Tutorial 2 - POA Irradiance and Module Temperature.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![tutorialpromo](images/tutorial_banner.PNG)\n",
    "\n",
    "\n",
    "# Tutorial 2 - POA Irradiance \n",
    "\n",
    "This notebook shows how to use pvlib to transform the three irradiance components (GHI, DHI, and DNI) into POA irradiance, the main driver of a PV system. Then this POA and weather data will be used to calculate Module Temperature\n",
    "\n",
    "![Overview](images/tutorial_2_overview.PNG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PV Concepts\n",
    "- Plane of Array Irradiance\n",
    "- Angle of Incidence\n",
    "- Time delta for solar position\n",
    "- GHI vs POA for fixed tilt system and tracked systems\n",
    "- Module temperature and it's dependence on POA and wind\n",
    "\n",
    "## Python Concepts\n",
    "- pandas Timedelta\n",
    "- making a new dataframe from existing columns\n",
    "- resampling\n",
    "- bar ploting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is transposition?\n",
    "\n",
    "The amount of sunlight collected by a PV panel depends on how well the panel orientation matches incoming sunlight.  For example, a rooftop array facing West will produce hardly any energy in the morning when the sun is in the East because the panel can only \"see\" the dim part of the sky away from the sun.  \n",
    "\n",
    "![Overview](images/t2_solarpanel_directions.PNG)\n",
    "\n",
    "\n",
    "As the sun comes into view and moves towards the center of the panel's field of view, the panel will collect more and more irradiance.  This concept is what defines plane-of-array irradiance -- the amount of sunlight available to be collected at a given panel orientation.  Like the three \"basic\" irradiance components, POA irradiance is measured in watts per square meter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Each irradiance component is considered separately when modeling POA irradiance.  For example, calculating the component of direct irradiance (DNI) that is incident on a panel is solved with straightforward geometry based on the angle of incidence.  Finding the POA component of diffuse irradiance (DHI) is more complex and can vary based on atmospheric conditions.  Many models, ranging from simple models with lots of assumptions to strongly empirical models, have been published to transpose DHI into the diffuse POA component.  A third component of POA irradiance is light that reflects off the ground before being collected by the PV panel.  Functions to calculate each of these components are provided by pvlib.\n",
    "\n",
    "\n",
    "![Overview](images/t2_POA.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How is array orientation defined?\n",
    "\n",
    "Two parameters define the panel orientation, one that measures the cardinal direction (North, East, South, West), and one that measures how high in the sky the panel faces:\n",
    "\n",
    "- tilt; measured in degrees from horizontal.  A flat panel is at tilt=0 and a panel standing on its edge has tilt=90.\n",
    "- azimuth; measured in degrees from North.  The direction along the horizon the panel is facing.  N=0, E=90, S=180, W=270.\n",
    "\n",
    "A fixed array has fixed tilt and azimuth, but a tracker array constantly changes its orientation to best match the sun's position.  So depending on the system configuration, tilt and azimuth may or may not be time series values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now to Code:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Step 0 If running on Google Collab:\n",
    "\n",
    "If running on google colab, uncomment the next cell and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install -r https://raw.githubusercontent.com/PV-Tutorials/2024_PVSC/requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import your libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11.2\n"
     ]
    }
   ],
   "source": [
    "import pvlib\n",
    "import pandas as pd  # for data wrangling\n",
    "import matplotlib.pyplot as plt  # for visualization\n",
    "import pathlib  # for finding the example dataset\n",
    "\n",
    "print(pvlib.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Download the Weather Data you will use\n",
    "\n",
    "We are making you code. Go to tutorial 1, and copy the lines you need to download the weather data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hint 1: You will need the API\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hint 2: You will need the get_psm3 function call\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have succeeded in copy-pasting the above, congratulations, you ARE Coding!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Calculate Sun Position\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9.4.dev19+ge4356f9\n"
     ]
    }
   ],
   "source": [
    "# make a Location object corresponding to this TMY\n",
    "location = pvlib.location.Location(latitude=metadata['latitude'],\n",
    "                                   longitude=metadata['longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'metadata' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWe are looking at data from \u001b[39m\u001b[38;5;124m\"\u001b[39m, metadata[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mName\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m, metadata[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mState\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'metadata' is not defined"
     ]
    }
   ],
   "source": [
    "print(\"We are looking at data from \", metadata['Name'], \",\", metadata['State'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "solar_position = location.get_solarposition(times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PSA: Because part of the transposition process requires knowing where the sun is in the sky, let's use pvlib to calculate solar position.  There is a gotcha here! Depending on the source of your weather data and how it was averaged, you might need to calculate solar position at the specific position of the timestamp, or at the middle of the previous or current hour. \n",
    "\n",
    "NSRDB provides irradiance data at the middle of the hour already, so sun position should be calculated at that exact time.\n",
    "TMY3, now deprecated, used to average data 'right labeled', so the sun should be calculated 30 minutes before.\n",
    "What does PVGis data does?\n",
    "\n",
    "But that is not the only factor to consider. Softwares have certain expectations on the input data unless you specify otherwise.\n",
    "SAM expects data to be left labeled, unless you provide minute data in which case it models at the specific timestamp given.\n",
    "PVSyst expects data left labeled, unless you use their data loading setup and specify this is not the case.\n",
    "PVlib will calculate sun position at the timestamp given.\n",
    "\n",
    "This issue trips most modelers, as shown by the many international round-robins, and can cause discrepancies on sun position, tracker position, and energy yield.\n",
    "\n",
    "Pvlib calculates solar position for the exact timestamps you specify. The following code example shows how to adjust the times by half an hour, in case it is needed:\n",
    "\n",
    "```\n",
    "times = df_tmy.index - pd.Timedelta('30min')\n",
    "solar_position = location.get_solarposition(times)\n",
    "solar_position.index += pd.Timedelta('30min') # but remember to shift the index back to line up with the TMY data:\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two values needed here are the solar zenith (how close the sun is to overhead) and azimuth (what direction along the horizon the sun is, like panel azimuth).  The difference between `apparent_zenith` and `zenith` is that `apparent_zenith` includes the effect of atmospheric refraction.\n",
    "\n",
    "Now that we have a time series of solar position that matches our irradiance data, let's run a transposition model using the convenient wrapper function [`pvlib.irradiance.get_total_irradiance`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.irradiance.get_total_irradiance.html). The more complex transposition models like Perez and Hay Davies require additional weather inputs, so for simplicity we'll just use the basic `isotropic` model here, which is the default if nothing is passed for `model` keyword argument.  As an example, we'll model a fixed array tilted south at 20 degrees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Calculate fixed Tilt POA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_poa = pvlib.irradiance.get_total_irradiance(\n",
    "    surface_tilt=20,  # tilted 20 degrees from horizontal\n",
    "    surface_azimuth=180,  # facing South\n",
    "    dni=df_tmy['dni'],\n",
    "    ghi=df_tmy['ghi'],\n",
    "    dhi=df_tmy['dhi'],\n",
    "    solar_zenith=solar_position['apparent_zenith'],\n",
    "    solar_azimuth=solar_position['azimuth'],\n",
    "    model='isotropic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`get_total_irradiance` returns a DataFrame containing each of the POA components mentioned earlier (direct, diffuse, and ground), along with the total in-plane irradiance.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['poa_global', 'poa_direct', 'poa_diffuse', 'poa_sky_diffuse',\n",
       "       'poa_ground_diffuse'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_poa.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What angle should you tilt your modules at?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[PVEducation Solar Radiation on a Tilted Surface](https://www.pveducation.org/pvcdrom/properties-of-sunlight/solar-radiation-on-a-tilted-surface)\n",
    "\n",
    "Model poa again, but now at a surface_tilt of 30 degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1087538627.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[1], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    df_poa2 =\u001b[0m\n\u001b[1;37m             ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "df_poa2 = pvlib.irradiance.get_total_irradiance( ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'poa_20': df_poa['poa_global'],\n",
    "    'poa_30': df_poa2['poa_global'],\n",
    "})\n",
    "df_monthly = df.resample('M').sum()\n",
    "df_monthly.plot.bar()\n",
    "plt.ylabel('Monthly Insolation [W h/m$^2$]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows that, compared with a flat array, a tilted array receives significantly more insolation in the winter.  However, it comes at the cost of slightly less insolation in the summer.  The difference is all about solar position -- tilting up from horizontal gives a better match to solar position in winter, when the sun is low in the sky.  However it gives a slightly worse match in summer when the sun is very high in the sky.\n",
    "\n",
    "As an example, here's a sunny day in winter vs a sunny day in summer.  Note that the daily profile doesn't just change its height, it also changes its width (summer POA is \"skinnier\" than GHI)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc['1990-01-15'].plot()\n",
    "plt.ylabel('Irradiance [W/m$^2$]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference between GHI and POA is of course dependent on the tilt defining POA, but because it also depends on solar position, it varies from location to location.  Luckily, tools like pvlib make it easy to model!\n",
    "\n",
    "### 4. Modeling POA for a tracking system\n",
    "\n",
    "The previous section calculated the transposition assuming a fixed array tilt and azimuth.  Now we'll do the same for a tracking array that follows the sun across the sky.  The most common type of tracking array is what's called a single-axis tracker (SAT) that rotates from East to West to follow the sun.  We can calculate the time-dependent orientation of a SAT array with pvlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tracker_data = pvlib.tracking.singleaxis(\n",
    "    solar_position['apparent_zenith'],\n",
    "    solar_position['azimuth'],\n",
    "    axis_azimuth=180,  # axis is aligned N-S\n",
    "    )  # leave the rest of the singleaxis parameters like backtrack and gcr at their defaults\n",
    "tilt = tracker_data['surface_tilt'].fillna(0)\n",
    "azimuth = tracker_data['surface_azimuth'].fillna(0)\n",
    "\n",
    "# plot a day to illustrate:\n",
    "tracker_data['tracker_theta'].fillna(0).head(24).plot()\n",
    "plt.ylabel('Tracker Rotation [degrees]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows a single day of tracker operation.  The y-axis shows the tracker rotation from horizontal, so 0 degrees means the panels are flat.  In the morning, the trackers rotate to negative angles to face East towards the morning sun; in the afternoon they rotate to positive angles to face West towards the evening sun.  In the middle of the day, the trackers are flat because the sun is more or less overhead.\n",
    "\n",
    "Now we can model the irradiance collected by a tracking array -- we follow the same procedure as before, but using the timeseries tilt and azimuth this time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_poa_tracker = pvlib.irradiance.get_total_irradiance(\n",
    "    surface_tilt=tilt,  # time series for tracking array\n",
    "    surface_azimuth=azimuth,  # time series for tracking array\n",
    "    dni=df_tmy['dni'],\n",
    "    ghi=df_tmy['ghi'],\n",
    "    dhi=df_tmy['dhi'],\n",
    "    solar_zenith=solar_position['apparent_zenith'],\n",
    "    solar_azimuth=solar_position['azimuth'])\n",
    "tracker_poa = df_poa_tracker['poa_global']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare GHI and POA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc['1990-01-15', 'ghi'].plot()\n",
    "tracker_poa.loc['1990-01-15'].plot()\n",
    "plt.legend()\n",
    "plt.ylabel('Irradiance [W/m$^2$]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how different the daily profile is for the tracker array!  This is because the array can tilt steeply East and West to face towards the sun in early morning and late afternoon, meaning the edges of day get much higher irradiance than for a south-facing array.\n",
    "\n",
    "Note also that in the middle of the day, GHI and POA just touch each other -- this is because at solar noon, the array lies flat, and so POA is momentarily identical to GHI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the POA calculations discussed above do not address the partial blocking of diffuse light in arrays with multiple tilted rows of PV modules, so the results will be slightly (typically 1-3%) optimistic relative to real conditions or to commercial modeling software. Such corrections are likely to be added to pvlib in the future for specific generic geometries like fixed racks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V. Model Temperature\n",
    "\n",
    "Now that we have the necessary weather inputs, all that is left are the thermal parameters.  These characterize the thermal properties of the module as well as the module's mounting configuration.  Parameter values covering the common system designs are provided with pvlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_parameters = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']\n",
    "list(all_parameters.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`open_rack_glass_polymer` is appropriate for many large-scale systems (polymer backsheet; open racking), so we will use it here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = all_parameters['open_rack_glass_polymer']\n",
    "# note the \"splat\" operator \"**\" which expands the dictionary \"parameters\"\n",
    "# into a comma separated list of keyword arguments\n",
    "cell_temperature = pvlib.temperature.sapm_cell(\n",
    "    tracker_poa, df_tmy['DryBulb'], df_tmy['Wspd'], **parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's compare ambient temperature with cell temperature.  Notice how our modeled cell temperature rises significantly above ambient temperature during the day, especially on sunny days:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmy['temp_air'].head(24*7).plot()\n",
    "cell_temperature.head(24*7).plot()\n",
    "plt.grid()\n",
    "plt.legend(['Dry Bulb', 'Cell Temperature'])\n",
    "# note Python 3 can use unicode characters like the degrees symbol\n",
    "plt.ylabel('Temperature [°C]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wind speed also has an effect, but it's harder to see in a time series plot like this.  To make it clearer, let's make a scatter plot: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature_difference = cell_temperature - df_tmy['temp_air']\n",
    "plt.scatter(tracker_poa, temperature_difference, c=df_tmy['wind_speed'])\n",
    "plt.colorbar()\n",
    "# note you can use LaTeX math in matplotlib labels\n",
    "# compare \\degree\" with the unicode symbol above\n",
    "plt.ylabel('Temperature rise above ambient [$\\degree C$]')\n",
    "plt.xlabel('POA Irradiance [$W/m^2$]');\n",
    "plt.title('Cell temperature rise, colored by wind speed');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main trend is a bigger temperature difference as incident irradiance increases.  However, this plot shows that higher wind speed reduces the effect -- faster wind means more convective cooling, so a lower cell temperature than it would be in calm air.\n",
    "\n",
    "Note: the gap at the upper edge of the trend is an artifact of the low resolution of wind speed values in this TMY dataset; there are no values between 0 and 0.3 m/s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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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