Tutorial C - Modeling Module's Performance Advanced.ipynb

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    "![tutorial banner](images/PVLib_PVPMCLogo.PNG)\n",
    "\n",
    "\n",
    "# Tutorial C - PV Module Performance Advanced\n",
    "\n",
    "Now that we know how to obtain the plane of array (POA) irradiance and cell temperature, let's calculate a module's performance assuming a subset of irradiance and temperature conditions. The objectives for this tutorial are to use pvlib python to do the following:\n",
    "\n",
    "1. Retrieve a set of module CEC parameters from the latest [NREL System Advisor Model (SAM)](https://sam.nrel.gov/) library.\n",
    "2. Calculate the single diode model (SDM) parameters at standard test conditions (STC) and for a set of PV module test conditions known as IEC61853.\n",
    "3. Produce an IV curve for each of the IEC61853 test conditions.\n",
    "4. Derive the California Energy Commission (CEC) model parameters based on standard CEC test measurements.\n",
    "\n",
    "![Overview](images/tutorial_4_overview.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  PV Concepts:\n",
    "\n",
    "- STC Parameters\n",
    "- IV Curve\n",
    "- PV module energy conversion models\n",
    "    - Point Models\n",
    "        - [PVWatts](https://pvwatts.nrel.gov/)\n",
    "        - [Sandia Array Performance Model (SAPM)](https://energy.sandia.gov/wp-content/gallery/uploads/043535.pdf)\n",
    "    - Continuous Models\n",
    "        - CEC\n",
    "        - [PVSyst](https://www.pvsyst.com/)\n",
    "        - [DeSoto](https://doi.org/10.1016/j.solener.2005.06.010)\n",
    "- Low light and temperature module performance\n",
    "- IEC 61853 standard \n",
    "\n",
    "## Python Concepts:\n",
    "\n",
    "- [`numpy.meshgrid`](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html)\n",
    "- `try: except` clauses to catch errors\n",
    "- Transpose a Pandas dataframe\n",
    "- [Pandas series and index string methods](https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html#string-methods): _e.g._ `df.index.str.startswith('Canadian')` where `df` is a dataframe of PV modules."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard Test Conditions (STC)\n",
    "\n",
    "The most basic condition is called \"standard test conditions\" or STC, which is considered the reference for most PV modules. For example, all of the PV modules in the SAM CEC module database list their nameplate power at STC.\n",
    "* irradiance: 1000-W/m&sup2;\n",
    "* cell temperature: 25&deg;C\n",
    "* angle of incidence (AOI): 0&deg;\n",
    "* spectrum: AM1.5g (ASTM G-173)\n",
    "\n",
    "### Air mass (AM)\n",
    "\n",
    "The standard reference AM1.5g (ASTM G-173) is defined as the solar spectrum of global irradiance that passes through 1.5 atmospheres. For more information see [NREL Solar Spectra](https://www.nrel.gov/grid/solar-resource/spectra.html)."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IEC 61853 test conditions\n",
    "\n",
    "Another common set of test conditions is the IEC 61853 standard which provides a PV module test matrix that covers the expected range of inicident irradiance and cell temperatures for PV modules assuming that the irradiance is normal and the solar spectrum is similar to AM1.5g.\n",
    "\n",
    "* irradiance (W/m&sup2;): 100, 200, 400, 600, 800, 1000, 1100\n",
    "* module temperature (&deg;C): 15, 25, 50, 75\n",
    "* angle of incidence: 0&deg;\n",
    "* spectrum: AM1.5g (ASTM G-173)\n",
    "\n",
    "The figure below shows IEC 61853 test results performed at CFV labs:\n",
    "\n",
    "![Overview](images/t4_PANOverview.PNG)"
   ]
  },
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   "metadata": {},
   "source": [
    "Certain combinations are excluded because they're unlikely: (1100-W/m&sup2;, 15&deg;C), (400-W/m&sup2;, 75&deg;C), (200-W/m&sup2;, 50&deg;C), (200-W/m&sup2;, 75&deg;C), (100-W/m&sup2;, 50&deg;C), and (100-W/m&sup2;, 75&deg;C). The figure below shows SAM parameters fit to IEC61853 test results for two different cell technologies. The white space shows combinations of irradiance and temperature which were intentionally excluded from testing.\n",
    "\n",
    "![Overview](images/t4_SingleDiodeParameterBehavior_to_TemperatureandIrradiance.PNG)\n",
    "\n",
    "Attribution: [NREL Dobos, Freeman 2019](https://www.nrel.gov/docs/fy19osti/68637.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
    "# !pip install -r https://raw.githubusercontent.com/PVSC-Python-Tutorials/PVPMC_2022/main/requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pvlib and other useful python packages\n",
    "import pvlib\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "data": {
      "text/plain": [
       "[array([[15, 25, 50, 75],\n",
       "        [15, 25, 50, 75],\n",
       "        [15, 25, 50, 75],\n",
       "        [15, 25, 50, 75],\n",
       "        [15, 25, 50, 75],\n",
       "        [15, 25, 50, 75],\n",
       "        [15, 25, 50, 75]]),\n",
       " array([[ 100,  100,  100,  100],\n",
       "        [ 200,  200,  200,  200],\n",
       "        [ 400,  400,  400,  400],\n",
       "        [ 600,  600,  600,  600],\n",
       "        [ 800,  800,  800,  800],\n",
       "        [1000, 1000, 1000, 1000],\n",
       "        [1100, 1100, 1100, 1100]])]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# set STC reference conditions\n",
    "E0 = 1000  # W/m^2\n",
    "T0 = 25  # degC\n",
    "\n",
    "# set the IEC61853 test matrix\n",
    "E_IEC61853 = [100, 200, 400, 600, 800, 1000, 1100]  # irradiances [W/m^2]\n",
    "T_IEC61853 = [15, 25, 50, 75]  # temperatures [degC]\n",
    "\n",
    "# create a meshgrid of temperatures and irradiances\n",
    "# for all 28 combinations in the test matrix\n",
    "IEC61853 = np.meshgrid(T_IEC61853, E_IEC61853)\n",
    "\n",
    "# meshgrid returns two 2-D arrays in the same order as the input arguments\n",
    "# so the first item in the output is a 2-D array of temperatures and\n",
    "# the second item is the mesh of irradiances\n",
    "# display temperature and irradiance test matrices\n",
    "IEC61853"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Diode Model (SDM) & IV Curve\n",
    "\n",
    "PV module performance can be modeled using *point* or *continuous* IV-curve models. \n",
    "\n",
    "Point models like PVWatts and The Sandia Array Performance Model (SAPM, _aka_ King model) yield the current (I), voltage (V), and power (P) at a single or discrete set of points. \n",
    "PVWatts only yields the performance at the max power point (MPP) of the module, whereas the SAPM also yields the short circuit current (Isc), open circuit voltage (Voc).\n",
    "\n",
    "Continuous IV curve models like the CEC, PVsyst, and DeSoto models yield a relation between current and voltage called an IV curve, and therefore yield a continuous set of (V, I) points spanning from Isc to Voc and beyond. The domain of the IV curve is in quadrants 1, 2, and 4 where voltage is on the horizontal and current is on the vertical axis. The figure below from [PV Education PVCDROM](https://www.pveducation.org/pvcdrom/welcome-to-pvcdrom) shows an IV curve of an \"ideal\" cell.\n",
    "\n",
    "[![IV curve](images/t4_PVEducation_IVCurve.PNG)](https://www.pveducation.org/pvcdrom/modules-and-arrays/mismatch-effects)\n",
    "\n",
    "Attribution: [PV Education, UNSW, ASU, _et al._](https://www.pveducation.org/pvcdrom/modules-and-arrays/mismatch-effects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The IV curve relationship is based on an electrical analog called the \"single diode model\" or SDM which is defined by 5 parameters: the light current ($I_L$), shunt resistance ($R_{sh}$), series resistance ($R_s$), diode saturation current ($I_o$ or $I_{sat}$), and the diode ideality factor ($n$). Other symbols for diode ideality factor are ($\\gamma$) used by PVsyst and ($a$) used by SAM, but ($\\gamma$) is also frequently used for power temperature coefficient. This \"ideal\" cell is described by the electrical schematic drawing below.\n",
    "\n",
    "[![single diode model](https://pvpmc.sandia.gov/wp-content/uploads/2012/04/Single-Diode-EC2.png)](https://pvpmc.sandia.gov/modeling-steps/2-dc-module-iv/diode-equivalent-circuit-models/)\n",
    "\n",
    "Attribution: [Sandia NL PV Performance Modeling Collaborative](https://pvpmc.sandia.gov/modeling-steps/2-dc-module-iv/diode-equivalent-circuit-models/)\n",
    "\n",
    "Combining the components in the SDM using Ohm's and Kirchhoff's laws yields the following equation, which is implicit because current (I) is on both sides of the equation, and cannot be solved explicitly:\n",
    "\n",
    "$$ I = I_L - I_o \\left( \\exp \\left( \\frac{V + I R_s}{n V_T} \\right) - 1 \\right) - \\frac{V + I R_s}{R_{sh}} $$\n",
    "\n",
    "with the diode voltage ($V_D = V + I R_s$), the diode current ($I_D$) given by the ideal diode equation:\n",
    "\n",
    "$$ I_D = I_o \\left( \\exp \\left( \\frac{V_D}{n V_T} \\right) - 1 \\right) $$\n",
    "\n",
    "the thermal voltage ($V_T = k_T / q_e$), elementary charge ($q_e$), Boltzmann constant ($k_T$), and the shunt current ($I_{sh} = V_D / R_{sh}$)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CEC Model (_aka_ SAM or 6-parameter model)\n",
    "\n",
    "The California Energy Commision (CEC) contracted authorized testing labs to measure at STC the nameplate power (Pmp), Isc, Voc, and the MPP voltage and current (Vmp, Imp), as well as Isc and Voc temperature coefficients, the module dimensions, the number of series cells (Ns), parallel substrings (Np), module area in m&sup2; (Ac), and more. Tables of the CEC module parameters are available from the [Solar Equipment Lists](https://www.energy.ca.gov/programs-and-topics/programs/solar-equipment-lists). These measurements have been fit to the SDM by the NREL System Advisor Model (SAM) and stored in a CSV file that is bundled with SAM. You can access the [SAM library on GitHub](https://github.com/NREL/SAM/tree/develop/deploy/libraries). This SAM library of module coefficients derived from the CEC measurements are collectively called CEC modules and the SAM model that uses the derived SDM coefficients is called the CEC model. The CEC model used in SAM is sometimes also called the 6-parameter model because of the `Adjust` additional parameter which differentiates it from the DeSoto model.\n",
    "\n",
    "### pvlib python\n",
    "\n",
    "There are several functions we can use in pvlib python:\n",
    "\n",
    "* [`pvlib.pvsystem.retrieve_sam()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.pvsystem.retrieve_sam.html#pvlib.pvsystem.retrieve_sam)\n",
    "* [`pvlib.pvsystem.calcparams_cec()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.pvsystem.calcparams_cec.html)\n",
    "* [`pvlib.pvsystem.singlediode()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.pvsystem.singlediode.html)\n",
    "* [`pvlib.ivtools.sdm.fit_cec_sam()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.ivtools.sdm.fit_cec_sam.html)"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Technology</th>\n",
       "      <th>Bifacial</th>\n",
       "      <th>STC</th>\n",
       "      <th>PTC</th>\n",
       "      <th>A_c</th>\n",
       "      <th>Length</th>\n",
       "      <th>Width</th>\n",
       "      <th>N_s</th>\n",
       "      <th>I_sc_ref</th>\n",
       "      <th>V_oc_ref</th>\n",
       "      <th>...</th>\n",
       "      <th>a_ref</th>\n",
       "      <th>I_L_ref</th>\n",
       "      <th>I_o_ref</th>\n",
       "      <th>R_s</th>\n",
       "      <th>R_sh_ref</th>\n",
       "      <th>Adjust</th>\n",
       "      <th>gamma_r</th>\n",
       "      <th>BIPV</th>\n",
       "      <th>Version</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A10Green_Technology_A10J_S72_175</th>\n",
       "      <td>Mono-c-Si</td>\n",
       "      <td>0</td>\n",
       "      <td>175.0914</td>\n",
       "      <td>151.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.576</td>\n",
       "      <td>0.825</td>\n",
       "      <td>72</td>\n",
       "      <td>5.17</td>\n",
       "      <td>43.99</td>\n",
       "      <td>...</td>\n",
       "      <td>1.981696</td>\n",
       "      <td>5.175703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.316688</td>\n",
       "      <td>287.102203</td>\n",
       "      <td>16.057121</td>\n",
       "      <td>-0.5072</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A10Green_Technology_A10J_S72_180</th>\n",
       "      <td>Mono-c-Si</td>\n",
       "      <td>0</td>\n",
       "      <td>179.928</td>\n",
       "      <td>155.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.576</td>\n",
       "      <td>0.825</td>\n",
       "      <td>72</td>\n",
       "      <td>5.31</td>\n",
       "      <td>44.06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.988414</td>\n",
       "      <td>5.316148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.299919</td>\n",
       "      <td>259.047943</td>\n",
       "      <td>16.418983</td>\n",
       "      <td>-0.5072</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A10Green_Technology_A10J_S72_185</th>\n",
       "      <td>Mono-c-Si</td>\n",
       "      <td>0</td>\n",
       "      <td>184.7016</td>\n",
       "      <td>160.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.576</td>\n",
       "      <td>0.825</td>\n",
       "      <td>72</td>\n",
       "      <td>5.43</td>\n",
       "      <td>44.14</td>\n",
       "      <td>...</td>\n",
       "      <td>1.984817</td>\n",
       "      <td>5.435676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.311962</td>\n",
       "      <td>298.424438</td>\n",
       "      <td>15.688233</td>\n",
       "      <td>-0.5072</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A10Green_Technology_A10J_M60_220</th>\n",
       "      <td>Multi-c-Si</td>\n",
       "      <td>0</td>\n",
       "      <td>219.876</td>\n",
       "      <td>189.1</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.632</td>\n",
       "      <td>0.995</td>\n",
       "      <td>60</td>\n",
       "      <td>7.95</td>\n",
       "      <td>36.06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.673094</td>\n",
       "      <td>7.959062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140393</td>\n",
       "      <td>123.168404</td>\n",
       "      <td>21.875164</td>\n",
       "      <td>-0.5196</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A10Green_Technology_A10J_M60_225</th>\n",
       "      <td>Multi-c-Si</td>\n",
       "      <td>0</td>\n",
       "      <td>224.9856</td>\n",
       "      <td>193.5</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.632</td>\n",
       "      <td>0.995</td>\n",
       "      <td>60</td>\n",
       "      <td>8.04</td>\n",
       "      <td>36.24</td>\n",
       "      <td>...</td>\n",
       "      <td>1.671782</td>\n",
       "      <td>8.047206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14737</td>\n",
       "      <td>164.419479</td>\n",
       "      <td>20.698376</td>\n",
       "      <td>-0.5196</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
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       "                                  Technology Bifacial       STC    PTC    A_c  \\\n",
       "A10Green_Technology_A10J_S72_175   Mono-c-Si        0  175.0914  151.2    1.3   \n",
       "A10Green_Technology_A10J_S72_180   Mono-c-Si        0   179.928  155.7    1.3   \n",
       "A10Green_Technology_A10J_S72_185   Mono-c-Si        0  184.7016  160.2    1.3   \n",
       "A10Green_Technology_A10J_M60_220  Multi-c-Si        0   219.876  189.1  1.624   \n",
       "A10Green_Technology_A10J_M60_225  Multi-c-Si        0  224.9856  193.5  1.624   \n",
       "\n",
       "                                 Length  Width N_s I_sc_ref V_oc_ref  ...  \\\n",
       "A10Green_Technology_A10J_S72_175  1.576  0.825  72     5.17    43.99  ...   \n",
       "A10Green_Technology_A10J_S72_180  1.576  0.825  72     5.31    44.06  ...   \n",
       "A10Green_Technology_A10J_S72_185  1.576  0.825  72     5.43    44.14  ...   \n",
       "A10Green_Technology_A10J_M60_220  1.632  0.995  60     7.95    36.06  ...   \n",
       "A10Green_Technology_A10J_M60_225  1.632  0.995  60     8.04    36.24  ...   \n",
       "\n",
       "                                     a_ref   I_L_ref I_o_ref       R_s  \\\n",
       "A10Green_Technology_A10J_S72_175  1.981696  5.175703     0.0  0.316688   \n",
       "A10Green_Technology_A10J_S72_180  1.988414  5.316148     0.0  0.299919   \n",
       "A10Green_Technology_A10J_S72_185  1.984817  5.435676     0.0  0.311962   \n",
       "A10Green_Technology_A10J_M60_220  1.673094  7.959062     0.0  0.140393   \n",
       "A10Green_Technology_A10J_M60_225  1.671782  8.047206     0.0   0.14737   \n",
       "\n",
       "                                    R_sh_ref     Adjust gamma_r BIPV  \\\n",
       "A10Green_Technology_A10J_S72_175  287.102203  16.057121 -0.5072    N   \n",
       "A10Green_Technology_A10J_S72_180  259.047943  16.418983 -0.5072    N   \n",
       "A10Green_Technology_A10J_S72_185  298.424438  15.688233 -0.5072    N   \n",
       "A10Green_Technology_A10J_M60_220  123.168404  21.875164 -0.5196    N   \n",
       "A10Green_Technology_A10J_M60_225  164.419479  20.698376 -0.5196    N   \n",
       "\n",
       "                                            Version      Date  \n",
       "A10Green_Technology_A10J_S72_175  SAM 2018.11.11 r2  1/3/2019  \n",
       "A10Green_Technology_A10J_S72_180  SAM 2018.11.11 r2  1/3/2019  \n",
       "A10Green_Technology_A10J_S72_185  SAM 2018.11.11 r2  1/3/2019  \n",
       "A10Green_Technology_A10J_M60_220  SAM 2018.11.11 r2  1/3/2019  \n",
       "A10Green_Technology_A10J_M60_225  SAM 2018.11.11 r2  1/3/2019  \n",
       "\n",
       "[5 rows x 25 columns]"
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   ],
   "source": [
    "# use pvlib python to retrieve CEC module parameters from the SAM libraries\n",
    "# with the \"name\" argument set to \"CECMod\"\n",
    "CECMODS = pvlib.pvsystem.retrieve_sam(name='CECMod')\n",
    "\n",
    "# the CEC modules are a pandas DataFrame oriented as columns, transpose to arrange\n",
    "# as indices\n",
    "CECMODS.T.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CEC modules library\n",
    "\n",
    "Periodically a static copy of CEC module parameters is copied from the SAM library to pvlib python. The modules are roughly named according the following scheme:\n",
    "\n",
    "    <manufacturer name> <model name>\n",
    "\n",
    "Whitespace, dashes, and other non-alphanumerical characters are all replaced by underscores in pvlib python.\n",
    "\n",
    "EG: \"Canadian Solar Inc. CS6X-300M\" becomes\n",
    "\n",
    "    Canadian_Solar_Inc__CS6X_300M\n",
    "    \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Let's Explore:</b> <a href='https://www.ecodirect.com/Canadian-Solar-CS6X-300M-300W-36V-PV-Panel-p/canadian-solar-cs6x-300m.htm'> Click here to see the panel's Datasheet. </a></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main CEC module parameters are defined as follows:\n",
    "\n",
    "| parameter    | data type | description and units                                                                         |\n",
    "| ------------ | --------- | --------------------------------------------------------------------------------------------- |\n",
    "| `Technology` | string    | one of \"Mono-c-Si\", \"Multi-c-Si\", \"Thin Film\", \"CdTe\", or \"CIGS\" families of cells            |\n",
    "| `Bifacial`   | boolean   | is bifacial?                                                                                  |\n",
    "| `STC`        | float     | nameplate in W at STC                                                                         |\n",
    "| `PTC`        | float     | nameplate in W at PVUSA test conditions (1-sun, 20&deg; ambient temperature, 1-m/s windspeed) |\n",
    "| `A_c`        | float     | module area in m&sup2;                                                                        |\n",
    "| `Length`     | float     | module length in m;                                                                           |\n",
    "| `Width`      | float     | module width in m;                                                                            |\n",
    "| `N_s`        | int       | number of cells in series                                                                     |\n",
    "| `I_sc_ref`   | float     | short circuit current in A at reference condition                                             |\n",
    "| `V_oc_ref`   | float     | open circuit voltage in V at reference condition                                              |\n",
    "| `I_mp_ref`   | float     | max power current in A at reference condition                                                 |\n",
    "| `V_mp_ref`   | float     | max power voltage in V at reference condition                                                 |\n",
    "| `alpha_sc`   | float     | short circuit current temperature coefficient in A/&Delta;&deg;C                              |\n",
    "| `beta_oc`    | float     | open circuit voltage temperature coefficient in V/&Delta;&deg;C                               |\n",
    "| `T_NOCT`     | float     | normal operating cell temperature in &deg;C                                                   |\n",
    "| `a_ref`      | float     | diode ideality factor                                                                         |\n",
    "| `I_L_ref`    | float     | light or photogenerated current at reference condition in A                                   |\n",
    "| `I_o_ref`    | float     | diode saturation current at reference condition in A                                          |\n",
    "| `R_s`        | float     | series resistance in &Omega;                                                                  |\n",
    "| `R_sh_ref`   | float     | shunt resistance at reference condition in &Omega;                                            |\n",
    "| `Adjust`     | float     | adjustment to short circuit temperature coefficient in %                                      |\n",
    "| `gamma_r`    | float     | power temperature coefficient at reference condition in %/&Delta;&deg;C                       |\n",
    "| `BIPV`       | boolean   | is building integrated PV?                                                                    |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>Technology</th>\n",
       "      <th>Bifacial</th>\n",
       "      <th>STC</th>\n",
       "      <th>PTC</th>\n",
       "      <th>A_c</th>\n",
       "      <th>Length</th>\n",
       "      <th>Width</th>\n",
       "      <th>N_s</th>\n",
       "      <th>I_sc_ref</th>\n",
       "      <th>V_oc_ref</th>\n",
       "      <th>...</th>\n",
       "      <th>a_ref</th>\n",
       "      <th>I_L_ref</th>\n",
       "      <th>I_o_ref</th>\n",
       "      <th>R_s</th>\n",
       "      <th>R_sh_ref</th>\n",
       "      <th>Adjust</th>\n",
       "      <th>gamma_r</th>\n",
       "      <th>BIPV</th>\n",
       "      <th>Version</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canadian_Solar_Inc__CS5P_220M</th>\n",
       "      <td>Mono-c-Si</td>\n",
       "      <td>0</td>\n",
       "      <td>219.961</td>\n",
       "      <td>200.1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.602</td>\n",
       "      <td>1.061</td>\n",
       "      <td>96</td>\n",
       "      <td>5.1</td>\n",
       "      <td>59.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2.635926</td>\n",
       "      <td>5.11426</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.066023</td>\n",
       "      <td>381.254425</td>\n",
       "      <td>8.619516</td>\n",
       "      <td>-0.476</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canadian_Solar_Inc__CS6P_220M</th>\n",
       "      <td>Mono-c-Si</td>\n",
       "      <td>0</td>\n",
       "      <td>219.775</td>\n",
       "      <td>198.5</td>\n",
       "      <td>1.549</td>\n",
       "      <td>1.615</td>\n",
       "      <td>0.959</td>\n",
       "      <td>60</td>\n",
       "      <td>7.97</td>\n",
       "      <td>36.9</td>\n",
       "      <td>...</td>\n",
       "      <td>1.515583</td>\n",
       "      <td>7.980784</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397651</td>\n",
       "      <td>293.871094</td>\n",
       "      <td>-3.311191</td>\n",
       "      <td>-0.436</td>\n",
       "      <td>N</td>\n",
       "      <td>SAM 2018.11.11 r2</td>\n",
       "      <td>1/3/2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Technology Bifacial      STC    PTC    A_c  \\\n",
       "Canadian_Solar_Inc__CS5P_220M  Mono-c-Si        0  219.961  200.1    1.7   \n",
       "Canadian_Solar_Inc__CS6P_220M  Mono-c-Si        0  219.775  198.5  1.549   \n",
       "\n",
       "                              Length  Width N_s I_sc_ref V_oc_ref  ...  \\\n",
       "Canadian_Solar_Inc__CS5P_220M  1.602  1.061  96      5.1     59.4  ...   \n",
       "Canadian_Solar_Inc__CS6P_220M  1.615  0.959  60     7.97     36.9  ...   \n",
       "\n",
       "                                  a_ref   I_L_ref I_o_ref       R_s  \\\n",
       "Canadian_Solar_Inc__CS5P_220M  2.635926   5.11426     0.0  1.066023   \n",
       "Canadian_Solar_Inc__CS6P_220M  1.515583  7.980784     0.0  0.397651   \n",
       "\n",
       "                                 R_sh_ref    Adjust gamma_r BIPV  \\\n",
       "Canadian_Solar_Inc__CS5P_220M  381.254425  8.619516  -0.476    N   \n",
       "Canadian_Solar_Inc__CS6P_220M  293.871094 -3.311191  -0.436    N   \n",
       "\n",
       "                                         Version      Date  \n",
       "Canadian_Solar_Inc__CS5P_220M  SAM 2018.11.11 r2  1/3/2019  \n",
       "Canadian_Solar_Inc__CS6P_220M  SAM 2018.11.11 r2  1/3/2019  \n",
       "\n",
       "[2 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One trick to find the modules is to search the indices using string filters\n",
    "# For example: find all Candian Solar 220-W mono-Si modules\n",
    "cs_220m_mods = CECMODS.T.index.str.startswith('Canadian_Solar') & CECMODS.T.index.str.contains('220M')\n",
    "CECMODS.T[cs_220m_mods]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Technology            Mono-c-Si\n",
       "Bifacial                      0\n",
       "STC                     219.961\n",
       "PTC                       200.1\n",
       "A_c                         1.7\n",
       "Length                    1.602\n",
       "Width                     1.061\n",
       "N_s                          96\n",
       "I_sc_ref                    5.1\n",
       "V_oc_ref                   59.4\n",
       "I_mp_ref                   4.69\n",
       "V_mp_ref                   46.9\n",
       "alpha_sc               0.004539\n",
       "beta_oc               -0.222156\n",
       "T_NOCT                     42.4\n",
       "a_ref                  2.635926\n",
       "I_L_ref                 5.11426\n",
       "I_o_ref                     0.0\n",
       "R_s                    1.066023\n",
       "R_sh_ref             381.254425\n",
       "Adjust                 8.619516\n",
       "gamma_r                  -0.476\n",
       "BIPV                          N\n",
       "Version       SAM 2018.11.11 r2\n",
       "Date                   1/3/2019\n",
       "Name: Canadian_Solar_Inc__CS5P_220M, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# that was almost too easy, let's use the CS5P-220M\n",
    "# NOTE: don't transpose CECMODS, get column with desired module \n",
    "CS_220M = CECMODS['Canadian_Solar_Inc__CS5P_220M']\n",
    "CS_220M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pop Quiz #1\n",
    "\n",
    "Get any CEC module from the `CECMODS` or pvfree.\n",
    "\n",
    "* Which module did you get?\n",
    "* How did you get it?\n",
    "* What is the module's nameplate power, Isc, Voc, Imp, and Vmp?\n",
    "* Who is the manufacturer?\n",
    "* What cell technology is it?\n",
    "* How does it differ from CS5P-220M?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Sorry, 'your module goes here' wasn't found in CECMODS. Please try again. ***\n"
     ]
    }
   ],
   "source": [
    "# use this cell to search CECMODS.T or pvfree\n",
    "your_mod = 'your module goes here'\n",
    "try:\n",
    "    your_mod = CECMODS[your_mod]\n",
    "except KeyError:\n",
    "    print(f\"*** Sorry, '{your_mod}' wasn't found in CECMODS. Please try again. ***\")\n",
    "else:\n",
    "    # display your module\n",
    "    your_mod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate CEC Parameters\n",
    "\n",
    "The module parameters are given at the reference condition. Use [`pvlib.pvsystem.calcparams_cec()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.pvsystem.calcparams_cec.html) to generate the five SDM parameters at your desired irradiance and temperature to use with [`pvlib.pvsystem.singlediode()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.pvsystem.singlediode.html) to calculate the IV curve information.\n",
    "\n",
    "### `nNsVth`, what's this?\n",
    "\n",
    "The diode ideality factor (n) is combined with the number of cells (Ns) and the thermal voltage (Vth) to create a convenience parameter. This is [syntactic sugar](https://en.wikipedia.org/wiki/Syntactic_sugar)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.50727824, 0.511426  , 0.5217954 , 0.5321648 ],\n",
       "       [1.01455648, 1.022852  , 1.0435908 , 1.0643296 ],\n",
       "       [2.02911296, 2.045704  , 2.0871816 , 2.1286592 ],\n",
       "       [3.04366944, 3.068556  , 3.1307724 , 3.19298881],\n",
       "       [4.05822592, 4.091408  , 4.1743632 , 4.25731841],\n",
       "       [5.0727824 , 5.11426   , 5.217954  , 5.32164801],\n",
       "       [5.58006064, 5.625686  , 5.7397494 , 5.85381281]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finally this is the magic\n",
    "temp_cell, effective_irradiance = IEC61853\n",
    "cecparams = pvlib.pvsystem.calcparams_cec(\n",
    "        effective_irradiance=effective_irradiance,\n",
    "        temp_cell=temp_cell,\n",
    "        alpha_sc=CS_220M.alpha_sc,\n",
    "        a_ref=CS_220M.a_ref,\n",
    "        I_L_ref=CS_220M.I_L_ref,\n",
    "        I_o_ref=CS_220M.I_o_ref,\n",
    "        R_sh_ref=CS_220M.R_sh_ref,\n",
    "        R_s=CS_220M.R_s,\n",
    "        Adjust=CS_220M.Adjust,\n",
    "        EgRef=1.121,\n",
    "        dEgdT=-0.0002677)\n",
    "IL, I0, Rs, Rsh, nNsVth = cecparams\n",
    "# display the photogenerated current\n",
    "IL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV Curve Info\n",
    "\n",
    "Now that we have the 5 SDM parameters (`IL`, `Io`, `Rs`, `Rsh`, and `nNsVth`) corresponding to each of the test conditions in the IEC61853 test matrix, we can calculate the IV curve information for that irradiance and cell temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 22.47344592,  21.33362028,  18.40249412,  15.37302335,\n",
       "        46.09151614,  43.87428661,  38.16846011,  32.26328112,\n",
       "        93.53042781,  89.2112251 ,  78.09399529,  66.58418353,\n",
       "       140.31159673, 133.92211364, 117.48149685, 100.47026643,\n",
       "       186.01692511, 177.5719005 , 155.85443596, 133.40606739,\n",
       "       230.45399426, 219.96096044, 192.99622092, 165.16045701,\n",
       "       252.16130617, 240.64592808, 211.06610839, 180.55358287])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# flatten the meshgrid to allow single diode to broadcast the output\n",
    "curve_info = pvlib.pvsystem.singlediode(\n",
    "    photocurrent=IL.flatten(),\n",
    "    saturation_current=I0.flatten(),\n",
    "    resistance_series=Rs,\n",
    "    resistance_shunt=Rsh.flatten(),\n",
    "    nNsVth=nNsVth.flatten(),\n",
    "    ivcurve_pnts=101,\n",
    "    method='lambertw')\n",
    "# display the max power points\n",
    "curve_info['p_mp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the calculated curves:\n",
    "exclude = [(1100, 15), (400, 75), (200, 50), (200, 75), (100, 50), (100, 75)]\n",
    "kolor = ['#1f77b4', '#2ca02c', '#8c564b', '#9467bd', '#d62728', '#e377c2', '#ff7f0e']\n",
    "f, ax = plt.subplots(2, 2, figsize=(16, 10), sharex=True, sharey=True)\n",
    "for m, irr in enumerate(E_IEC61853):\n",
    "    for n, tc in enumerate(T_IEC61853):\n",
    "        if (irr, tc) in exclude:\n",
    "            continue\n",
    "        i = n + 4*m\n",
    "        j = n // 2, n % 2\n",
    "        label = (\n",
    "            \"$G_{eff}$ \" + f\"{irr} $W/m^2$\"\n",
    "        )\n",
    "        ax[j].plot(curve_info['v'][i], curve_info['i'][i], label=label, c=kolor[m])\n",
    "        v_mp = curve_info['v_mp'][i]\n",
    "        i_mp = curve_info['i_mp'][i]\n",
    "        # mark the MPP\n",
    "        ax[j].plot(v_mp, i_mp, ls='', marker='o', c=kolor[m])\n",
    "        ax[j].vlines(v_mp, 0, i_mp, linestyle='dashed', color=kolor[m])\n",
    "        # just repeat this every time doesn't hurt anyone\n",
    "        ax[j].legend(loc='right')\n",
    "        if j[0] == 1:\n",
    "            ax[j].set_xlabel('Module voltage [V]')\n",
    "        if j[1] == 0:\n",
    "            ax[j].set_ylabel('Module current [A]')\n",
    "        ax[j].set_title(f\"{CS_220M.name}, \" + \"$T_{cell}$ \" + f\"{tc} \" + \"$^{\\circ}C$\")\n",
    "        ax[j].grid(True)\n",
    "        ax[j].set_xlim([0, 80])\n",
    "\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "So is this what you expected? What do you notice about the relation between voltage and temperature? What about the relation between current and irradiance? What other patterns do you observe?"
   ]
  },
  {
   "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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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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