pvlib-introduction_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "__dWsDewn7UD"
   },
   "source": [
    "![tutorial banner](https://github.com/PV-Tutorials/2025_pvlib_pvps_task13/blob/main/static/pvlib_pvps_logo.png?raw=true)\n",
    "\n",
    "# Workshop on Bifacial Tracking Systems\n",
    "\n",
    "Welcome to the Bifacial Tracking Systems Workshop organized by the IEA PVPS Task 13 in Rome, Italy on February 27th, 2025. The workshop will give an introduction to how to model single-axis tracking (SAT) with bifacial photovoltaic (PV) modules.\n",
    "\n",
    "In the tutorial, we will be simulating a real Italian PV SAT system with the following specifications:\n",
    "\n",
    "* **Modules**\n",
    "    * Module type: 550 W Bifacial Mono PERC modules JAM72D30 ([link to datasheet](https://www.jasolar.com/uploadfile/2021/0706/20210706053456650.pdf))\n",
    "    * Number of modules: 5859\n",
    "    * Bifaciality: 70% ± 10%\n",
    "* **Site and Trackers**\n",
    "    * Location: Canino (VT), Italy\n",
    "    * Configuration: Single-axis trackers – 1P\n",
    "    * Pitch: 4.5 m\n",
    "    * Torque tube height: 1.8 m\n",
    "* **Inverters**\n",
    "    * Inverter type: Huawei 215 kW KTL-H0 (string inverter) ([link to datasheet](https://solar.huawei.com/-/media/Solar/attachment/pdf/apac/datasheet/SUN2000-215KTL-H0.pdf))\n",
    "    * Number of inverters: 14\n",
    "    * Modules per string: 27"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "__dWsDewn7UD"
   },
   "source": [
    "## Step 0: Familiarize yourself with Google Colab\n",
    "\n",
    "The notebook can be seamlessly run in Google Colab, a free no-installation tool for running Python code from your web browser. \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",
    "In the code cell below, try out some math calculations to explore how the notebook and Python works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "__dWsDewn7UD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "# write some code and execute it here\n",
    "a = 1\n",
    "b = 2\n",
    "print(a*b)"
   ]
  },
  {
   "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": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c2QraVuun7UQ",
    "outputId": "55f8eb17-6be6-457a-a70a-a729543b0e4f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pvlib==0.11.2 in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (0.11.2)\n",
      "Requirement already satisfied: numpy>=1.19.3 in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from pvlib==0.11.2) (1.26.3)\n",
      "Requirement already satisfied: pandas>=1.3.0 in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from pvlib==0.11.2) (2.2.3)\n",
      "Requirement already satisfied: pytz in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from pvlib==0.11.2) (2023.3.post1)\n",
      "Requirement already satisfied: requests in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from pvlib==0.11.2) (2.31.0)\n",
      "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from pvlib==0.11.2) (1.11.4)\n",
      "Requirement already satisfied: h5py in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from pvlib==0.11.2) (3.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from pandas>=1.3.0->pvlib==0.11.2) (2.8.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from pandas>=1.3.0->pvlib==0.11.2) (2023.4)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from requests->pvlib==0.11.2) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from requests->pvlib==0.11.2) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from requests->pvlib==0.11.2) (2.1.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from requests->pvlib==0.11.2) (2023.11.17)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\arajen\\anaconda3\\envs\\pvlib\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->pvlib==0.11.2) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pvlib==0.11.2"
   ]
  },
  {
   "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 Rome for today (February 27)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "qkZrdCu7n7UY",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pvlib\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "-vw0gFIan7Ud",
    "tags": []
   },
   "outputs": [],
   "source": [
    "site_location = pvlib.location.Location(latitude=42.443, longitude=11.745)\n",
    "\n",
    "times = pd.date_range(start='2025-02-27 00:00', end='2025-02-27 23:59', freq='5min', tz='Europe/Rome')\n",
    "\n",
    "clearsky_irradiance = site_location.get_clearsky(times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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': 'Clear-Sky Simulation'}, ylabel='Irradiance [W/m2]'>"
      ]
     },
     "execution_count": 5,
     "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='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 weather data for Rome?**\n",
    "\n",
    "*Hint:* To calculate the solar position correctly, you need to shift the index to be in the middle of the period. The data is labeled by the left edge (start of interval)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "ywbl317d70dg"
   },
   "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>temp_air</th>\n",
       "      <th>relative_humidity</th>\n",
       "      <th>ghi</th>\n",
       "      <th>dni</th>\n",
       "      <th>dhi</th>\n",
       "      <th>IR(h)</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>pressure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time(UTC)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2018-01-01 00:30:00+00:00</th>\n",
       "      <td>8.70</td>\n",
       "      <td>87.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>294.13</td>\n",
       "      <td>3.25</td>\n",
       "      <td>156.0</td>\n",
       "      <td>98035.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 01:30:00+00:00</th>\n",
       "      <td>9.01</td>\n",
       "      <td>89.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>301.17</td>\n",
       "      <td>3.66</td>\n",
       "      <td>151.0</td>\n",
       "      <td>97976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 02:30:00+00:00</th>\n",
       "      <td>9.31</td>\n",
       "      <td>91.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>308.21</td>\n",
       "      <td>4.07</td>\n",
       "      <td>147.0</td>\n",
       "      <td>97927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 03:30:00+00:00</th>\n",
       "      <td>9.61</td>\n",
       "      <td>93.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>315.25</td>\n",
       "      <td>4.48</td>\n",
       "      <td>147.0</td>\n",
       "      <td>97869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 04:30:00+00:00</th>\n",
       "      <td>9.92</td>\n",
       "      <td>96.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>322.29</td>\n",
       "      <td>4.89</td>\n",
       "      <td>147.0</td>\n",
       "      <td>97761.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 19:30:00+00:00</th>\n",
       "      <td>7.18</td>\n",
       "      <td>76.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>258.92</td>\n",
       "      <td>1.21</td>\n",
       "      <td>62.0</td>\n",
       "      <td>98428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 20:30:00+00:00</th>\n",
       "      <td>7.49</td>\n",
       "      <td>78.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265.96</td>\n",
       "      <td>1.62</td>\n",
       "      <td>64.0</td>\n",
       "      <td>98408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:30:00+00:00</th>\n",
       "      <td>7.79</td>\n",
       "      <td>80.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>273.00</td>\n",
       "      <td>2.02</td>\n",
       "      <td>65.0</td>\n",
       "      <td>98388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:30:00+00:00</th>\n",
       "      <td>8.09</td>\n",
       "      <td>82.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>280.04</td>\n",
       "      <td>2.43</td>\n",
       "      <td>69.0</td>\n",
       "      <td>98388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:30:00+00:00</th>\n",
       "      <td>8.40</td>\n",
       "      <td>84.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>287.09</td>\n",
       "      <td>2.84</td>\n",
       "      <td>72.0</td>\n",
       "      <td>98369.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           temp_air  relative_humidity  ghi  dni  dhi   IR(h)  \\\n",
       "time(UTC)                                                                       \n",
       "2018-01-01 00:30:00+00:00      8.70              87.20  0.0  0.0  0.0  294.13   \n",
       "2018-01-01 01:30:00+00:00      9.01              89.42  0.0  0.0  0.0  301.17   \n",
       "2018-01-01 02:30:00+00:00      9.31              91.64  0.0  0.0  0.0  308.21   \n",
       "2018-01-01 03:30:00+00:00      9.61              93.85  0.0  0.0  0.0  315.25   \n",
       "2018-01-01 04:30:00+00:00      9.92              96.07  0.0  0.0  0.0  322.29   \n",
       "...                             ...                ...  ...  ...  ...     ...   \n",
       "2012-12-31 19:30:00+00:00      7.18              76.13  0.0  0.0  0.0  258.92   \n",
       "2012-12-31 20:30:00+00:00      7.49              78.34  0.0  0.0  0.0  265.96   \n",
       "2012-12-31 21:30:00+00:00      7.79              80.56  0.0  0.0  0.0  273.00   \n",
       "2012-12-31 22:30:00+00:00      8.09              82.77  0.0  0.0  0.0  280.04   \n",
       "2012-12-31 23:30:00+00:00      8.40              84.99  0.0  0.0  0.0  287.09   \n",
       "\n",
       "                           wind_speed  wind_direction  pressure  \n",
       "time(UTC)                                                        \n",
       "2018-01-01 00:30:00+00:00        3.25           156.0   98035.0  \n",
       "2018-01-01 01:30:00+00:00        3.66           151.0   97976.0  \n",
       "2018-01-01 02:30:00+00:00        4.07           147.0   97927.0  \n",
       "2018-01-01 03:30:00+00:00        4.48           147.0   97869.0  \n",
       "2018-01-01 04:30:00+00:00        4.89           147.0   97761.0  \n",
       "...                               ...             ...       ...  \n",
       "2012-12-31 19:30:00+00:00        1.21            62.0   98428.0  \n",
       "2012-12-31 20:30:00+00:00        1.62            64.0   98408.0  \n",
       "2012-12-31 21:30:00+00:00        2.02            65.0   98388.0  \n",
       "2012-12-31 22:30:00+00:00        2.43            69.0   98388.0  \n",
       "2012-12-31 23:30:00+00:00        2.84            72.0   98369.0  \n",
       "\n",
       "[8760 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "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=42.443,\n",
    "    longitude=11.745,\n",
    ")\n",
    "\n",
    "data.index = data.index + pd.Timedelta(minutes=30)\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 corresponding solar position for each time step.\n",
    "\n",
    "Can you figure out how to calculate the solar position for the time steps of the data downloaded in the previous step?\n",
    "\n",
    "*Hint: the time steps are the index of the ``data`` DataFrame. To access the index, you can use DataFrame.index.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "ywbl317d70dg"
   },
   "outputs": [
    {
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      "text/plain": [
       "                           apparent_zenith      zenith  apparent_elevation  \\\n",
       "time(UTC)                                                                    \n",
       "2018-01-01 00:30:00+00:00       155.274315  155.274315          -65.274315   \n",
       "2018-01-01 01:30:00+00:00       146.187240  146.187240          -56.187240   \n",
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       "2018-01-01 04:30:00+00:00       113.607076  113.607076          -23.607076   \n",
       "\n",
       "                           elevation     azimuth  equation_of_time  \n",
       "time(UTC)                                                           \n",
       "2018-01-01 00:30:00+00:00 -65.274315   44.019724         -3.336272  \n",
       "2018-01-01 01:30:00+00:00 -56.187240   65.585246         -3.355906  \n",
       "2018-01-01 02:30:00+00:00 -45.625328   79.792687         -3.375531  \n",
       "2018-01-01 03:30:00+00:00 -34.609825   90.713175         -3.395147  \n",
       "2018-01-01 04:30:00+00:00 -23.607076  100.270466         -3.414753  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here to calculate solar position\n",
    "solar_position = site_location.get_solarposition(data.index)\n",
    "\n",
    "solar_position.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S6IhoBRU9oid"
   },
   "source": [
    "## Step 5: Calculate tracker orientations\n",
    "\n",
    "The next step is to determine the orientation (rotation) of the single-axis trackers as this influences the incident irradiance.\n",
    "\n",
    "The orientation of a single-axis tracker can be calculated using the [``pvlib.tracking.singleaxis``](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.tracking.singleaxis.html) function.\n",
    "\n",
    "You may assume that the tracking axis has the typical orientation of north-south, which corresponds to ``axis_azimuth=0``. Also, we will assume that the SAT features backtracking and has a maximum tracker rotation angle of 60 degrees.\n",
    "\n",
    "Last, you may notice that the ground coverage ratio (GCR) is also needed but hasn't been provided. This dimensionless ratio can be calculated by dividing the module length (given that it is a 1P configuration) by the pitch (row-to-row distance)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "S6IhoBRU9oid"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracker_theta</th>\n",
       "      <th>aoi</th>\n",
       "      <th>surface_azimuth</th>\n",
       "      <th>surface_tilt</th>\n",
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       "      <th>time(UTC)</th>\n",
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      "text/plain": [
       "                           tracker_theta        aoi  surface_azimuth  \\\n",
       "time(UTC)                                                              \n",
       "2018-01-01 07:30:00+00:00       8.231975  77.220058        90.000000   \n",
       "2018-01-01 08:30:00+00:00      25.294967  61.122137        90.000000   \n",
       "2018-01-01 09:30:00+00:00      50.145898  57.490355        90.000000   \n",
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       "2018-01-01 11:30:00+00:00      -7.392794  65.243089       270.000004   \n",
       "\n",
       "                           surface_tilt  \n",
       "time(UTC)                                \n",
       "2018-01-01 07:30:00+00:00      8.231975  \n",
       "2018-01-01 08:30:00+00:00     25.294967  \n",
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       "2018-01-01 11:30:00+00:00      7.392794  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_tracker_angle = 60  # [degrees]\n",
    "pitch = 4.5  # [m] center-to-center tracker spacing in the horizontal dimension\n",
    "module_length = 2.285  # [m] from datasheet\n",
    "gcr = module_length / pitch  # [-] ground cover ratio\n",
    "\n",
    "# calculate tracker rotations for a single-axis tracker\n",
    "tracker_orientations = pvlib.tracking.singleaxis(\n",
    "    apparent_zenith=solar_position['apparent_zenith'],\n",
    "    apparent_azimuth=solar_position['azimuth'],\n",
    "    axis_tilt=0,  # flat terrain\n",
    "    axis_azimuth=0,  # north-south\n",
    "    max_angle=max_tracker_angle,\n",
    "    backtrack=True,\n",
    "    gcr=gcr,\n",
    ")\n",
    "\n",
    "tracker_orientations.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S6IhoBRU9oid"
   },
   "source": [
    "## Step 6: Calculate plane of array irradiance\n",
    "The irradiance data we obtained from PVGIS contains the three standard components:\n",
    "- global horizontal irradiance (GHI)\n",
    "- diffuse horizontal irradiance (DHI)\n",
    "- direct normal irradiance (DNI)\n",
    "\n",
    "However, to determine PV generation, we need to know the incident irradiance on the module surface, which is denoted as plane of array (POA) irradiance. Additionally, since we are working with bifacial modules, we need to estimate both the front and rear-side POA irradiance.\n",
    "\n",
    "To do this, we will be using pvlib's [infinite sheds model](https://pvlib-python.readthedocs.io/en/stable/user_guide/bifacial.html#infinite-sheds) which is based on view factors and assumes the PV system comprises of parallel, evenly spaced rows on a level, horizontal surface. The function is called [``pvlib.bifacial.infinite_sheds.get_irradiance``](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.bifacial.infinite_sheds.get_irradiance.html).\n",
    "\n",
    "**Can you use the infinite sheds model to calculate front and rear side irradiance?** Remember you have determined the tracker orientation in the previous step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "cQRma_bz9ovk"
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 09:30:00+00:00</th>\n",
       "      <td>68.050622</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.050622</td>\n",
       "      <td>1.323832</td>\n",
       "      <td>66.726790</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.201663</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.201663</td>\n",
       "      <td>7.641009</td>\n",
       "      <td>11.560654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.222963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 10:30:00+00:00</th>\n",
       "      <td>180.125376</td>\n",
       "      <td>25.395292</td>\n",
       "      <td>154.730084</td>\n",
       "      <td>0.601475</td>\n",
       "      <td>154.128609</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.036007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.036007</td>\n",
       "      <td>17.839388</td>\n",
       "      <td>5.196619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>195.928077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 11:30:00+00:00</th>\n",
       "      <td>208.747124</td>\n",
       "      <td>26.699086</td>\n",
       "      <td>182.048038</td>\n",
       "      <td>0.057187</td>\n",
       "      <td>181.990851</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.076153</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.076153</td>\n",
       "      <td>20.569773</td>\n",
       "      <td>0.506380</td>\n",
       "      <td>0.0</td>\n",
       "      <td>223.205365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            poa_front  poa_front_direct  poa_front_diffuse  \\\n",
       "time(UTC)                                                                    \n",
       "2018-01-01 07:30:00+00:00    4.949915          0.000000           4.949915   \n",
       "2018-01-01 08:30:00+00:00   42.965596          0.000000          42.965596   \n",
       "2018-01-01 09:30:00+00:00   68.050622          0.000000          68.050622   \n",
       "2018-01-01 10:30:00+00:00  180.125376         25.395292         154.730084   \n",
       "2018-01-01 11:30:00+00:00  208.747124         26.699086         182.048038   \n",
       "\n",
       "                           poa_front_ground_diffuse  poa_front_sky_diffuse  \\\n",
       "time(UTC)                                                                    \n",
       "2018-01-01 07:30:00+00:00                  0.001694               4.948221   \n",
       "2018-01-01 08:30:00+00:00                  0.157474              42.808122   \n",
       "2018-01-01 09:30:00+00:00                  1.323832              66.726790   \n",
       "2018-01-01 10:30:00+00:00                  0.601475             154.128609   \n",
       "2018-01-01 11:30:00+00:00                  0.057187             181.990851   \n",
       "\n",
       "                           shaded_fraction_front   poa_back  poa_back_direct  \\\n",
       "time(UTC)                                                                      \n",
       "2018-01-01 07:30:00+00:00                    0.0   0.507491              0.0   \n",
       "2018-01-01 08:30:00+00:00                    0.0   5.972947              0.0   \n",
       "2018-01-01 09:30:00+00:00                    0.0  19.201663              0.0   \n",
       "2018-01-01 10:30:00+00:00                    0.0  23.036007              0.0   \n",
       "2018-01-01 11:30:00+00:00                    0.0  21.076153              0.0   \n",
       "\n",
       "                           poa_back_diffuse  poa_back_ground_diffuse  \\\n",
       "time(UTC)                                                              \n",
       "2018-01-01 07:30:00+00:00          0.507491                 0.490387   \n",
       "2018-01-01 08:30:00+00:00          5.972947                 4.462223   \n",
       "2018-01-01 09:30:00+00:00         19.201663                 7.641009   \n",
       "2018-01-01 10:30:00+00:00         23.036007                17.839388   \n",
       "2018-01-01 11:30:00+00:00         21.076153                20.569773   \n",
       "\n",
       "                           poa_back_sky_diffuse  shaded_fraction_back  \\\n",
       "time(UTC)                                                               \n",
       "2018-01-01 07:30:00+00:00              0.017104                   0.0   \n",
       "2018-01-01 08:30:00+00:00              1.510724                   0.0   \n",
       "2018-01-01 09:30:00+00:00             11.560654                   0.0   \n",
       "2018-01-01 10:30:00+00:00              5.196619                   0.0   \n",
       "2018-01-01 11:30:00+00:00              0.506380                   0.0   \n",
       "\n",
       "                           poa_global  \n",
       "time(UTC)                              \n",
       "2018-01-01 07:30:00+00:00    5.298054  \n",
       "2018-01-01 08:30:00+00:00   47.063038  \n",
       "2018-01-01 09:30:00+00:00   81.222963  \n",
       "2018-01-01 10:30:00+00:00  195.928077  \n",
       "2018-01-01 11:30:00+00:00  223.205365  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here to calculate plane of array irradiance\n",
    "bifaciality = 0.7  # [-] from datasheet\n",
    "height = 1.8  # [m]\n",
    "albedo = 0.2  # [-]\n",
    "\n",
    "dni_extra = pvlib.irradiance.get_extra_radiation(data.index)\n",
    "\n",
    "front_rear_irradiance = pvlib.bifacial.infinite_sheds.get_irradiance(\n",
    "    surface_tilt=tracker_orientations['surface_tilt'],\n",
    "    surface_azimuth=tracker_orientations['surface_azimuth'],\n",
    "    solar_zenith=solar_position['apparent_zenith'],\n",
    "    solar_azimuth=solar_position['azimuth'],\n",
    "    gcr=gcr,\n",
    "    height=height,\n",
    "    pitch=pitch,\n",
    "    ghi=data['ghi'],\n",
    "    dhi=data['dhi'],\n",
    "    dni=data['dni'],\n",
    "    albedo=albedo,\n",
    "    model='haydavies',\n",
    "    dni_extra=dni_extra,\n",
    "    bifaciality=bifaciality,\n",
    ")\n",
    "\n",
    "front_rear_irradiance.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 7: 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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time(UTC)\n",
       "2018-01-01 07:30:00+00:00    10.909240\n",
       "2018-01-01 08:30:00+00:00    12.488489\n",
       "2018-01-01 09:30:00+00:00    13.696782\n",
       "2018-01-01 10:30:00+00:00    15.490398\n",
       "2018-01-01 11:30:00+00:00    16.363502\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "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=front_rear_irradiance['poa_global'],\n",
    "    temp_air=data['temp_air'],\n",
    "    # default values\n",
    "    wind_speed=data['wind_speed'],\n",
    "    u0=25.0,\n",
    "    u1=6.84)\n",
    "\n",
    "module_temperature.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zp8gfHoj-bZ3"
   },
   "source": [
    "# Step 7: Calculate DC 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 (MPP), 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 one module?\n",
    "\n",
    "*Note: for this exercise you may assume that the module and cell temperature are the same.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "k3xKoWFA-bkW"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "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=front_rear_irradiance['poa_global'],\n",
    "    # simplified assumptions that module and cell temperature are the same\n",
    "    temp_cell=module_temperature,\n",
    "    pdc0=550,\n",
    "    gamma_pdc=-0.0035,  # from datasheet\n",
    "    temp_ref=25.0,\n",
    ")\n",
    "\n",
    "dc_power.reset_index(drop=True).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "## Step 8: Reflect on the exercise\n",
    "\n",
    "We managed to model the output of one panel, how do you think we can extend the model workflow to simulate a full system?\n",
    "\n",
    "Last, this exercise made a number of simplifications. Can you name some? How do you think the accuracy was affected?"
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   "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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