pvlib-introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "__dWsDewn7UD"
   },
   "source": [
    "![tutorial banner](https://github.com/PVSC-Python-Tutorials/PVPMC_2022/blob/main/images/PVLib_PVPMCLogo.PNG?raw=true)\n",
    "\n",
    "\n",
    "# Welcome!\n",
    "\n",
    "**Welcome to the EU PVPMC 2024 pvlib-python introduction tutorial!**\n",
    "\n",
    "This notebook shows how to use Google Colab, a free no-installation tool for running python code from your web browser, for writing code and running pvlib for PV modeling tasks.\n",
    "\n",
    "Notebooks are organized by text and code \"cells\".  You can double-click a cell to start editing it.  To render a text cell, or execute a code cell, hit shift+enter.\n",
    "\n",
    "**NOTE: The tutorial has been updated after the PVPMC workshop and example code has been filled in the empty cells.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "orRWLccIn7UP"
   },
   "source": [
    "## Step 1: install pvlib\n",
    "\n",
    "First, we need to get pvlib installed in the Colab environment.  Active the following code cell (left click) and execute it (shift+enter) to automatically download and install pvlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c2QraVuun7UQ",
    "outputId": "55f8eb17-6be6-457a-a70a-a729543b0e4f"
   },
   "outputs": [],
   "source": [
    "!pip install pvlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1zHiQYXEn7UU"
   },
   "source": [
    "## Step 2: use pvlib\n",
    "\n",
    "Here's a simple example of running pvlib code: using pvlib and pandas (a library for data analysis), calculate a clear-sky irradiance profile for Copenhagen for today (August 22)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "qkZrdCu7n7UY",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pvlib\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "-vw0gFIan7Ud",
    "tags": []
   },
   "outputs": [],
   "source": [
    "cph = pvlib.location.Location(latitude=55.6846, longitude=12.5389)\n",
    "times = pd.date_range(start='2024-08-22 00:00', end='2024-08-22 23:59', freq='5min', tz='Europe/Copenhagen')\n",
    "clearsky_irradiance = cph.get_clearsky(times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "0pHB_2JWn7Un",
    "outputId": "db21b55b-3b54-4a82-dd53-a956e2f6b799",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'CPH Clear-Sky Simulation'}, ylabel='Irradiance [W/m2]'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clearsky_irradiance.plot(ylabel='Irradiance [W/m2]', title='CPH Clear-Sky Simulation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FKOImXx2n7U0"
   },
   "source": [
    "If you want to look up how to use a function, you can either look up its page in the pvlib documentation (https://pvlib-python.readthedocs.io/en/stable/reference/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fCeAgQ_J70SO"
   },
   "source": [
    "## Step 3: Irradiance data\n",
    "Now, let's get serious and get some actual irradiance data. For this purpose, we will use pvlib's iotools module. You can read about the type of weather data supported [here](https://pvlib-python.readthedocs.io/en/stable/user_guide/weather_data.html).\n",
    "\n",
    "In this tutorial, we will retrieve TMY irradiance data from PVGIS. The relevant pvlib function is [pvlib.iotools.get_pvgis_tmy](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_pvgis_tmy.html).\n",
    "\n",
    "Can you figure out how to download data for Copenhagen?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "ywbl317d70dg"
   },
   "outputs": [
    {
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       "      <th>relative_humidity</th>\n",
       "      <th>ghi</th>\n",
       "      <th>dni</th>\n",
       "      <th>dhi</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-31 19:00:00+00:00</th>\n",
       "      <td>4.97</td>\n",
       "      <td>88.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.03</td>\n",
       "      <td>5.77</td>\n",
       "      <td>171.0</td>\n",
       "      <td>101958.0</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-31 20:00:00+00:00</th>\n",
       "      <td>4.05</td>\n",
       "      <td>87.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>295.97</td>\n",
       "      <td>5.61</td>\n",
       "      <td>168.0</td>\n",
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       "      <th>2015-12-31 21:00:00+00:00</th>\n",
       "      <td>3.13</td>\n",
       "      <td>87.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>167.0</td>\n",
       "      <td>101998.0</td>\n",
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       "      <th>2015-12-31 22:00:00+00:00</th>\n",
       "      <td>2.21</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>5.28</td>\n",
       "      <td>165.0</td>\n",
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      "text/plain": [
       "                           temp_air  relative_humidity  ghi  dni  dhi   IR(h)  \\\n",
       "time(UTC)                                                                       \n",
       "2010-01-01 00:00:00+00:00      0.37              86.32  0.0  0.0  0.0  279.74   \n",
       "2010-01-01 01:00:00+00:00     -0.54              85.95  0.0  0.0  0.0  275.69   \n",
       "2010-01-01 02:00:00+00:00     -1.46              85.59  0.0  0.0  0.0  271.63   \n",
       "2010-01-01 03:00:00+00:00     -2.38              85.22  0.0  0.0  0.0  267.57   \n",
       "2010-01-01 04:00:00+00:00     -3.30              84.85  0.0  0.0  0.0  263.52   \n",
       "...                             ...                ...  ...  ...  ...     ...   \n",
       "2015-12-31 19:00:00+00:00      4.97              88.15  0.0  0.0  0.0  300.03   \n",
       "2015-12-31 20:00:00+00:00      4.05              87.78  0.0  0.0  0.0  295.97   \n",
       "2015-12-31 21:00:00+00:00      3.13              87.42  0.0  0.0  0.0  291.91   \n",
       "2015-12-31 22:00:00+00:00      2.21              87.05  0.0  0.0  0.0  287.86   \n",
       "2015-12-31 23:00:00+00:00      1.29              86.69  0.0  0.0  0.0  283.80   \n",
       "\n",
       "                           wind_speed  wind_direction  pressure  \n",
       "time(UTC)                                                        \n",
       "2010-01-01 00:00:00+00:00        4.95            46.0  100091.0  \n",
       "2010-01-01 01:00:00+00:00        4.78            47.0  100065.0  \n",
       "2010-01-01 02:00:00+00:00        4.62            48.0  100038.0  \n",
       "2010-01-01 03:00:00+00:00        4.45            48.0  100031.0  \n",
       "2010-01-01 04:00:00+00:00        4.29            48.0  100025.0  \n",
       "...                               ...             ...       ...  \n",
       "2015-12-31 19:00:00+00:00        5.77           171.0  101958.0  \n",
       "2015-12-31 20:00:00+00:00        5.61           168.0  101974.0  \n",
       "2015-12-31 21:00:00+00:00        5.44           167.0  101998.0  \n",
       "2015-12-31 22:00:00+00:00        5.28           165.0  102021.0  \n",
       "2015-12-31 23:00:00+00:00        5.11           164.0  102044.0  \n",
       "\n",
       "[8760 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here to retrieve irradiance data\n",
    "data, months, inputs, meta = pvlib.iotools.get_pvgis_tmy(\n",
    "    latitude=55.6846,\n",
    "    longitude=12.5389,\n",
    ")\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ywbl317d70dg"
   },
   "source": [
    "## Step 4: Calculate solar position\n",
    "\n",
    "When using irradiance data, we almost always need to know the solar position for each time step.\n",
    "\n",
    "Can you figure out how to calculate the solar position for the time steps of the time series downloaded in the previous step?\n",
    "\n",
    "*Hint: the time steps are the index of the DataFrame from the previous step, use DataFrame.index.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "ywbl317d70dg"
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2010-01-01 02:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2010-01-01 04:00:00+00:00</th>\n",
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       "      <td>-29.072812</td>\n",
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       "      <th>2015-12-31 19:00:00+00:00</th>\n",
       "      <td>123.886753</td>\n",
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       "      <td>278.422770</td>\n",
       "      <td>-2.971086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 20:00:00+00:00</th>\n",
       "      <td>132.015754</td>\n",
       "      <td>132.015754</td>\n",
       "      <td>-42.015754</td>\n",
       "      <td>-42.015754</td>\n",
       "      <td>292.595250</td>\n",
       "      <td>-2.990932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 21:00:00+00:00</th>\n",
       "      <td>139.251545</td>\n",
       "      <td>139.251545</td>\n",
       "      <td>-49.251545</td>\n",
       "      <td>-49.251545</td>\n",
       "      <td>309.460176</td>\n",
       "      <td>-3.010770</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-31 22:00:00+00:00</th>\n",
       "      <td>144.714399</td>\n",
       "      <td>144.714399</td>\n",
       "      <td>-54.714399</td>\n",
       "      <td>-54.714399</td>\n",
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       "      <td>-3.030600</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-31 23:00:00+00:00</th>\n",
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       "      <td>147.291854</td>\n",
       "      <td>-57.291854</td>\n",
       "      <td>-57.291854</td>\n",
       "      <td>354.504179</td>\n",
       "      <td>-3.050422</td>\n",
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       "<p>8760 rows × 6 columns</p>\n",
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      "text/plain": [
       "                           apparent_zenith      zenith  apparent_elevation  \\\n",
       "time(UTC)                                                                    \n",
       "2010-01-01 00:00:00+00:00       146.214221  146.214221          -56.214221   \n",
       "2010-01-01 01:00:00+00:00       141.863475  141.863475          -51.863475   \n",
       "2010-01-01 02:00:00+00:00       135.266987  135.266987          -45.266987   \n",
       "2010-01-01 03:00:00+00:00       127.435007  127.435007          -37.435007   \n",
       "2010-01-01 04:00:00+00:00       119.072812  119.072812          -29.072812   \n",
       "...                                    ...         ...                 ...   \n",
       "2015-12-31 19:00:00+00:00       123.886753  123.886753          -33.886753   \n",
       "2015-12-31 20:00:00+00:00       132.015754  132.015754          -42.015754   \n",
       "2015-12-31 21:00:00+00:00       139.251545  139.251545          -49.251545   \n",
       "2015-12-31 22:00:00+00:00       144.714399  144.714399          -54.714399   \n",
       "2015-12-31 23:00:00+00:00       147.291854  147.291854          -57.291854   \n",
       "\n",
       "                           elevation     azimuth  equation_of_time  \n",
       "time(UTC)                                                           \n",
       "2010-01-01 00:00:00+00:00 -56.214221   19.631435         -3.299708  \n",
       "2010-01-01 01:00:00+00:00 -51.863475   42.052113         -3.319362  \n",
       "2010-01-01 02:00:00+00:00 -45.266987   60.452896         -3.339007  \n",
       "2010-01-01 03:00:00+00:00 -37.435007   75.639157         -3.358643  \n",
       "2010-01-01 04:00:00+00:00 -29.072812   88.794833         -3.378271  \n",
       "...                              ...         ...               ...  \n",
       "2015-12-31 19:00:00+00:00 -33.886753  278.422770         -2.971086  \n",
       "2015-12-31 20:00:00+00:00 -42.015754  292.595250         -2.990932  \n",
       "2015-12-31 21:00:00+00:00 -49.251545  309.460176         -3.010770  \n",
       "2015-12-31 22:00:00+00:00 -54.714399  330.134063         -3.030600  \n",
       "2015-12-31 23:00:00+00:00 -57.291854  354.504179         -3.050422  \n",
       "\n",
       "[8760 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here to calculate solar position\n",
    "solar_position = cph.get_solarposition(data.index)\n",
    "\n",
    "solar_position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S6IhoBRU9oid"
   },
   "source": [
    "## Step 5: Calculate plane of array irradiance\n",
    "The irradiance data we have obtained contains the three components:\n",
    "- global horizontal irradiance (GHI)\n",
    "- diffuse horizontal irraddiance (DHI)\n",
    "- direct normal irradiance (DNI)\n",
    "\n",
    "However, to determine PV generation we are interested in the plane of array (POA) irradiance also known as global tilted irradiance. To transpose the standard irradiance components to tilted irradiance we need to use a transposition model.\n",
    "\n",
    "Can you find the suitable pvlib function to calculate plane of array irradiance and calculate the irradiance for a south-facing system with a 40-degree tilt?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "cQRma_bz9ovk"
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>time(UTC)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01 00:00:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:00:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 02:00:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 03:00:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 04:00:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 19:00:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 20:00:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 21:00:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 22:00:00+00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:00:00+00:00</th>\n",
       "      <td>0.0</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>8760 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           poa_global  poa_direct  poa_diffuse  \\\n",
       "time(UTC)                                                        \n",
       "2010-01-01 00:00:00+00:00         0.0         0.0          0.0   \n",
       "2010-01-01 01:00:00+00:00         0.0         0.0          0.0   \n",
       "2010-01-01 02:00:00+00:00         0.0         0.0          0.0   \n",
       "2010-01-01 03:00:00+00:00         0.0         0.0          0.0   \n",
       "2010-01-01 04:00:00+00:00         0.0         0.0          0.0   \n",
       "...                               ...         ...          ...   \n",
       "2015-12-31 19:00:00+00:00         0.0         0.0          0.0   \n",
       "2015-12-31 20:00:00+00:00         0.0         0.0          0.0   \n",
       "2015-12-31 21:00:00+00:00         0.0         0.0          0.0   \n",
       "2015-12-31 22:00:00+00:00         0.0         0.0          0.0   \n",
       "2015-12-31 23:00:00+00:00         0.0         0.0          0.0   \n",
       "\n",
       "                           poa_sky_diffuse  poa_ground_diffuse  \n",
       "time(UTC)                                                       \n",
       "2010-01-01 00:00:00+00:00              0.0                 0.0  \n",
       "2010-01-01 01:00:00+00:00              0.0                 0.0  \n",
       "2010-01-01 02:00:00+00:00              0.0                 0.0  \n",
       "2010-01-01 03:00:00+00:00              0.0                 0.0  \n",
       "2010-01-01 04:00:00+00:00              0.0                 0.0  \n",
       "...                                    ...                 ...  \n",
       "2015-12-31 19:00:00+00:00              0.0                 0.0  \n",
       "2015-12-31 20:00:00+00:00              0.0                 0.0  \n",
       "2015-12-31 21:00:00+00:00              0.0                 0.0  \n",
       "2015-12-31 22:00:00+00:00              0.0                 0.0  \n",
       "2015-12-31 23:00:00+00:00              0.0                 0.0  \n",
       "\n",
       "[8760 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here to calculate plane of array irradiance\n",
    "poa_irradiance = pvlib.irradiance.get_total_irradiance(\n",
    "    surface_tilt=40,\n",
    "    surface_azimuth=180,\n",
    "    dni=data['dni'],\n",
    "    ghi=data['ghi'],\n",
    "    dhi=data['dhi'],\n",
    "    solar_zenith=solar_position['apparent_zenith'],\n",
    "    solar_azimuth=solar_position['azimuth'])\n",
    "\n",
    "poa_irradiance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 6: Calculate module temperature\n",
    "\n",
    "The power efficiency of a PV module depends on its temperature (roughly, a 0.4% decrease for every increase in degree C).  Module temperature can be estimated based on ambient temperature and POA irradiance.\n",
    "\n",
    "pvlib includes several models for estimating module temperature. Can you find one in the documentation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time(UTC)\n",
       "2010-01-01 00:00:00+00:00    0.37\n",
       "2010-01-01 01:00:00+00:00   -0.54\n",
       "2010-01-01 02:00:00+00:00   -1.46\n",
       "2010-01-01 03:00:00+00:00   -2.38\n",
       "2010-01-01 04:00:00+00:00   -3.30\n",
       "                             ... \n",
       "2015-12-31 19:00:00+00:00    4.97\n",
       "2015-12-31 20:00:00+00:00    4.05\n",
       "2015-12-31 21:00:00+00:00    3.13\n",
       "2015-12-31 22:00:00+00:00    2.21\n",
       "2015-12-31 23:00:00+00:00    1.29\n",
       "Length: 8760, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here to calculate module temperature using a model of your choosing\n",
    "module_temperature = pvlib.temperature.faiman(\n",
    "    poa_global=poa_irradiance['poa_global'],\n",
    "    temp_air=data['temp_air'],\n",
    "    # default values\n",
    "    wind_speed=1.0,\n",
    "    u0=25.0,\n",
    "    u1=6.84)\n",
    "\n",
    "module_temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zp8gfHoj-bZ3"
   },
   "source": [
    "# Step 7: Calculate PV power\n",
    "\n",
    "With the plane of array irradiance and module temperature ready, we can now estimate the PV output power.  There are several modeling approaches available here, including:\n",
    "\n",
    "* Simple efficiency (like in PVWatts v5)\n",
    "* Single-diode models, like in PVsyst and SAM\n",
    "* Empirical methods like the SAPM\n",
    "\n",
    "Each type of these PV models requires different types of parameters.  They all produce estimates of the maximum power point (Pmp), while the fancier ones also estimate other quantities like open circuit voltage and short circuit current.\n",
    "\n",
    "The simplest option is to use [pvlib.pvsystem.pvwatts_dc](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.pvsystem.pvwatts_dc.html).  Can you use it with the POA irradiance and module temperature to estimate the output power for a 300 W module with a temperature coefficient of -0.004?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "k3xKoWFA-bkW"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write your code here to calculate PV power\n",
    "dc_power = pvlib.pvsystem.pvwatts_dc(\n",
    "    g_poa_effective=poa_irradiance['poa_global'],\n",
    "    # simplified assumptions that module and cell temperature are the same\n",
    "    temp_cell=module_temperature,\n",
    "    pdc0=300,\n",
    "    gamma_pdc=-0.004,\n",
    "    temp_ref=25.0,\n",
    ")\n",
    "\n",
    "dc_power.reset_index(drop=True).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZW1vJBdJn7U8"
   },
   "source": [
    "## Extra: next steps\n",
    "The purpose of this tutorial was to give a brief introduction to some of the functionality available in pvlib, show you how to use the pvlib documentation, and perform a simple weather-to-power calculation.\n",
    "\n",
    "Once you're comfortable running this example, now it's time to investigate a topic of your choice.  Feel free to choose your own topic, or you can try out one of the ideas listed here: https://github.com/PV-Tutorials/2024_pvlib_PVPMC-Salt-Lake/discussions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JreBQIskn7U9"
   },
   "source": [
    "[![Creative Commons License](https://i.creativecommons.org/l/by/4.0/88x31.png)](http://creativecommons.org/licenses/by/4.0/)\n",
    "\n",
    "This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/)."
   ]
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