run_single_simulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimal Wind+Hydrogen+Other+Battery+Solar Electricity Systems for European countries\n",
    "\n",
    "Download Jupyter notebook at https://github/PyPSA/WHOBS\n",
    "\n",
    "Required data:\n",
    "\n",
    "i) Solar time series \"ninja_pv_europe_v1.1_sarah.csv\" from \"PV v1.1 Europe\" https://www.renewables.ninja/downloads\n",
    "\n",
    "ii) Wind time series \"ninja_wind_europe_v1.1_current_on-offshore.csv\" from \"Wind v1.1 Europe\" https://www.renewables.ninja/downloads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pypsa\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "idx = pd.IndexSlice\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pyomo.environ import Constraint\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#read in renewables.ninja solar time series\n",
    "solar_pu = pd.read_csv('ninja_pv_europe_v1.1_sarah.csv',\n",
    "                       index_col=0,parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#read in renewables.ninja wind time series\n",
    "wind_pu = pd.read_csv('ninja_wind_europe_v1.1_current_on-offshore.csv',\n",
    "                       index_col=0,parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Major settings (see config.yaml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "add_hydrogen = True\n",
    "\n",
    "#years for weather data (solar is 1985-2015 inclusive, wind is 1980-2016)\n",
    "year_start = 2015\n",
    "year_end = 2015\n",
    "\n",
    "#1 is hourly, 3 is 3-hourly\n",
    "frequency = 1\n",
    "\n",
    "#Fixed load in MW\n",
    "load = 1\n",
    "\n",
    "#change to cbc or glpk for open-source solvers\n",
    "solver_name = \"gurobi\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Required functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def annuity(lifetime,rate):\n",
    "    if rate == 0.:\n",
    "        return 1/lifetime\n",
    "    else:\n",
    "        return rate/(1. - 1. / (1. + rate)**lifetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def prepare_assumptions(Nyears=1,usd_to_eur=1/1.2,assumptions_year=2020):\n",
    "    \"\"\"set all asset assumptions and other parameters\"\"\"\n",
    "\n",
    "    assumptions = pd.read_csv(\"assumptions.csv\",index_col=list(range(3))).sort_index()\n",
    "\n",
    "    #correct units to MW and EUR\n",
    "    assumptions.loc[assumptions.unit.str.contains(\"/kW\"),\"value\"]*=1e3\n",
    "    assumptions.loc[assumptions.unit.str.contains(\"USD\"),\"value\"]*=usd_to_eur\n",
    "\n",
    "    assumptions = assumptions.loc[idx[:,assumptions_year,:],\"value\"].unstack(level=2).groupby(level=\"technology\").sum(min_count=1)\n",
    "\n",
    "    #fill defaults\n",
    "    assumptions = assumptions.fillna({\"FOM\" : assumptions.at[\"default\",\"FOM\"],\n",
    "                                      \"discount rate\" : assumptions.at[\"default\",\"discount rate\"],\n",
    "                                      \"lifetime\" : assumptions.at[\"default\",\"lifetime\"]})\n",
    "\n",
    "    #annualise investment costs, add FOM\n",
    "    assumptions[\"fixed\"] = [(annuity(v[\"lifetime\"],v[\"discount rate\"])+v[\"FOM\"]/100.)*v[\"investment\"]*Nyears for i,v in assumptions.iterrows()]\n",
    "\n",
    "    return assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def solve_network(ct,scenario):\n",
    "\n",
    "    Nyears = year_end - year_start + 1\n",
    "\n",
    "    assumptions_year = int(scenario[:4])\n",
    "\n",
    "    assumptions = prepare_assumptions(Nyears=Nyears,\n",
    "                                      assumptions_year=assumptions_year)\n",
    "\n",
    "    if \"steel_tanks\" in scenario:\n",
    "        assumptions.loc[\"H2 storage\"] = assumptions.loc[\"H2 steel tank storage\"]\n",
    "    else:\n",
    "        assumptions.loc[\"H2 storage\"] = assumptions.loc[\"H2 underground storage\"]\n",
    "\n",
    "\n",
    "    network = pypsa.Network()\n",
    "\n",
    "    snapshots = pd.date_range(\"{}-01-01\".format(year_start),\"{}-12-31 23:00\".format(year_end),\n",
    "                              freq=str(frequency)+\"H\")\n",
    "\n",
    "    network.set_snapshots(snapshots)\n",
    "\n",
    "    network.snapshot_weightings = pd.Series(float(frequency),index=network.snapshots)\n",
    "\n",
    "    network.add(\"Bus\",ct)\n",
    "    network.add(\"Load\",ct,\n",
    "                bus=ct,\n",
    "                p_set=load)\n",
    "\n",
    "    network.add(\"Generator\",ct+\" solar\",\n",
    "                bus=ct,\n",
    "                p_max_pu = solar_pu[ct],\n",
    "                p_nom_extendable = True,\n",
    "                marginal_cost = 0.01, #Small cost to prefer curtailment to destroying energy in storage, solar curtails before wind\n",
    "                capital_cost = assumptions.at['utility solar PV','fixed'])\n",
    "\n",
    "    network.add(\"Generator\",ct+\" wind\",\n",
    "                bus=ct,\n",
    "                p_max_pu = wind_pu[ct+\"_ON\"],\n",
    "                p_nom_extendable = True,\n",
    "                marginal_cost = 0.02, #Small cost to prefer curtailment to destroying energy in storage, solar curtails before wind\n",
    "                capital_cost = assumptions.at['onshore wind','fixed'])\n",
    "\n",
    "    network.add(\"Bus\",ct + \" battery\")\n",
    "\n",
    "    network.add(\"Store\",ct + \" battery storage\",\n",
    "                bus = ct + \" battery\",\n",
    "                e_nom_extendable = True,\n",
    "                e_cyclic=True,\n",
    "                capital_cost=assumptions.at['battery storage','fixed'])\n",
    "\n",
    "    network.add(\"Link\",ct + \" battery charge\",\n",
    "                bus0 = ct,\n",
    "                bus1 = ct + \" battery\",\n",
    "                efficiency = assumptions.at['battery inverter','efficiency'],\n",
    "                p_nom_extendable = True,\n",
    "                capital_cost=assumptions.at['battery inverter','fixed'])\n",
    "\n",
    "    network.add(\"Link\",ct + \" battery discharge\",\n",
    "                bus0 = ct + \" battery\",\n",
    "                bus1 = ct,\n",
    "                p_nom_extendable = True,\n",
    "                efficiency = assumptions.at['battery inverter','efficiency'])\n",
    "\n",
    "    def extra_functionality(network,snapshots):\n",
    "        def battery(model):\n",
    "            return model.link_p_nom[ct + \" battery charge\"] == model.link_p_nom[ct + \" battery discharge\"]*network.links.at[ct + \" battery charge\",\"efficiency\"]\n",
    "\n",
    "        network.model.battery = Constraint(rule=battery)\n",
    "\n",
    "    if add_hydrogen:\n",
    "\n",
    "        network.add(\"Bus\",\n",
    "                     ct + \" H2\",\n",
    "                     carrier=\"H2\")\n",
    "\n",
    "        network.add(\"Link\",\n",
    "                    ct + \" H2 electrolysis\",\n",
    "                    bus1=ct + \" H2\",\n",
    "                    bus0=ct,\n",
    "                    p_nom_extendable=True,\n",
    "                    efficiency=assumptions.at[\"H2 electrolysis\",\"efficiency\"],\n",
    "                    capital_cost=assumptions.at[\"H2 electrolysis\",\"fixed\"])\n",
    "\n",
    "        network.add(\"Link\",\n",
    "                     ct + \" H2 to power\",\n",
    "                     bus0=ct + \" H2\",\n",
    "                     bus1=ct,\n",
    "                     p_nom_extendable=True,\n",
    "                     efficiency=assumptions.at[\"H2 CCGT\",\"efficiency\"],\n",
    "                     capital_cost=assumptions.at[\"H2 CCGT\",\"fixed\"]*assumptions.at[\"H2 CCGT\",\"efficiency\"])  #NB: fixed cost is per MWel\n",
    "\n",
    "        network.add(\"Store\",\n",
    "                     ct + \" H2 storage\",\n",
    "                     bus=ct + \" H2\",\n",
    "                     e_nom_extendable=True,\n",
    "                     e_cyclic=True,\n",
    "                     capital_cost=assumptions.at[\"H2 storage\",\"fixed\"])\n",
    "\n",
    "\n",
    "    if solver_name == \"gurobi\":\n",
    "        solver_options = {\"threads\" : 4,\n",
    "                          \"method\" : 2,\n",
    "                          \"crossover\" : 0,\n",
    "                          \"BarConvTol\": 1.e-5,\n",
    "                          \"FeasibilityTol\": 1.e-6 }\n",
    "    else:\n",
    "        solver_options = {}\n",
    "\n",
    "\n",
    "    network.consistency_check()\n",
    "\n",
    "    network.lopf(solver_name=solver_name,\n",
    "                 solver_options=solver_options,\n",
    "                 extra_functionality=extra_functionality)\n",
    "\n",
    "    return network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gather_stats(network,ct):\n",
    "    stats = pd.DataFrame()\n",
    "    \n",
    "    stats.loc[\"cost\",ct] = network.buses_t.marginal_price.mean()[ct]\n",
    "    \n",
    "    gens = [\"wind\",\"solar\"]\n",
    "    \n",
    "    for g in gens:\n",
    "        stats.loc[g,ct] = network.generators.p_nom_opt[ct + \" \" + g]\n",
    "    stats.loc[\"charger\",ct] = network.links.p_nom_opt[ct + \" battery charge\"]\n",
    "    stats.loc[\"elec\",ct] = network.links.p_nom_opt[ct + \" H2 electrolysis\"]\n",
    "    stats.loc[\"fc\",ct] = network.links.p_nom_opt[ct + \" H2 to power\"]\n",
    "    stats.loc[\"batt\",ct] = network.stores.e_nom_opt[ct + \" battery storage\"]\n",
    "    stats.loc[\"H2\",ct] = network.stores.e_nom_opt[ct + \" H2 storage\"]\n",
    "    available = network.generators_t.p_max_pu.multiply(network.generators.p_nom_opt).sum()\n",
    "    used = network.generators_t.p.sum()\n",
    "    curtailment = (available-used)/available\n",
    "    stats.loc[\"wcurt\",ct] = curtailment[ct + \" wind\"]\n",
    "    stats.loc[\"scurt\",ct] = curtailment[ct + \" solar\"]\n",
    "    \n",
    "    return stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solve the system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pypsa.pf:Slack bus for sub-network 0 is DE\n",
      "WARNING:pypsa.pf:No generators in sub-network 1, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 1 is DE battery\n",
      "WARNING:pypsa.pf:No generators in sub-network 2, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 2 is DE H2\n",
      "INFO:pypsa.opf:Performed preliminary steps\n",
      "INFO:pypsa.opf:Building pyomo model using `angles` formulation\n",
      "INFO:pypsa.opf:Solving model using gurobi\n",
      "INFO:pypsa.opf:Optimization successful\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ==========================================================\n",
      "# = Solver Results                                         =\n",
      "# ==========================================================\n",
      "# ----------------------------------------------------------\n",
      "#   Problem Information\n",
      "# ----------------------------------------------------------\n",
      "Problem: \n",
      "- Name: \n",
      "  Lower bound: 832444.027278\n",
      "  Upper bound: 832444.027278\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 210242\n",
      "  Number of variables: 113889\n",
      "  Number of binary variables: 0\n",
      "  Number of integer variables: 0\n",
      "  Number of continuous variables: 113889\n",
      "  Number of nonzeros: 389977\n",
      "  Sense: minimize\n",
      "# ----------------------------------------------------------\n",
      "#   Solver Information\n",
      "# ----------------------------------------------------------\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Return code: 0\n",
      "  Message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n",
      "  Termination condition: optimal\n",
      "  Termination message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n",
      "  Wall time: 29.3054258823\n",
      "  Error rc: 0\n",
      "  Time: 35.55070352554321\n",
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n"
     ]
    }
   ],
   "source": [
    "ct = \"DE\"\n",
    "scenario = \"2030\"\n",
    "\n",
    "network = solve_network(ct, scenario)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cost</th>\n",
       "      <td>95.027816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wind</th>\n",
       "      <td>3.866371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solar</th>\n",
       "      <td>4.125659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charger</th>\n",
       "      <td>0.369428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elec</th>\n",
       "      <td>1.267186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fc</th>\n",
       "      <td>1.332414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>batt</th>\n",
       "      <td>2.384782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H2</th>\n",
       "      <td>613.777029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wcurt</th>\n",
       "      <td>0.103601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scurt</th>\n",
       "      <td>0.009070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DE\n",
       "cost      95.027816\n",
       "wind       3.866371\n",
       "solar      4.125659\n",
       "charger    0.369428\n",
       "elec       1.267186\n",
       "fc         1.332414\n",
       "batt       2.384782\n",
       "H2       613.777029\n",
       "wcurt      0.103601\n",
       "scurt      0.009070"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = gather_stats(network,ct)\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DE solar    49.292438\n",
       "DE wind     64.042309\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#absolute market value in EUR/MWh\n",
    "(network.generators_t.p.multiply(network.buses_t.marginal_price[ct],axis=0).sum()/network.generators_t.p.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DE solar    49.282443\n",
       "DE wind     64.022315\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#market cost in EUR/MWh (LCOE) - for long-term equilibrium without additional constraints, same as market value\n",
    "(network.generators.capital_cost*network.generators.p_nom_opt)/network.generators_t.p.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DE solar    0.518716\n",
       "DE wind     0.673932\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#relative market value\n",
    "(network.generators_t.p.multiply(network.buses_t.marginal_price[ct],axis=0).sum()/network.generators_t.p.sum())/network.buses_t.marginal_price[ct].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DE battery charge       0.449096\n",
       "DE battery discharge    1.422199\n",
       "DE H2 electrolysis      0.333468\n",
       "DE H2 to power          1.811980\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#relative market value\n",
    "(network.links_t.p0.multiply(network.buses_t.marginal_price[ct],axis=0).sum()/network.links_t.p0.sum())/network.buses_t.marginal_price[ct].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tom/.virtualenvs/tom3/lib/python3.5/site-packages/matplotlib/axes/_base.py:3239: UserWarning:\n",
      "\n",
      "Attempting to set identical bottom==top results\n",
      "in singular transformations; automatically expanding.\n",
      "bottom=0.0, top=0\n",
      "\n"
     ]
    },
    {
     "data": {
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48cUXrX336dOHnJwcHnjggTbCBSAmJoZnnnmG2267jdzc3DbVfx295/nz55OX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86369f3860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches((8,6))\n",
    "\n",
    "start = \"2015-01-01\"\n",
    "\n",
    "stop = \"2015-01-31\"\n",
    "\n",
    "rename = {\"wind\" : \"onshore wind\",\n",
    "          \"solar\" : \"utility solar PV\",\n",
    "          \"battery discharge\" : \"battery discharge\",\n",
    "          \"battery charge\" : \"battery charge\",\n",
    "          \"H2 electrolysis\" : \"hydrogen electrolysis\",\n",
    "          \"H2 to power\" : \"hydrogen turbine\"}\n",
    "\n",
    "rename = {ct + \" \"+k : v for k,v in rename.items()}\n",
    "\n",
    "rename[ct] = \"load\"\n",
    "\n",
    "colors = {\"onshore wind\" : \"b\",\n",
    "           \"utility solar PV\" : \"y\",\n",
    "           \"battery discharge\" : \"gray\",\n",
    "           \"battery charge\" : \"gray\",\n",
    "           \"load\" : \"k\",\n",
    "           \"hydrogen electrolysis\" : \"m\",\n",
    "           \"hydrogen turbine\" : \"r\"\n",
    "          }\n",
    "\n",
    "positive = pd.concat((network.generators_t.p,-network.links_t.p1[[ct+\" H2 to power\",ct+\" battery discharge\"]]),axis=1).rename(columns=rename)\n",
    "\n",
    "negative = pd.concat((-network.loads_t.p,-network.links_t.p0[[ct+\" H2 electrolysis\",ct+\" battery charge\"]]),axis=1).rename(columns=rename)\n",
    "\n",
    "print((abs(positive.sum(axis=1) + negative.sum(axis=1)) > 0.1).any())\n",
    "\n",
    "positive.loc[start:stop].plot(kind=\"area\",stacked=True,ax=ax,linewidth=0,color=[colors[i] for i in positive.columns])\n",
    "\n",
    "negative.loc[start:stop].plot(kind=\"area\",stacked=True,ax=ax,linewidth=0,color=[colors[i] for i in negative.columns])\n",
    "\n",
    "ax.set_ylim([-2.5,3.5])\n",
    "\n",
    "ax.set_xlim([start,stop])\n",
    "\n",
    "ax.set_ylabel(\"Dispatch (generation is +ve, demand is -ve) [MW]\")\n",
    "\n",
    "ax.legend(ncol=3,loc=\"upper left\")\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig(\"img/{}-{}-{}-{}.png\".format(ct,scenario,start,stop),dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f86410be668>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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znOP/lDTKUQRFqCJAJUGSCXvLFxx8hPHzaspZ9O6TQ9L6Tzj/6ls4cEgfbr3t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\nAcAYYxLU/wdVJlYz81jDdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86389f2ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "network.buses_t.marginal_price.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DE            95.027816\n",
       "DE battery    93.525105\n",
       "DE H2         78.074242\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.buses_t.marginal_price.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DE battery charge       0.210566\n",
       "DE battery discharge    0.170559\n",
       "DE H2 electrolysis      0.301910\n",
       "DE H2 to power          0.229704\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.links_t.p0.mean()/network.links.p_nom_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f863b2b19b0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mYLu+422rc0PbSFof2AL4xTgCjIiI6RsluV8K7CRpR0kbAocCywbaLANeUt0+BPiypxrv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86386bd320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt_costs = pd.Series()\n",
    "\n",
    "\n",
    "opt_costs = pd.concat((opt_costs,network.generators.capital_cost*network.generators.p_nom_opt))\n",
    "\n",
    "opt_costs = pd.concat((opt_costs,network.links.capital_cost*network.links.p_nom_opt))\n",
    "\n",
    "opt_costs = pd.concat((opt_costs,network.stores.capital_cost*network.stores.e_nom_opt))\n",
    "\n",
    "\n",
    "(opt_costs/opt_costs.sum()).plot(kind=\"bar\",grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}