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@mikofski
Last active January 7, 2021 17:42
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spectral mismatch
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"import numpy as np\n",
"from scipy.constants import e as q, k, c, h\n",
"from scipy.interpolate import interp1d\n",
"from openpyxl import load_workbook\n",
"from matplotlib import pyplot as plt\n",
"from solar_utils import *\n",
"from datetime import datetime\n",
"#plt.ion()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Problem Statement\n",
"Determine the difference between power production using spectrum and quantum efficiency vs. using airmass function\n",
"$f(AM)$\n",
"\n",
"#Methods\n",
"Three methods are used to determine power production depending on the type of irradiance data\n",
"\n",
"##Solar Spectrum & Quantum Efficiency\n",
"The short circuit current density, $J_{sc}$, can be calculated from the incident solar spectrum, $I(\\lambda)$ and the quantum efficiency of the cell, $EQE(\\lambda)$.\n",
"\n",
"$$I_{sc} = A_{cell}*\\frac{q}{h*c}*\\int_{0}^{\\infty}I(\\lambda)*EQE(\\lambda)*\\lambda*d\\lambda$$\n",
"\n",
"Quantum efficiency is measured at NREL and represents the conversion efficiency of the cell to generate carriers from photons as a function of wavelength, $\\lambda$.\n",
"\n",
"###ASTM-G173\n",
"For reference conditions the [standard AM-1.5 solar spectrum](http://rredc.nrel.gov/solar/spectra/am1.5/) is specified in ASTM-G173.\n",
"\n",
"###SPECTRL2\n",
"Solar spectrum can also be predicted using [SPECTRL2 model from NREL](http://rredc.nrel.gov/solar/models/spectral/), for different airmass and atmospheric conditions. Solar spectrum is affected by aerosol optical depth, $AOD$, and total water vapor. Aerosol optical depth is the rate of attenuation of light due to scattering by condensed gases and particulate matter. Water vapor absorbs strongly in several IR bands from 1000[nm] and up."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total Global = 1000.37 [W/m^2]\n"
]
},
{
"data": {
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2AP4EbGlmj+bKqtLA+rrARmbMG2D995qxUS7tGTNWzeX9HPBFgptwK+B9Zvxt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\n9o1i2qDoRNc9MX2jXHrf9ZrZgxYhdM8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"text/plain": [
"<matplotlib.figure.Figure at 0x44efe48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# CONSTANTS\n",
"DIRNAME = %pwd # path to present working directory\n",
"\n",
"# SPECTRL2 CONSTANTS\n",
"UNITS = 1 # set spectrl2 units to [W/m^2]\n",
"LOCATION = [40, -105, -7] # [deg] latitude, longitude\n",
"DATETIME = datetime(2015, 2, 27, 12, 6) # date and time\n",
"WEATHER = [1013.3, 25] # ambient pressure [mB] and temperature [C]\n",
"ORIENTATION = [37, 180] # [deg] panel tilt and aspect\n",
"# turbidity alpha exponent, asymmetry factor, ozone conc., AOD and water vapor\n",
"ATMOS_COND = [1.14, 0.65, 0.34, 0.27, 1.42]\n",
"ALBEDO = [0.3, 0.7, 0.8, 1.3, 2.5, 4.0] + ([0.2] * 6) # albedo vs wavelength\n",
"\n",
"# SPR-E20-435 Paramters\n",
"ACELL = 153.33 # [cm^2] area of cell\n",
"Isc0 = 6.5579 # [A] short circuit current at STC\n",
"Voc0 = 86.626 # [V] open circuit voltage at STC\n",
"Imp0 = 6.1304 # [A] max power current at STC\n",
"Vmp0 = 72.3771 # [V] max power voltage at STC\n",
"# temperature coefficients\n",
"alpha_Isc0 = 0.000395 # [T^-1] short circuit current\n",
"alpha_Imp0 = -0.00023 # [T^-1] max power current\n",
"beta_Voc0 = -0.248 # [V/T] open circuit voltage\n",
"beta_Vmp0 =-0.2584 # [V/T] max power voltage\n",
"nDF = 1.011 # diode ideality factor\n",
"# airmass function\n",
"fAM = [0.957, 0.0402, -0.008515, 0.0007141, -0.00002132]\n",
"# angle of incidence function\n",
"fAOI = [1.0002, -0.000213, 3.63416E-05, -0.000002175, 5.2796E-08, -4.4351E-10]\n",
"# sandia model parameters\n",
"C0 = 1.0115\n",
"C1 = -0.0115\n",
"C2 = 0.218474\n",
"C3 = -7.224183\n",
"T0 = 25. # [C] ref temp\n",
"E0 = 1000. # [W/m^2] ref irradiance\n",
"deltaTc = nDF * k * (T0 + 273) / q # [V] thermal voltage\n",
"Ns = 128 # number of cells\n",
"\n",
"# filename of ASTMG173 AM1.5 standard\n",
"AM15_FULLPATH = os.path.join(DIRNAME, 'ASTMG173.csv')\n",
"\n",
"# get ASTMG173 AM1.5 standard\n",
"kwargs = {'delimiter': ',', 'skip_header': 1, 'names': True}\n",
"ASTMG173 = np.genfromtxt(AM15_FULLPATH, **kwargs)\n",
"E_AM15 = np.trapz(ASTMG173['Global_tilt__Wm2nm1'], x=ASTMG173['Wvlgth_nm'])\n",
"print 'Total Global = %g [W/m^2]' % E_AM15\n",
"f0 = plt.figure(0)\n",
"plt.plot(ASTMG173['Wvlgth_nm'], ASTMG173['Global_tilt__Wm2nm1'])\n",
"plt.title('ASTMG173 AM1.5')\n",
"plt.xlabel('Wavelength, $\\lambda \\ [nm]$')\n",
"plt.ylabel('Global Tilt, $I(\\lambda) \\ [W/m^2/nm]$')\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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uuqntJxob2QC31xYfUu2VMuoZalV/WrnIRS5ykatZLm7KflYy63RdxjFI0qSm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\nXOQi16+5yKq8fwIc1NgXbQz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"text/plain": [
"<matplotlib.figure.Figure at 0xb3d88d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# filename of measured EQE\n",
"EQE_FULLPATH = os.path.join(DIRNAME, 'meas_GenC-XUS-CS-AR.xlsx')\n",
"\n",
"# references of data in workbook\n",
"EQE_LAMBDA = 'G2:G2003' # lambda\n",
"EQE = 'H2:H2003' # measured EQE [%]\n",
"\n",
"# get EQE\n",
"wb = load_workbook(EQE_FULLPATH)\n",
"ws = wb.get_sheet_by_name('Sheet1')\n",
"dt = np.dtype([('lambda','float'), ('EQE','float')])\n",
"EQE_lambda = [[x[0].value for x in ws.iter_rows(EQE_LAMBDA)],\n",
" [y[0].value for y in ws.iter_rows(EQE)]]\n",
"EQE = np.array(zip(*EQE_lambda), dt)\n",
"f2 = plt.figure(2)\n",
"plt.plot(EQE['lambda'], EQE['EQE'])\n",
"plt.title('EQE GenC XUS AR')\n",
"plt.xlabel('Wavelength, $\\lambda \\ [nm]$')\n",
"plt.ylabel('Quantum Efficiency, $EQE(\\lambda) \\ [\\%]$')\n",
"plt.xlim([250, 1250])\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Effective Irradiance:\n",
"from EQE & AM1.5 = 1 [suns]\n",
"from fAM & AM1.5 = 1.00081 [suns]\n",
"Power = 443.701 [W]\n"
]
}
],
"source": [
"# calculate Isc0\n",
"SCALING_FACTOR = 1.0234 # set Ee = 1 with AM1.5\n",
"EQE_func = interp1d(EQE['lambda'], EQE['EQE'] * SCALING_FACTOR / 100.)\n",
"# EQE_interp = np.interp(ASTMG173['Wvlgth_nm'], EQE['lambda'], EQE['EQE'])\n",
"JscAM15 = np.trapz(ASTMG173['Global_tilt__Wm2nm1'] *\n",
" EQE_func(ASTMG173['Wvlgth_nm']) * ASTMG173['Wvlgth_nm'],\n",
" x=ASTMG173['Wvlgth_nm']) * q / h / c / 1.e9\n",
"IscAM15 = JscAM15 * ACELL / 100. / 100.\n",
"EeAM15 = IscAM15/Isc0\n",
"EefAM = np.polyval(fAM[::-1],1.5) * E_AM15 / E0\n",
"print 'Effective Irradiance:'\n",
"print 'from EQE & AM1.5 = %g [suns]' % EeAM15\n",
"print 'from fAM & AM1.5 = %g [suns]' % EefAM\n",
"print 'Power = %g [W]' % (Imp0 * Vmp0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"airmass = 1.50025 [atm]\n"
]
},
{
"data": {
"image/png": 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J51LxXGRmi33Y+18DnwAws1fN7F7/UDwf+BVwcELXs25H15Xtf/fDijNDi7cE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\nf0xfL9xlQSvbAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x14626a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# get solar spectrum\n",
"args = (UNITS, LOCATION, DATETIME.timetuple()[:6], WEATHER, ORIENTATION,\n",
" ATMOS_COND, ALBEDO)\n",
"specdif, specdir, specetr, specglo, specx = spectrl2(*args)\n",
"angles, airmass = solposAM(LOCATION, DATETIME.timetuple()[:6], WEATHER)\n",
"f1 = plt.figure(1)\n",
"plt.plot(specx, zip(specdif, specdir, specglo))\n",
"plt.title('SPECTRL2: Normal - Boulder, CO, 2/27/2015 12:06 PM')\n",
"plt.xlabel('Wavelength, $\\lambda \\ [\\mu m]$')\n",
"plt.ylabel('Solar Spectrum, $I(\\lambda) \\ [W/m^2/\\mu m]$')\n",
"plt.legend(('Diffuse Tilt', 'Direct Tilt', 'Global Tilt'))\n",
"plt.grid()\n",
"print 'airmass = %g [atm]' % airmass[1]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Effective Irradiance\n",
"from SPECTRL2 & EQE = 0.994864 [suns]\n",
"from SPECTRL2 & fAM = 1.00045 [suns]\n",
"Power = 441.425 [W]\n"
]
}
],
"source": [
"scaling = np.trapz(specglo, x=specx) / E0\n",
"Jsc = np.trapz(specglo / scaling * EQE_func(np.array(specx) * 1.e3) * specx,\n",
" x=specx) * q / h / c / 1.e6\n",
"Isc = Jsc * ACELL / 100. / 100.\n",
"Ee = Isc/Isc0\n",
"EefAM_spectrl2 = np.polyval(fAM[::-1],airmass[1])\n",
"print 'Effective Irradiance'\n",
"print 'from SPECTRL2 & EQE = %g [suns]' % Ee\n",
"print 'from SPECTRL2 & fAM = %g [suns]' % EefAM_spectrl2\n",
"Imp = Imp0 * (C0 * Ee + C1 *Ee**2)\n",
"Vmp = (Vmp0 + C2 * Ns * deltaTc * np.log(Ee) +\n",
" C3 * Ns * (deltaTc * np.log(Ee))**2)\n",
"Pmp = Imp * Vmp\n",
"print 'Power = %g [W]' % Pmp"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"airmass = 1.50025 [atm]\n"
]
},
{
"data": {
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chu6nETGVUhjx+41EVmFm8/BA/bmSPiRpsKReYfRX0n2fLNV7GXC8pF1DJpEz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\nPw1sOdjTYE+ATc5FJ1iozX6Vc0Z2Ltj1YH8FWw22EWwu2Of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"text/plain": [
"<matplotlib.figure.Figure at 0x14f2dc18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Dry year\n",
"args = (UNITS, LOCATION, DATETIME.timetuple()[:6], WEATHER, ORIENTATION,\n",
" [1.14, 0.65, 0.34, 0.55, 0.3], ALBEDO)\n",
"specdif, specdir, specetr, specglo, specx = spectrl2(*args)\n",
"angles, airmass = solposAM(LOCATION, DATETIME.timetuple()[:6], WEATHER)\n",
"f3 = plt.figure(1)\n",
"plt.plot(specx, zip(specdif, specdir, specglo))\n",
"plt.title('SPECTRL2: Dry - Boulder, CO, 2/27/2015 12:06 PM')\n",
"plt.xlabel('Wavelength, $\\lambda \\ [\\mu m]$')\n",
"plt.ylabel('Solar Spectrum, $I(\\lambda) \\ [W/m^2/\\mu m]$')\n",
"plt.legend(('Diffuse Tilt', 'Direct Tilt', 'Global Tilt'))\n",
"plt.grid()\n",
"print 'airmass = %g [atm]' % airmass[1]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Effective Irradiance\n",
"from SPECTRL2 & EQE = 0.969732 [suns]\n",
"from SPECTRL2 & fAM = 1.00045 [suns]\n",
"Power = 430.284 [W]\n",
"relative difference between Dry and Normal: -2.52378 [%]\n"
]
}
],
"source": [
"scaling = np.trapz(specglo, x=specx) / E0\n",
"JscDry = np.trapz(specglo / scaling * EQE_func(np.array(specx) * 1.e3) * specx,\n",
" x=specx) * q / h / c / 1.e6\n",
"IscDry = JscDry * ACELL / 100. / 100.\n",
"EeDry = IscDry/Isc0\n",
"EefAM_spectrl2Dry = np.polyval(fAM[::-1],airmass[1])\n",
"print 'Effective Irradiance'\n",
"print 'from SPECTRL2 & EQE = %g [suns]' % EeDry\n",
"print 'from SPECTRL2 & fAM = %g [suns]' % EefAM_spectrl2Dry\n",
"ImpDry = Imp0 * (C0 * EeDry + C1 * EeDry**2)\n",
"VmpDry = (Vmp0 + C2 * Ns * deltaTc * np.log(EeDry) +\n",
" C3 * Ns * (deltaTc * np.log(EeDry))**2)\n",
"PmpDry = ImpDry * VmpDry\n",
"print 'Power = %g [W]' % PmpDry\n",
"reldif = (PmpDry - Pmp) / Pmp * 100\n",
"print 'relative difference between Dry and Normal: %g [%%]' % reldif"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
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@shirubana
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Thought I'd share, if useful:

I've been doing spectra simulation with weather inputs for Golden, form the SRRL, using this two links:
AOD: http://midc.nrel.gov/apps/sitehome.pl?site=AODSRRL
PWV: http://midc.nrel.gov/apps/sitehome.pl?site=PWVSRRL (**units are mm)

Below is the list of available weather inputs similar to what SPECTRL2 uses. Convention name below follows SMARTS2.

SRRL-BMS:

  • Relative Humidity
  • Temperature
  • Site Pressure

SRRL-AOD:

  • ALPHA = SRRL-AOD['Alpha [Angstrom exp]'] # Ångström's wavelength exponent
  •     BETA = SRRL-AOD['Beta']                                      #  Ångström’s turbidity coefficient Beta specified at an AOD of 1000 nm        
    
  •     TAU5 = SRRL-AOD['AOD [500nm]'])                        # Aerosol optical depth -- used to calculate BETA.
    
  •     GG = SRRL-AOD['Asymmetry [500nm]']                  # aerosol asymettry parameter
    
  •     OMEGL = SRRL-AOD['SSA [500nm]']                       # aerosol single scattering albedo
    

SRRL-PWD

  • W = SRRL-PWD['Precipitable Water [mm]']/10

@mikofski
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mikofski commented Jan 7, 2021

Awesome @shirubana. Any ideas where we can collect EQE data for different cell tech? I assume we'll also need sensor spectrum as well.

@shirubana
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I emailed you what I have, not much.

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