{ "cells": [ { "cell_type": "markdown", "id": "880ff16d", "metadata": {}, "source": [ "![tutorial banner](images/PVLib_PVPMCLogo.PNG)\n", "\n", "# Tutorial D (extra) - pySAM Simulation\n", "\n", "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](https://sam.nrel.gov/software-development-kit-sdk/pysam.html) 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.\n", "\n", "
\n", "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.\n", "
\n", "\n", "### STEPS:\n", "
    \n", "
  1. Create default PV
    2. \n", "
  2. Modify default values
    3. \n", "
  3. Assign Solar Resource
    4. \n", "
  4. Obtaining the MODULE Data
    5. \n", " \n", "
  5. Run, Save and Explore Outputs
    6. \n", "
  • Extra: Comparison of pySAM Default vs SAM GUI exported values
    • \n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "a1ada6cc-8ed9-4568-8959-111d81ef4e9d", "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": 1, "id": "ca7b8b3c", "metadata": {}, "outputs": [], "source": [ "datafolder = r'data'\n", "exampleflag = False\n", "debugflag = False" ] }, { "cell_type": "code", "execution_count": 2, "id": "c4965b07", "metadata": {}, "outputs": [], "source": [ "import PySAM.Pvsamv1 as pv\n", "import PySAM\n", "import xlsxwriter\n", "import json\n", "import pandas as pd\n", "import os\n", "import pprint as pp\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "cd6fde1b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.2.4'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "PySAM.__version__" ] }, { "cell_type": "markdown", "id": "80d8c2b3", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "3b510431", "metadata": {}, "source": [ "# 1. Create default PV \n", "\n", "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 " ] }, { "cell_type": "code", "execution_count": 4, "id": "1195abe4", "metadata": {}, "outputs": [], "source": [ "sam1 = pv.default(\"FlatPlatePVCommercial\")" ] }, { "cell_type": "markdown", "id": "dee4e7c1", "metadata": {}, "source": [ "You can explore attributes of the pySAM object with the below cell. This will give variables, methods, etc." ] }, { "cell_type": "code", "execution_count": 5, "id": "352d6fcb", "metadata": {}, "outputs": [], "source": [ "#dir(sam1)" ] }, { "cell_type": "markdown", "id": "65734f44", "metadata": {}, "source": [ "You can also query a single attribute. And export it to see the contents more easily " ] }, { "cell_type": "code", "execution_count": 6, "id": "9fe6b3ac", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'SolarResource': {'albedo': (0.2,\n", " 0.2,\n", " 0.2,\n", " 0.2,\n", " 0.2,\n", " 0.2,\n", " 0.2,\n", " 0.2,\n", " 0.2,\n", " 0.2,\n", " 0.2,\n", " 0.2),\n", " 'irrad_mode': 0.0,\n", " 'sky_model': 2.0,\n", " 'use_wf_albedo': 0.0},\n", " 'Losses': {'acwiring_loss': 1.0,\n", " 'dcoptimizer_loss': 0.0,\n", " 'en_snow_model': 0.0,\n", " 'subarray1_dcwiring_loss': 2.0,\n", " 'subarray1_diodeconn_loss': 0.5,\n", " 'subarray1_mismatch_loss': 2.0,\n", " 'subarray1_nameplate_loss': 0.0,\n", " 'subarray1_rear_irradiance_loss': 0.0,\n", " 'subarray1_soiling': (5.0,\n", " 5.0,\n", " 5.0,\n", " 5.0,\n", " 5.0,\n", " 5.0,\n", " 5.0,\n", " 5.0,\n", " 5.0,\n", " 5.0,\n", " 5.0,\n", " 5.0),\n", " 'subarray1_tracking_loss': 0.0,\n", " 'subarray2_dcwiring_loss': 2.0,\n", " 'subarray2_diodeconn_loss': 0.5,\n", " 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1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " 1e+38,\n", " ...),\n", " 'grid_interconnection_limit_kwac': 100000.0},\n", " 'AdjustmentFactors': {'constant': 0.0, 'dc_constant': 0.0},\n", " 'Outputs': {}}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sam1.__getattribute__('SolarResource').use_wf_albedo\n", "dic = sam1.export()\n", "dic" ] }, { "cell_type": "markdown", "id": "96660264", "metadata": {}, "source": [ "Or save it as a csv if you want to explore the categories and variables" ] }, { "cell_type": "code", "execution_count": 7, "id": "9b03156d", "metadata": {}, "outputs": [], "source": [ "filesave = os.path.join(datafolder,'JSON_Default.xlsx')\n", "\n", "workbook = xlsxwriter.Workbook(filesave)\n", "worksheet = workbook.add_worksheet()\n", "row = 0\n", "col = 0\n", "order=sorted(dic.keys())\n", "for key in order:\n", " row += 1\n", " worksheet.write(row, col, key)\n", " for item in dic[key]:\n", " worksheet.write(row, col + 1, item)\n", " worksheet.write(row, col + 2, str(dic[key][item]))\n", "\n", " row += 1\n", "\n", "workbook.close()" ] }, { "cell_type": "markdown", "id": "b0b59f17", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "203bdc29", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "96ef790d", "metadata": {}, "source": [ "# 2. Set values that should not be default to represent our system:" ] }, { "cell_type": "code", "execution_count": 8, "id": "58437195", "metadata": {}, "outputs": [], "source": [ "solar_resource_file = r'data/SRRL_WeatherFile_SAM_60_2020.csv'\n", "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]\n", "\n", "module_aspect_ratio = 2\n", "\n", "subarray1_track_mode = 1\n", "subarray1_backtrack = 1\n", "subarray1_rotlim = 50\n", "\n", "subarray1_gcr = 0.34903\n", "subarray1_modules_per_string = 20\n", "subarray1_nstrings = 10\n", "subarray1_nmodx = 20\n", "subarray1_nmody = 1\n", "subarray1_shade_mode = 1\n", "\n", "inverter_count = 10\n", "\n", "subarray1_rear_irradiance_loss = 10\n", "subarray1_soiling = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", "use_wf_albedo = 1\n", "# cec_a_ref = 1.885731\n", "# inv_snl_vdco = 310\n", "# part of Inverter mppt_low_inverter = 100\n", "subarray1_tilt = 0 \n" ] }, { "cell_type": "code", "execution_count": 9, "id": "b5525137", "metadata": {}, "outputs": [], "source": [ "newval = { 'SolarResource': {\n", " 'albedo': albedo,\n", " 'use_wf_albedo': use_wf_albedo},\n", " 'SystemDesign' : {\n", " 'inverter_count':inverter_count,\n", " 'subarray1_backtrack':subarray1_backtrack,\n", " 'subarray1_gcr':subarray1_gcr,\n", " 'subarray1_modules_per_string':subarray1_modules_per_string,\n", " 'subarray1_nstrings':subarray1_nstrings,\n", " 'subarray1_rotlim':subarray1_rotlim,\n", " 'subarray1_track_mode':subarray1_track_mode,\n", " 'subarray1_tilt': subarray1_tilt},\n", " 'Layout': {\n", " 'module_aspect_ratio': module_aspect_ratio,\n", " 'subarray1_nmodx':subarray1_nmodx,\n", " 'subarray1_nmody':subarray1_nmody},\n", " 'Shading' : {'subarray1_shade_mode':subarray1_shade_mode},\n", " 'Losses' : {\n", " 'subarray1_soiling':subarray1_soiling,\n", " 'subarray1_rear_irradiance_loss':subarray1_rear_irradiance_loss},\n", " }\n", "\n" ] }, { "cell_type": "markdown", "id": "d23f4ff4", "metadata": {}, "source": [ "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. " ] }, { "cell_type": "code", "execution_count": 10, "id": "8862f54d", "metadata": {}, "outputs": [], "source": [ "sam1.assign(newval)" ] }, { "cell_type": "markdown", "id": "0478e419", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "79b45802", "metadata": {}, "source": [ "# 3. Assign Solar Resource\n", "\n", "No solar resource is assigned by default. If this is not assigned the simulation will not run." ] }, { "cell_type": "code", "execution_count": 11, "id": "134c8d20", "metadata": {}, "outputs": [], "source": [ "sam1.SolarResource.solar_resource_file = solar_resource_file" ] }, { "cell_type": "markdown", "id": "52e62fca", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "bc2c4af4", "metadata": {}, "source": [ "# 4. Obtaining the MODULE Data\n", "\n", "Two methods are explored: \n", " 1. Using PVLib\n", " 2. getting SAM's CEC library URL and reading the csv." ] }, { "cell_type": "markdown", "id": "b25177ac", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "e81648e2", "metadata": {}, "source": [ "### 4A. Using PVLib" ] }, { "cell_type": "code", "execution_count": 12, "id": "6503fb05", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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    " ], "text/plain": [ " 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]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pvlib\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", "id": "1f024d5d", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 13, "id": "bcad5158", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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    Prism_Solar_Technologies_Bi72_457BSTC
    TechnologyMulti-c-Si
    Bifacial1
    STC360.214
    PTC327.6
    A_c1.979
    Length2.011
    Width0.984
    N_s72
    I_sc_ref9.8
    V_oc_ref48.2
    I_mp_ref9.26
    V_mp_ref38.9
    alpha_sc0.004675
    beta_oc-0.139925
    T_NOCT47.4
    a_ref1.885731
    I_L_ref9.80468
    I_o_ref0.0
    R_s0.396799
    R_sh_ref830.975952
    Adjust10.40003
    gamma_r-0.4028
    BIPVN
    VersionSAM 2018.11.11 r2
    Date1/3/2019
    \n", "
    " ], "text/plain": [ " Prism_Solar_Technologies_Bi72_457BSTC\n", "Technology Multi-c-Si\n", "Bifacial 1\n", "STC 360.214\n", "PTC 327.6\n", "A_c 1.979\n", "Length 2.011\n", "Width 0.984\n", "N_s 72\n", "I_sc_ref 9.8\n", "V_oc_ref 48.2\n", "I_mp_ref 9.26\n", "V_mp_ref 38.9\n", "alpha_sc 0.004675\n", "beta_oc -0.139925\n", "T_NOCT 47.4\n", "a_ref 1.885731\n", "I_L_ref 9.80468\n", "I_o_ref 0.0\n", "R_s 0.396799\n", "R_sh_ref 830.975952\n", "Adjust 10.40003\n", "gamma_r -0.4028\n", "BIPV N\n", "Version SAM 2018.11.11 r2\n", "Date 1/3/2019" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cs_220m_mods = CECMODS.T.index.str.startswith('Prism') & CECMODS.T.index.str.contains('457BSTC')\n", "mymod = CECMODS.T[cs_220m_mods]\n", "mymod.T" ] }, { "cell_type": "markdown", "id": "4d28bf7e", "metadata": {}, "source": [ "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" ] }, { "cell_type": "markdown", "id": "cb283a62", "metadata": {}, "source": [ "## B. Get File from the SAM repository URL" ] }, { "cell_type": "code", "execution_count": 14, "id": "d729b965", "metadata": {}, "outputs": [], "source": [ "import requests" ] }, { "cell_type": "markdown", "id": "9e85f003", "metadata": {}, "source": [ "The URL you see when you navigate to the github looks like:\n", "\n", " >> https://github.com/NREL/SAM/blob/master/deploy/libraries/CEC%20Modules.csv\n", " \n", "However, to access it you must rename the github.com to raw.githubusercontent.com, and also remove the 'blob' part, so\n", "\n", " >> https://raw.githubusercontent.com/NREL/SAM/master/deploy/libraries/CEC%20Modules.csv'\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "36ee14d2", "metadata": {}, "outputs": [], "source": [ "url = 'https://raw.githubusercontent.com/NREL/SAM/master/deploy/libraries/CEC%20Modules.csv'\n", "df = pd.read_csv(url, index_col=0)\n", "modfilter = df.index.str.startswith('Prism') & df.index.str.contains('457BSTC')\n", "mymod = df[modfilter]\n" ] }, { "cell_type": "markdown", "id": "010d4f41", "metadata": {}, "source": [ "
    \n", "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\n", "
    \n" ] }, { "cell_type": "code", "execution_count": 16, "id": "9bbecb88", "metadata": {}, "outputs": [], "source": [ "masterdate = mymod['Date'][0]" ] }, { "cell_type": "code", "execution_count": 17, "id": "e3986c94", "metadata": {}, "outputs": [], "source": [ "#url = 'https://raw.githubusercontent.com/NREL/SAM/develop/deploy/libraries/CEC%20Modules.csv'\n", "url = 'https://raw.githubusercontent.com/NREL/SAM/patch/deploy/libraries/CEC%20Modules.csv'\n", "df = pd.read_csv(url, index_col=0)\n", "modfilter = df.index.str.startswith('Prism') & df.index.str.contains('Prism')\n", "mymod = df[modfilter].iloc[0]\n", "otherbranchdate = mymod['Date']" ] }, { "cell_type": "code", "execution_count": 18, "id": "d439f096", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Master database is dated: 11/25/2020\n", "Patch database is dated: 5/28/2022\n" ] } ], "source": [ "print(\"Master database is dated:\", masterdate)\n", "print(\"Patch database is dated:\", otherbranchdate)" ] }, { "cell_type": "markdown", "id": "9d617eb9", "metadata": {}, "source": [ "#### Assign the Variables to the SAM object from the CEC data\n", "\n", "This are read as strings, so we need to make " ] }, { "cell_type": "code", "execution_count": 19, "id": "f404d62a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.5188'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mymod.a_ref" ] }, { "cell_type": "code", "execution_count": 20, "id": "393adf08", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.65'" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mymod.A_c" ] }, { "cell_type": "code", "execution_count": 21, "id": "ff56aafa", "metadata": {}, "outputs": [], "source": [ "# Values on comments of each line were for a different module not found in the CEC database anymore (name changed?). \n", "# Leaving them for reference of the scale of values expected.\n", "cec_a_ref = float(mymod.a_ref) # 1.885731\n", "cec_adjust = float(mymod.Adjust) # 10.400029\n", "cec_alpha_sc = float(mymod.alpha_sc) # 0.004675\n", "cec_area = float(mymod.A_c) # 1.979\n", "\n", "cec_beta_oc = float(mymod.beta_oc) # -0.139925\n", "cec_gamma_r = float(mymod.gamma_r) # -0.4028\n", "cec_i_l_ref = float(mymod.I_L_ref) # 9.80468\n", "cec_i_mp_ref = float(mymod.I_mp_ref) # 9.26\n", "cec_i_o_ref = float(mymod.I_o_ref) # 7.72903e-11 \n", "cec_i_sc_ref = float(mymod.I_sc_ref) # 9.8\n", "cec_is_bifacial = int(mymod.Bifacial) # 1\n", "cec_module_length = float(mymod.Length) # 2.011\n", "cec_module_width = float(mymod.Width) # 0.984\n", "module_aspect_ratio = cec_module_length/cec_module_width # 2.043699187\n", "\n", "cec_n_s = float(mymod.N_s) # 72\n", "cec_r_s = float(mymod.R_s) #0.396799\n", "cec_r_sh_ref = float(mymod.R_sh_ref) # 830.975492\n", "cec_t_noct = float(mymod.T_NOCT) # 47.4\n", "cec_v_mp_ref = float(mymod.V_mp_ref) # 38.9\n", "cec_v_oc_ref = float(mymod.V_oc_ref) # 48.2\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "8fc288c7", "metadata": {}, "outputs": [], "source": [ "cec_bifacial_ground_clearance_height = 1.5\n", "cec_bifacial_transmission_factor = 0\n", "cec_bifaciality = 0.694 # WHY IS THIS NOT IN THE CEC Data?\n", "\n", "# Temperature corrections -- We are not doing this. Should we?\n", "# cec_temp_corr_mode = 1?\n", "# cec_array_cols = 10 # Already default\n", "# cec_array_rows = 1 # Already default\n", "# ?? cec_temp_corr_mode\t0 ???\n", "# ?? cec_transient_thermal_model_unit_mass\t11.0919\n" ] }, { "cell_type": "code", "execution_count": 23, "id": "826adcfa", "metadata": {}, "outputs": [], "source": [ "newval = { 'Layout':{'module_aspect_ratio':module_aspect_ratio},\n", " 'CECPerformanceModelWithModuleDatabase': {\n", " 'cec_a_ref': cec_a_ref,\n", " 'cec_adjust': cec_adjust,\n", " 'cec_alpha_sc': cec_alpha_sc,\n", " 'cec_area': cec_area,\n", " 'cec_beta_oc': cec_beta_oc,\n", " 'cec_gamma_r': cec_gamma_r,\n", " 'cec_i_l_ref': cec_i_l_ref,\n", " 'cec_i_mp_ref': cec_i_mp_ref,\n", " 'cec_i_o_ref': cec_i_o_ref,\n", " 'cec_i_sc_ref': cec_i_sc_ref,\n", " 'cec_is_bifacial': cec_is_bifacial,\n", " 'cec_module_length': cec_module_length,\n", " 'cec_module_width': cec_module_width,\n", " 'cec_n_s': cec_n_s,\n", " 'cec_r_s': cec_r_s,\n", " 'cec_r_sh_ref': cec_r_sh_ref,\n", " 'cec_t_noct': cec_t_noct,\n", " 'cec_v_mp_ref': cec_v_mp_ref,\n", " 'cec_v_oc_ref': cec_v_oc_ref,\n", " 'cec_bifacial_ground_clearance_height': cec_bifacial_ground_clearance_height,\n", " 'cec_bifacial_transmission_factor': cec_bifacial_transmission_factor,\n", " 'cec_bifaciality': cec_bifaciality\n", " }}\n", "\n", "sam1.assign(newval)" ] }, { "cell_type": "markdown", "id": "61d28441", "metadata": {}, "source": [ "## Get Inverter Values\n", "\n", "First let's download the database" ] }, { "cell_type": "code", "execution_count": 24, "id": "87212bbc", "metadata": {}, "outputs": [], "source": [ "#url = 'https://raw.githubusercontent.com/NREL/SAM/develop/deploy/libraries/CEC%20Modules.csv'\n", "url = 'https://raw.githubusercontent.com/NREL/SAM/patch/deploy/libraries/CEC%20Inverters.csv'\n", "df = pd.read_csv(url, index_col=0)\n" ] }, { "cell_type": "markdown", "id": "d85057d5", "metadata": {}, "source": [ "Our inverter is 'Fronius USA: Fronius Symo 10.0-3 480 [480V]'. Using the same look-up technique as with the module" ] }, { "cell_type": "code", "execution_count": 25, "id": "186794e2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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    VacPsoPacoPdcoVdcoC0C1C2C3PntVdcmaxIdcmaxMppt_lowMppt_highCEC_DateCEC_hybrid
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    Fronius International GmbH: Fronius Symo 10.0-3 480 [480V]48064.1251999510260.1670-2.75409e-07-2.25745e-05-0.0003850460.001688740.8680015.31351008003/1/2019N
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    " ], "text/plain": [ " Vac Pso Paco \\\n", "Name \n", "Fronius International GmbH: Fronius Symo 10.0-3... 480 64.1251 9995 \n", "\n", " Pdco Vdco \\\n", "Name \n", "Fronius International GmbH: Fronius Symo 10.0-3... 10260.1 670 \n", "\n", " C0 \\\n", "Name \n", "Fronius International GmbH: Fronius Symo 10.0-3... -2.75409e-07 \n", "\n", " C1 \\\n", "Name \n", "Fronius International GmbH: Fronius Symo 10.0-3... -2.25745e-05 \n", "\n", " C2 C3 \\\n", "Name \n", "Fronius International GmbH: Fronius Symo 10.0-3... -0.000385046 0.00168874 \n", "\n", " Pnt Vdcmax Idcmax \\\n", "Name \n", "Fronius International GmbH: Fronius Symo 10.0-3... 0.86 800 15.3135 \n", "\n", " Mppt_low Mppt_high \\\n", "Name \n", "Fronius International GmbH: Fronius Symo 10.0-3... 100 800 \n", "\n", " CEC_Date CEC_hybrid \n", "Name \n", "Fronius International GmbH: Fronius Symo 10.0-3... 3/1/2019 N " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "modfilter = df.index.str.startswith('Fronius') & df.index.str.contains('480V') & df.index.str.contains('10.0')\n", "myinv = df[modfilter]\n", "myinv" ] }, { "cell_type": "code", "execution_count": 26, "id": "dc4ee2ba", "metadata": {}, "outputs": [], "source": [ "inv_snl_c0 = float(myinv['C0'])\n", "inv_snl_c1 = float(myinv['C1'])\n", "inv_snl_c2 = float(myinv['C2'])\n", "inv_snl_c3 = float(myinv['C3'])\n", "inv_snl_paco = float(myinv['Paco'])\n", "inv_snl_pdco = float(myinv['Pdco']) \n", "inv_snl_pnt = float(myinv['Pnt'])\n", "inv_snl_pso = float(myinv['Pso'])\n", "inv_snl_vdcmax = float(myinv['Vdcmax'])\n", "inv_snl_vdco = float(myinv['Vdco'])\n", "mppt_low_inverter = float(myinv['Mppt_low'])\n", "mppt_hi_inverter = float(myinv['Mppt_high'])" ] }, { "cell_type": "markdown", "id": "4f99adf6", "metadata": {}, "source": [ "You can set an individual value with __setattr__" ] }, { "cell_type": "code", "execution_count": 27, "id": "86815b58", "metadata": {}, "outputs": [], "source": [ "sam1.Inverter.__setattr__('inv_snl_paco', inv_snl_paco)" ] }, { "cell_type": "markdown", "id": "93ae1308", "metadata": {}, "source": [ "Or do them all together like we've been doing in dictionary format" ] }, { "cell_type": "code", "execution_count": 28, "id": "cf39ceb5", "metadata": {}, "outputs": [], "source": [ "newval = { 'Inverter': { 'inv_snl_paco': inv_snl_paco,\n", " 'mppt_low_inverter': mppt_low_inverter,\n", " 'mppt_hi_inverter': mppt_hi_inverter},\n", " 'InverterCECDatabase': {\n", " 'inv_snl_c0': inv_snl_c0,\n", " 'inv_snl_c1': inv_snl_c1,\n", " 'inv_snl_c2': inv_snl_c2,\n", " 'inv_snl_c3': inv_snl_c3,\n", " 'inv_snl_paco': inv_snl_paco,\n", " 'inv_snl_pdco': inv_snl_pdco,\n", " 'inv_snl_pnt': inv_snl_pnt,\n", " 'inv_snl_pso': inv_snl_pso,\n", " 'inv_snl_vdcmax': inv_snl_vdcmax,\n", " 'inv_snl_vdco': inv_snl_vdco\n", " }}\n", "\n", "sam1.assign(newval)" ] }, { "cell_type": "markdown", "id": "76f20483", "metadata": {}, "source": [ "# Assign VALUES From the GUI\n", "\n", "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\".\n", "\n", "\n", "
    \n", "Note: There is no way for pySAM to calculate these updates internally at the moment. \n", "
    \n", "\n", "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.\n" ] }, { "cell_type": "code", "execution_count": 29, "id": "5e828164", "metadata": {}, "outputs": [], "source": [ "inv_snl_eff_cec = 96.776\n", "system_capacity = 72.04280090332031\n", "inverter_count = 10\n", "inv_tdc_cec_db = [[1, 52.79999923706055, -0.020999999716877937]] # Temperature derate curves for CEC Database [(Vdc, C, %/C)]\n", "\n", "sam1.SystemDesign.__setattr__('system_capacity', system_capacity)\n", "sam1.Inverter.__setattr__('inv_snl_eff_cec', inv_snl_eff_cec)\n", "sam1.Inverter.__setattr__('inverter_count', inverter_count)\n", "sam1.InverterCECDatabase.__setattr__('inv_tdc_cec_db', inv_tdc_cec_db)" ] }, { "cell_type": "markdown", "id": "c3c61d97", "metadata": {}, "source": [ "# SAVING" ] }, { "cell_type": "code", "execution_count": null, "id": "bc774dec", "metadata": {}, "outputs": [], "source": [ "sam1.execute()" ] }, { "cell_type": "code", "execution_count": null, "id": "25ff57af", "metadata": {}, "outputs": [], "source": [ "foo = sam1.Outputs.export()" ] }, { "cell_type": "code", "execution_count": null, "id": "95b9e8d0", "metadata": {}, "outputs": [], "source": [ "#foo['subarray1_poa_rear']\n", "#foo['subarray1_dc_gross']\n", "list(foo.keys())" ] }, { "cell_type": "code", "execution_count": null, "id": "0b55e86d", "metadata": {}, "outputs": [], "source": [ "filesave = os.path.join(datafolder,'pySAM_Outputs.xlsx')\n", "\n", "workbook = xlsxwriter.Workbook(filesave)\n", "worksheet = workbook.add_worksheet()\n", "\n", "row=0\n", "col=0\n", "\n", "for key in foo.keys():\n", " row += 1\n", " worksheet.write(row, col, key)\n", " worksheet.write(row, col + 1, str(foo[key]))\n", "\n", "workbook.close()" ] }, { "cell_type": "markdown", "id": "ecd45a30", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "c8d31c31", "metadata": {}, "source": [ "# ::Extra:: Comparison of pySAM Default vs SAM GUI exported values" ] }, { "cell_type": "markdown", "id": "6ed5fdeb", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": null, "id": "a596b731", "metadata": {}, "outputs": [], "source": [ "# Prism File\n", "Prismfile = os.path.join(datafolder,'Row2Prism.json')\n", "\n", "with open(Prismfile) as f:\n", " dic = json.load(f)" ] }, { "cell_type": "code", "execution_count": null, "id": "da34f889", "metadata": {}, "outputs": [], "source": [ "'''\n", "filesave = os.path.join(datafolder,'JSON_Prism.xlsx')\n", "\n", "workbook = xlsxwriter.Workbook(filesave)\n", "worksheet = workbook.add_worksheet()\n", "\n", "row=0\n", "col=0\n", "\n", "for key in dic.keys():\n", " row += 1\n", " worksheet.write(row, col, key)\n", " worksheet.write(row, col + 1, str(dic[key]))\n", "\n", "workbook.close()\n", "''';" ] }, { "cell_type": "code", "execution_count": null, "id": "293af29a", "metadata": {}, "outputs": [], "source": [ "maincomparisonvars = [sam1.AdjustmentFactors,\n", " sam1.CECPerformanceModelWithModuleDatabase,\n", " sam1.Inverter,\n", " sam1.InverterCECDatabase,\n", " sam1.Layout,\n", " sam1.Lifetime,\n", " sam1.Load,\n", " sam1.Losses,\n", " sam1.MermoudLejeuneSingleDiodeModel,\n", " sam1.Module,\n", " sam1.Shading,\n", " sam1.SolarResource,\n", " sam1.SystemDesign]" ] }, { "cell_type": "code", "execution_count": null, "id": "71272d4e", "metadata": {}, "outputs": [], "source": [ "d4 = dict(maincomparisonvars[0].export())\n", "for category in maincomparisonvars[1:]:\n", " d4.update(category.export())\n", " \n", "pySAMdic = pd.DataFrame.from_dict(d4, orient='index')" ] }, { "cell_type": "code", "execution_count": null, "id": "e7494cf6", "metadata": {}, "outputs": [], "source": [ "guiSAMdic = pd.DataFrame.from_dict(dic,orient='index')" ] }, { "cell_type": "code", "execution_count": null, "id": "8d09fc93", "metadata": {}, "outputs": [], "source": [ "guiSAMdic" ] }, { "cell_type": "code", "execution_count": null, "id": "b6fa259d", "metadata": {}, "outputs": [], "source": [ "result = pd.concat([guiSAMdic, pySAMdic], axis=1, join=\"inner\")\n", "result.columns = ['guiSAM', 'pySAM']\n", "result.to_csv(os.path.join(Resultsfolder, 'Side Comparison.csv'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }