ+IqEjLdSHo5eWFM2fOoFixYvDy8oIgCMjsUkEQEBJSMCf7sxAkIjK+2bO/w2+//ZzpvwkrV65Dt249AGSMIvH1rY74+Bfa805OTtoex9Kly+Do0dMoVozFYGGU/DgCZ4f2184JfJN1cTf4r98KqbU1Up4+wf31fyItOgol23VEqTaZ7z/8YNN63F+3BhplOsr17ocqH3zIvSaJiHIpu0IwyzmCR48ehaurq/b3RERkmZYtW4RFi+ZDEARMnDgJ06bNwI4dW3H//j29a0+cOKYtBG/cuK5TBALQ2YLiyZPH2LZtEz74YFy+5qf8EXPpvF4RWLxJUwiCALmLKyoMHAqptTU0SiUufTwOqZEZi8REnzsLQRBQsnU7vXtW6D8I5fsNAEQRglRqkvdBRGSpstw+okyZMtpP4RYuXAgnJyeUKVNG55ednR1mz55tsrBERGRaZ86cwpdfTsezZ0/x9OkTTJ8+FefPn4Ovb71Mr69Zs5b299WqVYOtre1b75+DaepUwNhXrKzXVqJZS/h8/zOqfzJdu3/gixvB2iLwP8/+PZLlfQWJhEUgEZEJZNkjGBYWhtjYjCW+d+7ciTZt2sDJyUnnmtDQUJw6dSp/ExIRkdkEBJzRazt37gy+/vo7REQ8woUL5yAIAgRBQN++/TFo0FDtdc7OLli0aAU+/3wqIiOfoX37jggOvobH/9+AvFSp0ujTh1sQFVYutX1QYcBghG/dBFGtRsnW7VCqXUfteU16OjQqJRRuJQBBAF4r+hUlSpgjMhERvSbLOYInTpzA6NGjMy7KYn4gAAwePBgzZszIv4R5wDmCRES5t3XrJixbthhXr17Wad+2bQ+aNWsBAEhIiIdMJodarYa9febzEERRhFKphFwuR1xcLLZv3/r/xWL6cX5gEaBMTISoVkP+2ofFDzaux70/V0KTloaSbdpBXtwN4Rv/AkQRtmXLod7chRkFIhER5ZtcLxYDAJGRkRBFES1btsSOHTu0cwb/Y2dnl+U//AUBC0EiopyLi4vFwoXzsGDBPG2bIAiwt7fHhAmT8PHHn2R7j/j4eIwd+z5CQm6gUaPG+O23hbCxscnH1FRQJN4LQ8CIQTptXh9/guJ+/kiLiYGTlzeHfhIRmUCuF4sBAHf3jM1gb926leU1aWlpBX4/QSIiMsyJE8cwbNgAJCcn67SLoog1azZoewKz06pVE0REPAIAbN++BeHhD7B/PxceswQvw+7ot929g7I9+8KmZCkzJCIiosy8tRD8T1xcHJYuXYrbt29D/f8Vwv4b6nP37l1cunQpX0MSEZFpfPvtV3pFIJDRI+jhUdagezx+HKEtAv8TGHgRarUaUvYEFXkudepCsLKCqFJp2wzdS5Ask1qpxoUfzyBs9x04lHNE46+ao0Qdd3PHIiryslw19HVfffUV9u7dC3d3d1y6dAmlS5eGUqnElStXMGbMmPzOSERGlJgIfPONNbp1s8H338uRyc/8ZMGiop5n2j5x4mRUrFjJoHtktlKotbU1i0ALoShRAnW++wkOVb1gU8YDld4bhZd3b+PqV5/jyaH95o5HBdCVRZdweeElJDyMx+PTj7Bv4A6o0zPfn5KIjMegHsGAgADMnTsXzZo1w40bNzBs2DB4e3tj1qxZBXYzeSLK3OTJCuzcKQMAnD9vhchICebPTzVzKioo+vcfiHnzftEeu7oWQ1xcLDZv/hv16jVAx46ds72Hi4srevfui+3bt2rbJk/+LF/yUsHk5tcEbn5NAAAXJ47Bi2tXAADPT/wLVWIiyvXuZ8Z0VNA8Oh6uc5wSlYzoG1Fw9y1ppkRElsGgHsGUlBRUqVIFAFCxYkXcvHkTADBgwABcvHgx/9IRkVGJIrB3r+7nP7t3G/R5EFmIadNmYPbsX9G1aw80bNgIsbExEEURT58+wdix7yMx8aVB91m6dBV27dqPSZOm4syZS5g0KfsFZqjoSXn6RFsE/ucpewXpDcVquOkcW9lYwbmSs3nCEFkQgwrBMmXK4N69ewAyCsH/egGlUikSEhLyLx0RGZUgAGXL6q6kW768xkxpqCCSSCQYMWIUVq1aB4lEdyhnUlIiwsLuGnyvxo39MX36l/D0rGrsmFRIWNnbQyKT67TJXVze+hqNUonE8PvQKJUAAFHDv6OKugZT/ODRrBwAQFHMBq3mtYe1k8LMqaigk768BocbY+FwfRSs4tkxlRsGdQX06NEDU6dOxezZs9GqVSu899578PDwwOnTp1GtWrX8zkhERjR7dipGjbJBQoIAZ2cRP/yQZu5IVED5+zfDuXNntcfFixeHl1d1Myaiwkbm4IhK772PuyuWAKIImaMjKr83Ksvr465dwbWvpiM9Lg4yJ2co3N3x8nYo7Ct7oua0L+HADxWKJIWrDbpv64vUuBTI7OWQyjifmN5OkhoBl4sdIKiTAADWkTsR53cGajv+HZETb91H8D+iKGL16tWoVKkSWrZsiSVLlmDt2rVwdXXFTz/9hBo1apgia45xH0GizCUlAXfuSFCtmgbc2o2ykpaWhq+++hy7d+9E+fLlMWvWj2jQoBH27NmF2bO/RUJCAoYOfQ9Tp043d1Qq4JKfPEZyxCM416wNq0wWE/rP2eEDkPTgfqbn7MqVR5O1m/IrIhEVIjYPl8A+VHfueVKlz5FceZqZEhVMedpQPjsajQbPnj1D6dKlc3uLfMVCkIjIuB4+DIefny9Ur20NMHfuAgwePMyMqaioONLGH6I669UiW+45DJmDgwkTEVFBJI/cAadruv/uvPSej1SP4eYJVEBlVwgaNEewbdu22L59u157bGws2rRpk/t0RERUqJw/H6BTBALA5MkTMGzYQCQlJZkpFRUVbv7NszxnX6kyi0AiAgCku3VFerHW2mOlsx9SS72jPZYmhsD27izYhC+CoIw3R8RCwaA5ghEREZg5cybu3LmDTz/9FIIgaM/loUORiIgKGR+fupm279+/F4sXz+cwUcqxFzeuI3zTemiU6SjdsSusXYvhxc1gOHpWQ2p0FOKuXoZjlarw/oR/tojo/yQyxNfdCav4IEBUQeXUIGNFPABW8UFwvtQBgiZjDQTFk/WI8zsFCJx7+iaD141fuHChthicN28e7O0zuhlfLwqJqPA5dUqKH36wRnS0gHffVWLy5HTw25qy4uLiChcXV8TFxeqdu3492AyJqDBLfR6JwMkfQpOW8QNbzPlzaLD4D3h9zO1GiCh7Kif9DycVj1dri0AAsEq8DlnsSSiLtTJltELBoKGhAFCrVi1s2bIFsbGx6Nu3L8LDwyGRGPxyIiqAYmOBIUNsEBgoRXi4BHPmWOPvv7mvIGVtw4Z1mRaBANCiBf+Rpaxp0tP12qICTmuLQAAQNWo8P3nMlLGogEsIj8eZL4/j+CdH8PxqpLnjUGEgsdZrEqVcGS8zBlVy//X6ubu7Y8OGDfD09ES/fv0QGBiYr+GIKH9duCBFcrJu99+JEywEKWvJyfrzAF1di2HKlM8wfPhIMySigi75cQTOjx2Bo+2bI2DkYLwMu6M9Z1OylN71mbWRZUp9kYptnTbg6rIg3Fx7DTu6bkTMzShzx6ICLqXsaGhkr/YrTXdtBZVTIzMmKrgMKgRfnweoUCiwYMECDBgwAJMmTcq3YESU/6pX10Ai0Z3nW6MGN2+mrPXvPxD29q8W7ChVqjQuXLiCzz77gqNEKFMhc+cgIeQmACAx7C6u//CN9lyxBn4o2ba99ti1XgOU6tDJ5BnJfERRxNNzj/EkIALiGyu9Pzh4DynRKdpjdZoat7eGmDoiFTJqO0/ENgnES+8FiK/zN+J9t4FzXjJn0Ef/48ePh+0b+/5MmjQJlStXxpYtW/IlGBHlv3LlRPz4Yxq+/94aiYlAly4qjBqlP3yL6D8VK1bCkSMn8Pff66FQKDB48DA4OjqZOxYVYAm3dH9wTwy7C3V6OqRyOQSJBLVmzELl4aOgUalgX6GimVKSOajTVNjdbxuennsMAHCvVwo9tveFlY0MAKBwUei9RuHCIX6UPVFeHKke3NYoO3naR7Cg4z6CRIZJTwfS0gCuzE5Exnbli08RdeakTptjNW/U/WU+t4OwcLe3huDIuP06ba3mtYf3wJoAAI1ag739tyPi5EMAgLOnK3rv7c9ikMhA2e0jmGWPYM2aNXHy5Em4urqiRo0ab10d9Pr163lLSURmJZdn/CIiMjbvKZ9BmRCPF8FXtW0JoSGI2L0DFQcNNWMyMreU6OS3tkmkEnTb0gdPAiKgSlHBo3k5SGXcAoDIWLIsBL/99lvtFhHffvstt4kgIiKiHLN2LQaPnn10CkEASH3+zEyJqKCo1NUTF+achTJJCQCwsrVC5W5Vda4RBAFlmpQ1RzyiIo9DQ4mIyGQSExO1HzKS5VC+TMDpgX2hepmQ0SAIqPfbIrj66O8BRpYl5mYUgldegSiKqPmeD9xqlTB3JKIiI7uhoVkWgl9++aVBDxAEAbNmzcpdunzGQpCIKP+Iooj09HRYW+vv2fSmsLA7GD16JK5du4Jq1bywePEfqFWrtglSUkGReC8MDzb+BVVSIsp07Qm3xv7mjkREVKTluhAcMmSIwQ9Zt25dzpOZAAtBIqL8cfjwAXz66WQ8efIY7dp1wKJFy+Hk5Jzl9b16dcGZM6e0x9Wr18Tx42dNkJSIiMgy5boQLApYCBIRGV9SUhLq1PFCQkK8tm3UqDH4/vufsnxNpUplkJj4UqftyZNYWFkZtIsRERER5VCuVw0NCgpCnTp1IJVKERQUlOUNBEGAr69v3lISEVGhce9emE4RCACXL2f97wQANG3aHAcO7NMe+/k1YRFIRERkRln+Kzxw4ECcOXMGxYoVw8CBAyEIAjLrPBQEASEhIZncgYiIiqKqVavBxcUVcXGx2ramTZu/9TW//jofMpkMAQGn4etbD3PmzM3vmERERPQWWQ4Nffz4MUqXLg1BEPD48eO33qRMmTL5Ei6vODSUiMj4bt0KQfv2LZCamgoAcHZ2QWDgdThwc3AiyoQqRYlnl57CqaIzHDwczR2HyGJkNzRUktWJMmXKaPcOXLhwIZycnFCmTBmdX3Z2dpg9e7bxU5NpqFQQoqLMnYKICpm1a1dpi0AAePEiDufPc+EXItIXfSMK6+qtxO4+W/FX/ZW4sviSuSMR0f9lOTQ0LCwMsbEZw3527tyJNm3awMnJSeea0NBQnDp1KrOXUwEnP3oI9h99COnzSCh9fJGw6i9oPLhhK+VOVJSAP/6QITJSQJ8+KjRrpjZ3JMpHUqn+Px1BQYGoXdsXJUpwDzAieuXinLNIiU4GAIgaEed/PAPvQTVh7aQwczIiynJo6IkTJzB69OiMi7KYHwgAgwcPxowZM/IvYR5waGgW0tNRzMcLkuhobVNqj954uWKN+TJRoaVWA82b2+LOHSkAQBBE/P13Clq3ZjFYVN27F4aOHVvhxYsXOu3W1tZYvfovtG3bwTzBiKjA2dphPZ5fjtRpG3RhBJwqOJsnEJEFyfWqoS1atMCJEycgiiJatmyJHTt2wNXVVecaOzs72NtnfXMqmCTPI3WKQACwunndTGmosLtwQaotAgFAFAX8/beMhWARVqlSZZw6dRG7d+/ADz/MQlJSIgAgLS0N3333DQtBItLy7OOtUwi61yvFIpCogHjr2t3u7u4AgFu3bpkkDJmGpowHVFWrwep2qLYtvVUbMyaiwszZWb/XPbM2Klrc3d0xdOh7+Oqrz3XaY2NjzJSIiAqiOh/UhdxejvsHwuBc2QV1JzQwdySyRKIatmE/wjpyOzSKskjy/AYqRx9zpzK7LAvBpUuXZv4CKys4OTmhVq1a8PLyyrdglI8EAQlr/4bdzM9hdesW0tu1R9IXX2tPW10JgvW2LdC4lUDq0OEQnV3Ml5UKPG9vDd59V4mNG2UAgBIlNBg3Lt3MqcgUrK2t0atXX2zZslHbNmDAIDMmIqKCyHtgTXgPrJmne4gaEeo0FVRpasTff4HiNdwglUuzfyERAJuHS2B3/6eMg+S7sLp8HTFNbwBSy56rmuUcwdatW2f6AlEUER8fj5SUFLRq1Qq///47ZDJZvobMLc4RzDmrcwFw7t0FgkoFAFB510Dcv6cBKf+ypbcLCpIgMlKC5s1VsLMzdxoypuDgq7hw4TwaNGiI2rV9dM6lpaVh1aoVuHo1CE2btsCgQUO1K04TERlD2N47ODX9XyRHJkGQCBA1Imzd7dB1Qy8Ur8UFqiiDkB4F66j90MjdkV68HSC82hzBKbAH5LHHdK6Pa3AYKudGpo5pUtnNEcyyEMzOrVu3MHnyZHTo0AEfffRRrgPmJxaCOefw4QdQvPbpPgC82LEPSv9mZkpEROa0Zs1KfPrpJO3xjz/+gpEjPzBjIiKyJGkJafiz9nKokpV65zxalEf3LX3MkIoKGmniTThf7ACJKh4AkObWBQk+f2vP24VOh+3DRdpjUWKNmGa3IMqLmTyrKeV6H8HseHl5YfLkyfjnn39yewsqgMRMunIyayOioishIR4LFszD559PxezZ3+qc+/XXOWZKRUSW6MWd2EyLQAB4+TDexGmooLIJX6QtAgHAOmofrBKuao+TK01FumsLAIDGyhkvvecV+SLQEG9dLCY71apVw7Nnz4yVhQqAlNHjYL17ByT/30MyrWMXqHzqmjkVEZmKKIro27c7rly5nOn5tLQ0EyciIktWrHpxKFwVSI1N1TsnVUihSlHCyqZgTlEi0xE0KfqNmld/ZkSZK+Lr7YGQHgXRyhGQWJswXcGV6x5BAEhKSoKtra2xslABoK7sidhzl5GwaDlebN6JhD83mDsSEZnQ5cuBWRaBADBq1BgTpiEiS2dlI0PrBR1hV8oeglwCvDYFOTYkBkELLpovHBUYqR7vQxRe9W8pHX2hcmqod50od2MR+Jo89Qj+/fffqFOnjrGyUAEhOrsgrd+75o5BRGZgb++g1+bv3wx16viiQYNG6NKlmxlSEZGl0qg1CPjmJJKeJmZ6PjLwqYkTUUGkdGmCFw2PZWwPYe2O1NJDAC5clq0cbx+h0WiQmJiIoKAghISEYP369fkWjnJP8jgCDpPGQ3Y+AMp6DfBy7gJoKlQ07MWiCOm9u1C7lwLss55gSoXf9esSnD8vha+vGnXraswdh8zo3LmzWLNmJWxsbNCuXQccPnwQAODk5IwffvgZ3t7VzZyQiCzRs4tPEHc7NsvzpRqVMWEaKshUjnWgcmQHVU5kWQhu3rw503aZTAZHR0fUqFED33//PSpXrpxv4Sj3HCaNh/z4vwAA+emTcJwwBi/2HMz2dZLwB3Aa/A6sQm9BY2ePxJ/msnewiNqwwQoff2yjPf7mm1SMHZv5hHwq2oKDr6J3765Q/X/bGEdHJ6xevR7JyUlo27Y9XFxczZyQiCyVwll/nzdrJ2toVBp49vaC74f1zZCKqGjI9fYRhYElbx9RvLw7hBTdibNRkfHZdpM7jH4Pih3btMcaewfEXAtlz2ARIorAyZNSjB6tQGzsa3vsOIm4fTvRoJEUO3ZYYd48OVQqYPRoJYYOZQFZmM2aNRMLF87Tafvtt4UYNGioeQIREb3m34kHcWvjDQCAjZsteu99F04Vnc0biqgQyG77iDzNEaSCS1mvAeSnT746rlvPoLHS0rAwnWNJ4ktInz+D2r6K0TOSeUycqMCmTforrCmVGUVidn9Mbt6UYMwYBUQx48JPPpGicmUN/P3V+RGXTMDd3d2gNiIic2g9vwNqDK+NpGdJKNuiPCIDn+LK4ktwLOeEGsNrQ+7AxT+IciNPq4ZSwfVy7gIoGzUGkFEEvly43KDXpXfopHOsquYFdUUO/y0qHjwQMi0CAWDkyHRIDPgb4dQpqbYI/M+JE1JjxCMzGTRoKHx8fLXHHTt2RuvW7cyYiIhIl3vdUqjUuQoe/nsfu/tuxY0/ryHg21PYO2CHuaMRFVrsESyiNBUqZswJfLOLJy0NinWrYXXjOpQtWiGtZx+d1yVPmgoIAuQH/oG6ShUkTZ/JVZeKkLQ0/f+X5ctrMHNmGrp2VRl0j5o19ReVqVGDC80UZvb2Djh48DguXrwAGxsFatXiZHsiKphurgvWOX524Qlib0XD1au4mRIRFV6cI2hhHMaMhGL7Fu1x4lffIeXDiWZMRKbWp48NTp3K+AxIIhGxbl0K2rXL2bDOH3+UY8kSOdRqYMgQJX74Ic2g3kQiIqK8ODhyD8L23HnVIABDAt+Hg4ej+UIRFVDZzRFkIWhBhJcJKOZZDoLmVe+NqlJlxJ3LevNoKnqSk4GNG2V4+FCCrl2VqF//1Z+HtDRg924rPHuWca5ixay/f1JSAI0GsLMzRWoiIiIg6lokdvXagvSX6QCAGsProMVPbcyciqhgYiHIQvCV1FQU864ESdKrTVmVvnXx4uBx82WiAuX13kKFQsSuXcnw9eWwTyIiKjhSY1Pw6Hg4HMo5omT90uaOQ1RgZVcIcjCXJVEokDx1uvZQlMuR9NkXZgxE+S0kRIL162W4ezf7eZ5Xrki0RSAApKYKWLFCnp/xiIiIckzhagPP3l4sAonyiIvFWJiUcROQ3rotrG5eh7JJU2hKljJ3JMonq1fL8NlnGRvxSiQiFixIRb9+hi0IQ0REZArJkUk4NuUwHp9+hBJ1SqDl3PZwruxi7lhEFoFDQ4mKIFEEqle3Q0zMq07/cuU0uHQpSXscFiYgKUlA7dqvhn5yaCgREZnS/uG7cf+fu9pjtzru6Hd4kBkTERUdBWpoaHR0NMaMGYN69erB398fCxcuzPQ6tVqNuXPnomXLlqhXrx769euHc+fOmTIqUaEmikBysu5w0OTkV7+fMEGBxo3t0batHTp2tMXLlxntGzakYNGiFHz5ZRpOnEhiEUhERPnq6bkIneOoq5FQJivNlIbIspi0EJw0aRJcXFxw5swZrFmzBtu2bcPevXv1rlu2bBmOHTuGdevW4cKFC3jnnXcwZswYPHnyxJRxiQotiQQYOlT3H9LhwzOOAwKkOpvKBwVJsXZtxrG1NdCvnwoTJqS/dcVQIiIiYyhRV3eKiqt3cchsZVlcTUTGZLJCMDw8HBcuXMCUKVOgUCjg6emJYcOGYf369XrXJiYm4sMPP0TZsmUhlUrRr18/yOVy3Lhxw1RxiQq9b75Jw8KFKRgxIh0rVqTg008zltqOiNBfOObxY64bRUREptfipzYo3cQDAFC8phvaLu5k5kRElsNki8XcuXMHzs7OKF68uLatUqVKCA0N1bv2008/1Tk+f/48Xr58CW9v73zPSVRUSCTAO++o8M47ugvEtGmjgqOjiISEjIJQEER0785FZIiIyPQcPBzRc+c70Kg1kEj5oSSRKZmsEExKSoKNjY1Om42NDVJTU9/6uuDgYHz00UeYMGECPDw8cvTMt02OJLJUbm7AqVPAzz8DCQnA6NECOne2NXcsIiIiIjIhkxWCtra2ekVfSkoK7O2zLtZ27tyJb775Bh9++CHef//9HD+Tq4YSZa5UKWDu3FfHUVHmy0JERESUH+RR+2F3ZyYk6VFILT0ISZ6zAEFq7lgmk92qoSYrBD09PREXF4fY2Fi4uroCAMLCwlClShW9a0VRxOzZs7F9+3bMmzcPLVq0MFVMIiIiIiIq5IS053C8NhSCJg0AYBu+AGqbCkgtO8rMyQoOkw3GrlChAnx9ffHTTz8hOTkZd+/exdq1a9GrVy+9axcuXIi9e/di8+bNLAKJiIiIiChHZAmB2iJQ2xZ3xkxpCiaTzsqdP38+EhMT0apVKwwfPhz9+vVDv3798OTJE/j6+uLSpUtIT0/HihUr8OLFC/Tu3Ru+vr7aX7t37zZlXCIiIiIiKoRUDnUgCrqDH1VO9c2UpmASRFEsspPoOEeQiIiIiKgIUqfA4dZkWD/bAbVNWSRWmwNlsdY6l1g/3QS7219CooxBaql3kej9GyCRmymw6WU3R5CFIBERERERFSq2d7+D3f2ftMcaK0fENLsFWGVS+Ihqi1ok5j/ZFYLcsIWIiIiIiAoV2YtzOscSVQKsEm9kfrEFFoGGYCFIRERERESFitK5oc6xRuoAtX11M6UpnFgIEhERERFRoZJc8ROkluoPUZBDZVsZCbXXQLRyMHesQoVzBImIiIiIiIoYzhEkIiIiIiIiHSwEiYiIiIiILAwLQSIiIiIiIgvDQpCIiIiIiMjCsBAkIiIiIiKyMCwEiSzUjRsSbN9uhefPBXNHISIiIiITszJ3ACIyrQsXJFi1So7t22UAABsbERs2pMDfX23mZERERERkKiwEiSzIe+8psG+fTKctJUXAL7/I4e+fYqZURERERGRqHBpKZCEuXJDoFYH/iY/n8FAiIiIiS8JCkMhCxMVlXewNGaI0YRIiIiIiMjcODSWyEC1aqFGihAbPn2d8/iMIItq3V2HAABU6d1aZOR0RERERmRILQSILERAgRXT0q17BunXVWLcu1YyJiIiIiMhcODSUyEL88YccGs2rQjAw0Ap373JuIBEREZElYiFIZCGsrES9Nqk07/dNTQXS0/N+HyIiIiIyHRaCRBZi3DglFIpXxWCvXkpUrKhfHBpKowE+/dQalSvbo1o1e8yfLzdGTCIiIiIyAUEUxdz/JFjAxcQkQqMpsm+PKMfCwwUcOmSFcuU0aNdODUkePgrassUKH35oo9N28GASfH01eUxJRERERHklkQgoVsw+6/MmzEJEZla+vIhRo5Ro1UqNqVOtUbasPerWtcOePTlfN+raNf1xpVevGmGsKRERERHlOxaCRBZoxQoZ1q2TIy1NQESEBGPHKvD8edYLx0RHC1i7VoY9e6yg/P+Wg82b6245IZGIaNqU21AQERERFQbcPoLIAl26pNtzl54uIDhYgjZt1HrX3rsnoFMnO+2G9I0bq7BjRwratVNj1qxUrFwph42NiClT0lGlCodiExERERUG7BEkskCNGukWfNbWIurUyXxu36pVcm0RCAABAVY4ezajkBwzRomLF5Nw8mQyevRgbyARERFRYcFCkMgCvf++EiNGpMPeXkSlShosX56K4sUz783LbGsIbhdBREREVLhx1VAieqvr1yXo0sUWKSkZvYLe3mocPZoMKw4sJyIiIiqwsls1lIUgEWXr7l0B27bJ4Ooqon9/JRwdzZ2IiIgoQ1pCGu7/cxcSmQQVO1WBzFZm7khEBQILQRaCRDru3xewbJkc8fECBg9Wwt9ff4EYIiKiwiAlOhlb2q9HYsRLAICrdzH02T+QxSARsi8EObiLyIIkJgJdu9oiKipjevCOHVbYtStFb/EYIiKiwuDWxhvaIhAAYkNicP+fu6ja19uMqYgKBy4WQ2RBjh2z0haBAKDRCNi2jZ8HERFR4aRO1/8gM7M2ItLHQpDIgri56Q+VzqyNiIioMKj2TnVYO1trj+3LOKBSV08zJiIqPDhHkMjCjBqlwK5dGXMnPD3V2L07BcWK8fuEiIgKp5cRCQjddBNSuRTV3q0BWzdbc0ciKhC4WAwLQSI9wcESxMcL8PNTcxsIIiIioiKIi8UQkZ5atTTmjkBEREREZsQ5gkRERERERBaGhSAREREREZGFYSFIRERERERkYVgIEhERERERWRgWgkRERERERBaGhSARERERmVz8gxdIeBhv7hhEFovbRxARERGRyaiVahz+YB/u7bsLAKjSqxraLu4EiZT9E0SmxO84IiIiIjKZe3vuaItAALi7IxQPDoSZMRGRZWIhSEREREQmE//ghX7bff02IspfLASJiIiIyGQqdKgMQSpojyUyCSp0rGzGRESWSRBFUTR3iPwSE5MIjabIvj0iIiKiQunhvw9wdVkgBIkAn7H14dG8nLkjERU5EomAYsXsszzPQpCIiIiIiKiIya4Q5NBQIiIiIiIiC8NCkIiIiIiIyMKwECQiIiIiIrIwLASJKFuHD0vRo4cNunSxxZ49VuaOQ0RERER5xMViiOitbt+WoGVLW6hUGUt9C4KI/fuTUbeuxszJiIiIiCgrXCyGiPLk6FGptggEAFEUcPgwewWJiIiICjMWgkT0Vp6e+j1/mbURERERUeHBQpCI3qpNGzWGDk2HRCJCEET06aNE9+4qc8ciIiIiojzgHEEiMkhUlACNBnB35/cUERERUUGX3RxBTvQhIoO4ubEAJCIiIioqODSUiIiIiIjIwrAQJCIiIiIisjAsBImIiIiIiCwMC0EiIiIiIiILw0KQiHKt6K45TERERFS0sRAkolz5/ns5Kla0R9Wq9li6VGbuOERERESUA9xHkIhybO9eK4wYYaPTduBAEurW1ZgpERERERG9Lrt9BNkjSEQ5FhSk/1dHUJDUDEmIiIiIKDdYCBJRjjVurNZra9RIv42IiIiICiYWgkSUY+3aqTFjRhrc3DTw8NBg7txU1KrFYaFEREREhQXnCBIRERERERUxnCNIREREREREOlgIEhERERERWRgWgkRERERERBaGhSAREREREZGFYSFIRDoOHJCiZ08b9OplgyNHuDcgERERUVHEVUOJSCs4WIJ27Wyh0QgAAKlUxLFjyfDy4tYQRERERIUJVw0lIoMdOmSlLQIBQK0WcPiwlRkTEREREVF+YCFIRFpVquj3/GXWRkRERESFGwtBItLq2lWFPn2UEAQRgiBiwAAlOnRQmTsWERERERmZSecIRkdHY8aMGbh48SIUCgUGDBiA8ePHZ3rt1q1bsWTJEsTExKB69eqYNWsWqlSpkqPncY4gUe48eyZAEAB3d37/EBERERVGBWqO4KRJk+Di4oIzZ85gzZo12LZtG/bu3at33fnz5zF79mzMnTsXFy5cQIMGDTBhwgQU4XVtiMzu1i0June3QZUq9vjyS2tYW/P7jYiIiKioMlkhGB4ejgsXLmDKlClQKBTw9PTEsGHDsH79er1rt2zZgi5duqBOnTqQy+WYOHEiYmJiEBAQYKq4RBZFFIGRIxU4d84KCQkCdu2SYcYMhbljEREREVE+MdlygHfu3IGzszOKFy+ubatUqRJCQ0P1rr179y769u2rPZZKpahQoQJCQ0PRpEkTg58pkQjZX0REiIoScOeO7p6BAQFSfg8RERERFVLZ/RxnskIwKSkJNjY2Om02NjZITU3N9FqFQrc3QqFQICUlJUfPdHGxy3lQIgtUrFhGr6AuCYCsx5UTERERUeFlsqGhtra2ekVfSkoK7O31f9C0sbFBWlqaTltqamqm1xIREREREVHOmKwQ9PT0RFxcHGJjY7VtYWFhma4E6unpiXv37mmP1Wo1Hjx4AE9PT5NkJSIiIiIiKspMVghWqFABvr7/a+/Og6q40gYO/7jkAhGGuKDghlE0120QEMQdBzUogrJEBQkmigZFxwVFmXwFKhONYAqD2yCCo4gR445YJJFMcImKqFgucdwSFxAFhdKRTbbvD8uu3KAGowKG96m6VdfT3adPt5c+/Z6l25qIiAiKioq4cuUK8fHxuLu7V1vXw8ODpKQkTp48yaNHj1ixYgXGxsbY2trWVnGFEEIIIYQQ4k+rVt8jmJubS1hYGBkZGajVasaNG0dAQAC3bt1ixIgRrFu3Tgn2duzYQUxMDLm5uXTr1o1FixZhYWFRW0UVQgghhBBCiD+tWg0EhRBCCCGEEELUvVp9obwQQgghhBBCiLongaAQQgghhBBCNDASCAohhBBCCCFEAyOBoBBCCCHqjZs3b9Z1EYQQokGQQLCB2LlzJ46Ojq8831u3bmFtbc2tW7fIyspCo9GQlZX1yvcjXi2NRkNAQEC19DVr1uDr66uVdunSJebNm4eDgwM9evSgT58+zJgxg/Pnz9dWccULqo2/xZUrV1b7rbxu6enpaDSaWt2n0Pbr35a1tTUnTpx46TxDQ0MJDQ0FIC0tjQkTJrx0nqJ+0mg0WFpaYm1tjZWVFXZ2dgQEBHD79m1lneDgYLp164a1tbXWp1+/flp5/fDDD/j5+dG7d2969OiBo6MjixYt4u7du7V9WA3Gm3CfV1VVxZw5c7CyssLJyeml8nqZe+dJkyYRHR0NgK+vLytXrnypsrwuEgiKl9KqVSsyMzNp1apVXRdFvKDvv/+e+Pj4565z5MgRRo8ejYmJCQkJCWRmZpKcnEzPnj358MMPuXTpUi2VVghR32RmZr6S9/uGhYURFhYGQH5+PpWVlS+dp6i/1q1bR2ZmJqdPn+aHH35AR0eHoKAgrXVcXV3JzMzU+vz444/K8tWrVzN//nyGDRtGcnIymZmZbNiwgZKSEnx8fCgpKantwxL1RG5uLsnJyWzcuJFvv/22zsoRGxvLlClT6mz/NSWBYD1y/vx5fH19sba2ZuDAgfzrX/8CHreCOzg4MHv2bGxtbYmPjyc4OJjg4GBl29u3b2u10ly9elXJy9XVlZ9++klrX2fOnOHDDz/Ezs4OJycnEhMTn1mukpISwsPDcXBwwN7eHj8/P65cuQK8Ga1D4ul8fHxYtmxZtd/GExUVFXz66aeMGzeOefPm0bZtW1QqFc2aNeOjjz4iKCiIBw8e1HKpxYtISkpi2LBhWFlZMWnSJPLy8gAoKysjIiKC4cOHY2NjQ79+/QgPD1e28/X1Zf78+Tg4ODB48GBKS0s5deoUnp6eWFlZ4eXl9bt/8/Hx8QwdOhRra2s8PT05efIkAJWVlaxdu5bBgwdja2uLt7c3p06dUrbLzMxk/Pjx9O/fH0tLS8aOHcu5c+dew9kRL0uj0ZCeng7A9evXGT9+PDY2Nri6urJmzRqlJX3nzp14e3vz2WefYW9vT79+/QgNDaW8vBxAqc/S09NZsGCBMtKkoKCAR48esXz5chwdHbG3tycgIICcnJw6O2bxahkZGTFmzBjOnj1b421+/vlnVq1aRUREhNJQqVKpMDc3Z/HixTg5OVFQUPAaSy2eVbc8Webi4kLPnj0ZPXq01qiBq1ev4u/vz6BBg7C0tMTV1ZVDhw4BEBQUxLx587T24+fnR2RkJH5+fixatEhrmbe3Nxs3btRKu3jxotIL+NFHH7F69erfvV/Oy8sjMDCQvn37MnDgQBYvXlzjhoTU1FTc3NywsbHB2dmZHTt2KMvqcy/gr0kgWE8UFBQwceJE7O3tSU9PZ+PGjSQkJLBv3z7g8Q+3S5cuHD16FE9Pz+fmVVZWhr+/v1JJR0ZGkpqaqiy/c+cOH3/8McOHD+fIkSNERUURHR2t7Ou3Fi5cSHp6Ol999RUHDx6ke/fuTJgwgf/973+v7gSIWufk5IS7uzuzZ8+msLCw2vLMzExycnLw9vZ+6vbjxo17Jb0B4vW5ePEi27dvJy0tjby8PFavXg3Axo0bOXz4MJs2beLUqVOsWrWK+Ph4jh49qmx7/Phxtm/fzq5duygqKsLf35/hw4eTkZFBUFCQ1jXlt3bs2MHq1atZtmwZJ0+eZNSoUUydOpXi4mJWr17N1q1bWbNmDUePHmXkyJH4+fmRlZVFaWkpU6ZMwdnZmYMHD3Ls2DFatmzJ559//trPlfjjysvL8ff3x8LCgqNHj7J8+XK2b9+utc6pU6do1qwZhw8fJiYmhr1791b7Ddnb27No0SJlpEmTJk2IjIzk4MGDxMfHc+DAATp06MAnn3yiBJHizVZQUMDOnTsZOnRojbf55ptvMDU1ZdCgQdWWqVQqAgMDadmy5SsspfitZ9Ut//nPfwgLC2PhwoUcP36cyZMnM3nyZGXe74wZM+jcuTP79+/nxIkT9OnTh5CQEABGjx7N/v37KSoqAh737B07dgxPT088PDxISUmhrKwMeDyP+OzZs7i6umqVS6PRkJycDEBycjLTpk177nFUVlYydepU1Go13333Hbt27eLKlSssWbLkd8/BsWPHmDVrFtOnTycjI4OwsDDCw8NJSUl5gTNZ9yQQrCfS0tLQ19dn2rRp6Onp0b59ezZu3IidnZ2yjpubG2q1GkNDw+fmlZmZye3bt5k7dy56enp06tRJa85FUlISnTp1wsfHB7VaTefOnfH19WXz5s3V8iotLSU5OZlZs2bRunVr9PX1mTlzJiqVirS0tFd2/KJufPrpp6jV6motbfC4wQDAzMxMSduxYwe2trbY2tpibW3NxIkTa62s4sX5+/tjZGRE48aNcXBw4Pr168DjCnf9+vWYmJhw7949SkpKMDQ0VP7PAQYMGEDz5s0xNjYmLS2NRo0a4efnh1qtpmfPns9tkNq9ezdjx47FysoKlUqFr68v0dHR6OrqsmPHDiZPnoxGo0GtVuPt7a1U3m+99RZbt27Fy8uL8vJycnJyaNy4sdb8IVH/nD59mps3bzJ//nz09fXp2LEjkyZN0lrHwMCAKVOmoFar6datGxqNhqtXrz4336qqKrZu3UpgYCBt2rTBwMCA2bNnc+vWLTIyMl7nIYnXaMqUKdja2mJjY0Pv3r358ccfqzU4JicnK3XNk8/p06eBxw3jv66X4HFd9mS9Hj16sGbNmto6nAbpWXXLli1b8Pb2xtbWFl1dXd5//3169eql9JRFR0czbdo0dHR0yMnJwdjYWLm+9+rVixYtWvDdd98Bj+9VbWxsaNeuHUOGDKGiokLpPdy9ezcDBw6kadOmL3Uc586d48KFC4SGhmJkZESzZs0IDAxk+/btPHr06LnbPmnAGDJkCLq6usoIl23btr1UmWrbW3VdAPFYXl4eLVu2REdHR0nr2LEjAL/88gsAzZs3r1Fed+7coXHjxhgYGChp5ubmyvfs7GzOnz+v1ZtTWVlJ48aNq+V1//59ysrKaN26tZKmUqlo1aoVOTk5WFtb1+wARb1kYGBAZGQko0ePpm/fvlrLTExMgMetcm3atAHA09NTCQBWrlzJ8ePHa7fA4oUYGxsr39VqtdKLUlxczGeffUZGRgYtWrSgS5cuVFVVac3NevL/D4+vKWZmZlrXJ3Nzcy5cuPDU/ebl5WnNG9bR0cHGxgaAu3fval1PANq0acOtW7fQ1dXl+PHj+Pv7U1hYSIcOHVCr1VRVVb3EWRCv2507d2jSpIlWnfPkmvFEs2bNtH4/arX6d+cC5ufnU1RUpDQ+PlFeXi7TEd5g0dHR2NvbA4+nnmzZsoXx48ezdetWunXrBoCLiwtLly596vbNmzfn8OHDWmlLlixRenF8fX2pqKh4jUcgnlW3ZGdnc/z4cbZs2aIsr6io4J133gHgv//9L9OnTyc3N5d3332XZs2aaV3fPT092bNnD25ubuzZswc/Pz8A9PX1cXZ2JikpCUdHR/bs2aM13POPysrKoqKiAgcHB610XV1dsrOzn7vtvXv36NKli1ZamzZt6nRe4h8hgWA9YWZmxu3bt6mqqlIqy5SUFHR0dGjSpAmAViWqUqm0Wit+PR7ezMyM/Px8CgsLld7DX7eom5qa0rt3b2JjY5W0u3fvPrX1w8TEBH19fbKysrCwsAAeB43Z2dlaN4rizfXee+8RHBzMokWLtIbn2NjYYGJiwvbt25k1a1bdFVC8ciEhIRgbG3Pw4EH09fWprKzUGn0A2tcbMzMzsrOzqaysVG7In9dL17Jly2rzuL744gvGjh1L69atq93E37x5k/79+3PmzBnCwsJITEyke/fuAKxfv15pDBP1U8uWLSkoKKC4uJi3334b4JXM42vSpAl6enr8+9//pkePHkr65cuXqwWa4s1kYGDAhAkTiI6O5siRI0og+DxDhw5l1apVHD16lD59+tRCKUVNmZqa4ubmxieffKKk3bx5EyMjI3Jzc5k5cyarVq1S5g9/++237N+/X1nXzc2NFStWcOjQIXJychg2bJiyzNPTE19fX9LT0yksLKwWvD3L8+6XTU1Nefvtt0lPT0dXVxd43FB669YtzM3NyczMfGa+rVq1qlaX3bhxo8adNvWFDA2tJxwcHHj06BGxsbGUlZVx9epVFi9e/MwW0/bt23Ps2DGys7MpLCwkJiZGWWZjY0P79u35/PPPKS4u5tq1a6xfv15Z7uLiwsmTJ9m3bx8VFRXk5OTg5+fHqlWrqu1HpVLh5uZGVFQU2dnZlJaWEhUVRWlp6VPH54s3k7e3NwMGDGDPnj1KmlqtZunSpWzYsIHIyEjlxq6goICEhAQ2bdpEixYt6qrI4iU8ePAAAwMDVCoVDx8+JCIigocPHz5z3pWjoyNVVVWsXr2asrIyzpw589zhL+7u7mzbto3z589TWVlJQkIC27dvp2nTpnh6ehIbG8vFixcpKytjy5Yt/PTTTzg7O3P//n1UKpXSs3T69Gni4+OVeSGifrKysqJDhw5ERERQWlrK9evXteqcF6Gnp0dRURHl5eWoVCrc3d2JjIzkzp07VFZWsnnzZtzd3bUeTiHeXOXl5ezYsYMHDx7Qs2fPGm3TuXNn/v73vzNr1iy2bdumPK/g5s2bLF26lJMnT75xN+N/Fp6eniQkJCgP+Dp58iRubm4cOHCAhw8fUlFRQaNGjQC4cuWKMrfwyTW+RYsWDBgwgAULFjBixAitUQaWlpa0adOGpUuX4uLiglqtrlGZnne/bGlpSevWrVm2bBnFxcUUFhaycOHCp75e67c8PDxITU0lNTWViooKTpw4QWJiIm5ubjUqV30hPYL1xDvvvMP69etZsmQJMTExGBkZMWnSJJydnZWnsv2al5cXZ8+exc3NDSMjI6ZNm6Z0R+vq6hITE0NoaCh9+/bFxMSEwYMHK+Ou27ZtS0xMDJGRkSxcuBA9PT3ef/995s+f/9SyBQcHExUVxbhx43jw4AE9evRg48aNNG3aVJnUK958//znP6s9nXHAgAHs3LmTuLg4fHx8KCgoQE9Pj27duhESEsKIESPqqLTiZYSEhBAaGkqvXr0wNDRk0KBB9O/fn8uXLz91fWNjY+Li4li4cCFxcXG0a9cOJyenZ/bUjRw5kgcPHhAYGEheXh7vvfcesbGxGBoaMnHiRCoqKggICCA/P59OnToRGxuLhYUFHTp0YNy4cfj4+FBZWUnbtm3x8fHhyy+/JD8//3WeEvESVCoVUVFRhISEYG9vj7m5OX369NF6+FBN2dnZ0bRpU+zs7Ni5cyf/+Mc/WLFiBV5eXty/f5/27dsTExOjNd1BvFkmT56s9L7o6Ojw7rvvEhkZqQwfr4np06djZWVFQkICy5cvp6ioCGNjY+zs7Ni0aVONg0rxarm4uFBYWMi8efPIycnBxMSEmTNnKsHRvHnzmDt3LsXFxZiZmTFmzBgiIiK4cuWKMszygw8+ICAg4Knz0N3d3Vm2bFmNHubyxPPul9VqNWvXriU8PJzBgwdTXl6OtbU1sbGxym/0WaytrVm+fDkrV64kKCiIFi1aMGvWrN99oGN9o1Mlky+EEEII8QeVlJRw+vRpevXqpQwdTkhIIDk5+bmvJhJCiBfxzTffEB0dze7du+u6KH8aMjRUCCGEEH+YWq1mxowZ7N69m6qqKnJyckhMTJTpA0KIVyI/P58LFy4QExODj49PXRfnT0UCQSGEEEL8Ybq6uqxcuZKEhATlJdJ/+9vflCf+CSHEyzh37hxeXl60a9cODw+Pui7On4oMDRVCCCGEEEKIBkZ6BIUQQgghhBCigZFAUAghhBBCCCEaGAkEhRBCCCGEEKKBkUBQCCHEn56vry8ajeapn4SEhFeyj6ysLDQaDSdOnHjq8uLiYjZv3vxK9iWEEEK8LHmhvBBCiAbBxcWF4ODgaulGRka1sv8NGzawbds2efy5EEKIekECQSGEEA2CgYEBzZs3r7P9y0O6hRBC1CcyNFQIIYQAHB0dWbNmzXPTUlNTGTlyJH/9618ZNmwYcXFxVFZW/m7eO3fuJCoqiuzsbDQaDSkpKXTv3p2UlBSt9YKCgpg2bRoAGo2GxMREPDw8sLS0xMPDg4yMDK31v/76a5ycnLC0tMTV1ZVdu3b90cMXQgjRwEggKIQQQtTAgQMHmDt3LuPHj2ffvn0EBQURHx9fLXh8GmdnZyZPnoyZmRmHDx9m8ODBDBgwgKSkJGWdoqIiUlNTcXd3V9IiIiLw8vJi165ddO/eHT8/P27evAnAV199xfLly5k9ezbJyclMmjSJxYsXSzAohBCiRiQQFEII0SDs3r0ba2trrc///d//1Xj76OhovL29+eCDDzA3N2fw4MHMmTOHdevW/W6voIGBAY0aNUJXV5fmzZujp6eHh4cHhw4doqCgAID9+/ejr6+Pg4ODst2YMWMYM2YMFhYWLFiwgObNm/P1118r5Zk+fTrDhg3D3NycUaNG4efnR3R09B84O0IIIRoamSMohBCiQRgyZAiBgYFaaYaGhjXe/sKFC5w9e5bExEQlrbKykpKSErKzs9HR0Xmh8gwaNAgjIyNSUlIYN24cSUlJuLi4oFarlXXs7OyU77q6unTv3p1Lly6Rn5/PnTt3CA8P54svvlDWKS8vp6KigkePHqGnp/dC5RFCCNGwSCAohBCiQTAyMqJdu3YvtE15ebnyXa1WM2nSJFxdXautZ2pqSm5u7gvlrVarcXFxITk5maFDh3L06FHmzJmjtc5bb2lX05WVlejo6CjBYkhICL169aqW92+3E0IIIX5LhoYKIYQQPA7MHj58qPz74cOH3Lt3T/l3x44duXbtGu3atVM+ly5dYvny5TXK/2k9hh4eHmRmZrJt2zYsLCzo2rWr1vLz588r38vLyzl//jxdu3blL3/5C6ampmRlZWmV58iRI8TFxaFSSfUuhBDi+aSmEEIIIQArKyv27dtHZmYmly9fJjg4GF1dXWX51KlT2bdvHzExMVy7do20tDRCQ0MxMDCo0TBMQ0ND7t+/z88//0xpaSkAXbt2pVOnTqxduxYPD49q26xfv56UlBSuXr1KaGgo9+/fZ+zYsUp5NmzYwNatW7lx4wZ79+5l6dKldfqKDCGEEG8OCQSFEEIIIDAwkM6dO/Pxxx8zYcIEbGxssLGxUZYPHDiQiIgI9u7di4uLC6Ghobi5uREWFlaj/J2cnGjdujUjR44kLS1NSXdzc6O8vPypQ07HjBlDdHQ07u7u3Lhxg/j4eExNTQHw9vYmMDCQuLg4nJ2d+fLLLwkICGD69OkvdyKEEEI0CDpV8oZbIYQQos6Eh4fzyy+/VHvap0ajISIiglGjRtVRyYQQQvyZyWxyIYQQog6cOHGCq1evkpiYyKpVq+q6OEIIIRoYCQSFEEKIOvD999+zZcsWfH196devX10XRwghRAMjQ0OFEEIIIYQQooGRh8UIIYQQQgghRAMjgaAQQgghhBBCNDASCAohhBBCCCFEAyOBoBBCCCGEEEI0MBIICiGEEEIIIUQDI4GgEEIIIYQQQjQw/w9mk6JpxPYVyQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOTTING OF UTILIZATION FACTORS\n",
    "\n",
    "# yearly utilization factors grouped by fuel type\n",
    "ut_plot = sns.stripplot(data=ut_data, x='Fuel', y='Utilization ratio [MWh_el/MWh_th]', palette=colors_ft)\n",
    "ut_plot.set(title=\"Yearly utilization ratios per fuel type\", ylim=(0, 1), xlabel='Fuel type', ylabel='Utilization ratio in MWh_el/MWh_th')\n",
    "plt.savefig('output/plots/utilization_ratio-fuel_type.png')\n",
    "plt.show()\n",
    "\n",
    "# yearly emission factors grouped by individual installations\n",
    "ut_plot = sns.stripplot(data=ut_data, x='ETSID', y='Utilization ratio [MWh_el/MWh_th]', order=ut_data.sort_values('Utilization ratio [MWh_el/MWh_th]').ETSID.unique())\n",
    "ut_plot.set(title=\"Yearly utilization ratios per ETS installation (sorted by avg. utilization ratio)\", ylim=(0, 1), xticklabels=[], xlabel='ETS-ID', ylabel='Utilization ratio in MWh_el/MWh_th')\n",
    "plt.savefig('output/plots/utilization_ratio-ETSID.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1caaeb95",
   "metadata": {},
   "source": [
    "# Output\n",
    "\n",
    "Additionally to the block-wise input file, three output files are generated:\n",
    "* Block-wise (long format): Yearly generation\n",
    "* Installation-wise (wide format): Plant information overview containing fuel type, sigma, heat share, avg. emission factor\n",
    "* Installation-wise (long format): Detailed yearly values including heat/electricity generation, emissions, emission factor, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "5f8d255e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PREPARE BLOCK-WISE LONG FORMAT (GENERATION DATA)\n",
    "\n",
    "out_block = []\n",
    "for eic in eic_list:\n",
    "    for y in years:\n",
    "        # access generation information\n",
    "        gen = blocks.loc[blocks['EIC']==eic, 'Generation elec. '+str(y)+' [MWh_el]'].values[0]\n",
    "        bna = blocks.loc[blocks['EIC']==eic, 'BNA-ID'].values[0]\n",
    "        bna_name = blocks.loc[blocks['EIC']==eic, 'Name BNA'].values[0]\n",
    "        plant_name = blocks.loc[blocks['EIC']==eic, 'Plant name'].values[0]\n",
    "        block_name = str(blocks.loc[blocks['EIC']==eic, 'Block name'].values[0]) if str(blocks.loc[blocks['EIC']==eic, 'Block name'].values[0])!='nan' else \"\"\n",
    "        name = plant_name + \" \" + block_name\n",
    "        # store information\n",
    "        out_block.append([eic, bna, bna_name, name, y, gen])\n",
    "# convert to DataFrame\n",
    "out_block = pd.DataFrame(out_block, columns=['EIC', 'BNA-ID', 'Name BNA', 'Plant & Block name', 'Year', 'Generation elec. [MWh_el]'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "d778847a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PREPARE INSTALLATION-WISE WIDE FORMAT\n",
    "\n",
    "out_installation_wide = []\n",
    "for id in ets_list:\n",
    "    # access information\n",
    "    city = installations.loc[installations['ETS-ID']==id, 'City'].values[0]\n",
    "    name = installations.loc[installations['ETS-ID']==id, 'Plant name'].values[0]\n",
    "    power_elec = installations.loc[installations['ETS-ID']==id, 'Net electrical power [MW_el]'].values[0]\n",
    "    power_heat = installations.loc[installations['ETS-ID']==id, 'Heat power [MW_th]'].values[0]\n",
    "    chp = installations.loc[installations['ETS-ID']==id, 'CHP'].values[0]\n",
    "    ft = installations.loc[installations['ETS-ID']==id, 'Fuel'].values[0]\n",
    "    ft_em_fac = installations.loc[installations['ETS-ID']==id, 'Thermal fuel emission factor [t/MWh_th]'].values[0]\n",
    "    sigma = installations.loc[installations['ETS-ID']==id, 'Sigma'].values[0]\n",
    "    em_fac_avg = installations.loc[installations['ETS-ID']==id, 'Emission factor avg. [t/MWh_el]'].values[0]\n",
    "    ut_avg = installations.loc[installations['ETS-ID']==id, 'Utilization ratio avg. [MWh_el/MWh_th]'].values[0]\n",
    "\n",
    "    gen_cols = installations.columns[installations.columns.str.contains('Generation elec.')]\n",
    "    gen_data = installations.loc[installations['ETS-ID']==id, gen_cols].values[0]\n",
    "    gen_avg = sum(gen_data)/len(gen_data)\n",
    "    gen_min = min(gen_data)\n",
    "    gen_max = max(gen_data)\n",
    "\n",
    "    em_cols = ['Emissions ' + str(y) + ' [t]' for y in years]\n",
    "    em_data = installations.loc[installations['ETS-ID']==id, em_cols].values[0]\n",
    "    em_avg = sum(em_data)/len(em_data)\n",
    "    em_min = min(em_data)\n",
    "    em_max = max(em_data)\n",
    "\n",
    "    # emission share calculations\n",
    "    em_total = sum(em_data)\n",
    "    em_elec_cols = ['Emissions (elec.) ' + str(y) + ' [t]' for y in years]\n",
    "    em_elec_data = installations.loc[installations['ETS-ID']==id, em_elec_cols].values[0]\n",
    "    em_elec = sum(em_elec_data)\n",
    "    em_heat_cols = ['Emissions (heat) ' + str(y) + ' [t]' for y in years]\n",
    "    em_heat_data = installations.loc[installations['ETS-ID']==id, em_heat_cols].values[0]\n",
    "    em_heat = sum(em_heat_data)\n",
    "    em_share_elec = em_elec/em_total\n",
    "    em_share_heat = em_heat/em_total\n",
    "\n",
    "    # store information\n",
    "    out_installation_wide.append([id, city, name, power_elec, power_heat, chp, ft, ft_em_fac, sigma, em_fac_avg, ut_avg, gen_avg, gen_min, gen_max, em_avg, em_min, em_max, em_share_elec, em_share_heat])\n",
    "\n",
    "# convert to DataFrame\n",
    "out_installation_wide = pd.DataFrame(out_installation_wide, columns = ['ETS-ID', 'City', 'Name', 'Net electrical power [MW_el]', 'Heat power [MW_th]', 'CHP', 'Fuel', 'Thermal fuel emission factor [t/MWh_th]', 'Sigma', 'Emission factor avg. [t/MWh_el]', 'Utilization ratio avg. [MWh_el/MWh_th]', 'Generation elec. avg. [MWh_el]', 'Generation elec. min [MWh_el]', 'Generation elec. max [MWh_el]', 'Emissions avg. [t]', 'Emissions min. [t]', 'Emissions max. [t]', 'Electricity share of total emissions', 'Heat share of total emissions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "5ce642bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PREPARE INSTALLATION-WISE LONG FORMAT\n",
    "\n",
    "out_installation_long = []\n",
    "for id in ets_list:\n",
    "    for y in years:\n",
    "        # access information\n",
    "        city = installations.loc[installations['ETS-ID']==id, 'City'].values[0]\n",
    "        name = installations.loc[installations['ETS-ID']==id, 'Plant name'].values[0]\n",
    "        gen_elec = installations.loc[installations['ETS-ID']==id, 'Generation elec. ' + str(y) + ' [MWh_el]'].values[0]\n",
    "        gen_heat = installations.loc[installations['ETS-ID']==id, 'Generation heat ' + str(y) + ' [MWh_th]'].values[0]\n",
    "        em_total = installations.loc[installations['ETS-ID']==id, 'Emissions ' + str(y) + ' [t]'].values[0]\n",
    "        em_elec = installations.loc[installations['ETS-ID']==id, 'Emissions (elec.) ' + str(y) + ' [t]'].values[0]\n",
    "        em_heat = installations.loc[installations['ETS-ID']==id, 'Emissions (heat) ' + str(y) + ' [t]'].values[0]\n",
    "        em_fac = installations.loc[installations['ETS-ID']==id, 'Emission factor ' + str(y) + ' [t/MWh_el]'].values[0]\n",
    "        ut_fac = installations.loc[installations['ETS-ID']==id, 'Utilization ratio ' + str(y) + ' [MWh_el/MWh_th]'].values[0]\n",
    "\n",
    "        # calculation of capacity factor\n",
    "        power_elec = installations.loc[installations['ETS-ID']==id, 'Net electrical power [MW_el]'].values[0]\n",
    "        cap_fac = gen_elec / (power_elec * 8760)\n",
    "\n",
    "        # store information\n",
    "        out_installation_long.append([id, y, city, name, gen_elec, gen_heat, cap_fac, em_total, em_elec, em_heat, em_fac, ut_fac])\n",
    "\n",
    "# convert to DataFrame\n",
    "out_installation_long = pd.DataFrame(out_installation_long, columns=['ETS-ID', 'Year', 'City', 'Name', 'Generation elec. [MWh_el]', 'Generation heat [MWh_th]', 'Capacity factor', 'Emissions (total) [t]', 'Emissions (elec.) [t]', 'Emissions (heat) [t]', 'Emission factor [t/MWh_el]', 'Utilization ratio [MWh_el/MWh/th]'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "614dc6a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EXPORT OUTPUT DATA\n",
    "out_block.to_csv('output/block-generation.csv', float_format=\"%.3f\", index=False)\n",
    "out_installation_wide.to_csv('output/installation-results.csv', float_format=\"%.3f\", index=False)\n",
    "out_installation_long.to_csv('output/installation-results-yearly.csv', float_format=\"%.3f\", index=False)"
   ]
  }
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