wind.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wind power expansion in Quebec: average availability factors per time slice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We used data of hourly production of wind to calculate the availability factors. The data comes from the Pan-Canadian Wind Integration Study (PCWIS) and it can be downloaded in the [Canwea website](http://canwea.ca/wind-integration-study/wind-data/). (35% target, actual data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import libraries and setup plotting style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import urllib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SIZE = 8\n",
    "MEDIUM_SIZE = 10\n",
    "BIGGER_SIZE = 12\n",
    "\n",
    "plt.rc('font', size=SIZE)                # controls default text sizes\n",
    "plt.rc('axes', titlesize=SIZE)           # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SIZE)          # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SIZE)          # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SIZE)          # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "\n",
    "plt.style.use('seaborn-deep')\n",
    "\n",
    "# witdth of figures\n",
    "smallwidth=3.54\n",
    "mediumwidth=5.51\n",
    "bigwidth=7.48"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## download the data \n",
    "(if you do not have it yet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "urllib.request.urlretrieve('https://canwea.ca/wp-content/uploads/2016/10/CanWEA_Wind_Site_Metadata.xlsx',\n",
    "                           'wind/CanWEA_Wind_Site_Metadata.xlsx');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('wind/35TRGT_2008-2010_Actual.zip', <http.client.HTTPMessage at 0x10eade390>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urllib.request.urlretrieve('https://canwea.ca/wp-content/uploads/2016/09/35TRGT_2008-2010_Actual.zip',\n",
    "                           'wind/35TRGT_2008-2010_Actual.zip');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "unzip the data in the wind folder (windows machines may need to use the library zipfile instead)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  wind/35TRGT_2008-2010_Actual.zip\n",
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     ]
    }
   ],
   "source": [
    "!unzip wind/35TRGT_2008-2010_Actual.zip -d wind;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "metadata=pd.read_excel('wind/CanWEA_Wind_Site_Metadata.xlsx',sheet_name='Metadata')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "metadata['Site ID #']=metadata['Site ID #'].astype(str).str.zfill(4) # format site ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4984, 18)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Pan-Canadian study considered 4984 locations. 53 of them are used to describe the high penetration scenario in Quebec."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5BAU Scenario      28\n",
       "20DISP Scenario    40\n",
       "20CONC Scenario    16\n",
       "35TRGT Scenario    53\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.loc[(metadata['Province']=='Quebec'),['5BAU Scenario',\n",
    "                                               '20DISP Scenario',\n",
    "                                               '20CONC Scenario',\n",
    "                                               '35TRGT Scenario']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Site ID #</th>\n",
       "      <th>Number of Grid Cells</th>\n",
       "      <th>Capacity (MW)</th>\n",
       "      <th>Gross Energy (GWh)</th>\n",
       "      <th>Maintenance Losses (GWh)</th>\n",
       "      <th>Electrical Losses (GWh)</th>\n",
       "      <th>Net Energy (GWh)</th>\n",
       "      <th>Capacity Factor (%)</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Province</th>\n",
       "      <th>Onshore or Offshore</th>\n",
       "      <th>Dist to High Volt Transmission</th>\n",
       "      <th>5BAU Scenario</th>\n",
       "      <th>20DISP Scenario</th>\n",
       "      <th>20CONC Scenario</th>\n",
       "      <th>35TRGT Scenario</th>\n",
       "      <th>PCWIS Scalar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>108.621663</td>\n",
       "      <td>5.431083</td>\n",
       "      <td>3.095717</td>\n",
       "      <td>100.094862</td>\n",
       "      <td>0.357074</td>\n",
       "      <td>-82.279000</td>\n",
       "      <td>42.172150</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Onshore</td>\n",
       "      <td>25 km</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>113.677474</td>\n",
       "      <td>5.683874</td>\n",
       "      <td>3.239808</td>\n",
       "      <td>104.753792</td>\n",
       "      <td>0.373694</td>\n",
       "      <td>-81.945000</td>\n",
       "      <td>42.263450</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Onshore</td>\n",
       "      <td>25 km</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003</td>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>449.030320</td>\n",
       "      <td>22.451516</td>\n",
       "      <td>12.797364</td>\n",
       "      <td>413.781440</td>\n",
       "      <td>0.369026</td>\n",
       "      <td>-82.111375</td>\n",
       "      <td>42.253462</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>Onshore</td>\n",
       "      <td>25 km</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.003125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Site ID #  Number of Grid Cells  Capacity (MW)  Gross Energy (GWh)  \\\n",
       "0      0001                     2             32          108.621663   \n",
       "1      0002                     2             32          113.677474   \n",
       "2      0003                     8            128          449.030320   \n",
       "\n",
       "   Maintenance Losses (GWh)  Electrical Losses (GWh)  Net Energy (GWh)  \\\n",
       "0                  5.431083                 3.095717        100.094862   \n",
       "1                  5.683874                 3.239808        104.753792   \n",
       "2                 22.451516                12.797364        413.781440   \n",
       "\n",
       "   Capacity Factor (%)  Longitude   Latitude Province Onshore or Offshore  \\\n",
       "0             0.357074 -82.279000  42.172150  Ontario             Onshore   \n",
       "1             0.373694 -81.945000  42.263450  Ontario             Onshore   \n",
       "2             0.369026 -82.111375  42.253462  Ontario             Onshore   \n",
       "\n",
       "  Dist to High Volt Transmission  5BAU Scenario  20DISP Scenario  \\\n",
       "0                          25 km              0                0   \n",
       "1                          25 km              0                0   \n",
       "2                          25 km              1                0   \n",
       "\n",
       "   20CONC Scenario  35TRGT Scenario  PCWIS Scalar  \n",
       "0                0                0      0.000000  \n",
       "1                0                0      0.000000  \n",
       "2                0                0      1.003125  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average capacity factor in Quebec:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37749815255905556"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.loc[(metadata['Province']=='Quebec') &\n",
    "             (metadata['35TRGT Scenario']==1),'Capacity Factor (%)'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read hourly wind production from the sites representative of the 35% penetration scenario."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identification of the files with production data for the sites of our scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "idQC=metadata.loc[(metadata['Province']=='Quebec') &\n",
    "             (metadata['35TRGT Scenario']==1) ,'Site ID #'].get_values()\n",
    "\n",
    "idQC='wind/'+idQC+'Pwr1HB.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'wind/0471Pwr1HB.csv'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idQC[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loop through the array of file names reading them and create a dataframe where each column is one site with the ID on the header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_list=[]\n",
    "for file in idQC:\n",
    "    w_list.append(pd.read_csv(file,\n",
    "                              skiprows=2,\n",
    "                              usecols=[1]).rename({' Value':pd.read_csv(file).columns[1].strip()},\n",
    "                                                   axis=1))\n",
    "    \n",
    "wdf=pd.concat(w_list,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>471</th>\n",
       "      <th>1018</th>\n",
       "      <th>1067</th>\n",
       "      <th>1068</th>\n",
       "      <th>1094</th>\n",
       "      <th>1095</th>\n",
       "      <th>1100</th>\n",
       "      <th>1105</th>\n",
       "      <th>1106</th>\n",
       "      <th>1125</th>\n",
       "      <th>...</th>\n",
       "      <th>1487</th>\n",
       "      <th>2214</th>\n",
       "      <th>3645</th>\n",
       "      <th>3669</th>\n",
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       "      <th>3692</th>\n",
       "      <th>3734</th>\n",
       "      <th>3751</th>\n",
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       "      <td>47.088440</td>\n",
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       "      <td>53.634608</td>\n",
       "      <td>85.575654</td>\n",
       "      <td>12.085572</td>\n",
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       "      <td>6.594878</td>\n",
       "      <td>23.282481</td>\n",
       "      <td>8.254024</td>\n",
       "      <td>30.128034</td>\n",
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       "      <td>12.941560</td>\n",
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       "      <td>114.106980</td>\n",
       "      <td>169.564281</td>\n",
       "      <td>98.781127</td>\n",
       "      <td>86.774755</td>\n",
       "      <td>27.899195</td>\n",
       "      <td>175.792418</td>\n",
       "      <td>81.785460</td>\n",
       "      <td>141.042111</td>\n",
       "      <td>26.946931</td>\n",
       "      <td>...</td>\n",
       "      <td>149.546328</td>\n",
       "      <td>242.918566</td>\n",
       "      <td>17.456520</td>\n",
       "      <td>39.309347</td>\n",
       "      <td>7.913547</td>\n",
       "      <td>38.227146</td>\n",
       "      <td>8.882720</td>\n",
       "      <td>30.582091</td>\n",
       "      <td>16.608383</td>\n",
       "      <td>19.572912</td>\n",
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       "      <th>2</th>\n",
       "      <td>53.936974</td>\n",
       "      <td>122.000506</td>\n",
       "      <td>179.136987</td>\n",
       "      <td>95.239415</td>\n",
       "      <td>103.878951</td>\n",
       "      <td>33.177226</td>\n",
       "      <td>170.021171</td>\n",
       "      <td>107.306374</td>\n",
       "      <td>203.492071</td>\n",
       "      <td>48.432183</td>\n",
       "      <td>...</td>\n",
       "      <td>150.766211</td>\n",
       "      <td>292.997423</td>\n",
       "      <td>26.459165</td>\n",
       "      <td>40.735028</td>\n",
       "      <td>11.364794</td>\n",
       "      <td>50.109251</td>\n",
       "      <td>7.756202</td>\n",
       "      <td>28.576315</td>\n",
       "      <td>27.956965</td>\n",
       "      <td>33.752373</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62.825949</td>\n",
       "      <td>123.833869</td>\n",
       "      <td>159.972005</td>\n",
       "      <td>66.047915</td>\n",
       "      <td>99.746765</td>\n",
       "      <td>29.957824</td>\n",
       "      <td>164.850292</td>\n",
       "      <td>154.964162</td>\n",
       "      <td>280.621703</td>\n",
       "      <td>91.434585</td>\n",
       "      <td>...</td>\n",
       "      <td>128.862628</td>\n",
       "      <td>322.006459</td>\n",
       "      <td>30.029726</td>\n",
       "      <td>51.363429</td>\n",
       "      <td>14.287551</td>\n",
       "      <td>62.687057</td>\n",
       "      <td>10.057875</td>\n",
       "      <td>38.962240</td>\n",
       "      <td>41.237376</td>\n",
       "      <td>49.918604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57.425502</td>\n",
       "      <td>105.240263</td>\n",
       "      <td>73.595490</td>\n",
       "      <td>20.857419</td>\n",
       "      <td>57.801515</td>\n",
       "      <td>17.526703</td>\n",
       "      <td>134.145145</td>\n",
       "      <td>175.581241</td>\n",
       "      <td>284.156912</td>\n",
       "      <td>136.425282</td>\n",
       "      <td>...</td>\n",
       "      <td>125.141527</td>\n",
       "      <td>341.585593</td>\n",
       "      <td>37.195993</td>\n",
       "      <td>54.117488</td>\n",
       "      <td>23.524509</td>\n",
       "      <td>88.684104</td>\n",
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       "      <td>38.556863</td>\n",
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       "<p>5 rows × 53 columns</p>\n",
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       "         471        1018        1067       1068        1094       1095  \\\n",
       "0  43.386995   98.311608  108.037867  72.540224   47.088440  12.986280   \n",
       "1  41.975833  114.106980  169.564281  98.781127   86.774755  27.899195   \n",
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       "4  57.425502  105.240263   73.595490  20.857419   57.801515  17.526703   \n",
       "\n",
       "         1100        1105        1106        1125    ...            1487  \\\n",
       "0  211.866612   53.634608   85.575654   12.085572    ...      152.189273   \n",
       "1  175.792418   81.785460  141.042111   26.946931    ...      149.546328   \n",
       "2  170.021171  107.306374  203.492071   48.432183    ...      150.766211   \n",
       "3  164.850292  154.964162  280.621703   91.434585    ...      128.862628   \n",
       "4  134.145145  175.581241  284.156912  136.425282    ...      125.141527   \n",
       "\n",
       "         2214       3645       3669       3671       3672       3691  \\\n",
       "0  201.279867  13.510124  35.542816   6.594878  23.282481   8.254024   \n",
       "1  242.918566  17.456520  39.309347   7.913547  38.227146   8.882720   \n",
       "2  292.997423  26.459165  40.735028  11.364794  50.109251   7.756202   \n",
       "3  322.006459  30.029726  51.363429  14.287551  62.687057  10.057875   \n",
       "4  341.585593  37.195993  54.117488  23.524509  88.684104  11.352177   \n",
       "\n",
       "        3692       3734       3751  \n",
       "0  30.128034  11.768342  12.941560  \n",
       "1  30.582091  16.608383  19.572912  \n",
       "2  28.576315  27.956965  33.752373  \n",
       "3  38.962240  41.237376  49.918604  \n",
       "4  38.556863  54.431934  67.534270  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we take the index from one of the files, as all have the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "windindex=pd.read_csv(idQC[0],skiprows=2,index_col='Date/Time ',parse_dates=True).index\n",
    "wdf.index=windindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2010-12-31 19:00:00</th>\n",
       "      <td>19.458469</td>\n",
       "      <td>70.185479</td>\n",
       "      <td>28.521656</td>\n",
       "      <td>40.742359</td>\n",
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       "      <th>2010-12-31 20:00:00</th>\n",
       "      <td>28.376684</td>\n",
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       "      <td>77.306186</td>\n",
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       "      <td>119.508981</td>\n",
       "      <td>145.448330</td>\n",
       "      <td>53.364308</td>\n",
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       "    <tr>\n",
       "      <th>2010-12-31 21:00:00</th>\n",
       "      <td>37.635215</td>\n",
       "      <td>151.117980</td>\n",
       "      <td>69.454031</td>\n",
       "      <td>93.020372</td>\n",
       "      <td>47.409794</td>\n",
       "      <td>23.496754</td>\n",
       "      <td>99.857188</td>\n",
       "      <td>152.439458</td>\n",
       "      <td>156.481960</td>\n",
       "      <td>76.309153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.587001</td>\n",
       "      <td>25.361630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.599320</td>\n",
       "      <td>91.478041</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-12-31 22:00:00</th>\n",
       "      <td>35.355530</td>\n",
       "      <td>169.208837</td>\n",
       "      <td>72.183341</td>\n",
       "      <td>73.877239</td>\n",
       "      <td>59.171990</td>\n",
       "      <td>28.092270</td>\n",
       "      <td>105.605814</td>\n",
       "      <td>185.518251</td>\n",
       "      <td>178.126366</td>\n",
       "      <td>129.280799</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>5.237689</td>\n",
       "      <td>49.834314</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>88.928420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-12-31 23:00:00</th>\n",
       "      <td>32.486903</td>\n",
       "      <td>185.128554</td>\n",
       "      <td>69.947958</td>\n",
       "      <td>51.422545</td>\n",
       "      <td>73.109216</td>\n",
       "      <td>38.039911</td>\n",
       "      <td>125.403069</td>\n",
       "      <td>201.036700</td>\n",
       "      <td>226.818647</td>\n",
       "      <td>160.253972</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.253283</td>\n",
       "      <td>52.722286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.174922</td>\n",
       "      <td>102.719735</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           471        1018       1067       1068       1094  \\\n",
       "Date/Time                                                                     \n",
       "2010-12-31 19:00:00  19.458469   70.185479  28.521656  40.742359  21.178782   \n",
       "2010-12-31 20:00:00  28.376684  108.115448  46.686212  77.306186  36.394423   \n",
       "2010-12-31 21:00:00  37.635215  151.117980  69.454031  93.020372  47.409794   \n",
       "2010-12-31 22:00:00  35.355530  169.208837  72.183341  73.877239  59.171990   \n",
       "2010-12-31 23:00:00  32.486903  185.128554  69.947958  51.422545  73.109216   \n",
       "\n",
       "                          1095        1100        1105        1106  \\\n",
       "Date/Time                                                            \n",
       "2010-12-31 19:00:00  18.435549   57.512439  122.359026  149.911866   \n",
       "2010-12-31 20:00:00  19.545809   81.237001  119.508981  145.448330   \n",
       "2010-12-31 21:00:00  23.496754   99.857188  152.439458  156.481960   \n",
       "2010-12-31 22:00:00  28.092270  105.605814  185.518251  178.126366   \n",
       "2010-12-31 23:00:00  38.039911  125.403069  201.036700  226.818647   \n",
       "\n",
       "                           1125  ...   1487       2214       3645  3669  \\\n",
       "Date/Time                        ...                                      \n",
       "2010-12-31 19:00:00   38.932510  ...    0.0   0.564462   0.000000   0.0   \n",
       "2010-12-31 20:00:00   53.364308  ...    0.0   5.543133   0.000000   0.0   \n",
       "2010-12-31 21:00:00   76.309153  ...    0.0   6.587001  25.361630   0.0   \n",
       "2010-12-31 22:00:00  129.280799  ...    0.0   5.237689  49.834314   0.0   \n",
       "2010-12-31 23:00:00  160.253972  ...    0.0  11.253283  52.722286   0.0   \n",
       "\n",
       "                          3671        3672  3691  3692  3734  3751  \n",
       "Date/Time                                                           \n",
       "2010-12-31 19:00:00   7.263620   49.797562   0.0   0.0   0.0   0.0  \n",
       "2010-12-31 20:00:00   8.024248   59.623555   0.0   0.0   0.0   0.0  \n",
       "2010-12-31 21:00:00  19.599320   91.478041   0.0   0.0   0.0   0.0  \n",
       "2010-12-31 22:00:00  24.040354   88.928420   0.0   0.0   0.0   0.0  \n",
       "2010-12-31 23:00:00  27.174922  102.719735   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**validation**: compare the total production per site in the dataframe containing the hourly data with the values of *Gross energy* provided in the metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d90a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=metadata.loc[(metadata['Province']=='Quebec') &\n",
    "                           (metadata['35TRGT Scenario']==1) ,\\\n",
    "                           ['Gross Energy (GWh)','Site ID #']]\\\n",
    "                           .set_index('Site ID #').values,\n",
    "            y=(wdf.sum()/1000/3).values);\n",
    "\n",
    "plt.xlabel('Metadata values')\n",
    "plt.ylabel('Calculated values')\n",
    "plt.title('Gross energy production per site (GWh) metadata vs file data');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some differences but roughly the same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I erase the leap day of 2008 because it will mess up with my dayofyear grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wdf=wdf[~((wdf.index.month == 2) & (wdf.index.day == 29))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sum all sites, we are not interested in intra-site variability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wdfagg=wdf.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## calculate the average capacity factor per time slice "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create a column with a tag indicating the time slice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we create a dataframe instead of a series because otherwise gives some problems adding extra columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_wdfagg=pd.DataFrame({'production':wdfagg})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_wdfagg['season']='empty'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_wdfagg.loc[(df_wdfagg.index.month==3) & (df_wdfagg.index.day>20),'season']='spring'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==4),'season']='spring'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==5),'season']='spring'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==6) & (df_wdfagg.index.day<21),'season']='spring'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_wdfagg.loc[(df_wdfagg.index.month==6) & (df_wdfagg.index.day>=21),'season']='summer'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==7),'season']='summer'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==8),'season']='summer'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==9) & (df_wdfagg.index.day<21),'season']='summer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_wdfagg.loc[(df_wdfagg.index.month==9) & (df_wdfagg.index.day>=21),'season']='fall'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==10),'season']='fall'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==11),'season']='fall'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==12) & (df_wdfagg.index.day<21),'season']='fall'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_wdfagg.loc[(df_wdfagg.index.month==12) & (df_wdfagg.index.day>=21),'season']='winter'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==1),'season']='winter'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==2),'season']='winter'\n",
    "df_wdfagg.loc[(df_wdfagg.index.month==3) & (df_wdfagg.index.day<21),'season']='winter'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14410f828>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x143b42a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_wdfagg.boxplot(by='season',grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_tag_ the slices per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_wdfagg['dailyts']='empty'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_wdfagg.loc[(df_wdfagg.index.hour==6) | \n",
    "              (df_wdfagg.index.hour==7) | \n",
    "              (df_wdfagg.index.hour==8),'dailyts']='P1' \n",
    "\n",
    "df_wdfagg.loc[(df_wdfagg.index.hour==17) | \n",
    "              (df_wdfagg.index.hour==18) | \n",
    "              (df_wdfagg.index.hour==19),'dailyts']='P2' \n",
    "\n",
    "df_wdfagg.loc[(wdfagg.index.hour>8) & \n",
    "              (wdfagg.index.hour<17),'dailyts']='D' \n",
    "\n",
    "df_wdfagg.loc[(wdfagg.index.hour<6) |\n",
    "              (wdfagg.index.hour>19),'dailyts']='N' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking we don't have empty values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all hours have a daily ts tag\n"
     ]
    }
   ],
   "source": [
    "if len(df_wdfagg['dailyts'].unique())!=4:\n",
    "    print('we have a problem')\n",
    "else:\n",
    "    print('all hours have a daily ts tag')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create a tag combining the seasonal and daily conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_wdfagg['tstag']='empty' # initialise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='N') & (df_wdfagg['season']=='spring'),'tstag']='RN'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='N') & (df_wdfagg['season']=='summer'),'tstag']='SN'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='N') & (df_wdfagg['season']=='winter'),'tstag']='WN'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='N') & (df_wdfagg['season']=='fall'),'tstag']='FN'\n",
    "\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='D') & (df_wdfagg['season']=='spring'),'tstag']='RD'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='D') & (df_wdfagg['season']=='summer'),'tstag']='SD'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='D') & (df_wdfagg['season']=='winter'),'tstag']='WD'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='D') & (df_wdfagg['season']=='fall'),'tstag']='FD'\n",
    "\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='P1') & (df_wdfagg['season']=='spring'),'tstag']='RP1'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='P1') & (df_wdfagg['season']=='summer'),'tstag']='SP1'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='P1') & (df_wdfagg['season']=='winter'),'tstag']='WP1'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='P1') & (df_wdfagg['season']=='fall'),'tstag']='FP1'\n",
    "\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='P2') & (df_wdfagg['season']=='spring'),'tstag']='RP2'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='P2') & (df_wdfagg['season']=='summer'),'tstag']='SP2'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='P2') & (df_wdfagg['season']=='winter'),'tstag']='WP2'\n",
    "df_wdfagg.loc[(df_wdfagg['dailyts']=='P2') & (df_wdfagg['season']=='fall'),'tstag']='FP2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e0c2240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp2=df_wdfagg.boxplot(by='tstag',figsize=(7,3))\n",
    "bp2.set_title('')\n",
    "bp2.set_xlabel('time slice')\n",
    "bp2.set_ylabel('average hourly electricity \\n production (MWh)')\n",
    "plt.suptitle('')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('boxplot_wind.png',dpi=600,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculation of availability factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the hourly availability factor is the hourly aggregated production divided by the total capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_wdfagg['CF']=df_wdfagg['production']/metadata.loc[(metadata['Province']=='Quebec') &\n",
    "             (metadata['35TRGT Scenario']==1),'Capacity (MW)'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39617675820713205"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wdfagg['CF'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cf_avg_metadata=metadata.loc[(metadata['Province']=='Quebec') &\n",
    "             (metadata['35TRGT Scenario']==1),'Capacity Factor (%)'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37749815255905556"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf_avg_metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The capacity factor in the metadata is slightly lower, maybe because of transmission and distribution losses. Just in case, we adjust the values so the average CF corresponds to the summary of the metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_wdfagg['CF2']=(df_wdfagg['CF']*cf_avg_metadata/df_wdfagg['CF'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "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>CF</th>\n",
       "      <th>CF2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tstag</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FD</th>\n",
       "      <td>0.438181</td>\n",
       "      <td>0.417522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FN</th>\n",
       "      <td>0.457169</td>\n",
       "      <td>0.435614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FP1</th>\n",
       "      <td>0.473116</td>\n",
       "      <td>0.450810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FP2</th>\n",
       "      <td>0.378074</td>\n",
       "      <td>0.360249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RD</th>\n",
       "      <td>0.344499</td>\n",
       "      <td>0.328257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RN</th>\n",
       "      <td>0.376761</td>\n",
       "      <td>0.358998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RP1</th>\n",
       "      <td>0.436327</td>\n",
       "      <td>0.415756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RP2</th>\n",
       "      <td>0.325859</td>\n",
       "      <td>0.310496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.328111</td>\n",
       "      <td>0.312642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SN</th>\n",
       "      <td>0.372321</td>\n",
       "      <td>0.354767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP1</th>\n",
       "      <td>0.430833</td>\n",
       "      <td>0.410520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP2</th>\n",
       "      <td>0.303485</td>\n",
       "      <td>0.289177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WD</th>\n",
       "      <td>0.411129</td>\n",
       "      <td>0.391745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>0.435841</td>\n",
       "      <td>0.415293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP1</th>\n",
       "      <td>0.448726</td>\n",
       "      <td>0.427570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP2</th>\n",
       "      <td>0.357249</td>\n",
       "      <td>0.340406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CF       CF2\n",
       "tstag                    \n",
       "FD     0.438181  0.417522\n",
       "FN     0.457169  0.435614\n",
       "FP1    0.473116  0.450810\n",
       "FP2    0.378074  0.360249\n",
       "RD     0.344499  0.328257\n",
       "RN     0.376761  0.358998\n",
       "RP1    0.436327  0.415756\n",
       "RP2    0.325859  0.310496\n",
       "SD     0.328111  0.312642\n",
       "SN     0.372321  0.354767\n",
       "SP1    0.430833  0.410520\n",
       "SP2    0.303485  0.289177\n",
       "WD     0.411129  0.391745\n",
       "WN     0.435841  0.415293\n",
       "WP1    0.448726  0.427570\n",
       "WP2    0.357249  0.340406"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wdfagg[['CF','CF2','tstag']].groupby('tstag').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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