Tutorial D (extra) - pySAM Simulation#

This is only one way to interact with SAM through locally definning ALL the necessary variables and dictionaries natively on the pySAM, and then executing the simulation. SAM’s webinars offer more accessible alternatives, where you create your system on the GUI, export the values as a JSON, and modify/run simulations based on that.

Note: At time of writing, this notebook may fail to execute successfully due to an unknown issue with PySAM. We hope to resolve this issue in the future.

Get File through PVLib
Get File from the SAM repository URL

Run, Save and Explore Outputs

Extra: Comparison of pySAM Default vs SAM GUI exported values

# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells:
# !pip install -r https://raw.githubusercontent.com/PVSC-Python-Tutorials/PVPMC_2022/main/requirements.txt

datafolder = r'data'
exampleflag = False
debugflag = False

import PySAM.Pvsamv1 as pv
import PySAM
import xlsxwriter
import json
import pandas as pd
import os
import pprint as pp

PySAM.__version__

'2.2.4'

1. Create default PV#

Doing Commercial type because the size is bigger and it’s tracking than a rooftop. We are not really interested in the financial model here though just in the irradiance and power generation so you can choose a different one if you want

sam1 = pv.default("FlatPlatePVCommercial")

You can explore attributes of the pySAM object with the below cell. This will give variables, methods, etc.

#dir(sam1)

You can also query a single attribute. And export it to see the contents more easily

sam1.__getattribute__('SolarResource').use_wf_albedo
dic = sam1.export()
dic

{'SolarResource': {'albedo': (0.2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2),
  'irrad_mode': 0.0,
  'sky_model': 2.0,
  'use_wf_albedo': 0.0},
 'Losses': {'acwiring_loss': 1.0,
  'dcoptimizer_loss': 0.0,
  'en_snow_model': 0.0,
  'subarray1_dcwiring_loss': 2.0,
  'subarray1_diodeconn_loss': 0.5,
  'subarray1_mismatch_loss': 2.0,
  'subarray1_nameplate_loss': 0.0,
  'subarray1_rear_irradiance_loss': 0.0,
  'subarray1_soiling': (5.0,
0,
0,
0,
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0,
0,
0,
0,
0,
0,
0),
  'subarray1_tracking_loss': 0.0,
  'subarray2_dcwiring_loss': 2.0,
  'subarray2_diodeconn_loss': 0.5,
  'subarray2_mismatch_loss': 2.0,
  'subarray2_nameplate_loss': 0.0,
  'subarray2_rear_irradiance_loss': 0.0,
  'subarray2_soiling': (5.0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0),
  'subarray2_tracking_loss': 0.0,
  'subarray3_dcwiring_loss': 2.0,
  'subarray3_diodeconn_loss': 0.5,
  'subarray3_mismatch_loss': 2.0,
  'subarray3_nameplate_loss': 0.0,
  'subarray3_rear_irradiance_loss': 0.0,
  'subarray3_soiling': (5.0,
0,
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0,
0,
0),
  'subarray3_tracking_loss': 0.0,
  'subarray4_dcwiring_loss': 2.0,
  'subarray4_diodeconn_loss': 0.5,
  'subarray4_mismatch_loss': 2.0,
  'subarray4_nameplate_loss': 0.0,
  'subarray4_rear_irradiance_loss': 0.0,
  'subarray4_soiling': (5.0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0),
  'subarray4_tracking_loss': 0.0,
  'transformer_load_loss': 0.0,
  'transformer_no_load_loss': 0.0,
  'transmission_loss': 0.0},
 'Lifetime': {'ac_lifetime_losses': (0.0,
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   ...),
  'analysis_period': 25.0,
  'dc_degradation': (0.5,),
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   ...),
  'grid_interconnection_limit_kwac': 100000.0},
 'AdjustmentFactors': {'constant': 0.0, 'dc_constant': 0.0},
 'Outputs': {}}

Or save it as a csv if you want to explore the categories and variables

filesave = os.path.join(datafolder,'JSON_Default.xlsx')

workbook = xlsxwriter.Workbook(filesave)
worksheet = workbook.add_worksheet()
row = 0
col = 0
order=sorted(dic.keys())
for key in order:
    row += 1
    worksheet.write(row, col,     key)
    for item in dic[key]:
        worksheet.write(row, col + 1, item)
        worksheet.write(row, col + 2, str(dic[key][item]))

        row += 1

workbook.close()

2. Set values that should not be default to represent our system:#

solar_resource_file = r'data/SRRL_WeatherFile_SAM_60_2020.csv'
albedo = [0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]

module_aspect_ratio = 2

subarray1_track_mode = 1
subarray1_backtrack = 1
subarray1_rotlim = 50

subarray1_gcr = 0.34903
subarray1_modules_per_string = 20
subarray1_nstrings = 10
subarray1_nmodx = 20
subarray1_nmody = 1
subarray1_shade_mode = 1

inverter_count = 10

subarray1_rear_irradiance_loss = 10
subarray1_soiling = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
use_wf_albedo = 1
# cec_a_ref = 1.885731
# inv_snl_vdco = 310
#  part of Inverter mppt_low_inverter = 100
subarray1_tilt = 0 

newval = { 'SolarResource': {
                            'albedo': albedo,
                            'use_wf_albedo': use_wf_albedo},
           'SystemDesign' : {
                            'inverter_count':inverter_count,
                            'subarray1_backtrack':subarray1_backtrack,
                            'subarray1_gcr':subarray1_gcr,
                            'subarray1_modules_per_string':subarray1_modules_per_string,
                            'subarray1_nstrings':subarray1_nstrings,
                            'subarray1_rotlim':subarray1_rotlim,
                            'subarray1_track_mode':subarray1_track_mode,
                            'subarray1_tilt': subarray1_tilt},
            'Layout':      {
                            'module_aspect_ratio': module_aspect_ratio,
                            'subarray1_nmodx':subarray1_nmodx,
                            'subarray1_nmody':subarray1_nmody},
            'Shading' :    {'subarray1_shade_mode':subarray1_shade_mode},
            'Losses' :     {
                            'subarray1_soiling':subarray1_soiling,
                            'subarray1_rear_irradiance_loss':subarray1_rear_irradiance_loss},
             }

We are going to use the method ’assign’, becasue that only replaces the values we are providing. ’replace’ replaces all the internally generated dictionary entries with only the ones we are providing.

sam1.assign(newval)

3. Assign Solar Resource#

No solar resource is assigned by default. If this is not assigned the simulation will not run.

sam1.SolarResource.solar_resource_file = solar_resource_file

4. Obtaining the MODULE Data#

Two methods are explored: 1. Using PVLib 2. getting SAM’s CEC library URL and reading the csv.

4A. Using PVLib#

import pvlib
CECMODS = pvlib.pvsystem.retrieve_sam(name='CECMod')

# the CEC modules are a pandas DataFrame oriented as columns, transpose to arrange
# as indices
CECMODS.T.head()

	Technology	STC	PTC	A_c	Length	Width	N_s	I_sc_ref	V_oc_ref	...	a_ref	I_L_ref	R_s	R_sh_ref	Adjust	gamma_r	BIPV	Version	Date
A10Green_Technology_A10J_S72_175	Mono-c-Si	175.0914	151.2	1.3	1.576	0.825	72	5.17	43.99	...	1.981696	5.175703	0.316688	287.102203	16.057121	-0.5072	N	SAM 2018.11.11 r2	1/3/2019
A10Green_Technology_A10J_S72_180	Mono-c-Si	179.928	155.7	1.3	1.576	0.825	72	5.31	44.06	...	1.988414	5.316148	0.299919	259.047943	16.418983	-0.5072	N	SAM 2018.11.11 r2	1/3/2019
A10Green_Technology_A10J_S72_185	Mono-c-Si	184.7016	160.2	1.3	1.576	0.825	72	5.43	44.14	...	1.984817	5.435676	0.311962	298.424438	15.688233	-0.5072	N	SAM 2018.11.11 r2	1/3/2019
A10Green_Technology_A10J_M60_220	Multi-c-Si	219.876	189.1	1.624	1.632	0.995	60	7.95	36.06	...	1.673094	7.959062	0.140393	123.168404	21.875164	-0.5196	N	SAM 2018.11.11 r2	1/3/2019
A10Green_Technology_A10J_M60_225	Multi-c-Si	224.9856	193.5	1.624	1.632	0.995	60	8.04	36.24	...	1.671782	8.047206	0.14737	164.419479	20.698376	-0.5196	N	SAM 2018.11.11 r2	1/3/2019

5 rows × 25 columns

The Module we want for this simulation has this name in SAM: “Prism Solar Technologies_ Inc. Bi72-457BSTC” . The name should be written similar with underscores, but is not always teh same in the database. To make it easier, we filter by keywords.

cs_220m_mods = CECMODS.T.index.str.startswith('Prism') & CECMODS.T.index.str.contains('457BSTC')
mymod = CECMODS.T[cs_220m_mods]
mymod.T

	Prism_Solar_Technologies_Bi72_457BSTC
Technology	Multi-c-Si
Bifacial	1
STC	360.214
PTC	327.6
A_c	1.979
Length	2.011
Width	0.984
N_s	72
I_sc_ref	9.8
V_oc_ref	48.2
I_mp_ref	9.26
V_mp_ref	38.9
alpha_sc	0.004675
beta_oc	-0.139925
T_NOCT	47.4
a_ref	1.885731
I_L_ref	9.80468
I_o_ref	0.0
R_s	0.396799
R_sh_ref	830.975952
Adjust	10.40003
gamma_r	-0.4028
BIPV	N
Version	SAM 2018.11.11 r2
Date	1/3/2019

Asigning variables about the bifaciality aspect of our site. This are saved inside the ‘CECPerformanceModelWithModuleDatabase’ but are not in the CEC table, they are default/user provided

B. Get File from the SAM repository URL#

import requests

The URL you see when you navigate to the github looks like:

>> https://github.com/NREL/SAM/blob/master/deploy/libraries/CEC%20Modules.csv

However, to access it you must rename the github.com to raw.githubusercontent.com, and also remove the ‘blob’ part, so

>> https://raw.githubusercontent.com/NREL/SAM/master/deploy/libraries/CEC%20Modules.csv'

url = 'https://raw.githubusercontent.com/NREL/SAM/master/deploy/libraries/CEC%20Modules.csv'
df = pd.read_csv(url, index_col=0)
modfilter = df.index.str.startswith('Prism') & df.index.str.contains('457BSTC')
mymod = df[modfilter]

Note: As of Oct 7th, the SAM GUI version corresponds to the 'patch' branch. This has a more updated version of the CEC csv. So we are atually going to use that one by repeating the above procedure but pointing to that branch. This might not be necessary or might change later on

masterdate = mymod['Date'][0]

#url = 'https://raw.githubusercontent.com/NREL/SAM/develop/deploy/libraries/CEC%20Modules.csv'
url = 'https://raw.githubusercontent.com/NREL/SAM/patch/deploy/libraries/CEC%20Modules.csv'
df = pd.read_csv(url, index_col=0)
modfilter = df.index.str.startswith('Prism') & df.index.str.contains('Prism')
mymod = df[modfilter].iloc[0]
otherbranchdate = mymod['Date']

print("Master database is dated:", masterdate)
print("Patch database is dated:", otherbranchdate)

Master database is dated: 11/25/2020
Patch database is dated: 5/28/2022

Assign the Variables to the SAM object from the CEC data#

This are read as strings, so we need to make

mymod.a_ref

'1.5188'

mymod.A_c

'1.65'

# Values on comments of each line were for a different module not found in the CEC database anymore (name changed?). 
# Leaving them for reference of the scale of values expected.
cec_a_ref = float(mymod.a_ref) # 1.885731
cec_adjust = float(mymod.Adjust) # 10.400029
cec_alpha_sc = float(mymod.alpha_sc) # 0.004675
cec_area = float(mymod.A_c) # 1.979

cec_beta_oc = float(mymod.beta_oc) # -0.139925
cec_gamma_r = float(mymod.gamma_r) # -0.4028
cec_i_l_ref = float(mymod.I_L_ref) # 9.80468
cec_i_mp_ref = float(mymod.I_mp_ref) # 9.26
cec_i_o_ref = float(mymod.I_o_ref) # 7.72903e-11       
cec_i_sc_ref = float(mymod.I_sc_ref) # 9.8
cec_is_bifacial = int(mymod.Bifacial) # 1
cec_module_length = float(mymod.Length) # 2.011
cec_module_width = float(mymod.Width) # 0.984
module_aspect_ratio = cec_module_length/cec_module_width    # 2.043699187

cec_n_s = float(mymod.N_s) # 72
cec_r_s = float(mymod.R_s) #0.396799
cec_r_sh_ref = float(mymod.R_sh_ref) # 830.975492
cec_t_noct = float(mymod.T_NOCT) # 47.4
cec_v_mp_ref = float(mymod.V_mp_ref) # 38.9
cec_v_oc_ref = float(mymod.V_oc_ref) # 48.2

cec_bifacial_ground_clearance_height = 1.5
cec_bifacial_transmission_factor = 0
cec_bifaciality = 0.694 # WHY IS THIS NOT IN THE CEC Data?

# Temperature corrections -- We are not doing this. Should we?
# cec_temp_corr_mode = 1?
# cec_array_cols = 10 #    Already default
# cec_array_rows = 1  #    Already default
# ?? cec_temp_corr_mode	0       ???
# ?? cec_transient_thermal_model_unit_mass	11.0919

newval = { 'Layout':{'module_aspect_ratio':module_aspect_ratio},
            'CECPerformanceModelWithModuleDatabase': {
                'cec_a_ref': cec_a_ref,
                'cec_adjust': cec_adjust,
                'cec_alpha_sc': cec_alpha_sc,
                'cec_area': cec_area,
                'cec_beta_oc': cec_beta_oc,
                'cec_gamma_r': cec_gamma_r,
                'cec_i_l_ref': cec_i_l_ref,
                'cec_i_mp_ref': cec_i_mp_ref,
                'cec_i_o_ref': cec_i_o_ref,
                'cec_i_sc_ref': cec_i_sc_ref,
                'cec_is_bifacial': cec_is_bifacial,
                'cec_module_length': cec_module_length,
                'cec_module_width': cec_module_width,
                'cec_n_s': cec_n_s,
                'cec_r_s': cec_r_s,
                'cec_r_sh_ref': cec_r_sh_ref,
                'cec_t_noct': cec_t_noct,
                'cec_v_mp_ref': cec_v_mp_ref,
                'cec_v_oc_ref': cec_v_oc_ref,
                'cec_bifacial_ground_clearance_height': cec_bifacial_ground_clearance_height,
                'cec_bifacial_transmission_factor': cec_bifacial_transmission_factor,
                'cec_bifaciality': cec_bifaciality
             }}

sam1.assign(newval)

Get Inverter Values#

First let’s download the database

#url = 'https://raw.githubusercontent.com/NREL/SAM/develop/deploy/libraries/CEC%20Modules.csv'
url = 'https://raw.githubusercontent.com/NREL/SAM/patch/deploy/libraries/CEC%20Inverters.csv'
df = pd.read_csv(url, index_col=0)

Our inverter is ‘Fronius USA: Fronius Symo 10.0-3 480 [480V]’. Using the same look-up technique as with the module

modfilter = df.index.str.startswith('Fronius') & df.index.str.contains('480V')  & df.index.str.contains('10.0')
myinv = df[modfilter]
myinv

	Vac	Pso	Paco	Pdco	Vdco	C0	C1	C2	C3	Pnt	Vdcmax	Idcmax	Mppt_low	Mppt_high	CEC_Date	CEC_hybrid
Name
Fronius International GmbH: Fronius Symo 10.0-3 480 [480V]	480	64.1251	9995	10260.1	670	-2.75409e-07	-2.25745e-05	-0.000385046	0.00168874	0.86	800	15.3135	100	800	3/1/2019	N

inv_snl_c0 = float(myinv['C0'])
inv_snl_c1 = float(myinv['C1'])
inv_snl_c2 = float(myinv['C2'])
inv_snl_c3 = float(myinv['C3'])
inv_snl_paco = float(myinv['Paco'])
inv_snl_pdco = float(myinv['Pdco']) 
inv_snl_pnt = float(myinv['Pnt'])
inv_snl_pso = float(myinv['Pso'])
inv_snl_vdcmax = float(myinv['Vdcmax'])
inv_snl_vdco =  float(myinv['Vdco'])
mppt_low_inverter = float(myinv['Mppt_low'])
mppt_hi_inverter = float(myinv['Mppt_high'])

You can set an individual value with setattr

sam1.Inverter.__setattr__('inv_snl_paco', inv_snl_paco)

Or do them all together like we’ve been doing in dictionary format

newval = {  'Inverter':            { 'inv_snl_paco': inv_snl_paco,
                                   'mppt_low_inverter': mppt_low_inverter,
                                   'mppt_hi_inverter': mppt_hi_inverter},
            'InverterCECDatabase': {
                                    'inv_snl_c0': inv_snl_c0,
                                    'inv_snl_c1': inv_snl_c1,
                                    'inv_snl_c2': inv_snl_c2,
                                    'inv_snl_c3': inv_snl_c3,
                                    'inv_snl_paco': inv_snl_paco,
                                    'inv_snl_pdco': inv_snl_pdco,
                                    'inv_snl_pnt': inv_snl_pnt,
                                    'inv_snl_pso': inv_snl_pso,
                                    'inv_snl_vdcmax': inv_snl_vdcmax,
                                    'inv_snl_vdco': inv_snl_vdco
             }}

sam1.assign(newval)

Assign VALUES From the GUI#

Some values are calculated internally by the SAM GUI based on other values you provide. To identify this items, you can look in the documentation for the warning “Changes to this variable may require updating the values of the following:” or “This variable may need to be updated if the values of the following have changed”.

Note: There is no way for pySAM to calculate these updates internally at the moment.

The easiest is to simulate your parameters so far in the GUI, and get the values from there. Alternatively, you could export the whole simulation as a JSON, load it and execute it. However for this tutorial we are starting from the default and modifying it here, so let’s assign those values now.

inv_snl_eff_cec = 96.776
system_capacity =  72.04280090332031
inverter_count = 10
inv_tdc_cec_db = [[1, 52.79999923706055, -0.020999999716877937]] # Temperature derate curves for CEC Database [(Vdc, C, %/C)]

sam1.SystemDesign.__setattr__('system_capacity', system_capacity)
sam1.Inverter.__setattr__('inv_snl_eff_cec', inv_snl_eff_cec)
sam1.Inverter.__setattr__('inverter_count', inverter_count)
sam1.InverterCECDatabase.__setattr__('inv_tdc_cec_db', inv_tdc_cec_db)

SAVING#

sam1.execute()

foo = sam1.Outputs.export()

#foo['subarray1_poa_rear']
#foo['subarray1_dc_gross']
list(foo.keys())

filesave = os.path.join(datafolder,'pySAM_Outputs.xlsx')

workbook = xlsxwriter.Workbook(filesave)
worksheet = workbook.add_worksheet()

row=0
col=0

for key in foo.keys():
    row += 1
    worksheet.write(row, col, key)
    worksheet.write(row, col + 1, str(foo[key]))

workbook.close()

::Extra:: Comparison of pySAM Default vs SAM GUI exported values#

If you have a JSON that you exported from SAM, it will look a bit different than the default pySAM object you created. pySAM groups all variables grouped by different categories. The below code will save that so you can compare the values between those of a SAM simulation and this default. Your SAM JSON will also have more variables for the grid, and economic calculation tabs which we have not created in the pySAM object yet.

# Prism File
Prismfile = os.path.join(datafolder,'Row2Prism.json')

with open(Prismfile) as f:
    dic = json.load(f)

'''
filesave = os.path.join(datafolder,'JSON_Prism.xlsx')

workbook = xlsxwriter.Workbook(filesave)
worksheet = workbook.add_worksheet()

row=0
col=0

for key in dic.keys():
    row += 1
    worksheet.write(row, col, key)
    worksheet.write(row, col + 1, str(dic[key]))

workbook.close()
''';

maincomparisonvars = [sam1.AdjustmentFactors,
 sam1.CECPerformanceModelWithModuleDatabase,
 sam1.Inverter,
 sam1.InverterCECDatabase,
 sam1.Layout,
 sam1.Lifetime,
 sam1.Load,
 sam1.Losses,
 sam1.MermoudLejeuneSingleDiodeModel,
 sam1.Module,
 sam1.Shading,
 sam1.SolarResource,
 sam1.SystemDesign]

d4 = dict(maincomparisonvars[0].export())
for category in maincomparisonvars[1:]:
    d4.update(category.export())
    
pySAMdic = pd.DataFrame.from_dict(d4, orient='index')

guiSAMdic = pd.DataFrame.from_dict(dic,orient='index')

guiSAMdic

result = pd.concat([guiSAMdic, pySAMdic], axis=1, join="inner")
result.columns = ['guiSAM', 'pySAM']
result.to_csv(os.path.join(Resultsfolder, 'Side Comparison.csv'))

PVPMC 2022

Tutorial D (extra) - pySAM Simulation

Contents