{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial VI.1\n",
    "\n",
    "Consider a long-term multi-year investment problem where **CSP (Concentrated Solar Power)** has a learning curve such that\n",
    "\n",
    "$$LCOE = c_0 \\left(\\frac{x_t}{x_0}\\right)^{-\\gamma} + c_1$$\n",
    "\n",
    "where $c_0$ is cost at start, $c_1$ is material cost and $x_t$ is cumulative\n",
    "capacity in the investment interval $t$. Thus, $x_0$ is the initial cumulative CSP capacity.\n",
    "\n",
    "Additionally, there are **nuclear** and **coal** generators for which there is no potential for reducing their LCOE.\n",
    "\n",
    "We build an optimisation to minimise the cost of supplying a flat demand $d=100$ with the given technologies between 2020 and 2050, where a CO$_2$ budget cap is applied.\n",
    "\n",
    "> **Hint:** Problem formulation is to be found further along this notebook.\n",
    "\n",
    "**Task:** Explore different discount rates, learning rates, CO$_2$ budgets. For instance\n",
    "* No learning for CSP and no CO$_2$ budget would result in a coal-reliant system.\n",
    "* A CO$_2$ budget and no learning prefers a system built on nuclear.\n",
    "* A CO$_2$ budget and learning results in a system with CSP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyomo.environ import ConcreteModel, Var, Objective, NonNegativeReals, Constraint, Suffix, exp\n",
    "from pyomo.opt import SolverFactory\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pwlf\n",
    "plt.style.use('bmh')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>coal</th>\n",
       "      <th>nuclear</th>\n",
       "      <th>CSP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>current annuity</th>\n",
       "      <td>131400.0</td>\n",
       "      <td>788400.0</td>\n",
       "      <td>1314000.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potential annuity</th>\n",
       "      <td>131400.0</td>\n",
       "      <td>788400.0</td>\n",
       "      <td>306600.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning parameter</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marginal cost</th>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>specific emissions</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lifetime</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>existing age</th>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>existing capacity</th>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current LCOE</th>\n",
       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>150.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potential LCOE</th>\n",
       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>35.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        coal   nuclear          CSP\n",
       "current annuity     131400.0  788400.0  1314000.000\n",
       "potential annuity   131400.0  788400.0   306600.000\n",
       "learning parameter       0.0       0.0        0.333\n",
       "marginal cost           35.0      10.0        0.000\n",
       "specific emissions       1.0       0.0        0.000\n",
       "lifetime                40.0      40.0       30.000\n",
       "existing age            20.0      30.0        0.000\n",
       "existing capacity       75.0      25.0        0.000\n",
       "current LCOE            50.0     100.0      150.000\n",
       "potential LCOE          50.0     100.0       35.000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techs = [\"coal\",\"nuclear\",\"CSP\"]\n",
    "colors = [\"#707070\",\"#ff9000\",\"#f9d002\"]\n",
    "parameters = pd.DataFrame(columns=techs)\n",
    "parameters.loc[\"current annuity\"] = [15.*8760,90.*8760,150.*8760] # EUR/MW/a\n",
    "parameters.loc[\"potential annuity\"] = [15.*8760,90.*8760,35.*8760] # EUR/MW/a\n",
    "parameters.loc[\"learning parameter\"] = [0.,0.,0.333]\n",
    "parameters.loc[\"marginal cost\"] = [35.,10.,0.] #EUR/MWhel\n",
    "parameters.loc[\"specific emissions\"] = [1.,0.,0.] #tcO2/MWhel\n",
    "parameters.loc[\"lifetime\"] = [40,40,30]  #years\n",
    "parameters.loc[\"existing age\"] = [20,30,0] #years\n",
    "parameters.loc[\"existing capacity\"] = [75,25,0] #GW\n",
    "\n",
    "parameters.loc[\"current LCOE\"] = parameters.loc[\"current annuity\"]/8760 + parameters.loc[\"marginal cost\"]\n",
    "parameters.loc[\"potential LCOE\"] = parameters.loc[\"potential annuity\"]/8760 + parameters.loc[\"marginal cost\"]\n",
    "\n",
    "parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1314000.0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0,300,0.5)\n",
    "y = parameters.at[\"potential annuity\",\"CSP\"]+(parameters.at[\"current annuity\",\"CSP\"]-parameters.at[\"potential annuity\",\"CSP\"])*(1+x)**(-parameters.at[\"learning parameter\",\"CSP\"])\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(x,y)\n",
    "\n",
    "ax.set_ylim([0,y.max()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1314000.0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#initialize piecewise linear fit with your x and y data\n",
    "my_pwlf = pwlf.PiecewiseLinFit(x, y)\n",
    "\n",
    "n_segments = 5\n",
    "\n",
    "# fit the data for four line segments\n",
    "res = my_pwlf.fit(n_segments)\n",
    "\n",
    "# predict for the determined points\n",
    "xHat = res\n",
    "yHat = my_pwlf.predict(res)\n",
    "#y = ax + b\n",
    "a = []\n",
    "b = []\n",
    "for i in range(len(xHat)-1):\n",
    "    a.append((yHat[i]-yHat[i+1])/(xHat[i]-xHat[i+1]))\n",
    "    b.append(yHat[i] - a[i]*xHat[i])\n",
    "    \n",
    "fig,ax = plt.subplots()\n",
    "\n",
    "\n",
    "for i in range(len(a)):\n",
    "    ax.plot(x,a[i]*x+b[i])\n",
    "ax.plot(x,y,color=\"k\")  \n",
    "ax.set_ylim([0,y.max()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#discount rate\n",
    "rate = 0.0\n",
    "\n",
    "#demand in GW\n",
    "demand = 100.\n",
    "\n",
    "# carbon budget in average tCO2/MWh_el\n",
    "co2_budget = 0.2\n",
    "\n",
    "# considered years\n",
    "years = list(range(2020,2070))\n",
    "\n",
    "scenario = \"co2-0p2-no_learning\"\n",
    "scenario = \"no_co2-no_learning\"\n",
    "scenario = \"co2-0p2-learning\"\n",
    "\n",
    "\n",
    "if \"no_learning\" in scenario:\n",
    "    parameters.loc[\"learning parameter\"] = 0\n",
    "if \"no_co2\" in scenario:\n",
    "    co2_budget = 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Model\n",
    "> **Note:** We use [`pyomo`](https://pyomo.readthedocs.io/en/stable/) for building optimisation problems in python. This is also what `pypsa` uses under the hood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ConcreteModel(\"discounted total costs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generator capacity available for tech $s$ in year $a$\n",
    "$$G_{s,a}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.generators = Var(techs, years, within=NonNegativeReals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generator dispatch for tech $s$ in year $a$\n",
    "$$g_{s,a}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.generators_dispatch = Var(techs, years, within=NonNegativeReals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New capacity built for tech $s$ in year $a$ \n",
    "$$Q_{s,a}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.generators_built = Var(techs, years, within=NonNegativeReals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$c_{s,a}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fixed_costs = Var(techs, years, within=NonNegativeReals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective is to minimise the system costs:\n",
    "\n",
    "$$\\min \\quad \\sum_{s\\in S, a\\in A} \\frac{1}{10^6\\cdot (1+r)^{a}} \\left( o_{s,a} \\cdot g_{s,a} \\cdot 8760 + \\sum_{b} c_{s,b} Q_{s,b} \\mathbb{I}(a \\geq b) \\mathbb{I}(a < b+L_s) \\right) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "394.1999999999998\n"
     ]
    }
   ],
   "source": [
    "# in billion EUR\n",
    "\n",
    "# annuities from existing generators\n",
    "# in billion (MW to GW *1e3, then devide by 1e9)\n",
    "constant =sum(parameters.at[\"existing capacity\",tech]*parameters.at[\"current annuity\",tech]/1e6/(1+rate)**(year-years[0]) for tech in techs for year in years if year < years[0] + parameters.at[\"lifetime\",tech] - parameters.at[\"existing age\",tech])\n",
    "print(constant)\n",
    "\n",
    "model.objective = Objective(expr=constant +\n",
    "                                 sum(model.generators_built[tech,year]*model.fixed_costs[tech,year]/1e6*sum(1/(1+rate)**(yearb-years[0]) for yearb in years if ((yearb>= year) and (yearb < year + parameters.at[\"lifetime\",tech])))\n",
    "                                     for year in years\n",
    "                                     for tech in techs) + \n",
    "                                 sum(model.generators_dispatch[tech,year]*parameters.at[\"marginal cost\",tech]*8760/1e6/(1+rate)**(year-years[0])\n",
    "                                     for year in years\n",
    "                                     for tech in techs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a constraint such that demand is met by generator dispatch:\n",
    "\n",
    "$$\\forall a\\in A: \\quad d = \\sum_{s \\in S} g_{s,a}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def balance_constraint(model, year):\n",
    "    return demand == sum(model.generators_dispatch[tech,year] for tech in techs)\n",
    "model.balance_constraint = Constraint(years, rule=balance_constraint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ g_{s,a} \\leq G_{s,a} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator_constraint(model, tech, year):\n",
    "    return model.generators_dispatch[tech,year] <= model.generators[tech,year]\n",
    "model.generator_constraint = Constraint(techs, years, rule=generator_constraint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a constraint on carbon dioxide emissions:\n",
    "\n",
    "$$\\sum_{s\\in S, a\\in A} G_{s,a} \\cdot e_{t} \\leq \\hat{e} \\cdot |A| \\cdot d$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def co2_constraint(model):\n",
    "    return co2_budget*len(years)*demand >= sum(model.generators_dispatch[tech,year]*parameters.at[\"specific emissions\",tech] for tech in techs for year in years)\n",
    "model.co2_constraint = Constraint(rule=co2_constraint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fixed_cost_constraint(model,tech,year):\n",
    "    if parameters.at[\"learning parameter\",tech] == 0:\n",
    "        return model.fixed_costs[tech,year] == parameters.at[\"current annuity\",tech]\n",
    "    else:\n",
    "        return model.fixed_costs[tech,year] == parameters.at[\"potential annuity\",tech] + (parameters.at[\"current annuity\",tech]-parameters.at[\"potential annuity\",tech])*(1+sum(model.generators[tech,yeart] for yeart in years if yeart < year))**(-parameters.at[\"learning parameter\",tech])\n",
    "model.fixed_cost_constraint = Constraint(techs, years, rule=fixed_cost_constraint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_years(model,tech,year):\n",
    "    if year < years[0] + parameters.at[\"lifetime\",tech] - parameters.at[\"existing age\",tech]:\n",
    "        constant = parameters.at[\"existing capacity\",tech]\n",
    "    else:\n",
    "        constant = 0.\n",
    "    \n",
    "    return model.generators[tech,year] == constant + sum(model.generators_built[tech,yearb] for yearb in years if ((year>= yearb) and (year < yearb + parameters.at[\"lifetime\",tech])))\n",
    "model.build_years = Constraint(techs, years, rule=build_years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Hint:** You can print the model formulation with `model.pprint()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solve Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = SolverFactory(\"ipopt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = opt.solve(model,suffixes=[\"dual\"],keepfiles=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimised cost (in billion euros NPV):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2559.453538442794\n"
     ]
    }
   ],
   "source": [
    "print(model.objective())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The unoptimized cost (where everything is supplied by coal) is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2190.0\n"
     ]
    }
   ],
   "source": [
    "print(8760*demand*parameters.at[\"current LCOE\",\"coal\"]*len(years)/1e6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "dispatch = pd.DataFrame(0.,index=years,columns=techs)\n",
    "for year in years:\n",
    "    for tech in techs:\n",
    "        dispatch.at[year,tech] = model.generators_dispatch[tech,year].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches((10,6))\n",
    "\n",
    "dispatch.plot(kind=\"area\",stacked=True,color=colors,ax=ax)\n",
    "ax.set_xlabel(\"year\")\n",
    "ax.set_ylabel(\"dispatch [GW]\")\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig(\"{}-dispatch.pdf\".format(scenario),transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the development of the technology mix of the optimal solution over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "capacities = pd.DataFrame(0.,index=years,columns=techs)\n",
    "for year in years:\n",
    "    for tech in techs:\n",
    "        capacities.at[year,tech] = model.generators[tech,year].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches((10,6))\n",
    "\n",
    "capacities.plot(kind=\"area\",stacked=True,color=colors,ax=ax)\n",
    "ax.set_xlabel(\"year\")\n",
    "ax.set_ylabel(\"capacity [GW]\")\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig(\"{}-capacity.pdf\".format(scenario),transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "build_years = pd.DataFrame(0.,index=years,columns=techs)\n",
    "for year in years:\n",
    "    for tech in techs:\n",
    "        build_years.at[year,tech] = model.generators_built[tech,year].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches((10,6))\n",
    "\n",
    "build_years.plot(kind=\"area\",stacked=True,color=colors,ax=ax)\n",
    "ax.set_xlabel(\"year\")\n",
    "ax.set_ylabel(\"new capacity built [GW]\")\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig(\"{}-new_capacity.pdf\".format(scenario),transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the development of the costs of the technology over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "costs = pd.DataFrame(0.,index=years,columns=techs)\n",
    "for year in years:\n",
    "    for tech in techs:\n",
    "        costs.at[year,tech] = model.fixed_costs[tech,year].value/8760. + parameters.at[\"marginal cost\",tech]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches((10,6))\n",
    "\n",
    "costs.plot(color=colors,ax=ax,linewidth=3)\n",
    "ax.set_xlabel(\"year\")\n",
    "ax.set_ylabel(\"LCOE [EUR/MWh]\")\n",
    "ax.set_ylim([0,160])\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig(\"{}-lcoe.pdf\".format(scenario),transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}