tutorial/westeros/westeros_emissions_bounds.ipynb

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   "source": [
    "# Westeros Tutorial - Introducing emissions (part1/2): Adding emission bounds\n",
    "\n",
    "In the first part, the model chose not base the power system on wind power as electricity from  wind turbines was more expensive than electricity produced from coal. However, now we introduce emissions to investigate the impact of climate policy. Let's see what happens then.\n",
    "\n",
    "**Pre-requisites**\n",
    "- You have the *MESSAGEix* framework installed and working\n",
    "- You have run Westeros baseline scenario (``westeros_baseline.ipynb``) and solved it successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import ixmp\n",
    "import message_ix\n",
    "\n",
    "from message_ix.util import make_df\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp = ixmp.Platform()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = 'Westeros Electrified'\n",
    "\n",
    "base = message_ix.Scenario(mp, model=model, scenario='baseline')\n",
    "scen = base.clone(model, 'emission_bound','introducing an upper bound on emissions',\n",
    "                  keep_solution=False)\n",
    "scen.check_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_df = scen.vintage_and_active_years()\n",
    "vintage_years, act_years = year_df['year_vtg'], year_df['year_act']\n",
    "model_horizon = scen.set('year')\n",
    "country = 'Westeros'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introducing Emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first we introduce the emission of CO2 and the emission category GHG\n",
    "scen.add_set('emission', 'CO2')\n",
    "scen.add_cat('emission', 'GHG', 'CO2')\n",
    "\n",
    "# we now add CO2 emissions to the coal powerplant\n",
    "base_emission_factor = {\n",
    "    'node_loc': country,\n",
    "    'year_vtg': vintage_years,\n",
    "    'year_act': act_years,\n",
    "    'mode': 'standard',\n",
    "    'unit': 'tCO2/kWa',\n",
    "}\n",
    "\n",
    "# adding new units to the model library (needed only once)\n",
    "mp.add_unit('tCO2/kWa')\n",
    "mp.add_unit('MtCO2')\n",
    "\n",
    "emission_factor = make_df(base_emission_factor, technology= 'coal_ppl', emission= 'CO2', value = 7.4)\n",
    "scen.add_par('emission_factor', emission_factor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a Bound on Emissions\n",
    "\n",
    "The `type_year: cumulative` assigns an upper bound on the *weighted average of emissions* over the entire time horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scen.add_par('bound_emission', [country, 'GHG', 'all', 'cumulative'],\n",
    "             value=500., unit='MtCO2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time to Solve the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scen.commit(comment='introducing emissions and setting an upper bound')\n",
    "scen.set_as_default()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scen.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scen.var('OBJ')['lvl']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from message_ix.reporting import Reporter\n",
    "from message_ix.util.tutorial import prepare_plots\n",
    "\n",
    "rep = Reporter.from_scenario(scen)\n",
    "prepare_plots(rep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Activity\n",
    "\n",
    "How much energy is generated in each time period from the different potential sources?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rep.set_filters(t=[\"coal_ppl\", \"wind_ppl\"])\n",
    "rep.get(\"plot activity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capacity\n",
    "\n",
    "How much capacity of each plant is installed in each period?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rep.get(\"plot capacity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Electricity Price\n",
    "\n",
    "And how much does the electricity cost? These prices are in fact **shadow prices** taken from the **dual variables** of the model solution. They reflect the marginal cost of electricity generation (i.e., the additional cost of the system for supplying one more unit of electricity), which is in fact the marginal cost of the most expensive generator.  \n",
    "\n",
    "Note the price drop when the most expensive technology is no longer in the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rep.set_filters(t=None, c=[\"light\"])\n",
    "rep.get(\"plot prices\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Close the connection to the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp.close_db()"
   ]
  }
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