Tutorial C - Modeling Module's Performance Advanced.ipynb

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    "![tutorialpromo](images/tutorial_banner.PNG)\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."
   ]
  },
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   "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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   "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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   "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 parametrs 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,
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   "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/PV-Tutorials/pyData-2021-Solar-PV-Modeling/main/requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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"
   ]
  },
  {
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   "execution_count": 2,
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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]])]"
      ]
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     "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)"
   ]
  },
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   "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/latest/generated/pvlib.pvsystem.retrieve_sam.html#pvlib.pvsystem.retrieve_sam)\n",
    "* [`pvlib.pvsystem.calcparams_cec()`](https://pvlib-python.readthedocs.io/en/latest/generated/pvlib.pvsystem.calcparams_cec.html)\n",
    "* [`pvlib.pvsystem.singlediode()`](https://pvlib-python.readthedocs.io/en/latest/generated/pvlib.pvsystem.singlediode.html)\n",
    "* [`pvlib.ivtools.sdm.fit_cec_sam()`](https://pvlib-python.readthedocs.io/en/latest/generated/pvlib.ivtools.sdm.fit_cec_sam.html)"
   ]
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       "  <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",
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       "      <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",
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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": 4,
   "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": 4,
     "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": 5,
   "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": 5,
     "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": 6,
   "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/latest/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/latest/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": 7,
   "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": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 22.47343394,  21.33361989,  18.40248885,  15.37301683,\n",
       "        46.09149166,  43.8742866 ,  38.16845408,  32.26328096,\n",
       "        93.53042326,  89.21121134,  78.09399194,  66.58417955,\n",
       "       140.31155148, 133.92211282, 117.4814962 , 100.47025604,\n",
       "       186.01690382, 177.57186861, 155.8544359 , 133.40606247,\n",
       "       230.45392244, 219.96093865, 192.99618253, 165.16041729,\n",
       "       252.16127408, 240.6459279 , 211.06605369, 180.55357125])"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "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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    "name": "ipython",
    "version": 3
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