{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Net Present Value (NPV) - Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PV Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "lifetime = 20 #years\n",
    "discount_rate = 0.04 #0.0775 #per unit\n",
    "size = 100 #kW\n",
    "specific_cost = 800 #EUR/kW\n",
    "fom = 20 #EUR/kW/a\n",
    "fit = 0.1 #EUR/kWh\n",
    "flh = 1000 #h/a\n",
    "flows = pd.DataFrame(index=range(lifetime+1))\n",
    "flows[\"investment\"] = [-size*specific_cost] + [0]*lifetime\n",
    "flows[\"FOM\"] = [0] + [-size*fom]*lifetime\n",
    "flows[\"income\"] = [0] + [size*flh*fit]*lifetime\n",
    "flows[\"total_flow\"] = flows.sum(axis=1)\n",
    "flows[\"discount_factor\"] = [(1+discount_rate)**(-t) for t in range(lifetime+1)]\n",
    "flows[\"discounted_total_flow\"] = flows[\"total_flow\"]*flows[\"discount_factor\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>investment</th>\n",
       "      <th>FOM</th>\n",
       "      <th>income</th>\n",
       "      <th>total_flow</th>\n",
       "      <th>discount_factor</th>\n",
       "      <th>discounted_total_flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-80000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-80000.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-80000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-2000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>7692.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-2000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0.924556</td>\n",
       "      <td>7396.449704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>-2000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0.888996</td>\n",
       "      <td>7111.970869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-2000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0.854804</td>\n",
       "      <td>6838.433528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   investment   FOM   income  total_flow  discount_factor  \\\n",
       "0      -80000     0      0.0    -80000.0         1.000000   \n",
       "1           0 -2000  10000.0      8000.0         0.961538   \n",
       "2           0 -2000  10000.0      8000.0         0.924556   \n",
       "3           0 -2000  10000.0      8000.0         0.888996   \n",
       "4           0 -2000  10000.0      8000.0         0.854804   \n",
       "\n",
       "   discounted_total_flow  \n",
       "0          -80000.000000  \n",
       "1            7692.307692  \n",
       "2            7396.449704  \n",
       "3            7111.970869  \n",
       "4            6838.433528  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "investment               -80000.000000\n",
       "FOM                      -40000.000000\n",
       "income                   200000.000000\n",
       "total_flow                80000.000000\n",
       "discount_factor              14.590326\n",
       "discounted_total_flow     28722.610760\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flows.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6650050203417118"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(size*specific_cost/(sum(flows[\"discount_factor\"])-1)+2500)/1e5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c3e060f28>"
      ]
     },
     "execution_count": 8,
     "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": [
    "flows[\"discount_factor\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nuclear Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lifetime = 40 #years\n",
    "discount_rate = 0.10 #per unit\n",
    "size = 3e6 #kW\n",
    "specific_cost = 5000 #EUR/kW\n",
    "decommissioning_cost = 0 #EUR/kW\n",
    "fom = 20 #EUR/kW/a\n",
    "fuel = 10 #EUR/MWh\n",
    "market_value = 50 #EUR/MWh\n",
    "flh = 8000 #h/a\n",
    "flows = pd.DataFrame(index=range(lifetime+1))\n",
    "flows[\"investment\"] = [-size*specific_cost] + [0]*(lifetime-1) + [-size*decommissioning_cost]\n",
    "flows[\"FOM\"] = [0] + [-size*fom]*lifetime\n",
    "flows[\"income\"] = [0] + [size*flh*market_value/1000]*lifetime\n",
    "flows[\"total_flow\"] = flows.sum(axis=1)\n",
    "flows[\"discount_factor\"] = [(1+discount_rate)**(-t) for t in range(lifetime+1)]\n",
    "flows[\"discounted_total_flow\"] = flows[\"total_flow\"]*flows[\"discount_factor\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>investment</th>\n",
       "      <th>FOM</th>\n",
       "      <th>income</th>\n",
       "      <th>total_flow</th>\n",
       "      <th>discount_factor</th>\n",
       "      <th>discounted_total_flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.500000e+10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.500000e+10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.500000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-60000000.0</td>\n",
       "      <td>1.200000e+09</td>\n",
       "      <td>1.140000e+09</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.036364e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-60000000.0</td>\n",
       "      <td>1.200000e+09</td>\n",
       "      <td>1.140000e+09</td>\n",
       "      <td>0.826446</td>\n",
       "      <td>9.421488e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-60000000.0</td>\n",
       "      <td>1.200000e+09</td>\n",
       "      <td>1.140000e+09</td>\n",
       "      <td>0.751315</td>\n",
       "      <td>8.564989e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-60000000.0</td>\n",
       "      <td>1.200000e+09</td>\n",
       "      <td>1.140000e+09</td>\n",
       "      <td>0.683013</td>\n",
       "      <td>7.786353e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     investment         FOM        income    total_flow  discount_factor  \\\n",
       "0 -1.500000e+10         0.0  0.000000e+00 -1.500000e+10         1.000000   \n",
       "1  0.000000e+00 -60000000.0  1.200000e+09  1.140000e+09         0.909091   \n",
       "2  0.000000e+00 -60000000.0  1.200000e+09  1.140000e+09         0.826446   \n",
       "3  0.000000e+00 -60000000.0  1.200000e+09  1.140000e+09         0.751315   \n",
       "4  0.000000e+00 -60000000.0  1.200000e+09  1.140000e+09         0.683013   \n",
       "\n",
       "   discounted_total_flow  \n",
       "0          -1.500000e+10  \n",
       "1           1.036364e+09  \n",
       "2           9.421488e+08  \n",
       "3           8.564989e+08  \n",
       "4           7.786353e+08  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "investment              -1.500000e+10\n",
       "FOM                     -2.400000e+09\n",
       "income                   4.800000e+10\n",
       "total_flow               3.060000e+10\n",
       "discount_factor          1.077905e+01\n",
       "discounted_total_flow   -3.851882e+09\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flows.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
   },
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