IAMs - Power System Model Soft-Link.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IAM - Power System Model soft-link tool\n",
    "\n",
    "Copyright (c) 2022 Maarten Brinkerink\n",
    "\n",
    "This work is released under the MIT License. See the LICENSE file for more information. \n",
    "\n",
    "Please refer to/cite https://doi.org/10.1016/j.envsoft.2022.105336 while using this methodological soft-link framework and refer to the README file for other sources to be cited as used for the overall workflow in this script. \n",
    "_______________________________________________________________________________________________________________________________\n",
    "\n",
    "This tool applies spatial downscaling of IAM output scenario data (electricity demand and powerplant capacities) developed with direct connections to IAM databases or IAM excel output files and the PLEXOS-World database on Harvard Dataverse. It furthermore extracts other required variables for power system modelling if available. The script can be used to standardize the overall workflow in the methodological soft-link framework as introduced in the paper 'Assessing global climate change mitigation scenarios from a power system perspective using a novel multi-model framework'. Linking the global IAM scenarios to PLEXOS-World (or any global power system model) has multiple purposes;\n",
    "\n",
    "I) To improve the spatial resolution of the IAM scenario to be able to assess its power system implications with higher locational detail.\n",
    "\n",
    "II) To assess the IAM scenario from a power system perspective with higher temporal and technical resolution.\n",
    "\n",
    "III) To use the power system model simulation output as a benchmark for the power system representation in the IAM.\n",
    "\n",
    "IV) To use the power system model simulation output and general modelling observations to update or improve the stylized power system representation in the IAM.\n",
    "\n",
    "The output of this script consists of multiple pandas DataFrames/CSV files with data that can be used as input for the power system model. Additional data conversion outside this script might be necessary depending on power system model specific data templates."
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Picture1.jpg](attachment:Picture1.jpg) Overview of the soft-link framework. Steps 1-3 in the framework can be performed with this script. The graph is extracted from Brinkerink et al., 2022 (https://doi.org/10.1016/j.envsoft.2022.105336)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xlsxwriter\n",
    "import pyam\n",
    "import os\n",
    "import requests\n",
    "import world_bank_data as wb\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define IAM model characteristics\n",
    "Determine for which models, scenarios, SSP pathway and snapshot year the scenario file needs to be created. <b><span style='color:red'>This is the only section WITHIN the script where manual entries are required. </span></b> Note that demand projections in this script are SSP pathway specific, hence if different SSP's are to be used the script needs to be rerun multiple times with different input parameters (models, scenarios and pathway). The same counts for other parameters such as the respective spatial mapping. For general pathway data it is advisable to use 'SSP2' as  'middle of the road' projection if the to be assessed scenarios are not directly linked to the SSP's.\n",
    "\n",
    "<b><span style='color:red'>The 'Custom Input' section - few cells down - defines the input data OUTSIDE the script where manual entries are required. Definition of mapping dictionaries (e.g. for defining spatial and technological representation) has to be pre-specified in the 'memberships' input excel file. </span></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "models = ['MESSAGE-GLOBIOM 1.0' , 'AIM/CGE 2.0' , 'IMAGE 3.0.1'] #Define the IAM models to be assessed\n",
    "\n",
    "scenarios = ['ADVANCE_2020_1.5C-2100'] #Define the scenarios to be assessed\n",
    "\n",
    "source = 'database' #Define whether scenario data is read from a 'database', 'xlsx' or 'csv' file.\n",
    "\n",
    "if source != 'database': #Define directory for IAM output data source file in case 'database' is not used. \n",
    "    IAM_output_directory = r''\n",
    "    IAMC_format = 'wide' #Define whether 'wide' or 'long' format is used in source file in case 'database' is not used.\n",
    "\n",
    "#Define where the output files of the script need to be saved.     \n",
    "script_output_directory = r''\n",
    "\n",
    "assert os.path.exists(script_output_directory), f'Folder {script_output_directory} does not exist! Change the script_output_directory variable to an available folder!'\n",
    "\n",
    "pathway = 'SSP2' #For general country-level SSP data such as GDP, population and urbanization data. \n",
    "POP_countries_source = 'IIASA-WiC POP' #Options are 'IIASA-WiC POP' and 'NCAR'\n",
    "GDPppp_countries_source = 'OECD Env-Growth' #Options are 'IIASA GDP' and 'OECD Env-Growth'. 'OECD Env-Growth' has more entries.\n",
    "\n",
    "spatial_mapping = 'R5' #Define the required spatial mapping. Custom mapping can be added to the 'memberships' input \n",
    "#excel file as a seperate sheet titled with the variable name followed by _spatial_mapping - E.g. R5_spatial_mapping. \n",
    "\n",
    "technology_mapping = 'ADVANCE' #Define the required technological mapping used to convert naming conventions or aggregate \n",
    "#technolgies from the IAM output to required power system model input. Custom mapping can be added to the 'memberships' input \n",
    "#excel file as a seperate sheet titled with the variable name followed by _technology_mapping - E.g. ADVANCE_technology_mapping. \n",
    "\n",
    "snapshot_year  = 2050 #Determine which year of the IAM scenario output the power system model is supposed to assess.\n",
    "\n",
    "urbanization = 'Yes' #Include urbanization as part of the linear regression? 'Yes' or 'No'\n",
    "\n",
    "electricity_demand_type = 'Final Energy|Electricity' #Determine which IAM variable should be used for the electricity \n",
    "#demand downscaling. For a modelling exercise where all technologies and demands are incorporated in the power system model\n",
    "#which require electricity (being at the final or secondary level) the 'Final Energy|Electricity' variable can be used. \n",
    "#In this case the script will calculate the electricity demand that needs to be met in the power system model as \n",
    "#'Final Energy|Electricity' + Domestic Transmission & Distribution losses. In a situation where the power system model is solely\n",
    "#used to simulate the power sector without interactions with other sectors (e.g. CHP or Electricity produced Hydrogen for other \n",
    "#sectors) or where certain technologies that require electricity at the secondary level are not included it can be more accurate\n",
    "#to use the 'Secondary Energy|Electricity' variable as starting point for the demand downscaling. If used, \n",
    "#'Secondary Energy|Electricity' - Domestic Transmission & Distribution losses will be used as the electricity demand that needs \n",
    "#to be met in the power system model. Both options have its clear limitations and a case by case approach should be used to \n",
    "#determine the best fit.\n",
    "\n",
    "peak_load_max = 1 #Change value if relative peak demand needs to change relatively to 2015 uniformally for all global nodes.\n",
    "\n",
    "hourly_demand_generator = 'No' #'Yes' in case hourly demand profiles need to be developed within the script. Only possible\n",
    "#when peak_load_max == 1 since within this script only linear scaling of timeseries is feasible. Non-linear scaling requires\n",
    "#optimization which is not supported."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loads IAM model and scenario specific data\n",
    "Automatically extracts the relevant scenario data from either the IAMC 1.5°C Scenario Explorer or from a seperate excel or csv file specific for the above identified ensemble. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pyam - INFO: Running in a notebook, setting up a basic logging at level INFO\n",
      "pyam.iiasa - INFO: You are connected to the IXSE_SR15 scenario explorer hosted by IIASA. If you use this data in any published format, please cite the data as provided in the explorer guidelines: https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/#/about\n",
      "pyam.iiasa - INFO: You are connected as an anonymous user\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>region</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IMAGE 3.0.1</td>\n",
       "      <td>ADVANCE_2020_1.5C-2100</td>\n",
       "      <td>Capacity|Electricity|Biomass</td>\n",
       "      <td>GW</td>\n",
       "      <td>R5ASIA</td>\n",
       "      <td>2050</td>\n",
       "      <td>632.314181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         model                scenario                      variable unit  \\\n",
       "9  IMAGE 3.0.1  ADVANCE_2020_1.5C-2100  Capacity|Electricity|Biomass   GW   \n",
       "\n",
       "   region  year       value  \n",
       "9  R5ASIA  2050  632.314181  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creates list of entries by means of entered model and scenario combinations. \n",
    "entries_base = [ (a + '|' + b)\n",
    "                for a in models\n",
    "                for b in scenarios ]\n",
    "entries = []\n",
    "\n",
    "#Creates empty dataframe dictionaries to be filled. \n",
    "import_scenario = {}\n",
    "import_year_scenario = {}\n",
    "import_year_scenario_generator_capacity = {}\n",
    "import_year_scenario_generator_efficiency = {}\n",
    "\n",
    "#Pulls scenario data from the IAMC 1.5°C scenario Explorer.\n",
    "if source == 'database':\n",
    "    conn = pyam.iiasa.Connection('IXSE_SR15')\n",
    "\n",
    "#Pulls scenario data from csv file.\n",
    "elif source == 'csv':\n",
    "    conn = pd.read_csv(IAM_output_directory , encoding = 'unicode_escape')\n",
    "\n",
    "#Pulls scenario data from xlsx file.\n",
    "else:\n",
    "    conn = pd.read_excel(IAM_output_directory , encoding = 'unicode_escape')\n",
    "\n",
    "for a in entries_base:\n",
    "    \n",
    "    model = a.split(\"|\",1)[0]\n",
    "    scenario = a.split(\"|\",1)[1]\n",
    "    \n",
    "    #converts 'wide' to 'long' IAMC data format if required. \n",
    "    if source != 'database' and IAMC_format == 'wide':\n",
    "        conn.columns = [str(x).lower() for x in conn.columns]\n",
    "        conn['year'] , conn['value'] = snapshot_year , conn[str(snapshot_year)]\n",
    "      \n",
    "    #Creates model and scenario specific dataframe.    \n",
    "    if source == 'database':\n",
    "        import_scenario[a] = (conn.query(model = model , scenario = scenario)).as_pandas()\n",
    "        #query function in pyam returns full dataframe if filter entry combination is non existing\n",
    "        import_scenario[a] = (import_scenario[a].loc[(import_scenario[a]['model'] == model) & \n",
    "                                                     (import_scenario[a]['scenario'] == scenario)])\n",
    "\n",
    "    else:\n",
    "        import_scenario[a] = (conn.loc[(conn['model'] == model) & (conn['scenario'] == scenario)])\n",
    "        \n",
    "    #Checks if scenario model combination exists if not removes it before filtering\n",
    "    if model not in import_scenario[a]['model'].values and scenario not in import_scenario[a]['scenario'].values:\n",
    "        print(f'{a} model|scenario combination does not exist in used database!')\n",
    "    \n",
    "    else:\n",
    "        entries.append(a)\n",
    "        \n",
    "        import_scenario[a] = import_scenario[a][['model' , 'scenario' , 'variable' , 'unit' , 'region' , 'year' , 'value']]\n",
    "\n",
    "        #Creates model and scenario specific dataframe for the specific snapshot year.\n",
    "        import_year_scenario[a] = import_scenario[a].loc[import_scenario[a]['year'] == snapshot_year]\n",
    "\n",
    "        #Creates model and scenario specific dataframe for Capacity|Electricity data for the specific snapshot year.\n",
    "        import_year_scenario_generator_capacity[a] = import_year_scenario[a].loc[import_year_scenario[a]['variable']\n",
    "                                                                                 .str.startswith('Capacity|Electricity|')]\n",
    "\n",
    "        #Creates model and scenario specific dataframe for Efficiency|Electricity data for the specific snapshot year.\n",
    "        import_year_scenario_generator_efficiency[a] = import_year_scenario[a][import_year_scenario[a]['variable']\n",
    "                                                                               .str.contains('Efficiency\\|Electricity')]\n",
    "\n",
    "#Will result in index error if no valid model|scenario combinations are given.\n",
    "import_year_scenario_generator_capacity[entries[-1]].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Input\n",
    "\n",
    "The workflow of the soft-linking exercise requires input data from external sources. Some of this can be automated (e.g. connections to the World Bank API and to the Harvard Dataverse for PLEXOS-World 2015 data) yet additional input is required that needs to be manually specified in input excel files. This is relevant for the following;\n",
    "\n",
    "<B>I)</B> 'memberships.xlsx' 'spatial_mapping' sheet needs to be defined for the required spatial mapping. 'country' column requires ISO-3 country codes as input, 'node' column requires PLEXOS-World 2015 naming conventions as input to link to baseline electricity demand and power plant capacity data. The 'region' column requires the required region mapping based on the specific IAM scenario ensemble. Note that by default data for all regions that are added in the 'region' column are processed in this script. If the focus is on some but not all regions from the global model the spatial mapping should ONLY be added for those regions. Naming conventions for PLEXOS-World 2015 can be found in the supplementary material file for https://doi.org/10.1016/j.esr.2020.100592 located at https://doi.org/10.7910/DVN/CBYXBY.\n",
    "\n",
    "<B>II)</B> 'memberships.xlsx' 'PLEXOS_World_2015_fuel_mapping' sheet needs to be defined to match technologies from the PLEXOS-World 2015 dataset (based on which baseline nodal powerplant capacities are determined) with the required power system model input categories.\n",
    "\n",
    "<B>III)</B> 'memberships.xlsx' 'technology_mapping' sheet needs to be defined to match IAM technology categories with power system model input categories. For example, aggregation of technologies can be relevant if the number of technologies need to be restricted in the power system model for computational reasoning (e.g. in the case of MESSAGEix-GLOBIOM Gas|w/o CCS|2 and Gas|w/o CCS|3 can be aggregated into Gas|OCGT|w/o CCS). Capacities in the script will be aggregated and the technology efficiency will be determined based on a capacity weighted average. The script checks whether all technologies from the IAM output are in the 'IAM' column of the sheet, if not it returns an error identifying the missing technology.\n",
    "\n",
    "The 'PSM_capacity' column should include the power system model input categories based on which IAM technology capacities are to be allocated. If a technology is to be excluded (e.g. to exclude the 'Coal' technology if 'Coal|w/ CCS' and 'Coal|w/o CCS' are specified to prevent double counting) the 'PSM_capacity' column requires an 'Exclude' entry. Conversion of technology names (e.g. from Wind to Wind|Onshore) can be relevant if the power system model requires technology specific inputs but the supplied IAM output does not provide this. If a technology name is to remain the same the 'PSM_capacity' column requires the same entry (e.g. enter 'Geothermal' in both columns). The 'PSM_efficiency' column requires power system model input categories as entries to which the specific IAM technology contributes in calculation of capacity weighted efficiencies and heat rates.\n",
    "\n",
    "<B>IV)</B> 'POP_GDP_URB_missing.xlsx' is used to add custom country-level projections for population, gdp and urbanization in case SSP data is not available. This is only relevant for smaller countries not covered by the SSP projections. The script checks whether country-level projections are available based on either SSP or custom data and if not it returns an error identifying the countries for which data is missing.\n",
    "\n",
    "<B>V)</B> 'T&D losses.xlsx' includes country-level T&D losses per 5-year interval based on Sadovskaia et al., 2019; https://doi.org/10.1016/j.ijepes.2018.11.012. Yet data does not exist for most smaller countries/islands and hence has to be manually added at the bottom of the 'T&D losses.xlsx' file. As a best estimate, data for these countries can be based on neighbouring countries of similar size and development status. The script checks whether country-level projections are available for all to be modelled countries and if not it returns an error identifying the countries for which data is missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input dictionaries\n",
    "\n",
    "Imports the required naming conventions and memberships to link and disaggregate IAM scenario data. Furthermore checks whether the dictionary entries are in line with the specific IAM scenario variables. If not it returns an error and identifies the issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "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>country</th>\n",
       "      <th>node</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>AS-AFG</td>\n",
       "      <td>R5ASIA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country    node  region\n",
       "0     AFG  AS-AFG  R5ASIA"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import_spatial_mapping = pd.read_excel('Input Data\\\\memberships.xlsx' , \n",
    "                                       sheet_name = f'{spatial_mapping}_spatial_mapping')\n",
    "\n",
    "import_memberships_technologies = pd.read_excel('Input Data\\\\memberships.xlsx', \n",
    "                                                sheet_name = f'{technology_mapping}_technology_mapping')\n",
    "\n",
    "import_memberships_fuels_PW2015 = pd.read_excel('Input Data\\\\memberships.xlsx' , \n",
    "                                                sheet_name = f'{technology_mapping}_PW2015_fuel_mapping')\n",
    "\n",
    "import_spatial_mapping.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Sets initial spatial mapping dictionaries ungrouped.\n",
    "dict_countries = dict(zip(import_spatial_mapping.country, import_spatial_mapping.region))\n",
    "dict_nodes = dict(zip(import_spatial_mapping.node, import_spatial_mapping.region))\n",
    "dict_technologies_capacity = dict(zip(import_memberships_technologies.IAM, import_memberships_technologies.PSM_capacity))\n",
    "dict_technologies_efficiency = dict(zip(import_memberships_technologies.IAM, import_memberships_technologies.PSM_efficiency))\n",
    "\n",
    "mapping_countries = {}\n",
    "mapping_nodes = {}\n",
    "\n",
    "#Groups all keys per value.\n",
    "for key, value in sorted(dict_countries.items()):\n",
    "    mapping_countries.setdefault(value, []).append(key)\n",
    "    \n",
    "for key, value in sorted(dict_nodes.items()):\n",
    "    mapping_nodes.setdefault(value, []).append(key)\n",
    "\n",
    "#Sets dictionary for Capacity|Electricity variables\n",
    "dict_technologies_capacity_variable = {'Capacity|Electricity|' + a: \n",
    "                                       'Capacity|Electricity|' + b for a, b in dict_technologies_capacity.items()}\n",
    "\n",
    "dict_technologies_efficiency_variable = {'Efficiency|Electricity|' + a: \n",
    "                                         'Efficiency|Electricity|' + b for a, b in dict_technologies_efficiency.items()}\n",
    "\n",
    "#Checks whether spatial and technology mapping in 'memberships.xlsx'are in line with IAM scenario output. Returns errors\n",
    "#if values are missing or incorrect. \n",
    "dic_check_capacity = pd.DataFrame()\n",
    "\n",
    "for c in entries:\n",
    "\n",
    "    dic_check_capacity = pd.concat([dic_check_capacity, import_year_scenario_generator_capacity[c][['variable' , 'region']]])\n",
    "    dic_check_capacity = dic_check_capacity[~dic_check_capacity['region'].str.contains('World')].drop_duplicates()\n",
    "    \n",
    "#Checks whether all IAM scenario Capacity|Electricity variables are defined in 'memberships.xlsx' technology_mapping sheet.\n",
    "for d in dic_check_capacity['variable'].unique():\n",
    "    \n",
    "    e = '|'.join(d.split(\"|\", 2)[2:])\n",
    "    assert d in dict_technologies_capacity_variable.keys(), f'{e} not defined in memberships.xlsx sheet {technology_mapping}_technology_mapping IAM column.'\n",
    "    \n",
    "#Checks whether all IAM scenario regions are defined in 'memberships.xlsx' spatial_mapping sheet and gives warning if not.\n",
    "#Not all regions are necessary to be defined in case a regional analysis is to be applied rather than all regions for the\n",
    "#specific scenario.\n",
    "\n",
    "for f in dic_check_capacity['region'].unique():\n",
    "    \n",
    "    if f not in dict_nodes.values():\n",
    "        print(f'Warning! {f} not defined in memberships.xlsx sheet {spatial_mapping}_spatial_mapping.')    \n",
    "    \n",
    "#Checks whether naming conventions for technologies as defined in memberships.xlsx sheet PW2015_fuel_mapping PSM column \n",
    "#are in line with memberships.xlsx sheet technology_mapping sheet PSM_capacity column. This determines whether the input \n",
    "#baseline powerplant capacities from PLEXOS-World 2015 are allocated to the correct technology for the power system model input.\n",
    "#In some cases (e.g. Wind|Offshore) where technologies are not part of the model ensemble a mismatch is possible.\n",
    "for g in import_memberships_fuels_PW2015.PSM.unique():\n",
    "    \n",
    "    if g not in dict_technologies_capacity.values():\n",
    "        print(f'Warning! {g} defined in memberships.xlsx sheet {technology_mapping}_PW2015_fuel_mapping PSM column but not in {technology_mapping}_technology_mapping PSM column.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loads SSP data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loads input excel files that include SSP projections for Population (POP), Gross Domesitic Product Purching Power Parity per person (GDPppp) and urbanization (URB) that can be found on the 'SSP Database (Shared Socioeconomic pathways) - Version 2.0' https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=30. For smaller countries that are not included in the SSP projections, custom data has to be included in the respective input xlsx files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "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>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>region</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>2010</th>\n",
       "      <th>2015</th>\n",
       "      <th>2020</th>\n",
       "      <th>2025</th>\n",
       "      <th>2030</th>\n",
       "      <th>...</th>\n",
       "      <th>2060</th>\n",
       "      <th>2065</th>\n",
       "      <th>2070</th>\n",
       "      <th>2075</th>\n",
       "      <th>2080</th>\n",
       "      <th>2085</th>\n",
       "      <th>2090</th>\n",
       "      <th>2095</th>\n",
       "      <th>2100</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OECD Env-Growth</td>\n",
       "      <td>SSP1</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP|PPP</td>\n",
       "      <td>billion US$2005/yr</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.492</td>\n",
       "      <td>...</td>\n",
       "      <td>2.586</td>\n",
       "      <td>3.165</td>\n",
       "      <td>3.797</td>\n",
       "      <td>4.47</td>\n",
       "      <td>5.171</td>\n",
       "      <td>5.842</td>\n",
       "      <td>6.469</td>\n",
       "      <td>7.034</td>\n",
       "      <td>7.505</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             model scenario region variable                unit   2010   2015  \\\n",
       "0  OECD Env-Growth     SSP1    ABW  GDP|PPP  billion US$2005/yr  0.151  0.205   \n",
       "\n",
       "    2020   2025   2030  ...   2060   2065   2070  2075   2080   2085   2090  \\\n",
       "0  0.266  0.357  0.492  ...  2.586  3.165  3.797  4.47  5.171  5.842  6.469   \n",
       "\n",
       "    2095   2100  notes  \n",
       "0  7.034  7.505    NaN  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Datafiles with country-level SSP projections as retrieved from the SSP Database.\n",
    "import_POP_SSP = pd.read_excel('Input Data\\\\iamc_db_POP_countries.xlsx')\n",
    "import_GDPppp_SSP = pd.read_excel('Input Data\\\\iamc_db_GDPppp_countries.xlsx')\n",
    "import_URB_SSP = pd.read_excel('Input Data\\\\iamc_db_URB_countries.xlsx')\n",
    "\n",
    "#Datafiles with custom country-level projections.\n",
    "import_POP_missing = pd.read_excel('Input Data\\\\POP_GDP_URB_missing.xlsx' , sheet_name = 'POP')\n",
    "import_POP_missing = import_POP_missing.loc[(import_POP_missing['SSP'] == pathway)]\n",
    "import_GDP_missing = pd.read_excel('Input Data\\\\POP_GDP_URB_missing.xlsx' , sheet_name = 'GDP|PPP')\n",
    "import_GDP_missing = import_GDP_missing.loc[(import_GDP_missing['SSP'] == pathway)]\n",
    "import_URB_missing = pd.read_excel('Input Data\\\\POP_GDP_URB_missing.xlsx' , sheet_name = 'URB')\n",
    "import_URB_missing = import_URB_missing.loc[(import_URB_missing['SSP'] == pathway)]\n",
    "\n",
    "import_GDPppp_SSP.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loads historic country-level data (GDPppp, & urbanization) from the World Bank\n",
    "Based on the World Bank's World Development Indicators (WDI) retrieved through the World Bank python API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "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>Series</th>\n",
       "      <th>year</th>\n",
       "      <th>WB_URB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ZWE</th>\n",
       "      <td>SP.URB.TOTL.IN.ZS</td>\n",
       "      <td>2020</td>\n",
       "      <td>32.242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Series  year  WB_URB\n",
       "country                                 \n",
       "ZWE      SP.URB.TOTL.IN.ZS  2020  32.242"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extracts historical GDP|PPP per capita from the World Bank API\n",
    "import_country_GDP_WB = pd.DataFrame(wb.get_series('NY.GDP.PCAP.PP.KD', date = '1985:2020' , id_or_value = 'id')\n",
    "                                    ).reset_index().rename(columns = {'NY.GDP.PCAP.PP.KD' : 'WB_GDPppp' , 'Country' : \n",
    "                                                                      'country' , 'Year' : 'year'}).set_index('country')\n",
    "\n",
    "#Extracts Urban population (% of total population) from the World Bank API.\n",
    "import_country_URB_WB = pd.DataFrame(wb.get_series('SP.URB.TOTL.IN.ZS', date = '1985:2020' , id_or_value = 'id')\n",
    "                                    ).reset_index().rename(columns = {'SP.URB.TOTL.IN.ZS' : 'WB_URB' , 'Country' : \n",
    "                                                                      'country' , 'Year' : 'year'}).set_index('country')\n",
    "\n",
    "import_country_URB_WB.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loads historic country-level per capita electricity consumption data from Our World In Data\n",
    "Based on data from BP and Ember. Retrieved through https://ourworldindata.org/energy-production-consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "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>year</th>\n",
       "      <th>OWID_CON</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ZWE</th>\n",
       "      <td>2019</td>\n",
       "      <td>617.692045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year    OWID_CON\n",
       "country                  \n",
       "ZWE      2019  617.692045"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import_country_CON_OWID = pd.read_csv('Input Data\\\\owid_pcconsumption.csv'\n",
    "                                   ).iloc[: , -3:].rename(columns = {'Per capita electricity (kWh)' : 'OWID_CON' , 'Code' : \n",
    "                                                                     'country' , 'Year' : 'year'}).set_index('country')\n",
    "\n",
    "import_country_CON_OWID['year'] = import_country_CON_OWID['year'].astype('str')\n",
    "\n",
    "import_country_CON_OWID.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loads model data from PLEXOS-World 2015 from Harvard Dataverse\n",
    "Automatically extracts the relevant data of the PLEXOS-World 2015 model from the dedicated Harvard Dataverse. More specifically, nodal level capacity- and demand data is extracted that is used as baseline for disaggregation of the regional IAM scenario data. The PLEXOS-World 2015 model includes over 30,000 powerplants and has a spatial representation of more than 250 nodes covering the globe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieves PLEXOS-World 2015 model data\n",
    "Used to calculate 2015 baseline generator capacities per node and technology."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Checks whether the PLEXOS-World 2015 raw model data file needs to be retrieved from the PLEXOS World Harvard Dataverse or \n",
    "#whether the file already exists in the local folder.\n",
    "try:\n",
    "    import_PLEXOS_2015_properties = pd.read_excel('Input Data\\\\PLEXOS World 2015 Gold V1.1.xlsx' , sheet_name = 'Properties' , \n",
    "                                                  usecols = ['child_class' , 'child_object' , 'property' , 'value'])\n",
    "\n",
    "    import_PLEXOS_2015_memberships = pd.read_excel('Input Data\\\\PLEXOS World 2015 Gold V1.1.xlsx' , sheet_name = 'Memberships', \n",
    "                                              usecols = ['parent_class' , 'child_class' , 'parent_object' , 'child_object'])\n",
    "    \n",
    "except IOError:\n",
    "    \n",
    "    url = 'https://dataverse.harvard.edu/api/access/datafile/4008393?format=original&gbrecs=true'\n",
    "    r = requests.get(url)\n",
    "    with open('Input Data\\\\PLEXOS World 2015 Gold V1.1.xlsx', 'wb') as outfile:\n",
    "        outfile.write(r.content)\n",
    "    \n",
    "    import_PLEXOS_2015_properties = pd.read_excel('Input Data\\\\PLEXOS World 2015 Gold V1.1.xlsx' , sheet_name = 'Properties' , \n",
    "                                                  usecols = ['child_class' , 'child_object' , 'property' , 'value'])\n",
    "\n",
    "    import_PLEXOS_2015_memberships = pd.read_excel('Input Data\\\\PLEXOS World 2015 Gold V1.1.xlsx' , sheet_name = 'Memberships', \n",
    "                                              usecols = ['parent_class' , 'child_class' , 'parent_object' , 'child_object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieves PLEXOS-World 2015 hourly demand data incl. T&D losses for all nodes\n",
    "Used to be able to disaggregate regional electricity demand to nodal level as well as calculate relative peak demand and optionally develop hourly demand profiles for future years based on linear scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "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>Datetime</th>\n",
       "      <th>EU-AUT</th>\n",
       "      <th>EU-BEL</th>\n",
       "      <th>EU-BGR</th>\n",
       "      <th>EU-BIH</th>\n",
       "      <th>EU-CHE</th>\n",
       "      <th>EU-CYP</th>\n",
       "      <th>EU-CZE</th>\n",
       "      <th>EU-DEU</th>\n",
       "      <th>EU-DNK</th>\n",
       "      <th>...</th>\n",
       "      <th>OC-FJI</th>\n",
       "      <th>OC-PNG</th>\n",
       "      <th>SA-BOL</th>\n",
       "      <th>SA-COL</th>\n",
       "      <th>SA-ECU</th>\n",
       "      <th>SA-GUF</th>\n",
       "      <th>SA-GUY</th>\n",
       "      <th>SA-PRY</th>\n",
       "      <th>SA-SUR</th>\n",
       "      <th>SA-VEN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2015</td>\n",
       "      <td>7847</td>\n",
       "      <td>10281</td>\n",
       "      <td>5267</td>\n",
       "      <td>1749</td>\n",
       "      <td>8205</td>\n",
       "      <td>437</td>\n",
       "      <td>6931</td>\n",
       "      <td>54544</td>\n",
       "      <td>3382</td>\n",
       "      <td>...</td>\n",
       "      <td>126</td>\n",
       "      <td>550</td>\n",
       "      <td>1300</td>\n",
       "      <td>9541</td>\n",
       "      <td>3911</td>\n",
       "      <td>39</td>\n",
       "      <td>136</td>\n",
       "      <td>2626</td>\n",
       "      <td>270</td>\n",
       "      <td>16344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/01/2015 01:00</td>\n",
       "      <td>7480</td>\n",
       "      <td>9785</td>\n",
       "      <td>5032</td>\n",
       "      <td>1658</td>\n",
       "      <td>8214</td>\n",
       "      <td>413</td>\n",
       "      <td>6816</td>\n",
       "      <td>52795</td>\n",
       "      <td>3301</td>\n",
       "      <td>...</td>\n",
       "      <td>125</td>\n",
       "      <td>544</td>\n",
       "      <td>1197</td>\n",
       "      <td>9215</td>\n",
       "      <td>3973</td>\n",
       "      <td>38</td>\n",
       "      <td>121</td>\n",
       "      <td>2514</td>\n",
       "      <td>266</td>\n",
       "      <td>14925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 271 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Datetime  EU-AUT  EU-BEL  EU-BGR  EU-BIH  EU-CHE  EU-CYP  EU-CZE  \\\n",
       "0        01/01/2015    7847   10281    5267    1749    8205     437    6931   \n",
       "1  01/01/2015 01:00    7480    9785    5032    1658    8214     413    6816   \n",
       "\n",
       "   EU-DEU  EU-DNK  ...  OC-FJI  OC-PNG  SA-BOL  SA-COL  SA-ECU  SA-GUF  \\\n",
       "0   54544    3382  ...     126     550    1300    9541    3911      39   \n",
       "1   52795    3301  ...     125     544    1197    9215    3973      38   \n",
       "\n",
       "   SA-GUY  SA-PRY  SA-SUR  SA-VEN  \n",
       "0     136    2626     270   16344  \n",
       "1     121    2514     266   14925  \n",
       "\n",
       "[2 rows x 271 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checks whether the All demand UTC 2015.csv datafile needs to be retrieved from the PLEXOS-World Harvard Dataverse or whether\n",
    "#the file already exists in the local folder.\n",
    "try:\n",
    "    \n",
    "    import_hourly_demand_2015 = pd.read_csv('Input Data\\\\All Demand UTC 2015.csv')\n",
    "    \n",
    "except IOError:\n",
    "    \n",
    "    url = 'https://dataverse.harvard.edu/api/access/datafile/3985039?format=original&gbrecs=true'\n",
    "    r = requests.get(url)\n",
    "    with open('Input Data\\\\All Demand UTC 2015.csv', 'wb') as outfile:\n",
    "        outfile.write(r.content)\n",
    "    \n",
    "    import_hourly_demand_2015 = pd.read_csv('Input Data\\\\All Demand UTC 2015.csv')\n",
    "    \n",
    "import_hourly_demand_2015.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loads projected electricity transmission & distribution losses\n",
    "\n",
    "Country-level T&D losses per 5-year interval are added to the scenario specific country-level Final Energy|Electricity based on Sadovskaia et al., 2019; https://doi.org/10.1016/j.ijepes.2018.11.012. T&D within nodes is generally not modelled in continental power system models for computational reasoning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import_losses = pd.read_excel('Input Data\\\\T&D losses.xlsx')\n",
    "\n",
    "#Checks whether T&D is available for all included countries. An assertion error pops up in case data is missing. \n",
    "#In this case either data needs to be added to 'T&D losses.xlsx' or the country needs to be removed from the \n",
    "#to be included countries. This is only relevant for smaller countries and islands that haven't been included to-date.\n",
    "losses_missing = import_spatial_mapping.country[(~import_spatial_mapping.country.isin(import_losses.country))]\n",
    "\n",
    "assert losses_missing.empty , print(losses_missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculates nodal level electricity demand\n",
    "In the following section the nodal level final electricity demand (including transmission and distribution (T&D) losses) and hourly peak demand will be determined for the IAM scenario and where needed hourly scenario specific demand profiles for all nodes can be developed. \n",
    "\n",
    "<B>The following steps are taken;</B>\n",
    "\n",
    "<B>I)</B> Socioeconomic pathway (SSP) specific country-level population (POP), urbanization (URB) and Gross Domestic Product Purchasing Power Parity (GDP|PPP) projections are extracted from the SSP database and formatted to be used to project country-level electricity demand. Country-level pathway data is available for the majority of countries globally, yet not for all (e.g. GDP|PPP for AS-PRK). Hence for these countries, if to be included in the modelling, custom data needs to be provided in the 'POP_GDP_URB_missing.xlsx' file. Both data sources are appended and formatted to be used as weight dataframe for the PYAM downscale_region function.\n",
    "\n",
    "SSP database can be found here;  'SSP Database (Shared Socioeconomic pathways) - Version 2.0' https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=30. Details on PYAM's downscale_region function can be found here; https://pyam-iamc.readthedocs.io/en/stable/tutorials/aggregating_downscaling_consistency.html.\n",
    "\n",
    "<B>II)</B> Historic regression relationships have been inferred per region (e.g. per R5 region) based on datasets from the World Bank's World Development Indicators (WDI) and Our World In Data (OWID). Country-level values for the period 1985-2020 are used as entries with electricity consumption as dependent variable (from OWID) and GDP|PPP and (optionally) URB as independent variables (from WB WDI). More information on the WDI can be found here; http://datatopics.worldbank.org/world-development-indicators/. More information on the World Bank's WDI python package can be found here; https://pypi.org/project/world-bank-data/. More information on OWID can be found here; https://ourworldindata.org/. More information on the underlying datasets as used in the OWID per capacity electricity consumption dataset can be found here; https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html and here; https://ember-climate.org/data/. \n",
    "\n",
    "<B>III)</B> The country-level POP and GDP|PPP (and optionally URB) (step I) are entered into the inferred relationships (step II) to project pathway specific country-level electricity demand. \n",
    "\n",
    "<B>IV)</B> Weight dataframes get created based on the projected country-level demand values (step III).\n",
    "\n",
    "<B>V)</B> Scenario specific regional electricity demand values (final or secondary) and transmission loss values (if applicable) are extracted from the IAM database. If transmission loss values based on the 'Secondary Energy|Electricity|Transmission Losses' variable are available they will either be added to the final demand or substracted from secondary demand depending on which demand type is chosen.\n",
    "\n",
    "<B>VI)</B> The weight dataframes (step IV) are used to downscale the scenario specific regional electricity demand values (final or secondary) (step V) to country-level.\n",
    "\n",
    "<B>VII)</B> If transmission loss values based on the 'Secondary Energy|Electricity|Transmission Losses' variable are NOT available for the specific scenario ensemble, custom country level T&D losses are retrieved and either added to the final demand or substracted from secondary demand depending on which demand type is chosen. Country-level T&D losses per 5-year interval are retreived from Sadovskaia et al., 2019; https://doi.org/10.1016/j.ijepes.2018.11.012.\n",
    "\n",
    "<B>VIII)</B> Specific for sub-country nodes (relevant for Australia, Brazil, Canada, China, India, Japan, Russia and the united States in the PLEXOS-World model), the 2015 relative share of electricity demand per sub-country node (based on PLEXOS-World data) is used to decompose the calculated country-level electricity demand (step VII) following the assumption that relative demand shares remain equal.\n",
    "\n",
    "<B>IX)</B> Constrains the forecasted electricity demand (step VIII) to 2015 baseline values in case the projected value for the scenario year is <= 0. In case of linear regression, small countries with signficantly lower projected independent variables (GDP|PPP, urbanization) compared to the regional average can lead to very low and often negative projected demand values (e.g. EU-KOS). Albeit a rough method, this is only relevant for small countries.\n",
    "\n",
    "<B>X)</B> In case negative values occured and demand values had to be scaled up (step IX), demand values of other areas belonging to the IAM region are comparitively scaled down as such that the sum of individual demands of all areas equals the reported demand from the IAM region.\n",
    "\n",
    "<B>XI)</B>  2015 hourly peak demand per node relative to the total demand (based on PLEXOS-World data) is used to calculate scenario and year specific hourly peak demand following the assumption that the relative peak to total demand ratio remains equal compared to 2015. Countries that weren't included in the 2015 dataset get assigned a dataset average peak to total demand ratio. The assumption of equal relative peak load compared to 2015 can be manually adjusted at the beginning of this script if required, yet note that if hourly demand profiles (step XII) are to be created within the script this can only occur in a linear fashion. If profiles are to be scaled in a non-linear fashion this becomes an optimization problem and needs to occur outside this script (e.g. PLEXOS has a built-in profile builder tool that is capable of doing this). The calculated electricity demand values (step IX), and adjusted peak min/max demand values can then be used as boundary constraints for the scaling of the baseline hourly demand profiles (e.g. from the PLEXOS-World dataset) outside this script.\n",
    "\n",
    "<B>XII)</B> As an optional cell - to be activated by setting the <i>hourly_demand_generator</i> variable to 'Yes' and the <i>peak_load_max</i> variable to 1 (linear) at the beginning of this script - scenario, year and node specific hourly demand profiles can be developed based on linear scaling. Note that this follows the setup of the 2015 calendar year, hence different order of weekdays/weekends or extra days following possible leap years are not considered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I) Extracts pathway and year specific country-level POP data and creates weights dataframe for downscaling of the regional data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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>2050</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>75.161958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              2050\n",
       "country           \n",
       "AFG      75.161958"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Copies relevant columns from the the 'iamc_db_POP_countries' datafile and filters relevant rows. In millions\n",
    "country_POP_raw = import_POP_SSP[['model' , 'scenario' , 'region' , snapshot_year]].copy()\n",
    "\n",
    "#Filters relevant data based on earlier given entries (SSP pathway and source for POP)\n",
    "country_POP_raw = country_POP_raw.loc[(country_POP_raw['model'] == POP_countries_source) & \n",
    "                                      (country_POP_raw['scenario'] == pathway)].rename(columns = {'region' : 'country'})\n",
    "\n",
    "country_POP_raw = pd.merge(import_spatial_mapping.country , country_POP_raw[['country' , snapshot_year]] , \n",
    "                           on = 'country' , how = 'outer').dropna()\n",
    "\n",
    "#Checks whether pathway data is available for all included countries.\n",
    "country_POP_raw_missing = import_spatial_mapping.country[(~import_spatial_mapping.country.isin(country_POP_raw.country))]\n",
    "\n",
    "#In case pathway data is not available for all countries it checks wheter custom data is provided in 'POP_GDP_URB_missing.xlsx' \n",
    "#If not, an assertion error pops up indicating for which country data is missing. In this case either data needs to be added or \n",
    "#the country needs to be removed from the to be included countries.\n",
    "country_POP_raw_missing = pd.merge(country_POP_raw_missing , import_POP_missing[['country' , snapshot_year]] , \n",
    "                           on = 'country' , how = 'left')\n",
    "\n",
    "assert country_POP_raw_missing.notnull().values.all() , print(country_POP_raw_missing)\n",
    "\n",
    "country_POP_raw = pd.concat([country_POP_raw, country_POP_raw_missing]).drop_duplicates().set_index('country')\n",
    "\n",
    "country_POP_raw.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I) Extracts pathway and year specific country-level GDPppp data and creates weighting dataframe for downscaling of the regional data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>2050</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>314.371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2050\n",
       "country         \n",
       "AFG      314.371"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Copies relevant columns from the the 'iamc_db_GDPppp_countries' datafile and filters relevant rows. In millions\n",
    "country_GDP_raw = import_GDPppp_SSP[['model' , 'scenario' , 'region' , snapshot_year]].copy()\n",
    "\n",
    "#Filters relevant data based on earlier given entries (SSP pathway and source for GDPppp)\n",
    "country_GDP_raw = country_GDP_raw.loc[(country_GDP_raw['model'] == GDPppp_countries_source) & \n",
    "                                                           (country_GDP_raw['scenario'] == pathway)\n",
    "                                     ].rename(columns = {'region' : 'country'})\n",
    "\n",
    "country_GDP_raw = pd.merge(import_spatial_mapping.country , country_GDP_raw[['country' , snapshot_year]] , \n",
    "                           on = 'country' , how = 'outer').dropna()\n",
    "\n",
    "#Checks whether pathway data is available for all included countries.\n",
    "country_GDP_raw_missing = import_spatial_mapping.country[(~import_spatial_mapping.country.isin(country_GDP_raw.country))]\n",
    "\n",
    "#In case pathway data is not available for all countries it checks wheter custom data is provided in 'POP_GDP_URB_missing.xlsx' \n",
    "#If not, an assertion error pops up indicating for which country data is missing. In this case either data needs to be added or \n",
    "#the country needs to be removed from the to be included countries.\n",
    "country_GDP_raw_missing = pd.merge(country_GDP_raw_missing , import_GDP_missing[['country' , snapshot_year]] , \n",
    "                           on = 'country' , how = 'left')\n",
    "\n",
    "assert country_GDP_raw_missing.notnull().values.all() , print(country_GDP_raw_missing)\n",
    "\n",
    "country_GDP_raw = pd.concat([country_GDP_raw, country_GDP_raw_missing]).drop_duplicates().set_index('country')\n",
    "\n",
    "country_GDP_raw.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I) Extracts pathway and year specific country-level urbanization data and creates weights dataframe for downscaling of the regional data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>2050</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>43.339553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              2050\n",
       "country           \n",
       "AFG      43.339553"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Copies relevant columns from the the 'iamc_db_URB_countries' datafile and filters relevant rows. In % of total population\n",
    "country_URB_raw = import_URB_SSP[['model' , 'scenario' , 'region' , snapshot_year]].copy()\n",
    "\n",
    "#Filters relevant data based on earlier given entries (SSP pathway and source for URB)\n",
    "country_URB_raw = country_URB_raw.loc[(country_URB_raw['model'] == 'NCAR') & \n",
    "                                                           (country_URB_raw['scenario'] == pathway)\n",
    "                                     ].rename(columns = {'region' : 'country'})\n",
    "\n",
    "country_URB_raw = pd.merge(import_spatial_mapping.country , country_URB_raw[['country' , snapshot_year]] , \n",
    "                           on = 'country' , how = 'outer').dropna()\n",
    "\n",
    "#Checks whether pathway data is available for all included countries.\n",
    "country_URB_raw_missing = import_spatial_mapping.country[(~import_spatial_mapping.country.isin(country_URB_raw.country))]\n",
    "\n",
    "#In case pathway data is not available for all countries it checks wheter custom data is provided in 'POP_GDP_URB_missing.xlsx' \n",
    "#If not, an assertion error URBs up indicating for which country data is missing. In this case either data needs to be added or \n",
    "#the country needs to be removed from the to be included countries.\n",
    "country_URB_raw_missing = pd.merge(country_URB_raw_missing , import_URB_missing[['country' , snapshot_year]] , \n",
    "                           on = 'country' , how = 'left')\n",
    "\n",
    "assert country_URB_raw_missing.notnull().values.all() , print(country_URB_raw_missing)\n",
    "\n",
    "country_URB_raw = pd.concat([country_URB_raw, country_URB_raw_missing]).drop_duplicates().set_index('country')\n",
    "\n",
    "country_URB_raw.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II) Creates historic GDP|PPP pp / Urbanization share / demand pp relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "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>country</th>\n",
       "      <th>node</th>\n",
       "      <th>year</th>\n",
       "      <th>WB_GDPppp</th>\n",
       "      <th>OWID_CON</th>\n",
       "      <th>WB_URB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R5REF</th>\n",
       "      <td>UKR</td>\n",
       "      <td>EU-UKR</td>\n",
       "      <td>2019</td>\n",
       "      <td>12804.955078</td>\n",
       "      <td>3247.056644</td>\n",
       "      <td>69.473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R5REF</th>\n",
       "      <td>UKR</td>\n",
       "      <td>EU-UKR</td>\n",
       "      <td>2020</td>\n",
       "      <td>12407.790039</td>\n",
       "      <td>3052.487690</td>\n",
       "      <td>69.608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country    node  year     WB_GDPppp     OWID_CON  WB_URB\n",
       "region                                                         \n",
       "R5REF      UKR  EU-UKR  2019  12804.955078  3247.056644  69.473\n",
       "R5REF      UKR  EU-UKR  2020  12407.790039  3052.487690  69.608"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merges input dataframes for electricity consumption and GDP|PPP.\n",
    "country_regression = pd.merge(import_country_GDP_WB , import_country_CON_OWID, how = 'inner' , \n",
    "                               left_on = ['country' , 'year'] , right_on = ['country' , 'year'])\n",
    "\n",
    "#Merges input dataframe for urbanization if urbanization is to be included in the regression.\n",
    "if urbanization == 'Yes':\n",
    "    country_regression = pd.merge(country_regression[['year' , 'WB_GDPppp' , 'OWID_CON']] , \n",
    "                                     import_country_URB_WB[['year' , 'WB_URB']] , left_on = ['year' , 'country'] , \n",
    "                                     right_on = ['year' , 'country'])\n",
    "    \n",
    "#Links the World Bank data entries to countries by making use of ISO 3 codes.\n",
    "country_regression = pd.merge(import_spatial_mapping , country_regression, \n",
    "                                 left_on = 'country' , right_index = True , how = 'left').set_index('region').dropna()\n",
    "\n",
    "#Calculates the regional linear fit based on all historical values in that region.\n",
    "sklearn_lr = LinearRegression()\n",
    "country_regression_grouped = pd.DataFrame()\n",
    "\n",
    "for x in country_regression.index.unique():\n",
    "    \n",
    "    country_regression_temp = country_regression.copy().loc[x]\n",
    "    \n",
    "    #If urbanization is included linear regression occurs with multiple independent variables (GDP|PPP and % urban population)\n",
    "    #for the dependent variable (electricity consumption). \n",
    "    if urbanization == 'Yes':\n",
    "        \n",
    "        sklearn_lr.fit(country_regression_temp[['WB_GDPppp' , 'WB_URB']] , country_regression_temp['OWID_CON'])\n",
    "        \n",
    "        country_regression_temp['intercept'] = sklearn_lr.intercept_\n",
    "        country_regression_temp['coef_GDPppp'] = float(sklearn_lr.coef_[0])\n",
    "        country_regression_temp['coef_URB'] = float(sklearn_lr.coef_[1])\n",
    "                                                                                                \n",
    "        country_regression_temp['R2_GDPppp_URB/CON'] = sklearn_lr.score(country_regression_temp[['WB_GDPppp' , 'WB_URB']]\n",
    "                                                                           , country_regression_temp['OWID_CON'])\n",
    "        \n",
    "        country_regression_grouped = pd.concat([country_regression_grouped, country_regression_temp])\n",
    "        \n",
    "    #If urbanization is not included linear regression occurs with single independent variables (GDP|PPP) for the dependent\n",
    "    #variable (electricity consumption).    \n",
    "    else:\n",
    "        \n",
    "        sklearn_lr.fit(country_regression_temp[['WB_GDPppp']] , country_regression_temp['OWID_CON'])\n",
    "              \n",
    "        country_regression_temp['intercept'] = sklearn_lr.intercept_\n",
    "        country_regression_temp['coef_GDPppp'] = float(sklearn_lr.coef_)\n",
    "        \n",
    "        country_regression_temp['R2_GDPppp/CON'] = sklearn_lr.score(country_regression_temp[['WB_GDPppp']] , \n",
    "                                                                        country_regression_temp['OWID_CON'])\n",
    "        \n",
    "        country_regression_grouped = pd.concat([country_regression_grouped, country_regression_temp])\n",
    "\n",
    "country_regression.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for a in country_regression_grouped.index.unique():\n",
    "    country_regression_plot = country_regression_grouped.loc[a]\n",
    "    b = country_regression_plot['WB_GDPppp']\n",
    "    c = country_regression_plot['OWID_CON']\n",
    "    x = country_regression_plot.loc[a , 'WB_GDPppp']\n",
    "    m = country_regression_plot.loc[a , 'coef_GDPppp']\n",
    "    z = country_regression_plot.loc[a , 'intercept']\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(111)\n",
    "    ax1.scatter(b , c , color = 'blue' , alpha = 0.5 , label = 'GDPppp per capita')\n",
    "    \n",
    "    if urbanization == 'Yes':\n",
    "        \n",
    "        w2 = country_regression_plot.loc[a , 'R2_GDPppp_URB/CON'].unique()\n",
    "        m2 = country_regression_plot.loc[a , 'coef_URB']\n",
    "        d = country_regression_plot['WB_URB']\n",
    "        ax2 = ax1.twiny()\n",
    "        ax2.set_xlim(0 , 100)\n",
    "        ax2.scatter(d , c , color = 'red' , alpha = 0.5 , label = 'Urban population') \n",
    "        ax2.set_xlabel('Urban population (% of total population)')\n",
    "        ax2.legend(loc = 'upper right')\n",
    "        plt.title(f'{a} R2 = {w2}')\n",
    "        \n",
    "    else:\n",
    "        ax1.plot(x , m * x + z , color = 'blue' , alpha = 0.5) \n",
    "        w = country_regression_plot.loc[a , 'R2_GDPppp/CON'].unique()\n",
    "        plt.title(f'{a} R2 = {w}')\n",
    "    \n",
    "    ax1.legend(loc='upper left')\n",
    "    ax1.set_xlabel('GDPppp per capita (constant 2017 international $)')\n",
    "    ax1.set_ylabel('Electricity demand per capita (kWh)')\n",
    "    plt.rcParams[\"figure.figsize\"] = [8,4]\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### III) Uses historic relationships and country-level SSP projections to project country-level electricity demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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>region</th>\n",
       "      <th>2050_GDP|PPPpp_raw</th>\n",
       "      <th>coef_GDPppp</th>\n",
       "      <th>coef_URB</th>\n",
       "      <th>intercept</th>\n",
       "      <th>2050_URB_raw</th>\n",
       "      <th>2050_demandpp_raw</th>\n",
       "      <th>2050_demand_total_raw</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>R5ASIA</td>\n",
       "      <td>4182.581324</td>\n",
       "      <td>0.058052</td>\n",
       "      <td>76.051465</td>\n",
       "      <td>-1512.874039</td>\n",
       "      <td>43.339553</td>\n",
       "      <td>2025.969913</td>\n",
       "      <td>1.522759e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         region  2050_GDP|PPPpp_raw  coef_GDPppp   coef_URB    intercept  \\\n",
       "country                                                                    \n",
       "AFG      R5ASIA         4182.581324     0.058052  76.051465 -1512.874039   \n",
       "\n",
       "         2050_URB_raw  2050_demandpp_raw  2050_demand_total_raw  \n",
       "country                                                          \n",
       "AFG         43.339553        2025.969913           1.522759e+08  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retrieves countries for which both GDP and POP data is available within the World Bank API. \n",
    "intersection_raw = list(set(country_GDP_raw.index.intersection(country_POP_raw.index)))\n",
    "\n",
    "#Sets the memberships dataframe solely for the above countries. \n",
    "country_demand_raw = import_spatial_mapping[['country' , 'region']].copy()\n",
    "    \n",
    "country_demand_raw = country_demand_raw[country_demand_raw['country'].isin(intersection_raw)\n",
    "                                       ].drop_duplicates().set_index('country')\n",
    "\n",
    "#Divides the country level GDP|PPP data (converted from billions to millions) with the population (millions) to get GDP|PPP pp.\n",
    "country_demand_raw[f'{snapshot_year}_GDP|PPPpp_raw'] = (country_GDP_raw[snapshot_year] * 1000) / country_POP_raw[snapshot_year]\n",
    "\n",
    "#Projects country-level projected demand pp (in kWh) based on multivariable linear regression if urbanization is included.\n",
    "if urbanization == 'Yes':  \n",
    "\n",
    "    country_demand_raw = pd.merge(country_demand_raw, country_regression_grouped[['coef_GDPppp' , 'coef_URB' , 'intercept']] \n",
    "                                  , left_on = 'region' , right_index = True)\n",
    "    \n",
    "    country_demand_raw[f'{snapshot_year}_URB_raw'] = country_URB_raw[snapshot_year]\n",
    "    \n",
    "    country_demand_raw[f'{snapshot_year}_demandpp_raw'] = country_demand_raw[\n",
    "        'coef_GDPppp'] * country_demand_raw[f'{snapshot_year}_GDP|PPPpp_raw'\n",
    "                                           ] + country_demand_raw['coef_URB'\n",
    "                                                                 ] * country_demand_raw[f'{snapshot_year}_URB_raw'\n",
    "                                                                                       ] + country_demand_raw['intercept']\n",
    "    \n",
    "#Projects country-level projected demand pp (in kWh) based on linear regression if urbanization is not included.\n",
    "else:\n",
    "\n",
    "    country_demand_raw = pd.merge(country_demand_raw, \n",
    "                                  country_regression_grouped[['coef_GDPppp' , 'intercept']] , \n",
    "                                  left_on = 'region' , right_index = True)\n",
    "    \n",
    "    country_demand_raw[f'{snapshot_year}_demandpp_raw'] = country_demand_raw['coef_GDPppp'\n",
    "                                                                       ] * country_demand_raw[f'{snapshot_year}_GDP|PPPpp_raw'\n",
    "                                                                                             ] + country_demand_raw['intercept']\n",
    "\n",
    "country_demand_raw = country_demand_raw.drop_duplicates()\n",
    "\n",
    "#Multiplies projected demand pp (in kWh) with population (in millions) to get country-level total projected demand (in MWh).\n",
    "country_demand_raw[f'{snapshot_year}_demand_total_raw'] = country_demand_raw[f'{snapshot_year}_demandpp_raw'\n",
    "                                                                       ] * (country_POP_raw[snapshot_year]) * 1000\n",
    "\n",
    "country_demand_raw.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IV) Creates weights dataframe for downscaling of the scenario specific regional Final Energy|Electricity data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "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>year</th>\n",
       "      <th>2050</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>1.522759e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year            2050\n",
       "region              \n",
       "AFG     1.522759e+08"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_demand_raw_weight = country_demand_raw[[f'{snapshot_year}_demand_total_raw']\n",
    "                                              ].copy().rename(columns = {f'{snapshot_year}_demand_total_raw' : snapshot_year})\n",
    "\n",
    "country_demand_raw_weight.index.names = ['region']\n",
    "\n",
    "country_demand_raw_weight ['year'] = snapshot_year\n",
    "\n",
    "country_demand_raw_weight = country_demand_raw_weight.pivot_table(index = 'region' , values = snapshot_year , columns = 'year')\n",
    "\n",
    "country_demand_raw_weight.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V) Extracts scenario specific demand and transmission loss variables (where applicable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>region</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IMAGE 3.0.1</td>\n",
       "      <td>ADVANCE_2020_1.5C-2100</td>\n",
       "      <td>R5ASIA</td>\n",
       "      <td>Final Energy|Electricity</td>\n",
       "      <td>EJ/yr</td>\n",
       "      <td>2050</td>\n",
       "      <td>52.904648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         model                scenario  region                  variable  \\\n",
       "0  IMAGE 3.0.1  ADVANCE_2020_1.5C-2100  R5ASIA  Final Energy|Electricity   \n",
       "\n",
       "    unit  year      value  \n",
       "0  EJ/yr  2050  52.904648  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import_region_final_demand_scenario = {}\n",
    "import_region_secondary_demand_scenario = {}\n",
    "import_year_transmission_losses_scenario = {}\n",
    "region_demand_scenario = {}\n",
    "peak_demand_ratio_scenario = {}\n",
    "\n",
    "for a in entries:\n",
    "\n",
    "    #Sets seperate copy for the scenario specific regional electricity demand/losses dataframes and filters the relevant rows\n",
    "\n",
    "    import_region_final_demand_scenario[a] = import_year_scenario[a].copy().loc[(import_year_scenario[a]['region'] != \n",
    "                                                                             'World') & \n",
    "                                                                            (import_year_scenario[a]['variable'] == \n",
    "                                                                             'Final Energy|Electricity')]\n",
    "    \n",
    "    import_region_secondary_demand_scenario[a] = import_year_scenario[a].copy().loc[(import_year_scenario[a]['region'] != \n",
    "                                                                         'World') & \n",
    "                                                                        (import_year_scenario[a]['variable'] == \n",
    "                                                                         'Secondary Energy|Electricity')]\n",
    "        \n",
    "    import_year_transmission_losses_scenario[a] = import_scenario[a].copy().loc[(import_scenario[a]['region'] != \n",
    "                                                                         'World') & \n",
    "                                                                        (import_scenario[a]['variable'] == \n",
    "                                                                        'Secondary Energy|Electricity|Transmission Losses')]\n",
    "    \n",
    "    if electricity_demand_type == 'Final Energy|Electricity':\n",
    "        region_demand_scenario[a] = import_region_final_demand_scenario[a].copy().set_index(['region', 'model', 'scenario'])\n",
    "    \n",
    "        if len(import_year_transmission_losses_scenario[a].index) != 0:\n",
    "            \n",
    "            region_demand_scenario[a]['value'] = region_demand_scenario[a]['value'] + \\\n",
    "                                                        import_year_transmission_losses_scenario[a].set_index([\n",
    "                                                            'region', 'model', 'scenario'])['value']\n",
    "            \n",
    "    if electricity_demand_type == 'Secondary Energy|Electricity':\n",
    "        region_demand_scenario[a] = import_region_secondary_demand_scenario[a].copy().set_index(['region', 'model', 'scenario'])\n",
    "            \n",
    "        if len(import_year_transmission_losses_scenario[a].index) != 0:\n",
    "            \n",
    "            region_demand_scenario[a]['value'] = region_demand_scenario[a]['value'] - \\\n",
    "                                                        import_year_transmission_losses_scenario[a].set_index([\n",
    "                                                            'region', 'model', 'scenario'])['value']\n",
    "        \n",
    "    #Converts pd.DataFrames to IamDataFrames to be able to use the PYAM downscale_region function.\n",
    "    region_demand_scenario[a] = pyam.IamDataFrame(region_demand_scenario[a])\n",
    "\n",
    "region_demand_scenario[a].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VI) Downscales scenario specific regional Final Energy|Electricity to country-level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>country_2050</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>IMAGE 3.0.1</td>\n",
       "      <td>ADVANCE_2020_1.5C-2100</td>\n",
       "      <td>Final Energy|Electricity</td>\n",
       "      <td>EJ/yr</td>\n",
       "      <td>0.408669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              model                scenario                  variable   unit  \\\n",
       "region                                                                         \n",
       "AFG     IMAGE 3.0.1  ADVANCE_2020_1.5C-2100  Final Energy|Electricity  EJ/yr   \n",
       "\n",
       "        country_2050  \n",
       "region                \n",
       "AFG         0.408669  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_demand_scenario = {}\n",
    "\n",
    "for a in entries:\n",
    "\n",
    "    #Loops through the keys (regions) and values (countries) in mapping dictionary.\n",
    "    for key, value in mapping_countries.items():\n",
    "    \n",
    "        #Filters the relevant weights (projected country-level electricity demand) based on the value entries (countries) \n",
    "        #for the specific key (region).\n",
    "        weight = country_demand_raw_weight.copy()[country_demand_raw_weight.index.isin(value)]\n",
    "        \n",
    "        if len(weight.index) == 1:\n",
    "            weight.loc[f'temp_{key}'] = [0]\n",
    "            value.append(f'temp_{key}')\n",
    "            \n",
    "        if len(weight.index) == 0:\n",
    "            weight.loc[f'temp_{key}'] = [0]\n",
    "            weight.loc[value[0]] = [1]\n",
    "            value.append(f'temp_{key}')\n",
    "    \n",
    "        #Downscales the IAM scenario electricity demand per region (key) to country level (value) by using the weights as proxy.\n",
    "        region_demand_scenario[a].downscale_region(electricity_demand_type , region = key , subregions = value , \n",
    "                                                   weight = weight , append = True)\n",
    "    \n",
    "    #Excludes the regional values from the final IamDataFrames\n",
    "    country_demand_scenario[a] = region_demand_scenario[a].filter(\n",
    "        region = list(dict_countries.keys()) , \n",
    "        keep = True).timeseries().reset_index().set_index('region').rename(columns = {snapshot_year : \n",
    "                                                                                      f'country_{snapshot_year}'})\n",
    "\n",
    "country_demand_scenario[a].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VII) Adds or substracts transmission and distribution losses to/from scenario specific country-level demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>country_2050</th>\n",
       "      <th>losses_%_2050</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>IMAGE 3.0.1</td>\n",
       "      <td>ADVANCE_2020_1.5C-2100</td>\n",
       "      <td>Final Energy|Electricity</td>\n",
       "      <td>MWh/yr</td>\n",
       "      <td>1.219331e+08</td>\n",
       "      <td>7.411865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model                scenario                  variable  \\\n",
       "country                                                                  \n",
       "AFG      IMAGE 3.0.1  ADVANCE_2020_1.5C-2100  Final Energy|Electricity   \n",
       "\n",
       "           unit  country_2050  losses_%_2050  \n",
       "country                                       \n",
       "AFG      MWh/yr  1.219331e+08       7.411865  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_demand_losses_scenario = {}\n",
    "\n",
    "for a in entries:\n",
    "    \n",
    "    if len(import_year_transmission_losses_scenario[a].index) == 0:\n",
    "\n",
    "        #Merges demand dataframe with losses dataframe and calculates scenario specific country-level demand including losses.\n",
    "        country_demand_losses_scenario[a] = pd.merge(country_demand_scenario[a] , \n",
    "                                                     import_losses [['country' , f'losses_%_{snapshot_year}']] , \n",
    "                                                     left_index = True , right_on = 'country').set_index('country')\n",
    "\n",
    "        if electricity_demand_type == 'Final Energy|Electricity':\n",
    "\n",
    "            country_demand_losses_scenario[a][\n",
    "                f'country_{snapshot_year}'] = country_demand_losses_scenario[a][\n",
    "                f'country_{snapshot_year}'] * country_demand_losses_scenario[a][\n",
    "                f'losses_%_{snapshot_year}'] / 100 + country_demand_losses_scenario[a][\n",
    "                f'country_{snapshot_year}']\n",
    "\n",
    "        if electricity_demand_type == 'Secondary Energy|Electricity':\n",
    "\n",
    "            country_demand_losses_scenario[a][\n",
    "                f'country_{snapshot_year}'] = country_demand_losses_scenario[a][\n",
    "                f'country_{snapshot_year}'] - (country_demand_losses_scenario[a][\n",
    "                f'country_{snapshot_year}'] * country_demand_losses_scenario[a][\n",
    "                f'losses_%_{snapshot_year}'] / 100)\n",
    "    \n",
    "    else:\n",
    "        country_demand_losses_scenario[a] = country_demand_scenario[a]\n",
    "\n",
    "    #Converts unit from EJ/yr to MWh/yr (1 EJ = 277778 GWh or 277778 * 1000 MWh).\n",
    "    country_demand_losses_scenario[a][f'country_{snapshot_year}'] = country_demand_losses_scenario[\n",
    "        a][f'country_{snapshot_year}'].apply(lambda x: x * 277778 * 1000)\n",
    "   \n",
    "    country_demand_losses_scenario[a]['unit'] = 'MWh/yr'\n",
    "\n",
    "country_demand_losses_scenario[entries[-1]].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VIII) Determines relative 2015 share of demand per sub-country node used to decompose country-level scenario specific electricity demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>country_2050</th>\n",
       "      <th>country</th>\n",
       "      <th>share_%_country_demand</th>\n",
       "      <th>node_2050_incl_losses</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NA-CAN-QC</th>\n",
       "      <td>IMAGE 3.0.1</td>\n",
       "      <td>ADVANCE_2020_1.5C-2100</td>\n",
       "      <td>Final Energy|Electricity</td>\n",
       "      <td>MWh/yr</td>\n",
       "      <td>4.777626e+08</td>\n",
       "      <td>CAN</td>\n",
       "      <td>0.334204</td>\n",
       "      <td>1.596703e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model                scenario                  variable  \\\n",
       "node                                                                       \n",
       "NA-CAN-QC  IMAGE 3.0.1  ADVANCE_2020_1.5C-2100  Final Energy|Electricity   \n",
       "\n",
       "             unit  country_2050 country  share_%_country_demand  \\\n",
       "node                                                              \n",
       "NA-CAN-QC  MWh/yr  4.777626e+08     CAN                0.334204   \n",
       "\n",
       "           node_2050_incl_losses  \n",
       "node                              \n",
       "NA-CAN-QC           1.596703e+08  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sums the hourly demand as retrieved from the PLEXOS-World dataset to year total (in MWh) and drops all hourly values.\n",
    "demand_2015_raw = import_hourly_demand_2015.drop(columns = ['Datetime'])\n",
    "\n",
    "demand_2015_raw.loc['node_demand_2015'] = demand_2015_raw.sum()\n",
    "\n",
    "demand_2015_raw = demand_2015_raw.iloc[8760:].transpose().reset_index().rename(columns = {'index' : 'node'})\n",
    "\n",
    "#Checks whether all countries as included for the modelling are also in the 2015 dataset and if not it adds entries manually.\n",
    "#NA replaced with 1 to indicate 'share_%_country_demand' to be the full country.\n",
    "demand_2015_missing = import_spatial_mapping.node[(~import_spatial_mapping.node.isin(demand_2015_raw.node))]\n",
    "\n",
    "demand_2015_raw = pd.concat([demand_2015_raw, demand_2015_missing]).fillna(1)\n",
    "\n",
    "#Adds country entry to dataframe (e.g. NA-USA-CA node gets column entry NA-USA country)\n",
    "demand_2015_raw.insert(loc = 0 , column = 'country' , value = demand_2015_raw['node'].str[:6])\n",
    "\n",
    "#Creates a dataframe excluding all sub-country nodes\n",
    "country_demand_2015 = demand_2015_raw[demand_2015_raw['country'] == demand_2015_raw['node']\n",
    "                                     ].drop(columns = ['node']\n",
    "                                           ).rename(columns = {'node_demand_2015' : 'country_demand_2015'})\n",
    "\n",
    "#Adds country-level 2015 demand in column adjacent to sub-country level 2015 demand and calculates relative share per country.\n",
    "node_demand_2015 = pd.merge(demand_2015_raw , country_demand_2015[['country' , 'country_demand_2015']] , \n",
    "                             on = 'country' , how ='left').dropna()\n",
    "\n",
    "node_demand_2015['share_%_country_demand'] = node_demand_2015['node_demand_2015'] / node_demand_2015['country_demand_2015']\n",
    "node_demand_2015['country'] = demand_2015_raw['country'].str[3:6]\n",
    "\n",
    "node_demand_scenario = {}\n",
    "\n",
    "for a in entries:\n",
    "\n",
    "    #Drops the non-required columns and merges the 2015 and 2050 demand dataframes\n",
    "    node_demand_scenario[a] = pd.merge(country_demand_losses_scenario[a][['model' , 'scenario' , 'variable' , 'unit' , \n",
    "                                                                          f'country_{snapshot_year}']] , \n",
    "                                       node_demand_2015[['country' , 'node' , 'share_%_country_demand']] , \n",
    "                                       left_index = True , right_on = 'country').set_index('node')\n",
    "\n",
    "    #Multiplies the sub-country share of 2015 demand by the calculated 2050 demand to retrieve the 2050 final demand including\n",
    "    #losses for all nodes (in MWh).\n",
    "    node_demand_scenario[a][f'node_{snapshot_year}_incl_losses'\n",
    "                           ] = (node_demand_scenario[a][f'country_{snapshot_year}'] * \\\n",
    "                                node_demand_scenario[a]['share_%_country_demand']).round(2)\n",
    "\n",
    "    #Filters out country rows for countries with sub-country nodes. \n",
    "    node_demand_scenario[a] = node_demand_scenario[a][node_demand_scenario[a].index.isin(import_spatial_mapping.node)]\n",
    "\n",
    "node_demand_scenario[entries[-1]].loc[node_demand_scenario[entries[-1]].index == 'NA-CAN-QC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IX) Constrains negative values to 2015 baseline values as minimum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "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>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>country</th>\n",
       "      <th>share_%_country_demand</th>\n",
       "      <th>node_2050_incl_losses</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NA-CAN-QC</th>\n",
       "      <td>IMAGE 3.0.1</td>\n",
       "      <td>ADVANCE_2020_1.5C-2100</td>\n",
       "      <td>Final Energy|Electricity</td>\n",
       "      <td>MWh/yr</td>\n",
       "      <td>CAN</td>\n",
       "      <td>0.334204</td>\n",
       "      <td>159670298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model                scenario                  variable  \\\n",
       "node                                                                       \n",
       "NA-CAN-QC  IMAGE 3.0.1  ADVANCE_2020_1.5C-2100  Final Energy|Electricity   \n",
       "\n",
       "             unit country  share_%_country_demand  node_2050_incl_losses  \n",
       "node                                                                      \n",
       "NA-CAN-QC  MWh/yr     CAN                0.334204              159670298  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_demand_scenario_clip = {}\n",
    "\n",
    "for a in entries:\n",
    "\n",
    "    node_demand_scenario_clip[a] = pd.DataFrame()\n",
    "\n",
    "    for b in node_demand_scenario[a].index.unique():\n",
    "        \n",
    "        node_demand_scenario_temp = node_demand_scenario[a].loc[node_demand_scenario[a].index == b]\n",
    "\n",
    "        node_demand_2015_temp = node_demand_2015[node_demand_2015['node'].str.contains(b)]\n",
    "        \n",
    "        if node_demand_scenario_temp[f'node_{snapshot_year}_incl_losses'].iloc[0] <= 0:\n",
    "            \n",
    "            print(f'{b} demand below 0. Value clipped to 2015 value.')\n",
    "        \n",
    "            node_demand_scenario_temp.loc[b, f'node_{snapshot_year}_incl_losses'] = node_demand_scenario_temp[\n",
    "                f'node_{snapshot_year}_incl_losses'].iloc[0].clip(node_demand_2015_temp.iloc[0]['node_demand_2015'])\n",
    "\n",
    "        node_demand_scenario_clip[a] = pd.concat([node_demand_scenario_clip[a], pd.DataFrame(node_demand_scenario_temp)])\n",
    "\n",
    "    node_demand_scenario_clip[a] = node_demand_scenario_clip[a][node_demand_scenario[a].columns\n",
    "                                                       ].drop(columns = {f'country_{snapshot_year}'})\n",
    "    \n",
    "    node_demand_scenario_clip[a] = node_demand_scenario_clip[a].astype({f'node_{snapshot_year}_incl_losses' : np.int64})\n",
    "\n",
    "node_demand_scenario_clip[entries[-1]].loc[node_demand_scenario_clip[entries[-1]].index == 'NA-CAN-QC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### X Rescales clipped demand values to regional scenario values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>country</th>\n",
       "      <th>share_%_country_demand</th>\n",
       "      <th>node_2050_incl_losses</th>\n",
       "      <th>original</th>\n",
       "      <th>IAM_region</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NA-CAN-QC</th>\n",
       "      <td>IMAGE 3.0.1</td>\n",
       "      <td>ADVANCE_2020_1.5C-2100</td>\n",
       "      <td>Final Energy|Electricity</td>\n",
       "      <td>MWh/yr</td>\n",
       "      <td>CAN</td>\n",
       "      <td>0.334204</td>\n",
       "      <td>159670298</td>\n",
       "      <td>1.596703e+08</td>\n",
       "      <td>R5OECD90+EU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model                scenario                  variable  \\\n",
       "node                                                                       \n",
       "NA-CAN-QC  IMAGE 3.0.1  ADVANCE_2020_1.5C-2100  Final Energy|Electricity   \n",
       "\n",
       "             unit country  share_%_country_demand  node_2050_incl_losses  \\\n",
       "node                                                                       \n",
       "NA-CAN-QC  MWh/yr     CAN                0.334204              159670298   \n",
       "\n",
       "               original   IAM_region  \n",
       "node                                  \n",
       "NA-CAN-QC  1.596703e+08  R5OECD90+EU  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_demand_scenario_final = {}\n",
    "\n",
    "for a in entries:\n",
    "    \n",
    "    col = f'node_{snapshot_year}_incl_losses'\n",
    "    \n",
    "    node_demand_scenario_final[a] = node_demand_scenario_clip[a].copy()\n",
    "    \n",
    "    #Sets the demand DF with clipped values and adds original values (unclipped) and its associated IAM regions.\n",
    "    node_demand_scenario_final[a]['original'] = node_demand_scenario[a].copy()[col]\n",
    "    node_demand_scenario_final[a]['IAM_region'] = node_demand_scenario_final[a].index.map(dict_nodes)\n",
    "    \n",
    "    for country in node_demand_scenario_final[a]['country'].unique():\n",
    "        tempdf = node_demand_scenario_final[a].loc[node_demand_scenario_final[a]['country'] == country]\n",
    "        \n",
    "        #For countries where the original values were <= 0 a scaler is calculated to decrease the demand for all other \n",
    "        #countries within the relevant IAM region to compensate for the clipped values to 2015 levels. I.e. this leads to \n",
    "        #the sum of the demands for all countries within a IAM region equal the regional scenario value after clipping.\n",
    "        if (tempdf['original'] <= 0).any():\n",
    "            iamregion = tempdf['IAM_region'].iloc[0]\n",
    "            tempdf2 = node_demand_scenario_final[a].loc[(node_demand_scenario_final[a]['original'] <= 0) & \n",
    "                                                       (node_demand_scenario_final[a]['IAM_region'] == \n",
    "                                                       iamregion)]\n",
    "            \n",
    "            tempdf3 = node_demand_scenario_final[a].loc[(node_demand_scenario_final[a]['original'] >= 0) & \n",
    "                                                        (node_demand_scenario_final[a]['IAM_region'] == \n",
    "                                                         iamregion)]\n",
    "\n",
    "            scaler = tempdf3['original'].sum() / (tempdf3['original'].sum() - \n",
    "                                                  ((tempdf2['original'].sum() * -1) +  tempdf2[col].sum()))\n",
    "   \n",
    "            node_demand_scenario_final[a].loc[\n",
    "                (node_demand_scenario_final[a]['original'] >= 0) & \n",
    "                (node_demand_scenario_final[a]['IAM_region'] == \n",
    "                 iamregion), col] = node_demand_scenario_final[a].loc[\n",
    "                                                       (node_demand_scenario_final[a]['original'] >= 0) & \n",
    "                                                       (node_demand_scenario_final[a]['IAM_region'] == \n",
    "                                                        iamregion), col].div(scaler)\n",
    "    \n",
    "            print(f'{country} demand below 0 and set to 2015 levels. Demand decreased for all other countries in {iamregion}')\n",
    "    \n",
    "  #  node_demand_scenario_final[a] = node_demand_scenario_final[a].drop(columns = {'original', 'IAM_region'})\n",
    "    \n",
    "node_demand_scenario_final[entries[-1]].loc[node_demand_scenario_final[entries[-1]].index == 'NA-CAN-QC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XI) Determines peak demand per node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "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>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>region</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>demand_total_2050</th>\n",
       "      <th>2050_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>IMAGE 3.0.1</td>\n",
       "      <td>ADVANCE_2020_1.5C-2100</td>\n",
       "      <td>NA-CAN-QC</td>\n",
       "      <td>Final Energy|Electricity</td>\n",
       "      <td>MWh/yr</td>\n",
       "      <td>159670298</td>\n",
       "      <td>33332.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          model                scenario     region                  variable  \\\n",
       "37  IMAGE 3.0.1  ADVANCE_2020_1.5C-2100  NA-CAN-QC  Final Energy|Electricity   \n",
       "\n",
       "      unit  demand_total_2050  2050_max  \n",
       "37  MWh/yr          159670298  33332.33  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_peak_demand_scenario = {}\n",
    "\n",
    "#Calculates 2015 max, min and sum per node.\n",
    "node_peak_demand_2015 = pd.DataFrame(list(zip(import_hourly_demand_2015.max() , import_hourly_demand_2015.min() , \n",
    "                                              import_hourly_demand_2015.sum() , import_hourly_demand_2015)) , \n",
    "                                     columns = ['2015_max' , '2015_min' , '2015_sum' , 'node']\n",
    "                                    ).iloc[1:].set_index('node')\n",
    "\n",
    "#Calculates 2015 peak demand / sum demand ratio\n",
    "node_peak_demand_2015['2015_ratio_max/sum'] = node_peak_demand_2015['2015_max'] / node_peak_demand_2015['2015_sum']\n",
    "node_peak_demand_2015['2015_ratio_min/sum'] = node_peak_demand_2015['2015_min'] / node_peak_demand_2015['2015_sum']\n",
    "\n",
    "#Countries that weren't included in the 2015 dataset get assigned an dataset average peak demand ratio.\n",
    "node_peak_demand_2015['2015_ratio_max/sum'] = node_peak_demand_2015['2015_ratio_max/sum'].fillna(\n",
    "    node_peak_demand_2015['2015_ratio_max/sum'].mean() , inplace = False)\n",
    "\n",
    "node_peak_demand_2015['2015_ratio_min/sum'] = node_peak_demand_2015['2015_ratio_min/sum'].fillna(\n",
    "    node_peak_demand_2015['2015_ratio_min/sum'].mean() , inplace = False)\n",
    "\n",
    "#Adds IAM region\n",
    "node_peak_demand_2015['IAM_region'] = node_peak_demand_2015.index.map(dict_nodes)\n",
    "\n",
    "#Calculates regional 2015 values\n",
    "node_peak_demand_2015['2015_sum_regional'] = node_peak_demand_2015['2015_sum'].groupby(node_peak_demand_2015['IAM_region']\n",
    "                                                                                      ).transform('sum')\n",
    "\n",
    "node_peak_demand_2015['2015_max_regional'] = node_peak_demand_2015['2015_max'].groupby(node_peak_demand_2015['IAM_region']\n",
    "                                                                                      ).transform('sum')\n",
    "\n",
    "node_peak_demand_2015['2015_ratio_max/sum_regional'] = node_peak_demand_2015['2015_max_regional'\n",
    "                                                                            ] / node_peak_demand_2015['2015_sum_regional']\n",
    "\n",
    "for a in entries:\n",
    "    \n",
    "    #Combines 2015 and snapshot_year demand datafiles.\n",
    "    node_peak_demand_scenario[a] = pd.merge(node_peak_demand_2015 , node_demand_scenario_final[a] , \n",
    "                                         left_index = True , right_index = True , how = 'right')\n",
    "\n",
    "    node_peak_demand_scenario[a][f'{snapshot_year}_multiplier'] = peak_load_max\n",
    "\n",
    "    #Calculates nodal peak demand. \n",
    "    node_peak_demand_scenario[a][f'{snapshot_year}_max'] = (node_peak_demand_scenario[a][\n",
    "        f'node_{snapshot_year}_incl_losses'] * node_peak_demand_scenario[a][\n",
    "        f'{snapshot_year}_multiplier'].astype(float) * node_peak_demand_scenario[a]['2015_ratio_max/sum']).round(2)\n",
    "    \n",
    "    #Cleans up dataframe.\n",
    "    node_peak_demand_scenario[a] = node_peak_demand_scenario[a].reset_index().rename(columns = {'node' : 'region' ,  \n",
    "                                                                                      f'node_{snapshot_year}_incl_losses' : \n",
    "                                                                                      f'demand_total_{snapshot_year}'})\n",
    "    \n",
    "    #Adds minimum load value in case hourly demand profiles need to be generated (only possible with linear scaling).\n",
    "    if hourly_demand_generator == 'Yes' and peak_load_max == 1:\n",
    "        node_peak_demand_scenario[a][f'{snapshot_year}_min'] = (node_peak_demand_scenario[a][\n",
    "            f'demand_total_{snapshot_year}'].astype(float) * node_peak_demand_scenario[a]['2015_ratio_min/sum'\n",
    "                                                                                         ].astype(float)).round(2)\n",
    "        \n",
    "    #Extracts relevant columns.\n",
    "        node_peak_demand_scenario[a] = node_peak_demand_scenario[a][['model' , 'scenario' , 'region' , 'variable' , \n",
    "                                                                 'unit' , f'demand_total_{snapshot_year}', \n",
    "                                                                 f'{snapshot_year}_max' , f'{snapshot_year}_min']\n",
    "                                                               ].sort_values(by = ['region'])\n",
    "        \n",
    "    else:\n",
    "        node_peak_demand_scenario[a] = node_peak_demand_scenario[a][['model' , 'scenario' , 'region' , 'variable' , \n",
    "                                                                 'unit' , f'demand_total_{snapshot_year}', \n",
    "                                                                 f'{snapshot_year}_max']].sort_values(by = ['region'])\n",
    "    \n",
    "node_peak_demand_scenario[entries[-1]].loc[node_peak_demand_scenario[entries[-1]]['region'] == 'NA-CAN-QC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XII) Creates scaled hourly demand profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hourly profiles not created because hourly_demand_generator != Yes and/or No peak_load_max != 1.\n"
     ]
    }
   ],
   "source": [
    "if hourly_demand_generator == 'Yes' and peak_load_max == 1:\n",
    "    \n",
    "    hourly_demand_scaled = {}\n",
    "  \n",
    "    for a in entries:\n",
    "\n",
    "        hourly_demand_scaled[a] = import_hourly_demand_2015.copy()\n",
    "\n",
    "        for b in node_peak_demand_scenario[entries[-1]].region.unique():\n",
    "     \n",
    "            peak_max = node_peak_demand_scenario[a].loc[node_peak_demand_scenario[a].region == \n",
    "                                                    b].iloc[0][f'{snapshot_year}_max']\n",
    "\n",
    "            peak_min = node_peak_demand_scenario[a].loc[node_peak_demand_scenario[a].region == \n",
    "                                                    b].iloc[0][f'{snapshot_year}_min']\n",
    "\n",
    "            scaler = MinMaxScaler(feature_range = (peak_min , peak_max))\n",
    "\n",
    "            hourly_demand_scaled[a][[b]] = scaler.fit_transform(hourly_demand_scaled[a][[b]]).round(0)\n",
    "            \n",
    "        hourly_demand_scaled[a] = hourly_demand_scaled[a].set_index('Datetime')\n",
    "        hourly_demand_scaled[a].index = hourly_demand_scaled[a].index.str.replace('/2015', f'/{snapshot_year}')\n",
    "    \n",
    "else:\n",
    "\n",
    "    print('Hourly profiles not created because hourly_demand_generator != Yes and/or No peak_load_max != 1.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculates region- and fuel type specific expansion and retirement constraints\n",
    "In the following section regional and fuel type specific expansion and retirement constraints are created by comparing the baseline generator capacities (based on the 2015 PLEXOS-World model) and the scenario specific generator capacities for the given scenario year following reported values in the IAM database.\n",
    "\n",
    "Based on the set of constraints per region, the Long Term capacity expansion Module in PLEXOS-World (or different tool) optimizes the allocation and retirement of generator capacities for all nodes by making use of its local characteristics (existing capacities, potential for renewables etc.). At all times, the cumulatively installed and retired generator capacity within the different nodes per region equals the reported regional capacities for the specific IAM scenario. This approach allows for improving the spatial resolution of the regional representation as well as co-optimize the expansion of supporting assets such as transmission infrastructure and electricity storage. Note that within the current framework no costs are attached to the allocation of generator capacities since this is already considered in the IAM. Refer to the journal paper as referenced at the beginning of the script for further details.\n",
    "\n",
    "<B>The following steps are taken;</B>\n",
    "\n",
    "<B>I)</B> 2015 baseline capacities are extracted from the 2015 PLEXOS-World model (over 30,000 seperate power plants) and grouped per fuel type and node.\n",
    "\n",
    "<B>II)</B> The fuel types as used in the 2015 PLEXOS-World model are linked to Capacity|Electricity variables as used in the IAM database and the 2015 baseline capacities are grouped per variable type and node. The mapping between PLEXOS-World 2015 fuel types and IAM variables has to be pre-specified in the 'memberships.xlsx PLEXOS_World_2015_fuel_mapping' sheet.\n",
    "\n",
    "<B>III)</B> 2015 baseline capacities are grouped per variable type and region to create regional baseline capacities.\n",
    "\n",
    "<B>IV)</B> Dataframe is created with the Capacity|Electricity variables for the given IAM scenario and the specific scenario year.\n",
    "\n",
    "<B>V)</B> Not all scenario's in the IAM databases have differentiated Capacity|Electricity for thermal plants (w/ & w/o CCS) Solar (PV, CSP) and wind (Onshore, Offshore). Cell checks whether variables are differentiated in the specific IAM scenario and adjusts the entries accordingly assuming that if the category is not differentiated the full capacity can be allocated to the most common technology (w/o CCS, Solar|PV and Wind|Onshore). Furthermore, where deemed appropriate, IAM variable categories are grouped into correct power system model technology entries. Both aspects have to be pre-specified in the 'memberships.xlsx technology_mapping' sheet. Refer to the 'Custom Input' section at the beginning of this script for more details.\n",
    "\n",
    "<B>VI)</B> The regional 2015 baseline capacities are compared to the scenario capacities for the specific year. Sets of expansion and retirement constraints are created specific per region and Capacity|Electricity variable. If the regional 2015 baseline capacity for a specific Capacity|Electricity variable is lower than the reported value for the given scenario year it means that expansion is required and vice versa retirement. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I) Calculates generator level total capacity in MW for all generators in the 2015 PLEXOS-World model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child_class</th>\n",
       "      <th>child_object</th>\n",
       "      <th>fuel</th>\n",
       "      <th>units</th>\n",
       "      <th>max_capacity</th>\n",
       "      <th>2015_total_capacity</th>\n",
       "      <th>node</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30657</th>\n",
       "      <td>Generator</td>\n",
       "      <td>ZWE_Hyd_KaribaDamSout29911</td>\n",
       "      <td>Hyd</td>\n",
       "      <td>2.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>AF-ZWE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      child_class                child_object fuel  units  max_capacity  \\\n",
       "30657   Generator  ZWE_Hyd_KaribaDamSout29911  Hyd    2.0         375.0   \n",
       "\n",
       "       2015_total_capacity    node  \n",
       "30657                750.0  AF-ZWE  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pulls out the number of generator units per powerplant in the 2015 PLEXOS-World model. Note that it also includes known \n",
    "#powerplants that were build after 2015 (yet are not included in model simulations for the 2015 calendar year in PLEXOS-World.)\n",
    "PLEXOS_2015_generator_units = import_PLEXOS_2015_properties.loc[(import_PLEXOS_2015_properties['child_class'] == 'Generator') &\n",
    "                                                                (import_PLEXOS_2015_properties['property'] == 'Units') &\n",
    "                                                                (import_PLEXOS_2015_properties['value'] != 0)]\n",
    "\n",
    "#Pulls out the max capacity per generator unit per powerplant in the 2015 PLEXOS-World model.\n",
    "PLEXOS_2015_generator_max_capacity = import_PLEXOS_2015_properties.loc[(import_PLEXOS_2015_properties['child_class'] == \n",
    "                                                                        'Generator') & \n",
    "                                                                       (import_PLEXOS_2015_properties['property'] == \n",
    "                                                                        'Max Capacity')]\n",
    "#Merges the units and max_capacity dataframes\n",
    "PLEXOS_2015_generator_capacity = pd.merge(PLEXOS_2015_generator_units , \n",
    "                                           PLEXOS_2015_generator_max_capacity [['property' , 'value' , 'child_object']] , \n",
    "                                           on = 'child_object' , how ='left')\n",
    "\n",
    "#Adds fuel type as seperate column\n",
    "PLEXOS_2015_generator_capacity.insert(2 , 'fuel' , PLEXOS_2015_generator_capacity['child_object'].str.split('_').str[1])\n",
    "\n",
    "#Renames the default column headers and drops non-required columns\n",
    "PLEXOS_2015_generator_capacity = PLEXOS_2015_generator_capacity.rename(columns = {'value_x' : 'units' , \n",
    "                                                                                  'value_y' : 'max_capacity'\n",
    "                                                                                 }).drop(columns = ['property_x' , \n",
    "                                                                                                    'property_y'])\n",
    "\n",
    "#Multiplies the number of units with the capacity per unit to return total capacity per generator\n",
    "PLEXOS_2015_generator_capacity['2015_total_capacity'] = PLEXOS_2015_generator_capacity ['units'] *\\\n",
    "                                                    PLEXOS_2015_generator_capacity ['max_capacity']\n",
    "\n",
    "#Pulls out the memberships of the generators with its related node. \n",
    "PLEXOS_2015_memberships_generator_node = import_PLEXOS_2015_memberships.loc[\n",
    "    (import_PLEXOS_2015_memberships['parent_class'] == 'Generator') & \n",
    "    (import_PLEXOS_2015_memberships['child_class'] == 'Node'\n",
    "    )].rename(columns = {'child_object' : 'node' , 'parent_object' : 'child_object'})\n",
    "\n",
    "#Merges the memberships with the generator capacity dataframe\n",
    "PLEXOS_2015_generator_capacity = pd.merge(PLEXOS_2015_generator_capacity , \n",
    "                                          PLEXOS_2015_memberships_generator_node[['child_object' , 'node']] , \n",
    "                                          on  = 'child_object' , how ='left')\n",
    "\n",
    "PLEXOS_2015_generator_capacity.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II) Links fuel entries in the 2015 PLEXOS-World model with generator technology groups from IAM databases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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>child_class</th>\n",
       "      <th>child_object</th>\n",
       "      <th>fuel</th>\n",
       "      <th>units</th>\n",
       "      <th>max_capacity</th>\n",
       "      <th>2015_total_capacity</th>\n",
       "      <th>node</th>\n",
       "      <th>variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30654</th>\n",
       "      <td>Generator</td>\n",
       "      <td>ZWE_Coa_Capacity Scaler</td>\n",
       "      <td>Coa</td>\n",
       "      <td>2.0</td>\n",
       "      <td>157.5</td>\n",
       "      <td>315.0</td>\n",
       "      <td>AF-ZWE</td>\n",
       "      <td>Capacity|Electricity|Coal|w/o CCS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30655</th>\n",
       "      <td>Generator</td>\n",
       "      <td>ZWE_Coa_HwangeCoalPow29910</td>\n",
       "      <td>Coa</td>\n",
       "      <td>4.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>AF-ZWE</td>\n",
       "      <td>Capacity|Electricity|Coal|w/o CCS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30656</th>\n",
       "      <td>Generator</td>\n",
       "      <td>ZWE_Hyd_Capacity Scaler</td>\n",
       "      <td>Hyd</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>AF-ZWE</td>\n",
       "      <td>Capacity|Electricity|Hydro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30657</th>\n",
       "      <td>Generator</td>\n",
       "      <td>ZWE_Hyd_KaribaDamSout29911</td>\n",
       "      <td>Hyd</td>\n",
       "      <td>2.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>AF-ZWE</td>\n",
       "      <td>Capacity|Electricity|Hydro</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      child_class                child_object fuel  units  max_capacity  \\\n",
       "30654   Generator     ZWE_Coa_Capacity Scaler  Coa    2.0         157.5   \n",
       "30655   Generator  ZWE_Coa_HwangeCoalPow29910  Coa    4.0         230.0   \n",
       "30656   Generator     ZWE_Hyd_Capacity Scaler  Hyd    1.0          40.0   \n",
       "30657   Generator  ZWE_Hyd_KaribaDamSout29911  Hyd    2.0         375.0   \n",
       "\n",
       "       2015_total_capacity    node                           variable  \n",
       "30654                315.0  AF-ZWE  Capacity|Electricity|Coal|w/o CCS  \n",
       "30655                920.0  AF-ZWE  Capacity|Electricity|Coal|w/o CCS  \n",
       "30656                 40.0  AF-ZWE         Capacity|Electricity|Hydro  \n",
       "30657                750.0  AF-ZWE         Capacity|Electricity|Hydro  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Distinguishes 'Gas' category into CCGT (>=130 MW power plants) and OCGT (<130 MW power plants) in line with PLEXOS-World.\n",
    "PLEXOS_2015_generator_capacity.loc[(PLEXOS_2015_generator_capacity['2015_total_capacity'] >= 130) &\n",
    "                                       (PLEXOS_2015_generator_capacity['fuel'] == 'Gas') , 'fuel'] = 'Ccg'\n",
    "\n",
    "PLEXOS_2015_generator_capacity.loc[(PLEXOS_2015_generator_capacity['2015_total_capacity'] < 130) &\n",
    "                                       (PLEXOS_2015_generator_capacity['fuel'] == 'Gas') , 'fuel'] = 'Ocg'\n",
    "\n",
    "#Links the different fuel entries in the 2015 PLEXOS-World model with generator groups as required for the power system model\n",
    "#input. #The specified mapping can be changed in the 'memberships' excel file in the 'PLEXOS_World_2015_fuel_mapping' sheet. \n",
    "fuels_conversion = import_memberships_fuels_PW2015.copy().rename(columns = {'PW2015_fuel' : 'fuel'})\n",
    "fuels_conversion['variable'] = 'Capacity|Electricity|' + import_memberships_fuels_PW2015['PSM']\n",
    "\n",
    "#Adds the IAM database 'variable' to the capacity dataframe.\n",
    "PLEXOS_2015_generator_capacity = pd.merge(PLEXOS_2015_generator_capacity , \n",
    "                                          fuels_conversion[['fuel' , 'variable']] , on = 'fuel' , how ='left')\n",
    "\n",
    "PLEXOS_2015_generator_capacity.tail(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### III) Aggregates 2015 generator capacity per variable group and IAM region and adds IAM scenario regional representation to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "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>variable</th>\n",
       "      <th>region</th>\n",
       "      <th>2015_total_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Capacity|Electricity|Biomass|w/o CCS</td>\n",
       "      <td>R5ASIA</td>\n",
       "      <td>24.906376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               variable  region  2015_total_capacity\n",
       "0  Capacity|Electricity|Biomass|w/o CCS  R5ASIA            24.906376"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Groups capacity based on the variable entries.\n",
    "PLEXOS_2015_generator_capacity_grouped_fuel = PLEXOS_2015_generator_capacity.groupby(['variable','node'\n",
    "                                                                                     ]).agg({'2015_total_capacity' : 'sum'\n",
    "                                                                                            }).reset_index()\n",
    "#Adds IAM scenario regional representation to PLEXOS_nodes\n",
    "PLEXOS_2015_generator_capacity_grouped_fuel = pd.merge(PLEXOS_2015_generator_capacity_grouped_fuel , \n",
    "                                                             import_spatial_mapping [['node' , 'region']] , \n",
    "                                                             on = 'node' , how = 'left')\n",
    "\n",
    "#Drops data from nodes that are in the 2015 PLEXOS-World model yet not included for the IAM scenario and rounds to 0 decimals.\n",
    "PLEXOS_2015_generator_capacity_grouped_fuel.dropna(axis = 0 , how = 'any' , inplace = True)\n",
    "\n",
    "#Groups capacity per scenario region and resets the index to return variables as column and not as index.\n",
    "PLEXOS_2015_generator_capacity_grouped_region = PLEXOS_2015_generator_capacity_grouped_fuel.groupby([\n",
    "    'variable','region']).agg({'2015_total_capacity' : 'sum'}).reset_index()\n",
    "\n",
    "#converts to GW.\n",
    "PLEXOS_2015_generator_capacity_grouped_region['2015_total_capacity'] = (PLEXOS_2015_generator_capacity_grouped_region[\n",
    "    '2015_total_capacity'] / 1000)\n",
    "\n",
    "PLEXOS_2015_generator_capacity_grouped_region.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "### IV) Creates dataframe with IAM scenario and year specific Capacity|Electricity variables for the simulation year with power system model naming conventions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>region</th>\n",
       "      <th>year</th>\n",
       "      <th>2050_total_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IMAGE 3.0.1</td>\n",
       "      <td>ADVANCE_2020_1.5C-2100</td>\n",
       "      <td>Capacity|Electricity|Biomass|w/o CCS</td>\n",
       "      <td>GW</td>\n",
       "      <td>R5ASIA</td>\n",
       "      <td>2050</td>\n",
       "      <td>632.314181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         model                scenario                              variable  \\\n",
       "0  IMAGE 3.0.1  ADVANCE_2020_1.5C-2100  Capacity|Electricity|Biomass|w/o CCS   \n",
       "\n",
       "  unit  region  year  2050_total_capacity  \n",
       "0   GW  R5ASIA  2050           632.314181  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_generator_capacity = {}\n",
    "\n",
    "for a in entries:\n",
    "\n",
    "    #Excludes 'World' from the scenario specific regional capacity dataframe\n",
    "    year_generator_capacity[a] = import_year_scenario_generator_capacity[\n",
    "        a][import_year_scenario_generator_capacity[a].region.isin(import_spatial_mapping.region)].rename(columns = {'value' : \n",
    "                                                                                           f'{snapshot_year}_total_capacity'})\n",
    "    \n",
    "    year_generator_capacity[a] = year_generator_capacity[a][year_generator_capacity[a].variable\n",
    "                                                            .isin(dict_technologies_capacity_variable.keys())]  \n",
    "    \n",
    "    #Replaces the IAM entries with to be used power system model naming conventions. \n",
    "    for b, c in dict_technologies_capacity_variable.items():\n",
    "        \n",
    "        year_generator_capacity[a].replace([b] ,  [c] , inplace=True)\n",
    "            \n",
    "    year_generator_capacity[a] = year_generator_capacity[a].groupby(['model' , 'scenario' , 'variable' , \n",
    "                                                                     'unit' , 'region' , 'year']).sum().reset_index()\n",
    "    \n",
    "    year_generator_capacity[a] = year_generator_capacity[a].loc[~year_generator_capacity[a]['variable'].str.contains('Exclude')]\n",
    "\n",
    "year_generator_capacity[entries[-1]].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VI) Merges the baseline and scenario specific capacity dataframes and creates expansion and retirement constraint values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "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>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>region</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>year</th>\n",
       "      <th>2015_total_capacity</th>\n",
       "      <th>2050_total_capacity</th>\n",
       "      <th>expansion</th>\n",
       "      <th>retirement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IMAGE 3.0.1</td>\n",
       "      <td>ADVANCE_2020_1.5C-2100</td>\n",
       "      <td>R5ASIA</td>\n",
       "      <td>Capacity|Electricity|Biomass|w/o CCS</td>\n",
       "      <td>GW</td>\n",
       "      <td>2050</td>\n",
       "      <td>24.906376</td>\n",
       "      <td>632.314181</td>\n",
       "      <td>607.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         model                scenario  region  \\\n",
       "0  IMAGE 3.0.1  ADVANCE_2020_1.5C-2100  R5ASIA   \n",
       "\n",
       "                               variable unit  year  2015_total_capacity  \\\n",
       "0  Capacity|Electricity|Biomass|w/o CCS   GW  2050            24.906376   \n",
       "\n",
       "   2050_total_capacity  expansion  retirement  \n",
       "0           632.314181      607.4         0.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expansion_constraints = {}\n",
    "\n",
    "for a in entries:\n",
    "\n",
    "    #Merges dataframes together\n",
    "    expansion_constraints[a] = pd.merge(PLEXOS_2015_generator_capacity_grouped_region , year_generator_capacity[a] , \n",
    "                                      on = ['variable' , 'region'] , how ='outer'\n",
    "                                        ).fillna(0).sort_values(by = ['variable' , 'region']).reset_index(drop = True)\n",
    "    \n",
    "    expansion_constraints[a]['model'] , expansion_constraints[a]['scenario'] = a.split(\"|\",1)[0] , a.split(\"|\",1)[1]\n",
    "    expansion_constraints[a]['unit'] , expansion_constraints[a]['year'] = 'GW' , snapshot_year\n",
    "    \n",
    "    #Calculates the difference between 2015 and snapshot_year capacity, negative values represent a reduction in capacity.\n",
    "    expansion_constraints[a]['dif_capacity'] = (expansion_constraints[a][\n",
    "        f'{snapshot_year}_total_capacity'] - expansion_constraints[a]['2015_total_capacity']).round(1)\n",
    "\n",
    "    #Specifies values for capacity expansion and retirement per region.\n",
    "    expansion_constraints[a]['expansion'] = np.where(expansion_constraints[a].dif_capacity > 0 , \n",
    "                                                   expansion_constraints[a].dif_capacity , 0)\n",
    "\n",
    "    expansion_constraints[a]['retirement'] = np.where(expansion_constraints[a].dif_capacity < 0 , \n",
    "                                                    - expansion_constraints[a].dif_capacity , 0)\n",
    "    \n",
    "    #Filters columns.\n",
    "    expansion_constraints[a] = expansion_constraints[a][['model' , 'scenario' , 'region' , 'variable' , 'unit' , 'year' , \n",
    "                                                        '2015_total_capacity' , f'{snapshot_year}_total_capacity' , \n",
    "                                                        'expansion' , 'retirement']]\n",
    "        \n",
    "expansion_constraints[entries[-1]].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculates region- and fuel type specific power plant efficiency and heat rates\n",
    "Calculates technology thermal efficiencies and heat rates for scenario ensembles where Efficiency|Electricity variables are specified. Similar to calculation of capacities, technology entries within the IAM can be aggregated for the power system model if deemed appropriate (as pre-specified in the 'memberships.xlsx' technology_mapping sheet PSM_efficiency column). For example, if Gas|w/o CCS|2 and Gas|w/o CCS|3 are to be aggregated in a single category, this cell determines a capacity weighted average for efficiencies and heat rates based on the relative capacity size per seperate IAM technology for the specific scenario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Efficiency|Electricity not specified in MESSAGE-GLOBIOM 1.0|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Efficiency|Electricity not specified in AIM/CGE 2.0|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Efficiency|Electricity not specified in IMAGE 3.0.1|ADVANCE_2020_1.5C-2100 scenario ensemble!\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>region</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>year</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>heatrate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [model, scenario, region, variable, unit, year, efficiency, heatrate]\n",
       "Index: []"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_generator_efficiency = {}\n",
    "\n",
    "for a in entries:\n",
    "\n",
    "    #Excludes 'World' from the scenario specific regional efficiency dataframe.\n",
    "    year_generator_efficiency[a] = import_year_scenario_generator_efficiency[\n",
    "        a][import_year_scenario_generator_efficiency[a].region.isin(import_spatial_mapping.region)].rename(columns = {'value' : \n",
    "                                                                                           f'{snapshot_year}_efficiency'})\n",
    "    \n",
    "    #Temporarily changes variable name to be able to merge capacity and efficiency dataframes.\n",
    "    year_generator_efficiency[a]['variable'] = year_generator_efficiency[a]['variable'].str.replace('Efficiency' , 'Capacity')\n",
    "    \n",
    "    year_generator_efficiency[a] = pd.merge(year_generator_efficiency[a] , import_year_scenario_generator_capacity[a]\n",
    "                                            [['value' , 'variable' , 'region']] , left_on = ['variable' , 'region'] , \n",
    "                                            right_on = ['variable' , 'region'] , how = 'inner')\n",
    "    \n",
    "    year_generator_efficiency[a]['variable'] = year_generator_efficiency[a]['variable'].str.replace('Capacity' , 'Efficiency')\n",
    "    \n",
    "    #Replaces the IAM entries with to be used power system model naming conventions. \n",
    "    for b, c in dict_technologies_efficiency_variable.items():\n",
    "        \n",
    "        year_generator_efficiency[a].replace([b] ,  [c] , inplace=True)\n",
    "    \n",
    "    #Filters out to be excluded rows and rows where efficiency == 0 (e.g. renewables). \n",
    "    year_generator_efficiency[a] = year_generator_efficiency[a].loc[(~year_generator_efficiency[a]\n",
    "                                                                    ['variable'].str.contains('Exclude')) & \n",
    "                                                                   (year_generator_efficiency[a]\n",
    "                                                                    [f'{snapshot_year}_efficiency'] != 0)]\n",
    "    \n",
    "    #Sets groupby function per power system model input category and region.    \n",
    "    x = year_generator_efficiency[a].groupby(['variable' , 'region'])\n",
    "    \n",
    "    #Calculates capacity weighted average efficiency per power system model input category and region.    \n",
    "    year_generator_efficiency[a]['y'] = year_generator_efficiency[a][f'{snapshot_year}_efficiency'\n",
    "                                                                    ] / x.value.transform('sum'\n",
    "                                                                                         ) * year_generator_efficiency[a].value\n",
    "    \n",
    "    year_generator_efficiency[a]['efficiency'] = x.y.transform('sum')\n",
    "    \n",
    "    #Sets standard variable efficiency for entries with 0 capacity.         \n",
    "    year_generator_efficiency[a]['efficiency'] = np.where(year_generator_efficiency[a].efficiency == 0 , \n",
    "                                                     x[f'{snapshot_year}_efficiency'].transform(np.mean) , \n",
    "                                                     year_generator_efficiency[a].efficiency).round(1)\n",
    "    \n",
    "    #Removes duplicate rows\n",
    "    year_generator_efficiency[a] = year_generator_efficiency[a][['model' , 'scenario' , 'region' , \n",
    "                                                                 'variable' , 'unit' , 'year' , 'efficiency']].drop_duplicates()\n",
    "    \n",
    "    #Adds heat rate column (GJ/MWh , 3.6 GJ = 1 MWh ). \n",
    "    year_generator_efficiency[a]['heatrate'] = (100 / year_generator_efficiency[a].efficiency * 3.6).round(1)\n",
    "    \n",
    "    year_generator_efficiency[a]['unit'] = '% or GJ/MWh'\n",
    "    \n",
    "    #Prints warning message in case Efficiency|Electricity variables are not specified in the specific scenario ensemble. \n",
    "    if year_generator_efficiency[a].empty:\n",
    "        print(f'Efficiency|Electricity not specified in {a} scenario ensemble!')\n",
    "\n",
    "year_generator_efficiency[entries[-1]].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracts storage and hydrogen capacities from scenario output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capacity|Electricity|Storage not specified in MESSAGE-GLOBIOM 1.0|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Capacity|Hydrogen|Electricity not specified in MESSAGE-GLOBIOM 1.0|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Capacity|Electricity|Hydrogen not specified in MESSAGE-GLOBIOM 1.0|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Capacity|Electricity|Storage not specified in AIM/CGE 2.0|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Capacity|Hydrogen|Electricity not specified in AIM/CGE 2.0|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Capacity|Electricity|Hydrogen not specified in AIM/CGE 2.0|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Capacity|Electricity|Storage not specified in IMAGE 3.0.1|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Capacity|Hydrogen|Electricity not specified in IMAGE 3.0.1|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Capacity|Electricity|Hydrogen not specified in IMAGE 3.0.1|ADVANCE_2020_1.5C-2100 scenario ensemble!\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>region</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [model, scenario, variable, unit, region, year, value]\n",
       "Index: []"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_storage_capacity = {}\n",
    "year_hydrogen_electricity_capacity = {}\n",
    "year_electricity_hydrogen_capacity = {}\n",
    "\n",
    "for a in entries:\n",
    "\n",
    "    year_storage_capacity[a] = import_year_scenario[a].loc[(import_year_scenario[a]['variable'] == \n",
    "                                                             'Capacity|Electricity|Storage') & \n",
    "                                                            (import_year_scenario[a]['region'] != 'World') & \n",
    "                                                            (import_year_scenario[a]['region'].isin(\n",
    "                                                                list(set(dict_nodes.values()))))].round(1)\n",
    "\n",
    "    year_hydrogen_electricity_capacity[a] = import_year_scenario[a].loc[(import_year_scenario[a]['variable'] == \n",
    "                                                              'Capacity|Hydrogen|Electricity') & \n",
    "                                                            (import_year_scenario[a]['region'] != 'World') & \n",
    "                                                            (import_year_scenario[a]['region'].isin(\n",
    "                                                                list(set(dict_nodes.values()))))].round(1)\n",
    "    \n",
    "    year_electricity_hydrogen_capacity[a] = import_year_scenario[a].loc[(import_year_scenario[a]['variable'] == \n",
    "                                                              'Capacity|Electricity|Hydrogen') & \n",
    "                                                            (import_year_scenario[a]['region'] != 'World') & \n",
    "                                                            (import_year_scenario[a]['region'].isin(\n",
    "                                                                list(set(dict_nodes.values()))))].round(1)\n",
    "    \n",
    "    #Prints warning message in case variables are not specified in the specific scenario ensemble. \n",
    "    if year_storage_capacity[a].empty:\n",
    "        print(f'Capacity|Electricity|Storage not specified in {a} scenario ensemble!')\n",
    "        \n",
    "    if year_hydrogen_electricity_capacity[a].empty:\n",
    "        print(f'Capacity|Hydrogen|Electricity not specified in {a} scenario ensemble!')\n",
    "\n",
    "    if year_electricity_hydrogen_capacity[a].empty:\n",
    "        print(f'Capacity|Electricity|Hydrogen not specified in {a} scenario ensemble!')\n",
    "        \n",
    "year_storage_capacity[entries[-1]].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sets scenario specific pricing data\n",
    "Extracts the year and scenario specific carbon and fuel prices if available for the specified scenario ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price|Secondary Energy|Hydrogen not specified in MESSAGE-GLOBIOM 1.0|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Price|Secondary Energy|Hydrogen not specified in AIM/CGE 2.0|ADVANCE_2020_1.5C-2100 scenario ensemble!\n",
      "Price|Secondary Energy|Hydrogen not specified in IMAGE 3.0.1|ADVANCE_2020_1.5C-2100 scenario ensemble!\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\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>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th>variable</th>\n",
       "      <th>unit</th>\n",
       "      <th>region</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23931</th>\n",
       "      <td>IMAGE 3.0.1</td>\n",
       "      <td>ADVANCE_2020_1.5C-2100</td>\n",
       "      <td>Price|Primary Energy|Coal</td>\n",
       "      <td>US$2010/GJ</td>\n",
       "      <td>World</td>\n",
       "      <td>2050</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             model                scenario                   variable  \\\n",
       "23931  IMAGE 3.0.1  ADVANCE_2020_1.5C-2100  Price|Primary Energy|Coal   \n",
       "\n",
       "             unit region  year  value  \n",
       "23931  US$2010/GJ  World  2050   2.21  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_carbon = {}\n",
    "price_fuels = {}\n",
    "price_hydrogen = {}\n",
    "\n",
    "list_regions_world = list(set(dict_nodes.values()))\n",
    "list_regions_world.append('World')\n",
    "\n",
    "for a in entries:\n",
    "\n",
    "    price_carbon[a] = import_year_scenario[a].loc[(import_year_scenario[a]['variable'] == 'Price|Carbon') & \n",
    "                                                  (import_year_scenario[a]['region'].isin(list_regions_world))].round(2)\n",
    "\n",
    "    price_fuels[a] = import_year_scenario[a].loc[import_year_scenario[a]['variable'].str.contains('Price\\|Primary Energy\\|') & \n",
    "                                                 (import_year_scenario[a]['region'].isin(list_regions_world))].round(2)\n",
    "    \n",
    "    price_hydrogen[a] = import_year_scenario[a].loc[(import_year_scenario[a]['variable'] == 'Price|Secondary Energy|Hydrogen')\n",
    "                                                    & (import_year_scenario[a]['region'].isin(list_regions_world))].round(2)\n",
    "    \n",
    "    #Prints warning message in case variables are not specified in the specific scenario ensemble.\n",
    "    if price_carbon[a].empty:\n",
    "        print(f'Carbon price not specified in {a} scenario ensemble!')\n",
    "        \n",
    "    if price_fuels[a].empty:\n",
    "        print(f'Price|Primary Energy for fossil fuels not specified in {a} scenario ensemble!')\n",
    "        \n",
    "    if price_hydrogen[a].empty:\n",
    "        print(f'Price|Secondary Energy|Hydrogen not specified in {a} scenario ensemble!')\n",
    "\n",
    "price_fuels[entries[-1]].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exports the downscaled and converted IAM scenario output data as input for the power system model soft-link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hourly_demand_scaled[MESSAGE-GLOBIOM 1.0|ADVANCE_2020_1.5C-2100] not defined\n",
      "hourly_demand_scaled[AIM/CGE 2.0|ADVANCE_2020_1.5C-2100] not defined\n",
      "hourly_demand_scaled[IMAGE 3.0.1|ADVANCE_2020_1.5C-2100] not defined\n"
     ]
    }
   ],
   "source": [
    "#Creates scenario subdirectory in output folder.\n",
    "for a in entries:\n",
    "        \n",
    "    output = f'{a.split(\"|\",1)[0]} {a.split(\"|\",1)[1]}'\n",
    "    \n",
    "    for ch in ['/' , '|']:\n",
    "        if ch in a:\n",
    "            output = output.replace(ch , '_')\n",
    "            \n",
    "    scenario_subdirectory = os.path.join(f'{script_output_directory}\\\\IAM_power system model soft_link output data', output)\n",
    "    \n",
    "    if not os.path.exists(scenario_subdirectory):\n",
    "        os.makedirs(scenario_subdirectory)\n",
    "    \n",
    "    #Saves script output dataframes as csv's.    \n",
    "    node_peak_demand_scenario[a].to_csv(f'{scenario_subdirectory}\\\\{snapshot_year} yearly demand {output}.csv' , index = False)\n",
    "    expansion_constraints[a].to_csv(f'{scenario_subdirectory}\\\\{snapshot_year} expansion constraints {output}.csv' , index = False)\n",
    "    year_generator_efficiency[a].to_csv(f'{scenario_subdirectory}\\\\{snapshot_year} generator efficiency {output}.csv' , index = False)\n",
    "    year_storage_capacity[a].to_csv(f'{scenario_subdirectory}\\\\{snapshot_year} storage capacity {output}.csv' , index = False)\n",
    "    year_hydrogen_electricity_capacity[a].to_csv(f'{scenario_subdirectory}\\\\{snapshot_year} hydrogen electrolysis capacity {output}.csv' , index = False)\n",
    "    year_electricity_hydrogen_capacity[a].to_csv(f'{scenario_subdirectory}\\\\{snapshot_year} hydrogen turbines capacity {output}.csv' , index = False)\n",
    "    price_carbon[a].to_csv(f'{scenario_subdirectory}\\\\{snapshot_year} carbon prices {output}.csv' , index = False)\n",
    "    price_fuels[a].to_csv(f'{scenario_subdirectory}\\\\{snapshot_year} fuel prices {output}.csv' , index = False)\n",
    "    price_hydrogen[a].to_csv(f'{scenario_subdirectory}\\\\{snapshot_year} hydrogen price {output}.csv' , index = False)\n",
    "    \n",
    "    try:\n",
    "        hourly_demand_scaled[a].to_csv(f'{scenario_subdirectory}\\\\{snapshot_year} hourly demand {output}.csv')\n",
    "    \n",
    "    except NameError:\n",
    "        print(f'hourly_demand_scaled[{a}] not defined')"
   ]
  }
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