{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capacity Price Data\n",
    "\n",
    "This notebook walk through how to use `gridstatus` to access to the NYISO latest capacity market report price data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gridstatus\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">NYCA</th>\n",
       "      <th colspan=\"3\" halign=\"left\">GHIJ</th>\n",
       "      <th colspan=\"3\" halign=\"left\">NYC</th>\n",
       "      <th colspan=\"3\" halign=\"left\">LI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Strip</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>Spot</th>\n",
       "      <th>Strip</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>Spot</th>\n",
       "      <th>Strip</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>Spot</th>\n",
       "      <th>Strip</th>\n",
       "      <th>Monthly</th>\n",
       "      <th>Spot</th>\n",
       "    </tr>\n",
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       "      <th></th>\n",
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       "      <th>2022-11-01</th>\n",
       "      <td>1.18</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.54</td>\n",
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       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>3.40</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.92</td>\n",
       "      <td>4.65</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.18</td>\n",
       "      <td>5.16</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.88</td>\n",
       "      <td>6.40</td>\n",
       "      <td>6.48</td>\n",
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       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>3.40</td>\n",
       "      <td>3.19</td>\n",
       "      <td>2.95</td>\n",
       "      <td>4.65</td>\n",
       "      <td>3.42</td>\n",
       "      <td>3.12</td>\n",
       "      <td>5.16</td>\n",
       "      <td>3.42</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.88</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>3.40</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.47</td>\n",
       "      <td>4.65</td>\n",
       "      <td>3.35</td>\n",
       "      <td>3.74</td>\n",
       "      <td>5.16</td>\n",
       "      <td>3.41</td>\n",
       "      <td>4.41</td>\n",
       "      <td>3.88</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>3.40</td>\n",
       "      <td>3.22</td>\n",
       "      <td>3.32</td>\n",
       "      <td>4.65</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.32</td>\n",
       "      <td>5.16</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.88</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>3.00</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.18</td>\n",
       "      <td>10.50</td>\n",
       "      <td>9.67</td>\n",
       "      <td>9.90</td>\n",
       "      <td>11.71</td>\n",
       "      <td>9.85</td>\n",
       "      <td>10.19</td>\n",
       "      <td>5.79</td>\n",
       "      <td>6.55</td>\n",
       "      <td>6.59</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>3.00</td>\n",
       "      <td>2.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>10.50</td>\n",
       "      <td>9.73</td>\n",
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       "      <td>6.68</td>\n",
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       "      <th>2017-07-01</th>\n",
       "      <td>3.00</td>\n",
       "      <td>3.15</td>\n",
       "      <td>2.26</td>\n",
       "      <td>10.50</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.75</td>\n",
       "      <td>11.71</td>\n",
       "      <td>10.25</td>\n",
       "      <td>9.86</td>\n",
       "      <td>5.79</td>\n",
       "      <td>6.55</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>3.00</td>\n",
       "      <td>2.41</td>\n",
       "      <td>3.89</td>\n",
       "      <td>10.50</td>\n",
       "      <td>10.25</td>\n",
       "      <td>10.01</td>\n",
       "      <td>11.71</td>\n",
       "      <td>10.55</td>\n",
       "      <td>10.24</td>\n",
       "      <td>5.79</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>3.00</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1.72</td>\n",
       "      <td>10.50</td>\n",
       "      <td>10.50</td>\n",
       "      <td>10.28</td>\n",
       "      <td>11.71</td>\n",
       "      <td>11.83</td>\n",
       "      <td>10.57</td>\n",
       "      <td>5.79</td>\n",
       "      <td>5.75</td>\n",
       "      <td>6.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>67 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "            NYCA                 GHIJ                   NYC                 \\\n",
       "           Strip Monthly  Spot  Strip Monthly   Spot  Strip Monthly   Spot   \n",
       "                                                                             \n",
       "2022-11-01  1.18    1.15  1.54   1.31    1.22   1.54   1.66    1.39   1.54   \n",
       "2022-10-01  3.40    2.94  2.92   4.65    3.21   3.18   5.16    3.21   3.27   \n",
       "2022-09-01  3.40    3.19  2.95   4.65    3.42   3.12   5.16    3.42   3.21   \n",
       "2022-08-01  3.40    3.25  3.47   4.65    3.35   3.74   5.16    3.41   4.41   \n",
       "2022-07-01  3.40    3.22  3.32   4.65    3.40   3.32   5.16    3.55   3.55   \n",
       "...          ...     ...   ...    ...     ...    ...    ...     ...    ...   \n",
       "2017-09-01  3.00    2.09  2.18  10.50    9.67   9.90  11.71    9.85  10.19   \n",
       "2017-08-01  3.00    2.24  2.18  10.50    9.73   9.69  11.71    9.90   9.85   \n",
       "2017-07-01  3.00    3.15  2.26  10.50    9.94   9.75  11.71   10.25   9.86   \n",
       "2017-06-01  3.00    2.41  3.89  10.50   10.25  10.01  11.71   10.55  10.24   \n",
       "2017-05-01  3.00    3.15  1.72  10.50   10.50  10.28  11.71   11.83  10.57   \n",
       "\n",
       "              LI                \n",
       "           Strip Monthly  Spot  \n",
       "                                \n",
       "2022-11-01  1.18    1.19  1.54  \n",
       "2022-10-01  3.88    6.40  6.48  \n",
       "2022-09-01  3.88    6.50  6.43  \n",
       "2022-08-01  3.88    6.50  6.71  \n",
       "2022-07-01  3.88    6.01  6.71  \n",
       "...          ...     ...   ...  \n",
       "2017-09-01  5.79    6.55  6.59  \n",
       "2017-08-01  5.79    6.68  6.67  \n",
       "2017-07-01  5.79    6.55  6.69  \n",
       "2017-06-01  5.79    6.50  6.69  \n",
       "2017-05-01  5.79    5.75  6.71  \n",
       "\n",
       "[67 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso = gridstatus.NYISO()\n",
    "df = iso.get_capacity_prices()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.47</td>\n",
       "      <td>3.74</td>\n",
       "      <td>4.41</td>\n",
       "      <td>6.71</td>\n",
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       "      <th>2022-07-01</th>\n",
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       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>2.18</td>\n",
       "      <td>9.90</td>\n",
       "      <td>10.19</td>\n",
       "      <td>6.59</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>2.18</td>\n",
       "      <td>9.69</td>\n",
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       "      <th>2017-07-01</th>\n",
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       "      <th>2017-06-01</th>\n",
       "      <td>3.89</td>\n",
       "      <td>10.01</td>\n",
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       "      <td>6.69</td>\n",
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       "      <th>2017-05-01</th>\n",
       "      <td>1.72</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.57</td>\n",
       "      <td>6.71</td>\n",
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      ],
      "text/plain": [
       "            NYCA   GHIJ    NYC    LI\n",
       "                                    \n",
       "2022-11-01  1.54   1.54   1.54  1.54\n",
       "2022-10-01  2.92   3.18   3.27  6.48\n",
       "2022-09-01  2.95   3.12   3.21  6.43\n",
       "2022-08-01  3.47   3.74   4.41  6.71\n",
       "2022-07-01  3.32   3.32   3.55  6.71\n",
       "...          ...    ...    ...   ...\n",
       "2017-09-01  2.18   9.90  10.19  6.59\n",
       "2017-08-01  2.18   9.69   9.85  6.67\n",
       "2017-07-01  2.26   9.75   9.86  6.69\n",
       "2017-06-01  3.89  10.01  10.24  6.69\n",
       "2017-05-01  1.72  10.28  10.57  6.71\n",
       "\n",
       "[67 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "spot_prices = df.loc[:, (slice(None), \"Spot\")].droplevel(1, axis='columns') #select just the spot market prices and drop the level 1 so plotly can plot it \n",
    "spot_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "fig = px.line(spot_prices, title=\"NYISO Capaciy Prices (Spot Auction)\")\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Date\", yaxis_title=\"Megawatts (MW)\"\n",
    ")\n",
    "fig.show(\"svg\")"
   ]
  }
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