Three case studies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building three case studies using the EU-SES modelling tool\n",
    "This jupyter notebook demonstrates the versatility of the EU-SES modelling tool by building three energy models with varying spatial contexts. The varied spatial characteristics are the spatial zones and the spatial scope. The spatial zones are determined by the spatial classification method applied. The characteristics of the three case studies are defined in the table below.\n",
    "\n",
    "| Model name |                                                  Countries                                                  | Classification method | Number of regions | Number of NUTS 2 areas |\n",
    "|:----------:|:-----------------------------------------------------------------------------------------------------------:|:---------------------:|:-----------------:|:----------------------:|\n",
    "| GER NUTS0  | Germany, Norway, Denmark, Poland, France, Netherlands, Belgium, Austria, Switzerland and the Czech Republic | National boundaries   | 10                |  134                      |\n",
    "| GER MAX-P  | Germany                                                                                                     | Max-p regions         | 9                 | 38                     |\n",
    "| GER NUTS1  | Germany                                                                                                     | State boundaries      | 16                | 38                     |\n",
    "\n",
    "\n",
    "The jupyter notebook is structured in four sections:\n",
    "1. The Areas dataset \n",
    "2. The case studies\n",
    "3. Solved models\n",
    "4. Results\n",
    "\n",
    "In steps 1 and 2, the models are built from scratch by using the Areas dataset and subsequently building and solving the models. The solved models of the three case studies can also be imported in step 3. Finally, in step 4, the results of the models are visualised. \n",
    "\n",
    "For all steps the neccesary packages for this notebook need to be imported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the neccesary pyton packages and the euses library\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import calliope\n",
    "import numpy as np\n",
    "import warnings\n",
    "import seaborn as sns\n",
    "\n",
    "import euses\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. The Areas dataset \n",
    "The first step is to obtain an Areas dataset. The areas dataset can either be built (Option 1) or imported from a pre-built dataset (Option 2). The reference year and the countries of interest are defined first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reference_year = 2011\n",
    "countries = ['Germany','Norway','Denmark','Poland','France','Netherlands',\n",
    "                                 'Belgium','Austria','Switzerland','Czech Rep.']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Areas dataset  (Option 1)\n",
    "This step takes approximately 25 minutes as data variables needed to build the Areas dataset are downloaded from different platforms and then processed into an xarray dataset. The Areas dataset is built using the build_dataset function for a selection of European countries that will be used in the three models. 2011 is the reference year given to the build_dataset function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# case study dataset\n",
    "cs_ds = euses.build_dataset(countries,reference_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the Areas dataset (Option 2)\n",
    "Alternatively an Areas dataset containing data for 30 European countries can be imported using the import_dataset function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eu wide dataset imported \n",
    "# countries of interest filtered in cs_ds\n",
    "eu_ds = euses.import_dataset('eu_2011.nc')\n",
    "cs_ds = eu_ds.filter_countries(countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. The case studies\n",
    "In all case studies, the optimised energy system models are constrained to a cumulative amount of CO2 emissions that is equal to 20% of the 1990 emission level. Considering this CO2 emission constraint, only existing renewable energy power plants and power plants that operate on natural gas are used in the models. The models are subsequently built and solved in sections 2.1, 2.2 and 2.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fuels_considered = ['Biomass and biogas','Natural gas','Solar','Wind']\n",
    "fuels_removed = np.setdiff1d(cs_ds.ds.coords['fuel'].values,fuels_considered)\n",
    "cs_ds.ds = cs_ds.ds.drop(fuels_removed,dim='fuel')\n",
    "tech_not_grouped = ['Solar','Wind','Wind Offshore']\n",
    "tech_list = np.setdiff1d(cs_ds.ds.coords['tech'].values,tech_not_grouped)\n",
    "# group natural gas and biomass/biogas based power_plants in single group\n",
    "sum_var = cs_ds.ds['power_plants'].loc[:,tech_list,:].sum(axis=1)\n",
    "tech_list = np.setdiff1d(tech_list,['Combined cycle'])\n",
    "cs_ds.ds['power_plants'].loc[:,['Combined cycle']] = [[g] for g in sum_var.values]\n",
    "cs_ds.ds = cs_ds.ds.drop(tech_list,dim='tech')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Build and optimise first case study model - GER NUTS0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_ds.create_regions('nuts0')\n",
    "cs_ds.create_calliope_model(op_mode='plan',sectors=['power','heat'],co2_cap_factor=0.2, national=True)\n",
    "ger_nuts0 = calliope.Model('calliope_model/model.yaml',scenario='time_3H',override_dict={'run.solver': 'cbc'})\n",
    "ger_nuts0_gpd = gpd.GeoDataFrame(cs_ds.ds_regions['regions'].values,\n",
    "    columns=['id'],geometry=cs_ds.ds_regions['geometry'].values)\n",
    "ger_nuts0.run()\n",
    "ger_nuts0.to_netcdf('calliope_model/results/cs_ger_nuts0.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Build and optimise second case study model - GER MAX-P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_ds = cs_ds.filter_countries(['Germany'])\n",
    "cs_ds.create_regions('max_p_regions',3.8)\n",
    "cs_ds.create_calliope_model(op_mode='plan',sectors=['power','heat'],co2_cap_factor=0.2, national=False)\n",
    "ger_max_p = calliope.Model('calliope_model/model.yaml',scenario='time_3H',override_dict={'run.solver': 'cbc'})\n",
    "ger_nuts0_gpd = gpd.GeoDataFrame(cs_ds.ds_regions['regions'].values,\n",
    "    columns=['id'],geometry=cs_ds.ds_regions['geometry'].values)\n",
    "ger_max_p.run()\n",
    "ger_max_p.to_netcdf('calliope_model/results/cs_ger_max_p.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Build and optimise third case study model - GER NUTS1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs_ds = cs_ds.filter_countries(['Germany'])\n",
    "cs_ds.create_regions('nuts1')\n",
    "cs_ds.create_calliope_model(op_mode='plan',sectors=['power','heat'],co2_cap_factor=0.2, national=False)\n",
    "ger_nuts1 = calliope.Model('calliope_model/model.yaml',scenario='time_3H',override_dict={'run.solver': 'cbc'})\n",
    "ger_nuts1_gpd = gpd.GeoDataFrame(cs_ds.ds_regions['regions'].values,\n",
    "    columns=['id'],geometry=cs_ds.ds_regions['geometry'].values)\n",
    "ger_nuts1.run()\n",
    "ger_nuts1.to_netcdf('calliope_model/results/cs_ger_nuts1.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Solved models\n",
    "The solved models of the three case studies can be imported from the data repository. As the model does not contain the models' spatial zones, the regions geodataframe is recreated separately. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import solved opimisation models\n",
    "ger_nuts0 = calliope.read_netcdf('calliope_model/results/cs_ger_nuts0.nc')\n",
    "ger_nuts1 = calliope.read_netcdf('calliope_model/results/cs_ger_nuts1.nc')\n",
    "ger_max_p = calliope.read_netcdf('calliope_model/results/cs_ger_max_p.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of regions is 9\n"
     ]
    }
   ],
   "source": [
    "# building GER NUTS0 regions\n",
    "eu_ds = euses.import_dataset('eu_2011.nc')\n",
    "\n",
    "countries = ['Germany','Norway','Denmark','Poland','France','Netherlands',\n",
    "                                 'Belgium','Austria','Switzerland','Czech Rep.']\n",
    "cs_ds = eu_ds.filter_countries(countries)\n",
    "cs_ds.create_regions('nuts0')\n",
    "ger_nuts0_gpd = gpd.GeoDataFrame(cs_ds.ds_regions['regions'].values,\n",
    "    columns=['id'],geometry=cs_ds.ds_regions['geometry'].values)\n",
    "\n",
    "# building GER MAXP regions\n",
    "cs_ds = cs_ds.filter_countries(['Germany'])\n",
    "cs_ds.create_regions('max_p_regions',3.8)\n",
    "ger_max_p_gpd = gpd.GeoDataFrame(cs_ds.ds_regions['regions'].values,\n",
    "    columns=['id'],geometry=cs_ds.ds_regions['geometry'].values)\n",
    "\n",
    "# building GER NUTS0 regions\n",
    "cs_ds.create_regions('nuts1')\n",
    "ger_nuts1_gpd = gpd.GeoDataFrame(cs_ds.ds_regions['regions'].values,\n",
    "    columns=['id'],geometry=cs_ds.ds_regions['geometry'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Plot results\n",
    "Finally the results of the models are illustrated. The generated figure contains four parts. Part a) illustrates the regions in the models. Part b), c) and d) are results collected from regions within the spatial scope of Germany. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x936 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate area usage by model and technology\n",
    "\n",
    "area_usage = pd.DataFrame(index=['GER NUTS0', 'GER NUTS1', 'GER MAX-P'])\n",
    "results_dic={'GER NUTS0':ger_nuts0, 'GER NUTS1':ger_nuts1, 'GER MAX-P':ger_max_p}\n",
    "\n",
    "for name, m in results_dic.items():\n",
    "    for tech in ['wind','solar','wind_offshore']:\n",
    "        filter = {'techs':tech}\n",
    "        if name == 'GER NUTS0':\n",
    "            filter = {'techs':tech,'locs':'DE'}\n",
    "        area_max = m.get_formatted_array('resource_area_max').loc[filter].sum()\n",
    "        area_used = m.get_formatted_array('resource_area').loc[filter].sum()\n",
    "        area_usage.loc[name,tech] = area_used/area_max*100\n",
    "\n",
    "# Calculate curtailment by model and technology\n",
    "curtailment = pd.DataFrame(index=['GER NUTS0', 'GER NUTS1', 'GER MAX-P'])\n",
    "\n",
    "for name, m in results_dic.items():\n",
    "    for tech in ['wind','solar','wind_offshore']:\n",
    "        filter = {'techs':tech}\n",
    "        if name == 'GER NUTS0':\n",
    "            filter = {'techs':tech,'locs':'DE'}\n",
    "        flh = m.get_formatted_array('carrier_prod').loc[dict(filter,**{'carriers':'power'})].sum()/m.get_formatted_array('energy_cap').loc[filter].sum()\n",
    "        flh_resource = m.get_formatted_array('resource').loc[filter].sum()\n",
    "        curtailment.loc[name,tech] = (1-flh/flh_resource).values*100\n",
    "\n",
    "\n",
    "techs= ['solar','wind', 'wind_offshore','combined_cycle', 'heat_pump_air', 'hdam',\n",
    "        'hphs', 'hror', 'hydrogen']\n",
    "\n",
    "ger_max_p.inputs['colors'].loc['dc_transmission'] = '#D2BB5F'\n",
    "colors = ger_max_p.inputs['colors'].loc[techs+['ac_transmission','dc_transmission']].values\n",
    "\n",
    "# load installed capaciy of technologies from model results into pandas dataframe\n",
    "df_IC = pd.DataFrame()\n",
    "fig_dic = {ger_nuts0:'GER_NUTS0',ger_nuts1:'GER_NUTS1',ger_max_p:'GER_MAXP'}\n",
    "\n",
    "for model, name in fig_dic.items():\n",
    "    if name == 'GER_NUTS0':\n",
    "        installed_capacity = model.get_formatted_array('energy_cap').loc['DE',:]\n",
    "    else:\n",
    "        installed_capacity = model.get_formatted_array('energy_cap').sum(axis=0)\n",
    "    df_IC[name] = installed_capacity.loc[techs].to_pandas()\n",
    "    for transmission in ['ac_transmission','dc_transmission']:\n",
    "        df_IC.loc[transmission,name] = installed_capacity.loc[installed_capacity.coords['techs'].str.contains(transmission)].sum()\n",
    "df_IC=df_IC/1e6 # convert to TW from GW\n",
    "\n",
    "def plot_graphs(w,l):\n",
    "    fig3 = plt.figure(figsize=(w,l),frameon=False)\n",
    "    gs = fig3.add_gridspec(4, 3)\n",
    "\n",
    "    # fig (a)\n",
    "    cmap = sns.color_palette(\"Spectral\", as_cmap=True)\n",
    "    ax_a1 = fig3.add_subplot(gs[0, 0])\n",
    "    ger_nuts0_gpd.plot(ax=ax_a1,column='id',cmap=cmap, edgecolor='grey')\n",
    "    ax_a1.set_title(label='GER NUTS0')\n",
    "    ax_a2 = fig3.add_subplot(gs[0, 1])\n",
    "    ger_nuts1_gpd.plot(ax=ax_a2,column='id',cmap=cmap,edgecolor='grey')\n",
    "    ax_a2.set_title(label='GER NUTS1')\n",
    "    ax_a3 = fig3.add_subplot(gs[0, 2])\n",
    "    ger_max_p_gpd.plot(ax=ax_a3,column='id',cmap=cmap,edgecolor='grey')\n",
    "    ax_a3.set_title(label='GER MAX-P')\n",
    "\n",
    "    [axs.set_axis_off() for axs in [ax_a1,ax_a2, ax_a3]]\n",
    "\n",
    "    # fig (b)\n",
    "    ax_b = fig3.add_subplot(gs[1, :])\n",
    "    techs_name= ['Solar','Onshore wind', 'Offshore wind','Cogeneration',\n",
    "     'Air-sourced heat pump', 'Reservoir-based hydropower',\n",
    "     'Pumped-storage', 'Run-of-river hydropower', 'Hydrogen','AC Transmission',\n",
    "     'DC Transmission']\n",
    "\n",
    "    df_IC.transpose().plot.bar(ax=ax_b,stacked=True, color=colors)\n",
    "    ax_b.legend(techs_name, loc='lower left', bbox_to_anchor=(1, -0.1),ncol=1)\n",
    "    ax_b.set_ylabel('Installed \\nCapacity (TW)')\n",
    "\n",
    "    ax_b.set_xticklabels(['GER NUTS0', 'GER NUTS1', 'GER MAX-P'], rotation=0)\n",
    "    ax_b.tick_params(length=0)\n",
    "\n",
    "    for side in ['top','right']:\n",
    "        ax_b.spines[side].set_visible(False)\n",
    "\n",
    "    # fig (c)\n",
    "    ax_c = fig3.add_subplot(gs[2, :])\n",
    "    colors_vre = ger_max_p.inputs['colors'].loc[curtailment.columns].values\n",
    "    curtailment.plot.bar(ax=ax_c,color=colors_vre)\n",
    "\n",
    "    ax_c.legend(curtailment.columns, loc='lower left', bbox_to_anchor=(1, 0),ncol=1)\n",
    "    ax_c.set_ylabel('Percentage \\ncurtailment')\n",
    "\n",
    "    ax_c.set_xticklabels(['GER NUTS0', 'GER NUTS1', 'GER MAX-P'], rotation=0)\n",
    "    ax_c.tick_params(length=0)\n",
    "    ax_c.legend(['Onshore wind','Solar','Offshore wind'], loc='lower left', bbox_to_anchor=(1, 0.1),ncol=1)\n",
    "\n",
    "    for side in ['top','right']:\n",
    "        ax_c.spines[side].set_visible(False)\n",
    "\n",
    "    # fig (d)\n",
    "\n",
    "    ax_d = fig3.add_subplot(gs[3, :])\n",
    "    colors_vre = ger_max_p.inputs['colors'].loc[curtailment.columns].values\n",
    "    area_usage.plot.bar(ax=ax_d,color=colors_vre)\n",
    "\n",
    "    ax_d.legend(curtailment.columns, loc='lower left', bbox_to_anchor=(1, 0),ncol=1)\n",
    "    ax_d.set_ylabel('Percentage \\nof available area used')\n",
    "\n",
    "    ax_d.set_xticklabels(['GER NUTS0', 'GER NUTS1', 'GER MAX-P'], rotation=0)\n",
    "    ax_d.tick_params(length=0)\n",
    "    ax_d.legend(['Onshore wind','Solar','Offshore wind'], loc='lower left', bbox_to_anchor=(1, 0.1),ncol=1)\n",
    "\n",
    "    for side in ['top','right']:\n",
    "        ax_d.spines[side].set_visible(False)\n",
    "\n",
    "    # ---\n",
    "\n",
    "    plt.text(-0.16, 2, '(a)',fontsize=13, transform=ax_b.transAxes, fontweight='semibold')\n",
    "    plt.text(-0.16, 0.8, '(b)',fontsize=13, transform=ax_b.transAxes, fontweight='semibold')\n",
    "    plt.text(-0.16, 0.8, '(c)',fontsize=13, transform=ax_c.transAxes, fontweight='semibold')\n",
    "    plt.text(-0.16, 0.8, '(d)',fontsize=13, transform=ax_d.transAxes, fontweight='semibold')\n",
    "\n",
    "    fig3.savefig('plots/case_studies_results.pdf',bbox_inches='tight')\n",
    "plot_graphs(8,13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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