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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Energy System Modelling - Solutions to Tutorial III"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Settings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pypsa\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "solver = \"gurobi\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**(a) Build a network in PyPSA with the two buses North and South and attach the load at each bus and attach the wind and solar generators with availability according to $G_{N,w}(t) = Cf_w(1+A_w\\sin \\omega_w t)$ and $G_{S,s}(t) = Cf_s(1+A_s\\sin \\omega_s t)$ for a year (you have to call `set_snapshots` for the year) and with `p_nom_extendable` set to `True`. As help you should have a look at the [minimal lopf example](https://www.pypsa.org/examples/minimal_example_lopf.html), understand what the [components documentation](https://pypsa.org/doc/components.html) of PyPSA gives you and that you can find the underlying objective function and constraints in the [LOPF documentation](https://pypsa.org/doc/optimal_power_flow.html#linear-optimal-power-flow).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = pypsa.Network()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add North and South bus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.add(\"Bus\",\n",
    "            \"North\",\n",
    "            carrier=\"AC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.add(\"Bus\",\n",
    "            \"South\",\n",
    "            carrier=\"AC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attach constant load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.add(\"Load\",\n",
    "            \"North Load\",\n",
    "            bus=\"North\",\n",
    "            p_set=20e3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.add(\"Load\",\n",
    "            \"South Load\",\n",
    "            bus=\"South\",\n",
    "            p_set=30e3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attach renewable generators according to given parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.set_snapshots(np.arange(0, 4*7*24))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cfw = 0.3\n",
    "Aw = 0.9\n",
    "omegaw = 2*np.pi/(7*24)\n",
    "\n",
    "Cfs = 0.12\n",
    "As = 1.\n",
    "omegas = 2*np.pi/24\n",
    "\n",
    "GNwt = Cfw * (1+Aw*np.sin(omegaw*network.snapshots.to_series()))\n",
    "GSst = Cfs * (1+As*np.sin(omegas*network.snapshots.to_series()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f38a43f4c88>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat([GNwt, GSst], keys=['wind', 'solar'], axis=1).loc[:4*7*24].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.add(\"Generator\",\n",
    "            \"Wind\",\n",
    "            bus=\"North\",\n",
    "            p_nom_extendable=True,\n",
    "            capital_cost=1.2e6,\n",
    "            p_max_pu=GNwt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.add(\"Generator\",\n",
    "            \"Solar\",\n",
    "            bus=\"South\",\n",
    "            p_nom_extendable=True,\n",
    "            capital_cost=0.6e6,\n",
    "            p_max_pu=GSst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**(b) Attach extendable storages at the North and the South! The storages have to be modelled as an `H2-bus` (a bus with `carrier='H2'`) linked to the `AC-bus` North with a `Link` where `p_nom_extendable=True` with the `capital_cost` of the power capacity and an also extendable `Store` with the `capital_cost` of the energy capacity, for instance. The losses can be set on the links as `efficiency`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for bus in [\"North\", \"South\"]:\n",
    "    \n",
    "    # H2 storage\n",
    "    network.add(\"Bus\",\n",
    "                bus + \" H2\",\n",
    "                carrier=\"H2\")\n",
    "    network.add(\"Store\",\n",
    "                bus + \" H2 St.\",\n",
    "                bus=bus + \" H2\",\n",
    "                e_nom_extendable=True,\n",
    "                capital_cost=10e3)\n",
    "    network.add(\"Link\",\n",
    "                bus + \"->H2\",\n",
    "                bus0=bus, bus1=bus + \" H2\",\n",
    "                p_nom_extendable=True,\n",
    "                capital_cost=0.3e6,\n",
    "                efficiency=0.75)\n",
    "    network.add(\"Link\",\n",
    "                \"H2->\" + bus,\n",
    "                bus0=bus + \" H2\", bus1=bus,\n",
    "                p_nom_extendable=True,\n",
    "                capital_cost=0.45e6,\n",
    "                efficiency=0.58)\n",
    "    \n",
    "    # Battery storage\n",
    "    network.add(\"Bus\",\n",
    "                bus + \" Battery\",\n",
    "                carrier=\"Battery\")\n",
    "    network.add(\"Store\",\n",
    "                bus + \" Battery St.\",\n",
    "                bus=bus + \" Battery\",\n",
    "                e_nom_extendable=True,\n",
    "                capital_cost=0.2e6)\n",
    "    network.add(\"Link\",\n",
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    "                bus + \"->Battery\",\n",
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    "                bus0=bus, bus1=bus + \" Battery\",\n",
    "                p_nom_extendable=True,\n",
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    "                capital_cost=0.15e6,\n",
    "                efficiency=0.9)\n",
    "    network.add(\"Link\",\n",
    "                \"Battery->\" + bus,\n",
    "                bus0=bus + \" Battery\", bus1=bus,\n",
    "                p_nom_extendable=True,\n",
    "                capital_cost=0.15e6,\n",
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    "                efficiency=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**(c) Run an investment optimization by calling the `lopf` function.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pypsa.pf:Slack bus for sub-network 0 is North\n",
      "INFO:pypsa.pf:Slack bus for sub-network 1 is South\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 North H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 3, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 3 is North Battery\n",
      "WARNING:pypsa.pf:No generators in sub-network 4, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 4 is South H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 5, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 5 is South Battery\n",
      "INFO:pypsa.opf:Performed preliminary steps\n",
      "INFO:pypsa.opf:Building pyomo model using `angles` formulation\n",
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/components.py:758: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "\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: x14797_copy\n",
      "  Lower bound: 340561629352.0\n",
      "  Upper bound: 340561629352.0\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 26881\n",
      "  Number of variables: 14797\n",
      "  Number of binary variables: 0\n",
      "  Number of integer variables: 0\n",
      "  Number of continuous variables: 14797\n",
      "  Number of nonzeros: 49697\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: 0.671092033386\n",
      "  Error rc: 0\n",
      "  Time: 0.9537255764007568\n",
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/opf.py:1207: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('ok', 'optimal')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.lopf(solver_name=solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**(d) How do your results `objective` and `{generators,stores,links}.p_nom_opt` compare with the results of III.1(d)?** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "340.561629352"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_v1 = network.objective / 1e9 # Mio. Euro\n",
    "obj_v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wind      87.378322\n",
       "Solar    250.000000\n",
       "Name: p_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (a) Capacities for wind and solar.\n",
    "res_cap_v1 = network.generators.p_nom_opt / 1e3 # GW\n",
    "res_cap_v1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North->H2          2.900176e+01\n",
       "H2->North          2.984945e+01\n",
       "North<->Battery    6.596843e-02\n",
       "South->H2         -9.701277e-15\n",
       "H2->South          8.109740e-14\n",
       "South<->Battery    3.000000e+01\n",
       "Name: p_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (b) Store and dispatch power capacity.\n",
    "sto_cap_v1 = network.links.p_nom_opt / 1e3 # GW\n",
    "sto_cap_v1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North H2 St.         1.350832e+00\n",
       "North Battery St.    1.484186e-04\n",
       "South H2 St.         2.891789e-16\n",
       "South Battery St.    2.050854e-01\n",
       "Name: e_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (c) Energy capacities.\n",
    "sto_engy_v1 = network.stores.e_nom_opt / 1e6 # TWh\n",
    "sto_engy_v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3895cd3828>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "network.stores_t.e.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**(e) Now we lift the restriction against transmission and allow North and South to bridge their 500 km\n",
    "separation with a transmission line. How does the cost optimal technology mix change?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add extendable link between North and South:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.add(\"Link\",\n",
    "            \"North<->South\",\n",
    "            bus0=\"North\", bus1=\"South\",\n",
    "            p_min_pu=-1,\n",
    "            p_nom_extendable=True,\n",
    "            capital_cost=0.2e6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run LOPF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:6201: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "\n",
      "INFO:pypsa.pf:Slack bus for sub-network 0 is North\n",
      "INFO:pypsa.pf:Slack bus for sub-network 1 is South\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 North H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 3, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 3 is North Battery\n",
      "WARNING:pypsa.pf:No generators in sub-network 4, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 4 is South H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 5, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 5 is South Battery\n",
      "INFO:pypsa.opf:Performed preliminary steps\n",
      "INFO:pypsa.opf:Building pyomo model using `angles` formulation\n",
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/components.py:758: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "\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: x15470_copy\n",
      "  Lower bound: 332221602177.0\n",
      "  Upper bound: 332221602177.0\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 28225\n",
      "  Number of variables: 15470\n",
      "  Number of binary variables: 0\n",
      "  Number of integer variables: 0\n",
      "  Number of continuous variables: 15470\n",
      "  Number of nonzeros: 53729\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: 1.08625793457\n",
      "  Error rc: 0\n",
      "  Time: 1.4113450050354004\n",
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/opf.py:1207: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('ok', 'optimal')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.lopf(solver_name=solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the results `objective` and `{generators,stores,links}.p_nom_opt` with real availability:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "332.221602177"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_v2 = network.objective / 1e9 # Mio. Euro\n",
    "obj_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wind      92.306059\n",
       "Solar    241.707119\n",
       "Name: p_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (a) Capacities for wind and solar.\n",
    "res_cap_v2 = network.generators.p_nom_opt / 1e3  # GW\n",
    "res_cap_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North->H2          23.402150\n",
       "H2->North          28.753000\n",
       "North<->Battery     0.000000\n",
       "South->H2          14.005796\n",
       "H2->South          12.651488\n",
       "South<->Battery    20.748098\n",
       "North<->South       9.259880\n",
       "Name: p_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (b) Store and dispatch power capacity.\n",
    "sto_cap_v2 = network.links.p_nom_opt / 1e3 # GW\n",
    "sto_cap_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North H2 St.         1.054641\n",
       "North Battery St.    0.000000\n",
       "South H2 St.         0.438776\n",
       "South Battery St.    0.117825\n",
       "Name: e_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (c) Energy capacities\n",
    "sto_engy_v2 = network.stores.e_nom_opt / 1e6 # TWh\n",
    "sto_engy_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3897557278>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "network.stores_t.e.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**(e) Replace the approximated availability time-series of the wind and the solar generators with the ones from `availability.csv` computed from reanalysis weather data available on the [course website](https://nworbmot.org/courses/complex_renewable_energy_networks/) and re-run the LOPF. Compare the results! Explain the differences by looking at the cumulative variations relative to the mean of the availability time-series!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adapt the network to new availabiltiy data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.remove(\"Generator\", \"Wind\")\n",
    "network.remove(\"Generator\", \"Solar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>solar</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.402412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.480648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.586046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.641201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     solar      wind\n",
       "name                                \n",
       "2012-01-01 00:00:00    0.0  0.402412\n",
       "2012-01-01 01:00:00    0.0  0.480648\n",
       "2012-01-01 02:00:00    0.0  0.542354\n",
       "2012-01-01 03:00:00    0.0  0.586046\n",
       "2012-01-01 04:00:00    0.0  0.641201"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "availability = pd.read_csv(\"availability.csv\", index_col=0, parse_dates=True)\n",
    "availability.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3896f49080>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "availability.loc[\"2012-7\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-07-01 00:00:00', '2012-07-01 01:00:00',\n",
       "               '2012-07-01 02:00:00', '2012-07-01 03:00:00',\n",
       "               '2012-07-01 04:00:00', '2012-07-01 05:00:00',\n",
       "               '2012-07-01 06:00:00', '2012-07-01 07:00:00',\n",
       "               '2012-07-01 08:00:00', '2012-07-01 09:00:00',\n",
       "               ...\n",
       "               '2012-07-31 14:00:00', '2012-07-31 15:00:00',\n",
       "               '2012-07-31 16:00:00', '2012-07-31 17:00:00',\n",
       "               '2012-07-31 18:00:00', '2012-07-31 19:00:00',\n",
       "               '2012-07-31 20:00:00', '2012-07-31 21:00:00',\n",
       "               '2012-07-31 22:00:00', '2012-07-31 23:00:00'],\n",
       "              dtype='datetime64[ns]', name='name', length=744, freq=None)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "availability.loc[\"2012-7\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.set_snapshots(availability.loc[\"2012-7\"].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.add(\"Generator\",\n",
    "            \"Wind\",\n",
    "            bus=\"North\",\n",
    "            p_nom_extendable=True,\n",
    "            capital_cost=1.2e6,\n",
    "            p_max_pu=availability[\"wind\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.add(\"Generator\",\n",
    "            \"Solar\",\n",
    "            bus=\"South\",\n",
    "            p_nom_extendable=True,\n",
    "            capital_cost=0.6e6,\n",
    "            p_max_pu=availability[\"solar\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run LOPF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:6201: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "\n",
      "INFO:pypsa.pf:Slack bus for sub-network 0 is North\n",
      "INFO:pypsa.pf:Slack bus for sub-network 1 is South\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 North H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 3, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 3 is North Battery\n",
      "WARNING:pypsa.pf:No generators in sub-network 4, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 4 is South H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 5, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 5 is South Battery\n",
      "INFO:pypsa.opf:Performed preliminary steps\n",
      "INFO:pypsa.opf:Building pyomo model using `angles` formulation\n",
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/components.py:758: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "\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: x17126_copy\n",
      "  Lower bound: 525509809363.0\n",
      "  Upper bound: 525509809363.0\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 31249\n",
      "  Number of variables: 17126\n",
      "  Number of binary variables: 0\n",
      "  Number of integer variables: 0\n",
      "  Number of continuous variables: 17126\n",
      "  Number of nonzeros: 59239\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: 0.608809947968\n",
      "  Error rc: 0\n",
      "  Time: 0.8882288932800293\n",
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/opf.py:1207: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('ok', 'optimal')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.lopf(solver_name=\"gurobi\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the results `objective` and `{generators,stores,links}.p_nom_opt` with real availability:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "525.509809363"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_v3 = network.objective / 1e9 # Mio. Euro\n",
    "obj_v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wind     365.287069\n",
       "Solar     11.102876\n",
       "Name: p_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (a) Capacities for wind and solar.\n",
    "res_cap_v3 = network.generators.p_nom_opt / 1e3\n",
    "res_cap_v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North->H2          71.679680\n",
       "H2->North          69.287835\n",
       "North<->Battery     2.484339\n",
       "South->H2           0.037960\n",
       "H2->South           0.155088\n",
       "South<->Battery     0.707198\n",
       "North<->South      30.000000\n",
       "Name: p_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (b) Store and dispatch power capacity.\n",
    "sto_cap_v3 = network.links.p_nom_opt / 1e3\n",
    "sto_cap_v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North H2 St.         1.953585\n",
       "North Battery St.    0.004337\n",
       "South H2 St.         0.001680\n",
       "South Battery St.    0.001807\n",
       "Name: e_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (c) Energy capacities\n",
    "sto_engy_v3 = network.stores.e_nom_opt / 1e6\n",
    "sto_engy_v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f38965e0048>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "network.stores_t.e.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3896909e10>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.cumsum(availability.loc[\"2012-7\"] - availability.loc[\"2012-7\"].mean()).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**(f) Compare all results for all three scenarios in terms of total system cost, renewable generation capacity, storage power capacity and storage energy capacity!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "scens = [\"without transmission\",\n",
    "         \"with transmission\",\n",
    "         \"with real data and transmission\"]\n",
    "\n",
    "attrs = [\"storage power capacity\",\n",
    "         \"storage energy capacity\",\n",
    "         \"renewable capacity\"]\n",
    "\n",
    "sto_cap_v1.name = attrs[0] + \" \" + scens[0]\n",
    "sto_cap_v2.name = attrs[0] + \" \" + scens[1]\n",
    "sto_cap_v3.name = attrs[0] + \" \" + scens[2]\n",
    "\n",
    "res_cap_v1.name = attrs[1] + \" \" + scens[0]\n",
    "res_cap_v2.name = attrs[1] + \" \" + scens[1]\n",
    "res_cap_v3.name = attrs[1] + \" \" + scens[2]\n",
    "\n",
    "sto_engy_v1.name = attrs[2] + \" \" + scens[0]\n",
    "sto_engy_v2.name = attrs[2] + \" \" + scens[1]\n",
    "sto_engy_v3.name = attrs[2] + \" \" + scens[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = [obj_v1, obj_v2, obj_v3]\n",
    "plt.bar(scens,values)\n",
    "plt.ylabel('Mio. Euro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3896b47048>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sto_caps = pd.concat([sto_cap_v1,sto_cap_v2,sto_cap_v3], axis=1, sort=False)\n",
    "sto_caps.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3896557b70>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_caps = pd.concat([res_cap_v1,res_cap_v2,res_cap_v3], axis=1, sort=False)\n",
    "res_caps.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3896905d68>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sto_engy = pd.concat([sto_engy_v1,sto_engy_v2,sto_engy_v3], axis=1, sort=False)\n",
    "sto_engy.plot.bar()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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