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delete mode 100644 tutorials_and_tools/tutorials_and_tools/images/tutorial_1_NSRDB_example.PNG delete mode 100644 tutorials_and_tools/tutorials_and_tools/pvd_job_temperature and degradation/temperature and degradation.json diff --git a/.github/workflows/nbval.yaml b/.github/workflows/nbval.yaml index 45ffbe289..2e63b9f4b 100644 --- a/.github/workflows/nbval.yaml +++ b/.github/workflows/nbval.yaml @@ -15,23 +15,25 @@ jobs: env: # Notebooks with estimated runtime in seconds (format: "notebook:runtime") ALL_NOTEBOOKS: | - "1 - Basics, Humidity, Design.ipynb:10" - "2 - Degradation.ipynb:5" - "3 - Spectral Degradation.ipynb:5" - "4 - Weather Database Access.ipynb:15" - "ASTM Live Demo.ipynb:10" - "B-O LID - Accelerated Test.ipynb:10" - "Custom-Functions-Nopython.ipynb:30" - "LETID - Accelerated Test.ipynb:120" - "LETID - Outdoor LETID.ipynb:30" - "LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb:45" - "LETID - Passivated Wafer.ipynb:80" - "Monte Carlo - Arrhenius.ipynb:180" - "Monte Carlo - Standoff.ipynb:30" - "Pysam - Single Location.ipynb:25" - "Tools - Edge Seal Oxygen Ingress.ipynb:15" - "Van't Hoff Degradation Model.ipynb:5" - "Geospatial - Local Scenario.ipynb:1" + "01_basics/01_basics_humidity_design.ipynb:10" + "01_basics/02_degradation.ipynb:5" + "01_basics/03_spectral_degradation.ipynb:5" + "01_basics/04_weather_database_access.ipynb:15" + "02_degradation/01_bo_lid_accelerated_test.ipynb:10" + "02_degradation/02_letid_accelerated_test.ipynb:120" + "02_degradation/03_letid_outdoor.ipynb:30" + "02_degradation/04_letid_outdoor_scenario.ipynb:45" + "02_degradation/05_letid_passivated_wafer.ipynb:80" + "02_degradation/06_vant_hoff_degradation_model.ipynb:5" + "03_monte_carlo/01_arrhenius.ipynb:180" + "03_monte_carlo/02_standoff.ipynb:30" + "04_geospatial/01_local_scenario.ipynb:1" + "05_advanced/01_custom_functions_nopython.ipynb:30" + "05_advanced/02_pysam_single_location.ipynb:25" + "07_workshop_demos/01_astm_live_demo.ipynb:10" + "../tools/Tools - Degradation.ipynb:15" + "../tools/Tools - Edge Seal Oxygen Ingress.ipynb:15" + "../tools/Tools - Module Standoff for IEC TS 63126.ipynb:20" steps: - name: Calculate batch matrix id: set-matrix @@ -151,7 +153,7 @@ jobs: fail-fast: false matrix: ${{ fromJson(needs.calculate-batches.outputs.matrix) }} env: - NOTEBOOK_DIR: "tutorials_and_tools/tutorials_and_tools" + NOTEBOOK_DIR: "tutorials" SANITIZE_CFG: "nbval_sanitization_rules.cfg" ALL_NOTEBOOKS: ${{ needs.calculate-batches.outputs.notebooks }} BATCH_ASSIGNMENTS: ${{ needs.calculate-batches.outputs.batch_assignments }} @@ -168,14 +170,14 @@ jobs: uses: actions/cache@v4 with: path: ~/.cache/pip - key: ${{ runner.os }}-pip-${{ hashFiles('tutorials_and_tools/requirements.txt', 'pyproject.toml') }} + key: ${{ runner.os }}-pip-${{ hashFiles('tutorials/requirements.txt', 'pyproject.toml') }} restore-keys: | ${{ runner.os }}-pip- - name: Install notebook environment run: | python -m pip install --upgrade pip wheel - pip install --timeout=300 -r tutorials_and_tools/requirements.txt + pip install --timeout=300 -r tutorials/requirements.txt pip install --timeout=300 -e .[test] pip install nbval global_land_mask dotenv imageio nrel-pysam pip install nbconvert[webpdf] diff --git a/.github/workflows/testbook.yaml b/.github/workflows/testbook.yaml index 161dc41ed..8ab761d4f 100644 --- a/.github/workflows/testbook.yaml +++ b/.github/workflows/testbook.yaml @@ -6,17 +6,16 @@ jobs: notebook-testbook-check: runs-on: ubuntu-latest env: - NOTEBOOK_DIR: "tutorials_and_tools/tutorials_and_tools" + NOTEBOOK_DIR: "tutorials" TESTBOOK_NOTEBOOK_LIST: | - "API Call Required - PYSam.ipynb" - "API Call Required - load_nsrdb_distributed.ipynb" - "HPC Connection Required - Scenario - Geographical Features.ipynb" - "HPC Connection Required - Scenario - Geospatial.ipynb" - "HPC Connection Required - Scenario - Non-uniform Mountain Prefferential Downselect.ipynb" - "HPC Connection Required - Scenario - Single Location.ipynb" - "HPC Connection Required - Scenario - Temperature.ipynb" - "HPC Connection Required - Tools - Module Standoff for IEC TS 63126.ipynb" - "HPC Connection Required - Geospatial Templates.ipynb" + "05_advanced/03_pysam_api.ipynb" + "05_advanced/04_nsrdb_distributed_api.ipynb" + "04_geospatial/03_scenario_geographical_features.ipynb" + "04_geospatial/04_scenario_geospatial.ipynb" + "04_geospatial/05_scenario_mountain_downselect.ipynb" + "04_geospatial/06_scenario_single_location.ipynb" + "04_geospatial/07_scenario_temperature.ipynb" + "04_geospatial/02_geospatial_templates.ipynb" steps: - uses: actions/checkout@v4 @@ -27,7 +26,7 @@ jobs: - name: Install notebook environment run: | python -m pip install --upgrade pip wheel - pip install --timeout=300 -r tutorials_and_tools/requirements.txt + pip install --timeout=300 -r tutorials/requirements.txt pip install --timeout=300 -e .[test] pip install testbook pip install global_land_mask dotenv imageio nrel-pysam diff --git a/.gitignore b/.gitignore index a4c303f76..0fe206002 100644 --- a/.gitignore +++ b/.gitignore @@ -70,6 +70,8 @@ docs/sphinx/_build/ # tutorial jupyter-book pvdeg_tutorials/_build/ +tutorials/_build/ +tutorials_and_tools/_build/ # PyBuilder target/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f641d7edb..80e6ee21f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ # See https://pre-commit.com for more information # See https://pre-commit.com/hooks.html for more hooks -exclude: '^(docs/source/|tutorials_and_tools/_build/)' +exclude: '^(docs/source/|tutorials/_build/)' repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v6.0.0 @@ -14,9 +14,17 @@ repos: rev: 25.9.0 hooks: - id: black - exclude: '^tutorials_and_tools/' + exclude: '^(tutorials/|tools/)' - repo: https://github.com/PyCQA/flake8 rev: 7.3.0 # Use the desired flake8 version hooks: - id: flake8 - exclude: '^tutorials_and_tools/' + exclude: '^(tutorials/|tools/)' +- repo: https://github.com/mwouts/jupytext + rev: v1.18.1 + hooks: + - id: jupytext + args: [--sync, --pipe, black] + files: '^(tutorials/|tools/).*\.ipynb$' + additional_dependencies: + - black==25.9.0 diff --git a/jupytext.toml b/jupytext.toml new file mode 100644 index 000000000..5345bcdcc --- /dev/null +++ b/jupytext.toml @@ -0,0 +1,9 @@ +# Jupytext configuration +# Sync notebooks to scripts/ subdirectories as Python files with percent format + +# Default formats for notebooks +formats = "ipynb,scripts//py:percent" + +# Notebook metadata filter (reduce diff noise) +notebook_metadata_filter = "-all" +cell_metadata_filter = "-all" diff --git a/pyproject.toml b/pyproject.toml index 75e157332..cbd7243a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,6 +57,7 @@ dependencies = [ "pre-commit", "sympy", "zarr", + "jupytext", ] dynamic = ["version"] diff --git a/tools/README.md b/tools/README.md new file mode 100644 index 000000000..c44cc3cb5 --- /dev/null +++ b/tools/README.md @@ -0,0 +1,25 @@ +# PVDeg Tools + +This folder contains standalone computational tools for specific PV degradation calculations. + +## Tools Overview + +### Tools - Degradation +General degradation calculation tools and utilities. + +### Tools - Edge Seal Oxygen Ingress +Calculations for oxygen ingress through edge seals in PV modules. + +### Tools - Module Standoff for IEC TS 63126 +**âš ï¸ Requires NREL HPC Access** + +Calculation of module standoff distance according to IEC TS 63126. This tool performs geospatial analysis across multiple locations and requires access to NREL's High Performance Computing (HPC) resources for execution. + +## Requirements + +Most tools can run locally with standard dependencies. Tools marked with âš ï¸ require: +- NREL HPC cluster access +- Additional authentication/credentials +- Specific environment setup + +See individual notebooks for detailed requirements. diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Degradation.ipynb b/tools/Tools - Degradation.ipynb similarity index 97% rename from tutorials_and_tools/tutorials_and_tools/Tools - Degradation.ipynb rename to tools/Tools - Degradation.ipynb index 8e3d190f5..4981b4a7c 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Degradation.ipynb +++ b/tools/Tools - Degradation.ipynb @@ -25,7 +25,7 @@ "5. Generate chamber or field data for environmental comparison\n", "6. Calculate degradation acceleration factor of field location to chamber (or another location)\n", "7. Produce a map of acceleration factors for a geographic region\n", - " Select a geographic region of interest \n", + " Select a geographic region of interest\n", " downsample to select specific site coordinates\n", " Download or access the meteorological data for the chosen site coordinates.\n", " Run the calculation" @@ -78,6 +78,7 @@ "from IPython.display import display, Math\n", "\n", "import pvlib\n", + "\n", "print(pvlib.__version__)\n", "from pvlib import iotools" ] @@ -109,6 +110,7 @@ "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", + "\n", "print(\"Working on a \", platform.system(), platform.release())\n", "print(\"Python version \", sys.version)\n", "print(\"Pandas version \", pd.__version__)\n", @@ -152,8 +154,8 @@ ], "source": [ "# Get data from a supplied data file (Do not use the next box of code if using your own file)\n", - "weather_file = os.path.join(DATA_DIR, 'psm3_demo.csv')\n", - "weather_df, meta = pvdeg.weather.read(weather_file,'csv', find_meta=True)\n", + "weather_file = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n", + "weather_df, meta = pvdeg.weather.read(weather_file, \"csv\", find_meta=True)\n", "print(weather_file)\n", "print(meta)" ] @@ -180,16 +182,16 @@ ], "source": [ "# This routine will get a meteorological dataset from anywhere in the world where it is available\n", - "#weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", - "#weather_id = (35.6754, 139.65) #Tokyo, Japan\n", - "#weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n", - "#weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n", - "#weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n", - "weather_id = (33.4152, -111.8315) #Mesa, Arizona\n", - "#weather_id = (0,0) # Somewhere else you are interested in.\n", + "# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", + "# weather_id = (35.6754, 139.65) #Tokyo, Japan\n", + "# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n", + "# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n", + "# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n", + "weather_id = (33.4152, -111.8315) # Mesa, Arizona\n", + "# weather_id = (0,0) # Somewhere else you are interested in.\n", "weather_df, meta = pvdeg.weather.get_anywhere(id=weather_id)\n", "print(meta)\n", - "#display(weather_df)" + "# display(weather_df)" ] }, { @@ -198,12 +200,12 @@ "source": [ "#### POA Irradiance\n", "Next we need to calculate the stress parameters including temperature and humidity. We start with POA irradiance.\n", - "Irradiance_kwarg governs the array orientation for doing the POA calculations. \n", - "It is defaulted to a north-south single axis tracking. A fixed tilt set of parameters is included but is blocked out. \n", - "Look in spectral.py and/or PVLib here for 1-axis kwargs, \n", - "https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.tracking.singleaxis.html#pvlib.tracking.singleaxis \n", - "and for fixed tilt, \n", - "https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.irradiance.gti_dirint.html?highlight=poa . \n", + "Irradiance_kwarg governs the array orientation for doing the POA calculations.\n", + "It is defaulted to a north-south single axis tracking. A fixed tilt set of parameters is included but is blocked out.\n", + "Look in spectral.py and/or PVLib here for 1-axis kwargs,\n", + "https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.tracking.singleaxis.html#pvlib.tracking.singleaxis\n", + "and for fixed tilt,\n", + "https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.irradiance.gti_dirint.html?highlight=poa .\n", "Here, the POA global calculation is appended to the meteorolgical data dataframe." ] }, @@ -220,17 +222,16 @@ }, "outputs": [], "source": [ - "#irradiance_kwarg ={\n", - " #\"tilt\": None,\n", - " #\"azimuth\": None,\n", - " #\"module_mount\": 'fixed'}\n", - "irradiance_kwarg ={\n", - " \"axis_tilt\": 0,\n", - " \"axis_azimuth\": 180,\n", - " \"module_mount\": 'single_axis'}\n", - "poa_df = pvdeg.spectral.poa_irradiance(weather_df=weather_df, meta=meta, **irradiance_kwarg)\n", + "# irradiance_kwarg ={\n", + "# \"tilt\": None,\n", + "# \"azimuth\": None,\n", + "# \"module_mount\": 'fixed'}\n", + "irradiance_kwarg = {\"axis_tilt\": 0, \"axis_azimuth\": 180, \"module_mount\": \"single_axis\"}\n", + "poa_df = pvdeg.spectral.poa_irradiance(\n", + " weather_df=weather_df, meta=meta, **irradiance_kwarg\n", + ")\n", "\n", - "weather_df['poa_global'] = poa_df['poa_global']" + "weather_df[\"poa_global\"] = poa_df[\"poa_global\"]" ] }, { @@ -239,7 +240,7 @@ "source": [ "\n", "#### Get Spectrally Resolved Irradiance Data\n", - "This first set of commands will calculate spectrally resolved irradiance data. This may or may not be needed for a given degradation model and can be skipped here. " + "This first set of commands will calculate spectrally resolved irradiance data. This may or may not be needed for a given degradation model and can be skipped here." ] }, { @@ -251,7 +252,8 @@ "iopub.status.busy": "2025-10-04T11:18:58.272896Z", "iopub.status.idle": "2025-10-04T11:18:58.290765Z", "shell.execute_reply": "2025-10-04T11:18:58.290052Z" - } + }, + "lines_to_next_cell": 2 }, "outputs": [ { @@ -266,21 +268,22 @@ "# this whole block needs to be replaced with call to calculate spectrally resolved irradiance.\n", "\n", "from pvdeg import TEST_DATA_DIR\n", + "\n", "INPUT_SPECTRA = os.path.join(TEST_DATA_DIR, r\"spectra_pytest.csv\")\n", "data = pd.read_csv(INPUT_SPECTRA)\n", - "#display(data)\n", + "# display(data)\n", "print(INPUT_SPECTRA)\n", "\n", - "#Test function\n", - "#cusotm_albedo['Summer']\n", - "#custom_albedo['Winter']\n", + "# Test function\n", + "# cusotm_albedo['Summer']\n", + "# custom_albedo['Winter']\n", "# custom_albedo['Snow']\n", - "#defaults - Grass, Dry Grass, Snow\n", - "#Flexible to add complexity later\n", - "#merge in development branch changes\n", - "#KGPCY Python package\n", - "custom_albedo_summer = 'A006'\n", - "custom_albedo_winter = { #required: startDate, wavelength (if len(albedo) > 1), albedo, isSnow defaults to False\n", + "# defaults - Grass, Dry Grass, Snow\n", + "# Flexible to add complexity later\n", + "# merge in development branch changes\n", + "# KGPCY Python package\n", + "custom_albedo_summer = \"A006\"\n", + "custom_albedo_winter = { # required: startDate, wavelength (if len(albedo) > 1), albedo, isSnow defaults to False\n", " \"data_entry_person\": \"Michael Kempe\",\n", " \"date_entered\": \"7/28/2025\",\n", " \"DOI\": \"10.3390/ijerph15071507\",\n", @@ -289,24 +292,26 @@ " \"reference\": \"Turner J, Parisi AV. Ultraviolet Radiation Albedo and Reflectance in Review: The Influence to Ultraviolet Exposure in Occupational Settings. Int J Environ Res Public Health. 2018 Jul 17;15(7):1507.\",\n", " \"keywords\": \"snow, ground\",\n", " \"months\": \"1,2,3,10,11,12\",\n", - " \"startDate\": \"January 1\", #Day of Year? 0-365\n", - " \"HourOfYear\": \"1\", #Hour of Year? 1-8760\n", + " \"startDate\": \"January 1\", # Day of Year? 0-365\n", + " \"HourOfYear\": \"1\", # Hour of Year? 1-8760\n", " \"isSnow\": \"False\",\n", " \"comments\": \"Data is emperically extrapolated from 280 nm to 297 nm. Data extracted from Turner et al. Figure 1 as a reference to Doda & Green Snow-Ground. Doda D., Green A. Surface Reflectance Measurements in the UV from an Airborne Platform. Part 1. Appl. Opt. 1980;19:2140-2145. doi: 10.1364/AO.19.002140. Doda D., Green A. Surface Reflectance Measurements in the Ultraviolet from an Airborne Platform. Part 2. Appl. Opt. 1981;20:636-642. doi: 10.1364/AO.20.000636.\",\n", " \"wavelength\": \"280, 297.32034, 300.02435, 301.8514, 305.79782, 310.10962, 313.21558, 317.4543, 322.86237, 329.9513, 331.19366, 339.89038, 343.06943, 350.34103, 360.02435, 369.96347, 380.3776, 386.77222, 390.0609, 400.14615\",\n", - " \"albedo\": \"20, 29.515152, 28.30303, 29.454546, 28.90909, 34.696968, 36.757576, 39.363636, 39.21212, 38.60606, 41.272728, 40.909092, 42.242424, 42.21212, 40.575756, 43.21212, 43.090908, 43.454544, 43.60606, 39.757576\"\n", + " \"albedo\": \"20, 29.515152, 28.30303, 29.454546, 28.90909, 34.696968, 36.757576, 39.363636, 39.21212, 38.60606, 41.272728, 40.909092, 42.242424, 42.21212, 40.575756, 43.21212, 43.090908, 43.454544, 43.60606, 39.757576\",\n", "}\n", - "#Startdate, albedo, wavelength -> then next one + boolean logic for snow ()\n", + "# Startdate, albedo, wavelength -> then next one + boolean logic for snow ()\n", "custom_albedo_snow = {}\n", "# custom_albedo_snow\n", - "spectra_folder = 'spectra' #If you have already pulled the spectra from SMARTS, pass the folder path to avoid going through the donwload process again.\n", - "wavelengths = np.arange(280, 400, 25) # Example wavelengths from 280 nm to 400 nm in steps of 25 nm\n", - "#data = pvdeg.spectral.spectrally_resolved_irradiance(weather_df=weather_df, meta=meta, wavelengths=wavelengths, frontResultsOnly=None,\n", + "spectra_folder = \"spectra\" # If you have already pulled the spectra from SMARTS, pass the folder path to avoid going through the donwload process again.\n", + "wavelengths = np.arange(\n", + " 280, 400, 25\n", + ") # Example wavelengths from 280 nm to 400 nm in steps of 25 nm\n", + "# data = pvdeg.spectral.spectrally_resolved_irradiance(weather_df=weather_df, meta=meta, wavelengths=wavelengths, frontResultsOnly=None,\n", "# spectra_folder=spectra_folder, custom_albedo_summer=custom_albedo_summer, custom_albedo_winter=custom_albedo_winter, **irradiance_kwarg)\n", - "#return front, back, or both (True, False, None)\n", - "#bool frontResultsonly = True for front only\n", - "#separate columns for front and back irradiance: spectra_front: etc. , spectra_back: etc. (see spectra_pytest.csv)\n", - "#Check albedo boolean snow, winter non-snow, summer non-snow\n" + "# return front, back, or both (True, False, None)\n", + "# bool frontResultsonly = True for front only\n", + "# separate columns for front and back irradiance: spectra_front: etc. , spectra_back: etc. (see spectra_pytest.csv)\n", + "# Check albedo boolean snow, winter non-snow, summer non-snow" ] }, { @@ -314,7 +319,7 @@ "metadata": {}, "source": [ "#### Get Cell Temperature and Module Surface Temperature\n", - "The following will calculate the cell and module surface temperature using the King model as a default. Other models can be used as described at, \n", + "The following will calculate the cell and module surface temperature using the King model as a default. Other models can be used as described at,\n", "https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html. The difference is less than one °C for ground mounted systems\n", "but can be as high as 3 °C for a high temperature building integrated system.\n", "\n", @@ -337,11 +342,11 @@ "temp_cell = pvdeg.temperature.cell(weather_df=weather_df, meta=meta, poa=poa_df)\n", "temp_module = pvdeg.temperature.module(weather_df=weather_df, meta=meta, poa=poa_df)\n", "\n", - "weather_df['temp_cell'] = temp_cell\n", - "weather_df['temp_module'] = temp_module\n", + "weather_df[\"temp_cell\"] = temp_cell\n", + "weather_df[\"temp_module\"] = temp_module\n", "\n", - "weather_df['temperature'] = weather_df['temp_module']\n", - "#weather_df['temperature'] = weather_df['temp_cell']" + "weather_df[\"temperature\"] = weather_df[\"temp_module\"]\n", + "# weather_df['temperature'] = weather_df['temp_cell']" ] }, { @@ -371,13 +376,14 @@ "RH_module = pvdeg.humidity.module(\n", " weather_df=weather_df,\n", " poa=poa_df,\n", - " temp_module = temp_module,\n", - " backsheet='W017',\n", + " temp_module=temp_module,\n", + " backsheet=\"W017\",\n", " backsheet_thickness=0.30,\n", - " encapsulant='W001',\n", - " back_encap_thickness=0.50)\n", + " encapsulant=\"W001\",\n", + " back_encap_thickness=0.50,\n", + ")\n", "\n", - "weather_df=pd.concat([weather_df, RH_module], axis=1)" + "weather_df = pd.concat([weather_df, RH_module], axis=1)" ] }, { @@ -404,11 +410,11 @@ "outputs": [], "source": [ "\n", - "weather_df['RH'] = RH_module['RH_surface_outside']\n", - "#weather_df['RH'] = RH_module['RH_front_encap']\n", - "#weather_df['RH'] = RH_module['Ce_back_encap']\n", - "#weather_df['RH'] = RH_module['RH_back_encap']\n", - "#weather_df['RH'] = RH_module['RH_backsheet']" + "weather_df[\"RH\"] = RH_module[\"RH_surface_outside\"]\n", + "# weather_df['RH'] = RH_module['RH_front_encap']\n", + "# weather_df['RH'] = RH_module['Ce_back_encap']\n", + "# weather_df['RH'] = RH_module['RH_back_encap']\n", + "# weather_df['RH'] = RH_module['RH_backsheet']" ] }, { @@ -455,8 +461,8 @@ } ], "source": [ - "#kwarg_variables = pvdeg.utilities._read_material(name=None, fname=\"DegradationDatabase\", item=(\"Material\", \"Equation\", \"KeyWords\", \"EquationType\"))\n", - "#print(json.dumps(kwarg_variables, skipkeys = True, indent = 0 ).replace(\"{\" + \"\\n\", \"{\").replace('\\\"' + \"\\n\", \"\\\"\").replace(': {' , ':' + \"\\n\" + \"{\").replace('},' + \"\\n\", '},' +'\\n' +'\\n'))\n", + "# kwarg_variables = pvdeg.utilities._read_material(name=None, fname=\"DegradationDatabase\", item=(\"Material\", \"Equation\", \"KeyWords\", \"EquationType\"))\n", + "# print(json.dumps(kwarg_variables, skipkeys = True, indent = 0 ).replace(\"{\" + \"\\n\", \"{\").replace('\\\"' + \"\\n\", \"\\\"\").replace(': {' , ':' + \"\\n\" + \"{\").replace('},' + \"\\n\", '},' +'\\n' +'\\n'))\n", "pvdeg.utilities.display_json(pvdeg_file=\"DegradationDatabase\", fp=DATA_DIR)" ] }, @@ -505,7 +511,9 @@ } ], "source": [ - "deg_data = pvdeg.utilities.read_material(fp=DATA_DIR, key=\"D036\", pvdeg_file=\"DegradationDatabase\")\n", + "deg_data = pvdeg.utilities.read_material(\n", + " fp=DATA_DIR, key=\"D036\", pvdeg_file=\"DegradationDatabase\"\n", + ")\n", "display(deg_data)" ] }, @@ -560,7 +568,7 @@ "source": [ "## 3. Calculate Absolute Degradation Rate\n", "\n", - "To do this calculation, we must have degradation parameter data for a process that is complete with all the necessary variables. " + "To do this calculation, we must have degradation parameter data for a process that is complete with all the necessary variables." ] }, { @@ -588,7 +596,9 @@ "source": [ "func_call = getattr(pvdeg.degradation, deg_data[\"EquationType\"])\n", "degradation = func_call(weather_df=weather_df, parameters=deg_data)\n", - "print(\"Average degradation rate for a year\", degradation/8760, deg_data[\"R_0\"][\"units\"])" + "print(\n", + " \"Average degradation rate for a year\", degradation / 8760, deg_data[\"R_0\"][\"units\"]\n", + ")" ] }, { @@ -596,9 +606,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "\n" - ] + "source": [] } ], "metadata": { diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb b/tools/Tools - Edge Seal Oxygen Ingress.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb rename to tools/Tools - Edge Seal Oxygen Ingress.ipynb index ed7f2c442..ffbaf7522 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb +++ b/tools/Tools - Edge Seal Oxygen Ingress.ipynb @@ -89,6 +89,7 @@ "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", + "\n", "print(\"Working on a \", platform.system(), platform.release())\n", "print(\"Python version \", sys.version)\n", "print(\"Pandas version \", pd.__version__)\n", @@ -244,8 +245,16 @@ "sn = 20 # This is the number of edge seal nodes to use\n", "en = 50 # This is the number of encapsulant nodes to use\n", "pressure = 0.2109 * (1 - 0.0065 * meta.get(\"altitude\") / 288.15) ** 5.25588\n", - "print(pvdeg.utilities.read_material(pvdeg_file=\"O2permeation\", key=\"OX003\", values_only=True))\n", - "print(pvdeg.utilities.read_material(pvdeg_file=\"H2Opermeation\", key=\"W003\", values_only=True))" + "print(\n", + " pvdeg.utilities.read_material(\n", + " pvdeg_file=\"O2permeation\", key=\"OX003\", values_only=True\n", + " )\n", + ")\n", + "print(\n", + " pvdeg.utilities.read_material(\n", + " pvdeg_file=\"H2Opermeation\", key=\"W003\", values_only=True\n", + " )\n", + ")" ] }, { diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Tools - Module Standoff for IEC TS 63126.ipynb b/tools/Tools - Module Standoff for IEC TS 63126.ipynb similarity index 92% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Tools - Module Standoff for IEC TS 63126.ipynb rename to tools/Tools - Module Standoff for IEC TS 63126.ipynb index 83b906aa8..fa4fd4c2a 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Tools - Module Standoff for IEC TS 63126.ipynb +++ b/tools/Tools - Module Standoff for IEC TS 63126.ipynb @@ -27,7 +27,7 @@ "\n", "where $T_0$ is the temperature for $X=0$ (insulated-back) and $\\Delta T$ is the temperature difference between an insulated-back ($X=0$) and open-rack mounting configuration ($X=\\infty)$.\n", "\n", - " We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification. \n", + " We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification.\n", "\n", "$*$ D. L. King, W. E. Boyson, and J. A. Kratochvil, \"Photovoltaic array performance model,\" SAND2004-3535, Sandia National Laboratories, Albuquerque, NM, 2004. '\\\n", "$**$ M. K. Fuentes, \"A simplified thermal model for Flat-Plate photovoltaic arrays,\" United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914\n" @@ -37,7 +37,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![alt text](T98_70C_standoff_Map.png)" + "![alt text](images/T98_70C_standoff_Map.png)" ] }, { @@ -118,14 +118,14 @@ "outputs": [], "source": [ "# This routine will get a meteorological dataset from anywhere in the world where it is available\n", - "#weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", - "#weather_id = (35.6754, 139.65) #Tokyo, Japan\n", - "#weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n", - "#weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n", - "#weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n", - "#weather_id = (33.4152, -111.8315) #Mesa, Arizona\n", - "#WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id)\n", - "#print(META)" + "# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", + "# weather_id = (35.6754, 139.65) #Tokyo, Japan\n", + "# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n", + "# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n", + "# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n", + "# weather_id = (33.4152, -111.8315) #Mesa, Arizona\n", + "# WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id)\n", + "# print(META)" ] }, { @@ -134,7 +134,7 @@ "source": [ "## 2. Calculate Installation Standoff Minimum - Level 1 and Level 2\n", "\n", - "According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to T₉₈<70°C, <80°C and <90°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of T₉₈<70°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as, \n", + "According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to T₉₈<70°C, <80°C and <90°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of T₉₈<70°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as,\n", "\n", "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$\n", "\n", @@ -174,7 +174,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following is a full function call for both T₉₈=70°C and 80°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98. " + "The following is a full function call for both T₉₈=70°C and 80°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98." ] }, { @@ -220,7 +220,7 @@ "source": [ "## 3. Calculate $X_{eff}$ from provided module temperature data.\n", "\n", - "To do this calculation, one must use a set of data with: \n", + "To do this calculation, one must use a set of data with:\n", " - meterological irradiance data sufficient to calculate the POA irradiance (DHI, GHI, and DNI),\n", " - ambient temperature data,\n", " - wind speed at module height, (wind_factor=0.33 will be used unless otherwise specified)\n", @@ -286,12 +286,9 @@ "source": [ "# This is the minimal function call using the common default settings to estimate T₉₈.\n", "T_98 = pvdeg.standards.T98_estimate(\n", - " weather_df=WEATHER_df,\n", - " meta=META,\n", - " tilt=-META['latitude'],\n", - " azimuth=None,\n", - " x_eff=10)\n", - "print ('The 98áµ—Ê° percential temperature is estimated to be' , '%.1f' % T_98 , '°C.')" + " weather_df=WEATHER_df, meta=META, tilt=-META[\"latitude\"], azimuth=None, x_eff=10\n", + ")\n", + "print(\"The 98áµ—Ê° percential temperature is estimated to be\", \"%.1f\" % T_98, \"°C.\")" ] }, { @@ -301,23 +298,27 @@ "outputs": [], "source": [ "# This code will calculate the temperature for an arbitrary x_eff distance. Either set of kwargs can be modified and use.\n", - "#irradiance_kwarg ={\n", + "# irradiance_kwarg ={\n", "# \"axis_tilt\": None,\n", "# \"axis_azimuth\": None,\n", "# \"x_eff\": 10,\n", "# \"module_mount\": '1_axis'}\n", - "irradiance_kwarg ={\n", - " \"tilt\": META['latitude'],\n", + "irradiance_kwarg = {\n", + " \"tilt\": META[\"latitude\"],\n", " \"azimuth\": None,\n", " \"x_eff\": 10,\n", - " \"module_mount\": \"fixed\"}\n", + " \"module_mount\": \"fixed\",\n", + "}\n", "\n", "T_xeff = pvdeg.standards.x_eff_temperature_estimate(\n", - " weather_df=WEATHER_df,\n", - " meta=META,\n", - " **irradiance_kwarg)\n", + " weather_df=WEATHER_df, meta=META, **irradiance_kwarg\n", + ")\n", "\n", - "print ('The 98áµ—Ê° percential temperature is estimated to be' , '%.1f' % np.percentile(T_xeff, 98) , '°C.')" + "print(\n", + " \"The 98áµ—Ê° percential temperature is estimated to be\",\n", + " \"%.1f\" % np.percentile(T_xeff, 98),\n", + " \"°C.\",\n", + ")" ] }, { @@ -565,7 +566,7 @@ "source": [ "## 7. Plot $X_{min}$ for a $T_{98}$, and plot $T_{98}$ for a given region.\n", "\n", - "This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. \n", + "This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance.\n", "For more in-depth instructions on doing this, look at the tutorial \"Scenario - Geospatial.ipynb\" here in PVDeg.\n", "\n", "Step #1: Create an object, \"geospatial_standoff_scenario\" to be used to run the computations." @@ -679,14 +680,22 @@ }, "outputs": [], "source": [ - "print (\"Your results will be stored in %s\" % output_folder)\n", - "print ('The folder must already exist or the file will not be created')\n", + "print(\"Your results will be stored in %s\" % output_folder)\n", + "print(\"The folder must already exist or the file will not be created\")\n", "\n", - "pvdeg.weather.write(data_df=WEATHER_df, metadata=META, savefile=os.path.join(output_folder, 'WeatherFile.csv')) #Writes the meterological data to an *.csv file.\n", + "pvdeg.weather.write(\n", + " data_df=WEATHER_df,\n", + " metadata=META,\n", + " savefile=os.path.join(output_folder, \"WeatherFile.csv\"),\n", + ") # Writes the meterological data to an *.csv file.\n", "\n", - "pd.DataFrame(standoff_series_df).to_csv(os.path.join(output_folder, 'Standoff_Scan.csv')) #Writes a file with the Tilt and Azimuth scan calculations of standoff.\n", + "pd.DataFrame(standoff_series_df).to_csv(\n", + " os.path.join(output_folder, \"Standoff_Scan.csv\")\n", + ") # Writes a file with the Tilt and Azimuth scan calculations of standoff.\n", "\n", - "pd.DataFrame(T98_series_df).to_csv(os.path.join(output_folder, 'T98_Scan.csv')) #Writes a file with the Tilt and Azimuth scan calculations of T98.\n" + "pd.DataFrame(T98_series_df).to_csv(\n", + " os.path.join(output_folder, \"T98_Scan.csv\")\n", + ") # Writes a file with the Tilt and Azimuth scan calculations of T98." ] } ], diff --git a/tools/_toc.yml b/tools/_toc.yml new file mode 100644 index 000000000..e3f485bd6 --- /dev/null +++ b/tools/_toc.yml @@ -0,0 +1,8 @@ +# Table of contents for Tools +# Learn more at https://jupyterbook.org/customize/toc.html + +format: jb-book +root: intro +chapters: +- file: Tools - Edge Seal Oxygen Ingress +- file: Tools - Degradation diff --git a/tutorials_and_tools/tutorials_and_tools/T98_70C_standoff_Map.png b/tools/images/T98_70C_standoff_Map.png similarity index 100% rename from tutorials_and_tools/tutorials_and_tools/T98_70C_standoff_Map.png rename to tools/images/T98_70C_standoff_Map.png diff --git a/tools/intro.md b/tools/intro.md new file mode 100644 index 000000000..66c3353cb --- /dev/null +++ b/tools/intro.md @@ -0,0 +1,8 @@ +# PVDegradationTools - Tools + +This section contains tools for photovoltaic degradation analysis. + +## Available Tools + +- **Edge Seal Oxygen Ingress**: Analysis tools for edge seal oxygen ingress +- **Degradation Tools**: Additional degradation analysis tools diff --git a/tools/scripts/Tools - Degradation.py b/tools/scripts/Tools - Degradation.py new file mode 100644 index 000000000..66dbe72d7 --- /dev/null +++ b/tools/scripts/Tools - Degradation.py @@ -0,0 +1,256 @@ +# %% [markdown] +# # Degradation and Acceleration Factors +# This tool will provide a simple method for estimating degradation and for calculating acceleration factors. It interfaces with the degradation database to simplify acquisition of the degradation parameters. +# +# **Requirements**: +# - compatible weather file (e.g., PSM3, TMY3, EPW...) +# - Accelerated testing chamber parameters +# - chamber irradiance [W/m^2] +# - chamber temperature [C] +# - chamber humidity [%] +# - & etc. +# - Activation energies for test material [kJ/mol] +# - Other degradation parameters +# +# **Objectives**: +# 1. Read in the weather data +# 2. Gather basic degradation modeling data for a material of interest +# 3. Calculate absolute degradation rate +# 4. Run Monte Carlo simulation at a single site +# 5. Generate chamber or field data for environmental comparison +# 6. Calculate degradation acceleration factor of field location to chamber (or another location) +# 7. Produce a map of acceleration factors for a geographic region +# Select a geographic region of interest +# downsample to select specific site coordinates +# Download or access the meteorological data for the chosen site coordinates. +# Run the calculation + +# %% +# 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 pvdeg==0.4.2 + +# %% +import os +import pvdeg +import pandas as pd +import numpy as np +from pvdeg import DATA_DIR +import json +from IPython.display import display, Math + +import pvlib + +print(pvlib.__version__) +from pvlib import iotools + +# %% +# This information helps with debugging and getting support :) +import sys, platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("Pandas version ", pd.__version__) +print("pvdeg version ", pvdeg.__version__) +print(DATA_DIR) + +# %% [markdown] +# ## 1. Read In the Weather Data +# +# The function has these minimum requirements when using a weather data file: +# - Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level. +# - Site meta-data containing (at least) latitude, longitude, and time zone +# +# Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial "Weather Database Access.ipynb" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information. + +# %% +# Get data from a supplied data file (Do not use the next box of code if using your own file) +weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") +weather_df, meta = pvdeg.weather.read(weather_file, "csv", find_meta=True) +print(weather_file) +print(meta) + +# %% +# This routine will get a meteorological dataset from anywhere in the world where it is available +# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia +# weather_id = (35.6754, 139.65) #Tokyo, Japan +# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand +# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska +# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland +weather_id = (33.4152, -111.8315) # Mesa, Arizona +# weather_id = (0,0) # Somewhere else you are interested in. +weather_df, meta = pvdeg.weather.get_anywhere(id=weather_id) +print(meta) +# display(weather_df) + +# %% [markdown] +# #### POA Irradiance +# Next we need to calculate the stress parameters including temperature and humidity. We start with POA irradiance. +# Irradiance_kwarg governs the array orientation for doing the POA calculations. +# It is defaulted to a north-south single axis tracking. A fixed tilt set of parameters is included but is blocked out. +# Look in spectral.py and/or PVLib here for 1-axis kwargs, +# https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.tracking.singleaxis.html#pvlib.tracking.singleaxis +# and for fixed tilt, +# https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.irradiance.gti_dirint.html?highlight=poa . +# Here, the POA global calculation is appended to the meteorolgical data dataframe. + +# %% +# irradiance_kwarg ={ +# "tilt": None, +# "azimuth": None, +# "module_mount": 'fixed'} +irradiance_kwarg = {"axis_tilt": 0, "axis_azimuth": 180, "module_mount": "single_axis"} +poa_df = pvdeg.spectral.poa_irradiance( + weather_df=weather_df, meta=meta, **irradiance_kwarg +) + +weather_df["poa_global"] = poa_df["poa_global"] + +# %% [markdown] +# +# #### Get Spectrally Resolved Irradiance Data +# This first set of commands will calculate spectrally resolved irradiance data. This may or may not be needed for a given degradation model and can be skipped here. + +# %% +# this whole block needs to be replaced with call to calculate spectrally resolved irradiance. + +from pvdeg import TEST_DATA_DIR + +INPUT_SPECTRA = os.path.join(TEST_DATA_DIR, r"spectra_pytest.csv") +data = pd.read_csv(INPUT_SPECTRA) +# display(data) +print(INPUT_SPECTRA) + +# Test function +# cusotm_albedo['Summer'] +# custom_albedo['Winter'] +# custom_albedo['Snow'] +# defaults - Grass, Dry Grass, Snow +# Flexible to add complexity later +# merge in development branch changes +# KGPCY Python package +custom_albedo_summer = "A006" +custom_albedo_winter = { # required: startDate, wavelength (if len(albedo) > 1), albedo, isSnow defaults to False + "data_entry_person": "Michael Kempe", + "date_entered": "7/28/2025", + "DOI": "10.3390/ijerph15071507", + "source_title": "Ultraviolet Radiation Albedo and Reflectance in Review: The Influence to Ultraviolet Exposure in Occupational Settings", + "authors": "Joanna Turner, Alfio V. Parisi", + "reference": "Turner J, Parisi AV. Ultraviolet Radiation Albedo and Reflectance in Review: The Influence to Ultraviolet Exposure in Occupational Settings. Int J Environ Res Public Health. 2018 Jul 17;15(7):1507.", + "keywords": "snow, ground", + "months": "1,2,3,10,11,12", + "startDate": "January 1", # Day of Year? 0-365 + "HourOfYear": "1", # Hour of Year? 1-8760 + "isSnow": "False", + "comments": "Data is emperically extrapolated from 280 nm to 297 nm. Data extracted from Turner et al. Figure 1 as a reference to Doda & Green Snow-Ground. Doda D., Green A. Surface Reflectance Measurements in the UV from an Airborne Platform. Part 1. Appl. Opt. 1980;19:2140-2145. doi: 10.1364/AO.19.002140. Doda D., Green A. Surface Reflectance Measurements in the Ultraviolet from an Airborne Platform. Part 2. Appl. Opt. 1981;20:636-642. doi: 10.1364/AO.20.000636.", + "wavelength": "280, 297.32034, 300.02435, 301.8514, 305.79782, 310.10962, 313.21558, 317.4543, 322.86237, 329.9513, 331.19366, 339.89038, 343.06943, 350.34103, 360.02435, 369.96347, 380.3776, 386.77222, 390.0609, 400.14615", + "albedo": "20, 29.515152, 28.30303, 29.454546, 28.90909, 34.696968, 36.757576, 39.363636, 39.21212, 38.60606, 41.272728, 40.909092, 42.242424, 42.21212, 40.575756, 43.21212, 43.090908, 43.454544, 43.60606, 39.757576", +} +# Startdate, albedo, wavelength -> then next one + boolean logic for snow () +custom_albedo_snow = {} +# custom_albedo_snow +spectra_folder = "spectra" # If you have already pulled the spectra from SMARTS, pass the folder path to avoid going through the donwload process again. +wavelengths = np.arange( + 280, 400, 25 +) # Example wavelengths from 280 nm to 400 nm in steps of 25 nm +# data = pvdeg.spectral.spectrally_resolved_irradiance(weather_df=weather_df, meta=meta, wavelengths=wavelengths, frontResultsOnly=None, +# spectra_folder=spectra_folder, custom_albedo_summer=custom_albedo_summer, custom_albedo_winter=custom_albedo_winter, **irradiance_kwarg) +# return front, back, or both (True, False, None) +# bool frontResultsonly = True for front only +# separate columns for front and back irradiance: spectra_front: etc. , spectra_back: etc. (see spectra_pytest.csv) +# Check albedo boolean snow, winter non-snow, summer non-snow + + +# %% [markdown] +# #### Get Cell Temperature and Module Surface Temperature +# The following will calculate the cell and module surface temperature using the King model as a default. Other models can be used as described at, +# https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html. The difference is less than one °C for ground mounted systems +# but can be as high as 3 °C for a high temperature building integrated system. +# +# Here the temperatures are added to the dataframe and the 'temp_module' temperature is selected as the default 'temperature' for the degradation calculations. If it is a cell degradation that is being investigated, 'temp_cell' should be used for the temperature. + +# %% +temp_cell = pvdeg.temperature.cell(weather_df=weather_df, meta=meta, poa=poa_df) +temp_module = pvdeg.temperature.module(weather_df=weather_df, meta=meta, poa=poa_df) + +weather_df["temp_cell"] = temp_cell +weather_df["temp_module"] = temp_module + +weather_df["temperature"] = weather_df["temp_module"] +# weather_df['temperature'] = weather_df['temp_cell'] + +# %% [markdown] +# #### Humidity +# Depending on the component for which the calculation is being run on, the desired humidity may be the atmospheric humidity, the module surface humidity, the humidity in front of a cell with a permeable backsheet, the humidity in the backsheet, the humidity in the back encapsulant or another custom humidity location such as a diffusion limited location. The folowing are options for doing all of these calculations. Here all the different humidities are put in the weather_df dataframe, but to select one to be specifically used it should be named 'RH' for most degradation functions (check the documentation of a specific degradation calculation if in doubt). Here the surface humidity is selected as a default. +# +# Append the calculated values into the weather DataFrame. +# Note: putting the values into the weather_df DataFrame is not strictly necessary, but may be convenient for later use in the degradation calculations. + +# %% +RH_module = pvdeg.humidity.module( + weather_df=weather_df, + poa=poa_df, + temp_module=temp_module, + backsheet="W017", + backsheet_thickness=0.30, + encapsulant="W001", + back_encap_thickness=0.50, +) + +weather_df = pd.concat([weather_df, RH_module], axis=1) + +# %% [markdown] +# Each of the necessary arrays of data can be individually sent to a function for calculation in the function call, or they can be combined into a single dataframe. The degradation functions are set up to first check for a specific data set in the function call but if not found it looks for specific data or a suitable substitute in the weather dataframe. +# +# You can select one of the RH values to be used as the relative humidity in the degradation model calculations by assigning it to to column "RH" in the dataframe. +# Alternatively, the "RH" data can be sent to the degradation function explicitly in the function call. + +# %% + +weather_df["RH"] = RH_module["RH_surface_outside"] +# weather_df['RH'] = RH_module['RH_front_encap'] +# weather_df['RH'] = RH_module['Ce_back_encap'] +# weather_df['RH'] = RH_module['RH_back_encap'] +# weather_df['RH'] = RH_module['RH_backsheet'] + +# %% [markdown] +# ## 2. Gather Basic Degradation Modeling Data for a Material of Interest +# +# First we need to gather in the parameters for the degradation process of interest. This includes things such as the activiation energy and parameters defining the sensitivity to moisture, UV light, voltage, and other stressors. +# For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions. +# The blocked out text will produce a list of key fields from the database for each entry. + +# %% +# kwarg_variables = pvdeg.utilities._read_material(name=None, fname="DegradationDatabase", item=("Material", "Equation", "KeyWords", "EquationType")) +# print(json.dumps(kwarg_variables, skipkeys = True, indent = 0 ).replace("{" + "\n", "{").replace('\"' + "\n", "\"").replace(': {' , ':' + "\n" + "{").replace('},' + "\n", '},' +'\n' +'\n')) +pvdeg.utilities.display_json(pvdeg_file="DegradationDatabase", fp=DATA_DIR) + +# %% [markdown] +# This next set of codes will take the data from the extracted portion of the Json library and create a list of variables from it. If more variables need to be modified or added, this is where it should be done. + +# %% +deg_data = pvdeg.utilities.read_material( + fp=DATA_DIR, key="D036", pvdeg_file="DegradationDatabase" +) +display(deg_data) + +# %% [markdown] +# Here we pull out the relevant equation code identifier needed for running the calculations. + +# %% +func = "pvdeg.degradation." + deg_data["EquationType"] +print(func) +display(Math("\\Large " + deg_data["Equation"])) + +# %% [markdown] +# ## 3. Calculate Absolute Degradation Rate +# +# To do this calculation, we must have degradation parameter data for a process that is complete with all the necessary variables. + +# %% +func_call = getattr(pvdeg.degradation, deg_data["EquationType"]) +degradation = func_call(weather_df=weather_df, parameters=deg_data) +print( + "Average degradation rate for a year", degradation / 8760, deg_data["R_0"]["units"] +) + +# %% diff --git a/tools/scripts/Tools - Edge Seal Oxygen Ingress.py b/tools/scripts/Tools - Edge Seal Oxygen Ingress.py new file mode 100644 index 000000000..45a347149 --- /dev/null +++ b/tools/scripts/Tools - Edge Seal Oxygen Ingress.py @@ -0,0 +1,201 @@ +# %% [markdown] +# # Tools - Edge Seal Oxygen Ingress Calculator tool +# +# ### Calculation of oxygen ingress profile through an edge seal and into the encapsulant. +# +# **Requirements:** +# - Local weather data file or site longitude and latittude. +# - Properties and dimensions of the edge seal. +# +# **Objectives:** +# 1. Import weather data. +# 2. Set up the calculations. +# 3. Calculate oxygen ingress into an edge seal. +# 3. Incorporate an oxygen consumption model. +# 4. Plot the data. +# +# **Background:** +# +# This performs a 1-D finite difference model for oxygen ingress through an edge seal and into an encapsulant. This is effectively an infinitely long module with a prescribed width.The output is then displayed graphically. + +# %% +# 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 pvdeg==0.4.2 + +# %% +import os +import pvdeg +import pandas as pd +from pvdeg import DATA_DIR +import matplotlib.pyplot as plt +import numpy as np +import json + +# %% +# This information helps with debugging and getting support :) +import sys, platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("Pandas version ", pd.__version__) +print("pvdeg version ", pvdeg.__version__) + +# %% [markdown] +# ## 1. Import Weather Data +# +# The function has these minimum requirements when using a weather data file: +# - Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level. +# - Site meta-data containing (at least) latitude, longitude, and time zone +# +# Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial "Weather Database Access.ipynb" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information. + +# %% +# Get data from a supplied data file (Do not use the next box of code if using your own file) +weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") +weather_df, meta = pvdeg.weather.read(weather_file, "psm") +print(sorted(meta.keys())) + +# %% +# This routine will get a meteorological dataset from anywhere in the world where it is available +# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia +# weather_id = (35.6754, 139.65) #Tokyo, Japan +# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand +# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska +# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland +weather_id = (33.4152, -111.8315) # Mesa, Arizona +weather_df, meta = pvdeg.weather.get_anywhere(id=weather_id, database="PVGIS") +print(meta) + +# %% +# This computes a module temperature. Here the default is an open rack system, but other options include: +# 'open_rack_glass_glass', +# 'close_mount_glass_glass', +# 'insulated_back_glass_polymer' + +temperature = pvdeg.temperature.temperature( + weather_df=weather_df, + meta=meta, + cell_or_mod="module", + temp_model="sapm", + conf="open_rack_glass_polymer", +) + +temperature = pd.DataFrame(temperature, columns=["module_temperature"]) +temperature["time"] = list(range(8760)) + +# %% [markdown] +# ## 2. Set up the Calculations +# +# There is a library of some materials and the relevant oxygen ingress parameters that can be used. + +# %% +es = "OX005" # This is the number for the edge seal in the json file +enc = "OX003" # This is the number for the encapsulant in the json file +esw = 1.5 # This is the edge seal width in [cm] +encw = 10 # This is the encapsulant width in [cm] +sn = 20 # This is the number of edge seal nodes to use +en = 50 # This is the number of encapsulant nodes to use +pressure = 0.2109 * (1 - 0.0065 * meta.get("altitude") / 288.15) ** 5.25588 +print( + pvdeg.utilities.read_material( + pvdeg_file="O2permeation", key="OX003", values_only=True + ) +) +print( + pvdeg.utilities.read_material( + pvdeg_file="H2Opermeation", key="W003", values_only=True + ) +) + +# %% [markdown] +# ## 3. Run the Calculations +# +# This runs the calculations for diffusion using a simple 1-D finite difference calculation. The first set of calculations is just for diffusion, then the next two (when written) will include some consumption of oxygen. In typical PV applications, it is common for oxygen ingress distance to be limited by its consumption rate in the encapsulant. + +# %% +oxygen_profile = pvdeg.diffusion.esdiffusion( + temperature=temperature, + edge_seal=es, + encapsulant=enc, + edge_seal_width=esw, + encapsulant_width=encw, + seal_nodes=sn, + encapsulant_nodes=en, + press=pressure, + repeat=2, +) + +# %% +# This sets up an a variable with the output folder information. +output_folder = os.path.join( + os.path.dirname(os.path.dirname(os.getcwd())), "TEMP", "results" +) +try: + os.makedirs(output_folder) + print(f"Created directory: {output_folder}") +except OSError as error: + print(error) + +# %% +n_lines = 10 +times = oxygen_profile.index.tolist() +for index in range(n_lines): + plt.plot( + oxygen_profile.iloc[ + int(np.trunc((index + 1) * (len(oxygen_profile) - 1) / n_lines)) + ], + label=np.round( + times[int(np.trunc((index + 1) * ((len(oxygen_profile) - 1) / n_lines)))] + / 365.25 + / 24, + 2, + ), + ) +plt.legend(title="Time [year]") +plt.ylabel("Oxygen Concentration [g/cm³]") +plt.xlabel("Distance From Edge [cm]") +plt.ticklabel_format(axis="y", style="plain") + +plt.savefig( + os.path.join(output_folder, "Edge_Seal_O2_ingress.png"), bbox_inches="tight" +) # Creates an image file of the standoff plot +plt.show() + +# %% [markdown] +# ## 5. Save data outputs. +# +# This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation. First check that the output folder exists. + +# %% +fpath = os.path.join(DATA_DIR, "O2permeation.json") +with open(fpath) as f: + data = json.load(f) +f.close() + +material_list = "" +for key in data: + if "name" in data[key].keys(): + material_list = material_list + key + "=" + data[key]["name"] + "\n" +material_list = material_list[0 : len(material_list) - 1] +print(material_list) + +# %% +print("Your results will be stored in %s" % output_folder) +print("The folder must already exist or the file will not be created") + +# Writes the meterological data to an *.csv file. +pvdeg.weather.write( + data_df=weather_df, + metadata=meta, + savefile=os.path.join(output_folder, "WeatherFile.csv"), +) + +# Writes a file with the edge seal oxygen profile calculations. +pd.DataFrame(oxygen_profile).to_csv( + os.path.join(output_folder, "ES_Oxygen_profile.csv") +) + +# Writes a file with temperature data used in the model calculations. +pd.DataFrame(temperature).to_csv( + os.path.join(output_folder, "ES_Temperature_profile.csv") +) diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.py b/tools/scripts/Tools - Module Standoff for IEC TS 63126.py similarity index 79% rename from tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.py rename to tools/scripts/Tools - Module Standoff for IEC TS 63126.py index 80efddf7e..27b5b4b63 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.py +++ b/tools/scripts/Tools - Module Standoff for IEC TS 63126.py @@ -1,9 +1,7 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # Tools - Module Standoff for IEC TS 63126 -# ## Calculation of module standoff distance according to IEC TS 63126 # +# ### Calculation of module standoff distance according to IEC TS 63126 # # **Requirements:** # - Local weather data file or site longitude and latittude @@ -30,18 +28,15 @@ # $**$ M. K. Fuentes, "A simplified thermal model for Flat-Plate photovoltaic arrays," United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914 # -# ![T98 70C standoff Map.png](attachment:62279573-41e3-45dd-bf62-4fa60c1e7e69.png) - -# In[ ]: - - -# 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 pvdeg==0.3.3 - - -# In[3]: +# %% [markdown] +# ![alt text](images/T98_70C_standoff_Map.png) +# %% +# 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 pvdeg==0.4.2 +# %% import os import pvdeg import pandas as pd @@ -50,11 +45,9 @@ import matplotlib.pyplot as plt import seaborn as sns import math +import numpy as np - -# In[2]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -66,47 +59,34 @@ print("dask version", dask.__version__) print(DATA_DIR) - +# %% [markdown] # ## 1. Import Weather Data # # The function has these minimum requirements when using a weather data file: # - Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level. # - Site meta-data containing (at least) latitude, longitude, and time zone # -# Alternatively one may can get meterological data from the NSRDB with just the longitude and latitude. +# Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial "Weather Database Access.ipynb" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information. # -# In[4]: - - +# %% # Get data from a supplied data file (Do not use the next box of code if using your own file) weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") -WEATHER, META = pvdeg.weather.read(weather_file, "psm") - - -# In[5]: - - -# From Tutorial 5 EXAMPLE, this works. -# API_KEY = 'your_api_key_here' -API_KEY = "DEMO_KEY" # you can activate this line to use the demonstration API key but it has limited usage. -# The example API key here is for demonstation and is rate-limited per IP. -# To get your own API key, visit https://developer.nrel.gov/signup/ - -weather_db = "PSM3" -weather_id = (33.4152, -111.8315) -weather_arg = { - "api_key": API_KEY, - "email": "user@mail.com", - "names": "tmy", - "attributes": [], - "map_variables": True, -} - -WEATHER_df, META = pvdeg.weather.get(weather_db, weather_id, **weather_arg) +WEATHER_df, META = pvdeg.weather.read(weather_file, "csv") print(META) - +# %% +# This routine will get a meteorological dataset from anywhere in the world where it is available +# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia +# weather_id = (35.6754, 139.65) #Tokyo, Japan +# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand +# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska +# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland +# weather_id = (33.4152, -111.8315) #Mesa, Arizona +# WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id) +# print(META) + +# %% [markdown] # ## 2. Calculate Installation Standoff Minimum - Level 1 and Level 2 # # According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to T₉₈<70°C, <80°C and <90°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of T₉₈<70°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as, @@ -127,21 +107,17 @@ # - x_0 : thermal decay constant [cm] (see documentation). The default is 6.5 cm. # - wind_factor : Wind speed power law correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM). The default is 0.33. -# The following is the minimum function call. It defaults to horizontal tilt. - -# In[6]: - +# %% [markdown] +# The following is the minimum function call. It defaults to horizontal tilt and T₉₈=70°C. +# %% standoff = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META) -output = pvdeg.standards.interpret_standoff(standoff) -print(output) - - -# The following is a full function call for both T₉₈=70°C and 80°C. This also includes the ability to print out a detailed interpretation of the results. With this function, one may also want to change the tilt, azimuth, or T_98 - -# In[8]: +print(pvdeg.standards.interpret_standoff(standoff)) +# %% [markdown] +# The following is a full function call for both T₉₈=70°C and 80°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98. +# %% standoff_1 = pvdeg.standards.standoff( weather_df=WEATHER_df, meta=META, @@ -170,10 +146,9 @@ wind_factor=0.33, ) print("Second calculation standoff = ", "%.1f" % standoff_2["x"].iloc[0], " cm.") -print("\n") -print(pvdeg.standards.interpret_standoff(standoff_1, standoff_2)) - +print(pvdeg.standards.interpret_standoff(standoff_1=standoff_1, standoff_2=standoff_2)) +# %% [markdown] # ## 3. Calculate $X_{eff}$ from provided module temperature data. # # To do this calculation, one must use a set of data with: @@ -187,37 +162,34 @@ # # To do this calculation, one should also filter the data to remove times when the sun is not shining or when snow is likely to be on the module. The recommendations and programmed defaults are to use poa_min=100 W/m² and data when the minimum ambient temperature t_amb_min=0. -# In[9]: - - +# %% # Read the weather file -weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") -Xeff_WEATHER, Xeff_META = pvdeg.weather.read(weather_file, "psm3") - -# Get module data from a supplied data file -measured = pd.read_csv(os.path.join(DATA_DIR, "module_temperature.csv")) - +weather_file = os.path.join(DATA_DIR, "xeff_demo.csv") +xeff_weather, xeff_meta = pvdeg.weather.read(weather_file, "csv") # Pull measured temperature and calculate theoretical insulated back module temperature and open rack module temperature -T_0, T_inf, T_measured, temp_air, POA = pvdeg.standards.eff_gap_parameters( - weather_df=Xeff_WEATHER, - meta=Xeff_META, - module_temp=measured["Module_Temperature"], +T_0, T_inf, xeff_poa = pvdeg.standards.eff_gap_parameters( + weather_df=xeff_weather, + meta=xeff_meta, sky_model="isotropic", temp_model="sapm", conf_0="insulated_back_glass_polymer", conf_inf="open_rack_glass_polymer", - tilt=39.73, - azimuth=180, wind_factor=0.33, ) - # Now calculate X_eff. x_eff = pvdeg.standards.eff_gap( - T_0, T_inf, T_measured, temp_air, POA, x_0=6.5, poa_min=100, t_amb_min=0 + T_0, + T_inf, + xeff_weather["module_temperature"], + xeff_weather["temp_air"], + xeff_poa["poa_global"], + x_0=6.5, + poa_min=100, + t_amb_min=0, ) print("The effective standoff for this system is", "%.1f" % x_eff, "cm.") - +# %% [markdown] # ## 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$. # # Equation 2 can be reorganized as, @@ -226,29 +198,45 @@ # # and used to calculate the $98^{th}$ percential temperature, $T_{98}$, for a PV system having a given effective standoff height, $X_{eff}$, for an arbitrarily oriented module. Here, $T_{98,0}$ is the $98^{th}$ percentile for an insulated-back module and $T_{98,inf}$ is the $98^{th}$ percentile for a rack-mounted module. The input parameter possibilities are the same as shown in Objective #2 above, but the example below uses the default parameters. The actual tilt [degrees], azimuth [degrees] and $X_{eff}$ [cm] can be modifed as desired. -# In[10]: - - +# %% # This is the minimal function call using the common default settings to estimate T₉₈. T_98 = pvdeg.standards.T98_estimate( - weather_df=WEATHER_df, - meta=META, - tilt=META["latitude"], - azimuth=None, - x_eff=0, + weather_df=WEATHER_df, meta=META, tilt=-META["latitude"], azimuth=None, x_eff=10 ) print("The 98áµ—Ê° percential temperature is estimated to be", "%.1f" % T_98, "°C.") +# %% +# This code will calculate the temperature for an arbitrary x_eff distance. Either set of kwargs can be modified and use. +# irradiance_kwarg ={ +# "axis_tilt": None, +# "axis_azimuth": None, +# "x_eff": 10, +# "module_mount": '1_axis'} +irradiance_kwarg = { + "tilt": META["latitude"], + "azimuth": None, + "x_eff": 10, + "module_mount": "fixed", +} + +T_xeff = pvdeg.standards.x_eff_temperature_estimate( + weather_df=WEATHER_df, meta=META, **irradiance_kwarg +) + +print( + "The 98áµ—Ê° percential temperature is estimated to be", + "%.1f" % np.percentile(T_xeff, 98), + "°C.", +) +# %% [markdown] # ## 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$. # # The temperature of a system is affected by the orientation. This section will scan all possible tilts and azimuths calculating the minimum standoff distance for a given $T_{98}$. Similar additional factors as above can also be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $T_{98}$=70°C, the boundary between Level 0 and Level 1 requirements. The temperature model information given below is unnecessary as these are default values that would get populated automatically. However, they were included here for clarity into a standard practice as per IEC TS 63126. # # $$ X_{min} = -X_0 \ln\left(1-\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\right), Equation 2 $$ -# In[11]: - - +# %% # Scans through all the azimuth and tilt running the minimum standoff calculation # Set up keyword parameters for the calculation @@ -273,7 +261,7 @@ **kwarg_x, ) - +# %% [markdown] # The next cell creates a plot of the calculated data. Some of the things you may want to change are: # - cmap="Spectral_r": Change to have different colors # - plt.title : This will change the plot title. @@ -284,9 +272,7 @@ # - h_ticks=10 : This changes the number of horizontal tick marks # - Unblock the last two lines to ouput the plot as an *.png image file -# In[12]: - - +# %% standoff_series_df = pd.DataFrame( { "Tilt": standoff_series[:, 0], @@ -337,23 +323,21 @@ os.path.dirname(os.path.dirname(os.getcwd())), "TEMP", "results" ) try: - os.mkdir(output_folder) + os.makedirs(output_folder) except OSError as error: print(error) plt.savefig( - output_folder + r"\Standoff_Scan.png", bbox_inches="tight" + os.path.join(output_folder, "Standoff_Scan.png"), bbox_inches="tight" ) # Creates an image file of the standoff plot plt.show() - +# %% [markdown] # ## 6. Plot $T_{98}$ for all azimuth and tilt for a given $X_{eff}$. # # The temperature of a system is affected by the orientation and the effective standoff, $X_{eff}$, of the system. This section will scan all possible tilts and azimuths calculating the $T_{98}$ for a given $X_{eff}$. As above, additional factors can be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $X_{eff}$=10 cm, a common effective standoff distance on a rooftop system. A value of $X_{eff}$=None will run the calculations for an open rack system and $X_{eff}$=0 for an insulated-back system. -# In[13]: - - +# %% # Scans through all the azimuth and tilt running the 98áµ—Ê° percentile temperature calculation. # Set up keyword parameters for the calculation kwarg_T = dict( @@ -377,7 +361,7 @@ **kwarg_T, ) - +# %% [markdown] # The next cell creates a plot of the calculated data. Some of the things you may want to change are: # - cmap="Spectral_r": Change to have different colors # - plt.title : This will change the plot title. @@ -388,9 +372,7 @@ # - h_ticks=10 : This changes the number of horizontal tick marks # - Unblock the last two lines to ouput the plot as an *.png image file -# In[14]: - - +# %% # This produces the plot of the data T98_series_df = pd.DataFrame( {"Tilt": T98_series[:, 0], "Azimuth": T98_series[:, 1], "T₉₈": T98_series[:, 2]} @@ -446,18 +428,59 @@ ) # Creates an image file of the standoff plot plt.show(T98_fig) - -# ## 7. Plot $X_{min}$ for Level 1, Level 2, and $T_{98}$ for a given region. +# %% [markdown] +# ## 7. Plot $X_{min}$ for a $T_{98}$, and plot $T_{98}$ for a given region. # -# This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. This is presented as doing the computations on Amazon Web Services (AWS) for which you will need a paid account, and will be covered on a subsequent journal. +# This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. +# For more in-depth instructions on doing this, look at the tutorial "Scenario - Geospatial.ipynb" here in PVDeg. +# +# Step #1: Create an object, "geospatial_standoff_scenario" to be used to run the computations. + +# %% +geospatial_standoff_scenario = pvdeg.GeospatialScenario( + name="standoff geospatial", + geospatial=True, +) + +# %% [markdown] +# Step #2: Identifies a subset of locations from the database to run the computations. +# Specifically all are from the NSRDB. + +# %% +geospatial_standoff_scenario.addLocation( + state="Colorado", county="Summit" +) # Identifies a subset of locations from the database to run the computations. Specifically all are from the NSRDB. + +# %% [markdown] +# Step #3: indicate which function will be run. Here the default is the standoff calculation, but it could be any other function with a key word argument dictionary. +# Here the 98th percential temperature is defined as 70C, but any arbitrary value can be specified. +# %% +geospatial_standoff_scenario.addJob( + func=pvdeg.standards.standoff, func_params={"T98": 70} +) + +# %% [markdown] +# Step #4: Run the scenario + +# %% +geospatial_standoff_scenario.run() + +# %% [markdown] +# Step #5: Create a plot of the standoff calculation. + +# %% +geospatial_standoff_scenario.plot_world("x") + +# %% +geospatial_standoff_scenario.plot_world("T98_inf") + +# %% [markdown] # ## 8. Save data outputs. # # This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation and is blocked by default. First check that the output folder exists, then unblock the code for data you would like to save. -# In[15]: - - +# %% print("Your results will be stored in %s" % output_folder) print("The folder must already exist or the file will not be created") @@ -471,4 +494,6 @@ os.path.join(output_folder, "Standoff_Scan.csv") ) # Writes a file with the Tilt and Azimuth scan calculations of standoff. -pd.DataFrame(T98_series_df).to_csv(os.path.join(output_folder, "T98_Scan.csv")) +pd.DataFrame(T98_series_df).to_csv( + os.path.join(output_folder, "T98_Scan.csv") +) # Writes a file with the Tilt and Azimuth scan calculations of T98. diff --git a/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb b/tutorials/01_basics/01_basics_humidity_design.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb rename to tutorials/01_basics/01_basics_humidity_design.ipynb index ac2fdd503..7c1357620 100644 --- a/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb +++ b/tutorials/01_basics/01_basics_humidity_design.ipynb @@ -1586,7 +1586,7 @@ "metadata": {}, "source": [ "## 3. Module Humidities\n", - "`PVDEG` can be used to calculate the relative humidity of several layers within a PV module: the outside surface of the module, front ecapsulant, back encapsulant, and backsheet. This can be done with 2 techniques: Automatically and Manually. " + "`PVDEG` can be used to calculate the relative humidity of several layers within a PV module: the outside surface of the module, front ecapsulant, back encapsulant, and backsheet. This can be done with 2 techniques: Automatically and Manually." ] }, { @@ -1711,7 +1711,9 @@ } ], "source": [ - "rh_module = pvdeg.humidity.module(weather_df=WEATHER, meta=META, backsheet_thickness=0.3, back_encap_thickness=0.46)\n", + "rh_module = pvdeg.humidity.module(\n", + " weather_df=WEATHER, meta=META, backsheet_thickness=0.3, back_encap_thickness=0.46\n", + ")\n", "rh_module.head()" ] }, @@ -1740,7 +1742,7 @@ }, "outputs": [], "source": [ - "surface_relative_humidity=WEATHER[\"relative_humidity\"]\n", + "surface_relative_humidity = WEATHER[\"relative_humidity\"]\n", "temperature_ambient = WEATHER[\"temp_air\"]\n", "\n", "rh_surface_relative = pvdeg.humidity.surface_relative(\n", @@ -1832,7 +1834,9 @@ } ], "source": [ - "water_saturation_pressure, avg_water_saturation_pressure = pvdeg.humidity.water_saturation_pressure(WEATHER[\"temp_air\"])\n", + "water_saturation_pressure, avg_water_saturation_pressure = (\n", + " pvdeg.humidity.water_saturation_pressure(WEATHER[\"temp_air\"])\n", + ")\n", "\n", "k = pvdeg.design.edge_seal_ingress_rate(avg_water_saturation_pressure)\n", "\n", diff --git a/tutorials_and_tools/tutorials_and_tools/2 - Degradation.ipynb b/tutorials/01_basics/02_degradation.ipynb similarity index 98% rename from tutorials_and_tools/tutorials_and_tools/2 - Degradation.ipynb rename to tutorials/01_basics/02_degradation.ipynb index a6a909a36..4654dbb05 100644 --- a/tutorials_and_tools/tutorials_and_tools/2 - Degradation.ipynb +++ b/tutorials/01_basics/02_degradation.ipynb @@ -373,7 +373,7 @@ "source": [ "## 6. Solder Fatigue\n", "\n", - "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. " + "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data." ] }, { @@ -440,9 +440,12 @@ "\n", "# calculate cell temperature over our selected date-time range.\n", "# specify the module configuration\n", - "temp_cell = pvdeg.temperature.cell(weather_df=WEATHER.loc[time_range],\n", - " meta=META,temp_model=\"sapm\",\n", - " conf=\"insulated_back_glass_polymer\")\n", + "temp_cell = pvdeg.temperature.cell(\n", + " weather_df=WEATHER.loc[time_range],\n", + " meta=META,\n", + " temp_model=\"sapm\",\n", + " conf=\"insulated_back_glass_polymer\",\n", + ")\n", "\n", "fatigue = pvdeg.fatigue.solder_fatigue(\n", " weather_df=WEATHER, meta=META, time_range=time_range, temp_cell=temp_cell\n", diff --git a/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.ipynb b/tutorials/01_basics/03_spectral_degradation.ipynb similarity index 97% rename from tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.ipynb rename to tutorials/01_basics/03_spectral_degradation.ipynb index 5a3e8c44c..e405ebf50 100644 --- a/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.ipynb +++ b/tutorials/01_basics/03_spectral_degradation.ipynb @@ -55,7 +55,9 @@ "import pvdeg\n", "from pvdeg import DATA_DIR\n", "\n", - "warnings.filterwarnings('ignore', message='.*Pyarrow will become a required dependency.*')" + "warnings.filterwarnings(\n", + " \"ignore\", message=\".*Pyarrow will become a required dependency.*\"\n", + ")" ] }, { @@ -100,14 +102,14 @@ "\n", "Spectral degradation has 4 main requirements:\n", "- Spectral Irradiance [W/m^2 nm]\n", - "- Wavelength [nm] \n", + "- Wavelength [nm]\n", "- Module Relative Humidity [%]\n", "- Module Temperature [C]\n", "\n", "For more advanced scenarios, you may want to calculate the degradation of a particular layer within the module. Below, we are using *backside* irradiance and therefore a slightly different temperature and humidity have been calculated. To calculate degradation on the backside, we used `pvdeg.humidity.rh_backsheet`. For the the front side, you should use `pvdeg.humidity.rh_surface_outside` or `rh_front_encap`\n", "\n", "\n", - "For this tutorial we are using pre-generated data from a ray-tracing simulation. To calculate the degradation rate, we will need the wavelengths used in the simulation. " + "For this tutorial we are using pre-generated data from a ray-tracing simulation. To calculate the degradation rate, we will need the wavelengths used in the simulation." ] }, { @@ -254,7 +256,7 @@ " rh=SPECTRA[\"RH\"],\n", " temp=SPECTRA[\"Temperature\"],\n", " wavelengths=wavelengths,\n", - " time=SPECTRA.index\n", + " time=SPECTRA.index,\n", ")" ] } diff --git a/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.ipynb b/tutorials/01_basics/04_weather_database_access.ipynb similarity index 98% rename from tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.ipynb rename to tutorials/01_basics/04_weather_database_access.ipynb index 4e52db21d..934571eaa 100644 --- a/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.ipynb +++ b/tutorials/01_basics/04_weather_database_access.ipynb @@ -253,7 +253,7 @@ "print(pvdeg.standards.interpret_standoff(res))\n", "\n", "# Clean metadata for consistent output\n", - "meta_clean = {k: v for k, v in meta.items() if k not in ['irradiance_time_offset']}\n", + "meta_clean = {k: v for k, v in meta.items() if k not in [\"irradiance_time_offset\"]}\n", "print(meta_clean)" ] }, @@ -318,7 +318,7 @@ "print(pvdeg.standards.interpret_standoff(res))\n", "\n", "# Clean metadata for consistent output (remove variable fields)\n", - "meta_clean = {k: v for k, v in meta.items() if k not in ['irradiance_time_offset']}\n", + "meta_clean = {k: v for k, v in meta.items() if k not in [\"irradiance_time_offset\"]}\n", "print(meta_clean)" ] }, diff --git a/tutorials/01_basics/README.md b/tutorials/01_basics/README.md new file mode 100644 index 000000000..56f425d90 --- /dev/null +++ b/tutorials/01_basics/README.md @@ -0,0 +1,16 @@ +# Basics Tutorials + +Introduction to PVDegradationTools covering fundamental concepts and workflows. + +## Tutorials + +1. **01_basics_humidity_design.ipynb** - Introduction to basic concepts, humidity calculations, and design parameters +2. **02_degradation.ipynb** - Overview of degradation modeling and calculations +3. **03_spectral_degradation.ipynb** - Spectral degradation analysis +4. **04_weather_database_access.ipynb** - Accessing and working with weather databases + +## Getting Started + +These tutorials are designed to be completed in sequence. Start with tutorial 01 and progress through to 04. + +All tutorials can be run locally or in Google Colab by clicking the rocket icon at the top of each notebook. diff --git a/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.py b/tutorials/01_basics/scripts/01_basics_humidity_design.py similarity index 85% rename from tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.py rename to tutorials/01_basics/scripts/01_basics_humidity_design.py index 66842b3ca..6b68499cb 100644 --- a/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.py +++ b/tutorials/01_basics/scripts/01_basics_humidity_design.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # 1 - Basics, Humidity, Design # ### Module Humidity and Edge Seal Width # @@ -14,26 +12,18 @@ # 3. Generate module humidities # 4. Calculate edge seal width -# In[1]: - - +# %% # 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 pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg==0.3.3 +# %% import os import pandas as pd import pvdeg from pvdeg import DATA_DIR - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -43,7 +33,7 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # ## 1. Reading in Weather Data and Site Meta-Data # Most `pvdeg` functions have been standardized to operate from popular weather files such as TMY3, EPW, and PSM3. For high-performance computer users, NSRDB and other database fetching tools can be used. For these tutorials, we will use local weather files. Unless otherwise stated, functions require the following fields within a weather file: # @@ -62,30 +52,24 @@ # - Longitude # - Altitude -# In[2]: - - +# %% PSM_FILE = os.path.join(DATA_DIR, "psm3_demo.csv") WEATHER, META = pvdeg.weather.read(PSM_FILE, "psm") - +# %% [markdown] # Let's take a closer look at the dataframe (weather) and dictionary (meta) imported above. The structure will be used by most `pvdeg` functions. -# In[3]: - - +# %% WEATHER.head() - +# %% [markdown] # Meta-Data will vary greatly between weather files. However, at a minimum they should all contain the fields required by `pvdeg` listed above. Let's look at the meta-data generated by our PSM3 weather file. It provides much more information that we need, but at the very end are the three most important fields. -# In[4]: - - +# %% META - +# %% [markdown] # We'll be working with a lot of time series data, generally with a date-time index. If you're # processing an entire year of data, you don't need to sort or filter the data. However, you may want # to filter the data by a few different criteria. Below are some common examples. @@ -94,38 +78,28 @@ # - range of months # - Filter by sun-up hours -# In[5]: - - +# %% # Let's select the month of June june_weather = WEATHER[WEATHER.index.month == 6] june_weather - -# In[6]: - - +# %% # Let's filter the summer months for the northern hemisphere summer_months = [6, 7, 8, 9] summer_weather = WEATHER[WEATHER.index.month.isin(summer_months)] summer_weather - -# In[7]: - - +# %% # lets just select hours from the year where the sun is above the horizon sunup_weather = WEATHER[WEATHER["ghi"] > 0] sunup_weather - +# %% [markdown] # ## 2. Solar Position, POA, and Module Temperature # # Many operations with `PVDEG` require solar position, POA irradiance, and module temperature. Usually a `pvdeg` method will calculate these data sets automatically when it is required, however it will not keep the data for external use. For now, we will calculate them directly. This is helpful when you need to use the data for an external calculation. Here, we generate the solar position (azimuth, elevation, etc), plane of array irradiance (POA), and module temperature. `PVDEG` has wrappers for quickly using `PVLIB` to generate these figures with minimal input -# In[8]: - - +# %% sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META) poa_df = pvdeg.spectral.poa_irradiance( @@ -134,7 +108,7 @@ temp_mod = pvdeg.temperature.module(weather_df=WEATHER, meta=META) # , poa=poa_df) - +# %% [markdown] # For more advanced usage of these functions, refer to the documentation for `pvdeg` and `pvlib`. # # Lets inspect the output from each of these functions @@ -142,20 +116,16 @@ # `solar_position` returns a datetime-indexed dataframe of solar position for the length and frequency given by the weather file. # -# In[9]: - - +# %% sol_pos - +# %% [markdown] # `poa_irradiance` returns a datetime-indexed dataframe as well. Each column contains either the global plane of array irradiance or a particular contribition. Lets look at a time in the index we expect to have daylight. -# In[10]: - - +# %% poa_df.loc["1999-01-01 13:30:00-07:00":"1999-01-01 17:30:00-07:00"] - +# %% [markdown] # `temperature.module` has several optional parameters. Below is an example which explicity uses all of the default values. This function will return a datetime-indexed series matching the index of the given weather file. For more options, see the pvlib documentation for temperature models. # # https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html @@ -166,34 +136,32 @@ # - conf : choose a recognized module configuration # - wind_speed_factor : change the empirical wind speed fit paramter -# In[11]: - - +# %% temp_mod = pvdeg.temperature.module( weather_df=WEATHER, meta=META, poa=poa_df, temp_model="sapm", conf="open_rack_glass_polymer", - wind_speed_factor=1, ) temp_mod.loc["1999-01-01 13:30:00-07:00":"1999-01-01 17:30:00-07:00"] - +# %% [markdown] # ## 3. Module Humidities # `PVDEG` can be used to calculate the relative humidity of several layers within a PV module: the outside surface of the module, front ecapsulant, back encapsulant, and backsheet. This can be done with 2 techniques: Automatically and Manually. +# %% [markdown] # ### 3.a. Automatic # Use the function `humidity.module`. This method does all of the calculations behind the scenes. It requires only 2 inputs (weather data and meta data). It will return a dataframe with all the layers of the module. -# In[12]: - - -rh_module = pvdeg.humidity.module(weather_df=WEATHER, meta=META) +# %% +rh_module = pvdeg.humidity.module( + weather_df=WEATHER, meta=META, backsheet_thickness=0.3, back_encap_thickness=0.46 +) rh_module.head() - +# %% [markdown] # ### 3.b. Manual # Use the individual functions to calculate the RH in each layer. These are named and require more extensive input parameters. This must be done in the correct order: # 1. Surface Outside @@ -201,52 +169,51 @@ # 2. Back Encapsulant # 3. Backsheet -# In[13]: +# %% +surface_relative_humidity = WEATHER["relative_humidity"] +temperature_ambient = WEATHER["temp_air"] - -rh_surface_outside = pvdeg.humidity.surface_relative( - rh_ambient=WEATHER["relative_humidity"], - temp_ambient=WEATHER["temp_air"], +rh_surface_relative = pvdeg.humidity.surface_relative( + rh_ambient=surface_relative_humidity, + temp_ambient=temperature_ambient, temp_module=temp_mod, ) -rh_front_encap = pvdeg.humidity.front_encapsulant( - rh_ambient=rh_surface_outside, - temp_ambient=WEATHER["temp_air"], +rh_front_encapsulant = pvdeg.humidity.front_encapsulant( + rh_ambient=surface_relative_humidity, + temp_ambient=temperature_ambient, temp_module=temp_mod, ) -rh_back_encap = pvdeg.humidity.back_encapsulant_water_concentration( - rh_ambient=rh_surface_relative, - temp_ambient=WEATHER["temp_air"], +rh_back_encap = pvdeg.humidity.back_encapsulant( + rh_ambient=surface_relative_humidity, + temp_ambient=temperature_ambient, temp_module=temp_mod, ) rh_backsheet = pvdeg.humidity.backsheet_from_encap( - rh_back_encap=rh_back_encap, rh_surface_outside=WEATHER["relative_humidity"] + rh_back_encap=rh_back_encap, rh_surface_outside=surface_relative_humidity ) - +# %% [markdown] # ## 4. Design: Edge Seal Width # `pvdeg.design.edge_seal_width` calculates the width [cm] required for several years of water ingress. If you do not specify the number of years, it will use the default value of 25. As with most `pvdeg` functions, we can automatically generate all necessary data if we pass the two arguments (weather_df, meta). -# In[14]: - - +# %% edge_seal_width = pvdeg.design.edge_seal_width(weather_df=WEATHER, meta=META) edge_seal_width - +# %% [markdown] # Lets look at the calculation step by step. To get the estimated edge seal width, we need the following: # 1. Humidity Saturation Point # 2. Edge seal ingress rate ( k ) or the estimated rate of water ingress through edge seal in [cm/hour^0.5] -# In[15]: - - -psat, psat_avg = pvdeg.humidity.psat(WEATHER["temp_air"]) +# %% +water_saturation_pressure, avg_water_saturation_pressure = ( + pvdeg.humidity.water_saturation_pressure(WEATHER["temp_air"]) +) -k = pvdeg.design.edge_seal_ingress_rate(avg_psat=psat_avg) +k = pvdeg.design.edge_seal_ingress_rate(avg_water_saturation_pressure) edge_seal_width = pvdeg.design.edge_seal_width( weather_df=WEATHER, meta=META, k=k, years=25 @@ -254,13 +221,11 @@ print(edge_seal_width) - +# %% [markdown] # ### Alternate Edge Seal Width # If you have the dew point, or wet-bulb temperature, you can calculate the edge seal width directly. -# In[17]: - - +# %% edge_seal_width = pvdeg.design.edge_seal_width( weather_df=WEATHER, meta=META, from_dew_point=True ) diff --git a/tutorials_and_tools/tutorials_and_tools/2 - Degradation.py b/tutorials/01_basics/scripts/02_degradation.py similarity index 95% rename from tutorials_and_tools/tutorials_and_tools/2 - Degradation.py rename to tutorials/01_basics/scripts/02_degradation.py index 6da786f50..0f4f59c78 100644 --- a/tutorials_and_tools/tutorials_and_tools/2 - Degradation.py +++ b/tutorials/01_basics/scripts/02_degradation.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # 2 - Degradation # Degradation and Solder Fatigue # @@ -20,26 +18,18 @@ # 5. Quick Method # 5. Solder Fatigue -# In[1]: - - +# %% # 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 pvdeg==0.3.3 - - -# In[2]: - +# # !pip install pvdeg==0.3.3 +# %% import os import pandas as pd import pvdeg from pvdeg import DATA_DIR - -# In[3]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -49,25 +39,21 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # ## 1. Read In the Weather File # # This is usually the first step. Use a PSM3, TMY3, or EPW file. For this demo, use the provided PSM3 weather file. -# In[2]: - - +# %% PSM_FILE = os.path.join(DATA_DIR, "psm3_demo.csv") WEATHER, META = pvdeg.weather.read(PSM_FILE, "psm") - +# %% [markdown] # ## 2. Generate Basic Modeling Data # # For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions. -# In[3]: - - +# %% sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META) poa_df = pvdeg.spectral.poa_irradiance( @@ -78,7 +64,7 @@ temp_module = pvdeg.temperature.module(weather_df=WEATHER, meta=META, poa=poa_df) - +# %% [markdown] # ## 3. VantHoff Degradation # # Van 't Hoff Irradiance Degradation @@ -87,9 +73,7 @@ # # As with most `pvdeg` functions, the following functions will always require two arguments (weather_df and meta) -# In[4]: - - +# %% # chamber irradiance (W/m^2) I_chamber = 1000 # chamber temperature (C) @@ -102,15 +86,15 @@ I_chamber=I_chamber, temp_chamber=temp_chamber, poa=poa_df, - temp_cell=temp_cell, + temp=temp_cell, ) # calculate the VantHoff weighted irradiance irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff( - weather_df=WEATHER, meta=META, poa=poa_df, temp_cell=temp_cell + weather_df=WEATHER, meta=META, poa=poa_df, temp=temp_cell ) - +# %% [markdown] # ## 4. Arrhenius # Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet # @@ -124,9 +108,7 @@ # Equation: # $$ I_{WA} = [ \frac{ \Sigma (I_{outdoor}^x * RH_{outdoor}^n e^{\frac{-E_a}{k T_{outdood}}}) }{ N * RH_{WA}^n * e^{- \frac{E_a}{k T_eq}} } ]^{\frac{1}{x}} $$ -# In[6]: - - +# %% # relative humidity within chamber (%) rh_chamber = 15 # arrhenius activation energy (kj/mol) @@ -146,7 +128,7 @@ rh_chamber=rh_chamber, temp_chamber=temp_chamber, poa=poa_df, - temp_cell=temp_cell, + temp=temp_cell, Ea=Ea, ) @@ -155,18 +137,16 @@ meta=META, poa=poa_df, rh_outdoor=WEATHER["relative_humidity"], - temp_cell=temp_cell, + temp=temp_cell, Ea=Ea, ) - +# %% [markdown] # ## 5. Quick Method (Degradation) # # For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration. -# In[7]: - - +# %% # chamber settings I_chamber = 1000 temp_chamber = 60 @@ -181,10 +161,7 @@ irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=WEATHER, meta=META) - -# In[8]: - - +# %% rh_surface = pvdeg.humidity.surface_relative( rh_ambient=WEATHER["relative_humidity"], temp_ambient=WEATHER["temp_air"], @@ -205,17 +182,15 @@ weather_df=WEATHER, meta=META, rh_outdoor=WEATHER["relative_humidity"], Ea=Ea ) - +# %% [markdown] # ## 6. Solder Fatigue # # Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. -# In[9]: - - +# %% fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META) - +# %% [markdown] # If you wish to reduce the span of time or use a non-default temperature model, you may specify the parameters manually. Let's try an explicit example. # We want the solder fatigue estimated over the month of June for a roof mounted glass-front polymer-back module. # @@ -223,9 +198,7 @@ # 2. Next, generate the cell temperature. Make sure to explicity restrict the weather data to our dt-index for June. Next, declare the PV module configuration. # 3. Calculate the fatigue. Explicity specify the time_range (our dt-index for June from step 1) and the cell temperature as we caculated in step 2 -# In[ ]: - - +# %% # select the month of June time_range = WEATHER.index[WEATHER.index.month == 6] @@ -238,7 +211,6 @@ conf="insulated_back_glass_polymer", ) - fatigue = pvdeg.fatigue.solder_fatigue( weather_df=WEATHER, meta=META, time_range=time_range, temp_cell=temp_cell ) diff --git a/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.py b/tutorials/01_basics/scripts/03_spectral_degradation.py similarity index 85% rename from tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.py rename to tutorials/01_basics/scripts/03_spectral_degradation.py index 36f5db8b6..43b605524 100644 --- a/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.py +++ b/tutorials/01_basics/scripts/03_spectral_degradation.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # 3 - Spectral Degradation # # **Requirements:** @@ -14,26 +12,23 @@ # 1. Read in spectral irradiance # 2. Calculate spectral degradation -# In[1]: - - +# %% # 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 pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg==0.3.3 +# %% import os +import warnings import pandas as pd import numpy as np import pvdeg from pvdeg import DATA_DIR +warnings.filterwarnings( + "ignore", message=".*Pyarrow will become a required dependency.*" +) -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -43,7 +38,7 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # ## 1. Read in spectral irradiance data # # Spectral degradation has 4 main requirements: @@ -57,25 +52,22 @@ # # For this tutorial we are using pre-generated data from a ray-tracing simulation. To calculate the degradation rate, we will need the wavelengths used in the simulation. -# In[2]: - - +# %% wavelengths = np.array(range(280, 420, 20)) SPECTRA = pd.read_csv(os.path.join(DATA_DIR, "spectra.csv"), header=0, index_col=0) SPECTRA.head() - +# %% [markdown] # ### 2. Calculate Degradation # # The spectral degradation function has several optional paramters. For more information, refer to the documentation. Below is a function call with the minimum required information. -# In[3]: - - -degradation = pvdeg.degradation.degradation( +# %% +degradation = pvdeg.degradation.degradation_spectral( spectra=SPECTRA["Spectra"], - rh_module=SPECTRA["RH"], - temp_module=SPECTRA["Temperature"], + rh=SPECTRA["RH"], + temp=SPECTRA["Temperature"], wavelengths=wavelengths, + time=SPECTRA.index, ) diff --git a/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.py b/tutorials/01_basics/scripts/04_weather_database_access.py similarity index 86% rename from tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.py rename to tutorials/01_basics/scripts/04_weather_database_access.py index a2406aad9..76f696422 100644 --- a/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.py +++ b/tutorials/01_basics/scripts/04_weather_database_access.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # 4 - Weather Database Access # # **Requirements:** @@ -21,6 +19,7 @@ # # For all users and all steps: This journal will run significantly longer than other tutorials and have significant internet traffic as you fetch large datasets. +# %% [markdown] # This example demonstrates the calculation of a minimum standoff distance necessary for roof-mounted PV modules to ensure that the $T_{98}$ operational temperature remains under 70°C, in which case the more rigorous thermal stability testing requirements of IEC TS 63126 would not needed to be considered. We use data from [Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as, # # $$ X = -X_0 \ln\left(1-\frac{T_0-T}{\Delta T}\right)$$ @@ -29,24 +28,17 @@ # # The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems. +# %% [markdown] # ## Single location example -# In[1]: - - +# %% # 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 pvdeg==0.3.3 - - -# In[2]: - +# # !pip install pvdeg==0.3.3 +# %% import pvdeg - -# In[3]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -55,8 +47,8 @@ print("Python version ", sys.version) print("pvdeg version ", pvdeg.__version__) - -# ## 1. NSRDB - HSDS on Eagle +# %% [markdown] +# ## 1. NSRDB - HSDS on Kestrel # # This method requires a direct connection to NREL's high performance computer "Eagle". If you are not running this journal from Eagle, skip this section and proceed to section **2.** # @@ -66,15 +58,12 @@ # # Next, we want to select a satellite, named dataset (year of data), and what weather attributes we want to fetch. For further options, see the documentation for `pvdeg.weather.get` -# In[ ]: - - +# %% # Get weather data weather_db = "NSRDB" # Latitude and Longitude weather_id = (33.448376, -112.074036) -weather_id = (39.741931, -105.169891) # weather_id = 1933572 weather_arg = { "satellite": "GOES", @@ -90,33 +79,21 @@ ], } -weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) +# Uncomment the following when working on NREL Kestrel + +# weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) +# res = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm', +# conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer', T98=70, x_0=6.5, wind_factor=0.33) +# print(pvdeg.standards.interpret_standoff(res)) +# print(meta) +# %% [markdown] # `pvdeg.weather.get` returns the same variables as `weather.read` which we have used in each journal before this. We get a weather DataFrame and a meta-data dicitonary. Each contains a minimum of consistent fields, but may have additional fields based on the database accessed or the attributes requested. # # Lets verify the weather data we fetched by running a familiar calculation; standoff distance. -# In[ ]: - - -res = pvdeg.standards.standoff( - weather_df=weather_df, - meta=meta, - tilt=None, - azimuth=180, - sky_model="isotropic", - temp_model="sapm", - conf_0="insulated_back_glass_polymer", - conf_inf="open_rack_glass_polymer", - T98=70, - x_0=6.5, - wind_factor=0.33, -) -print(pvdeg.standards.interpret_standoff(res)) -print(meta) - - +# %% [markdown] # ## 2. NSRDB - API # # To access the NREL NSRDB, you will need an API key. Key's are free, but require you to set up an account. Without an API key, you can use a demonstration API which is severely limited. To set up an account and get your API key, visit https://developer.nrel.gov/signup/ @@ -127,20 +104,16 @@ # - names = 'tmy' : generate a TMY-like weather dataframe aggregate. This will calculate the relative humidity from temperature and dew point. # - names = '2019' : collect a weather dataframe including measured relative humidity. -# In[ ]: - - +# %% API_KEY = "your_api_key_here" # The example API key here is for demonstation and is rate-limited per IP. # To get your own API key, visit https://developer.nrel.gov/signup/ -weather_db = "PSM3" +weather_db = "PSM4" weather_id = (39.741931, -105.169891) weather_arg = { "api_key": "DEMO_KEY", "email": "user@mail.com", - "names": "tmy", - "attributes": [], "map_variables": True, } @@ -160,16 +133,17 @@ wind_factor=0.33, ) print(pvdeg.standards.interpret_standoff(res)) -print(meta) +# Clean metadata for consistent output +meta_clean = {k: v for k, v in meta.items() if k not in ["irradiance_time_offset"]} +print(meta_clean) +# %% [markdown] # ## 3. PVGIS # # This method uses the PVGIS database, a public resource. It requires no API key or user account. -# In[ ]: - - +# %% weather_db = "PVGIS" # weather_id = (39.741931, -105.169891) weather_id = (24.7136, 46.6753) # Riyadh, Saudi Arabia @@ -193,7 +167,9 @@ wind_factor=0.33, ) print(pvdeg.standards.interpret_standoff(res)) -print(meta) +# Clean metadata for consistent output (remove variable fields) +meta_clean = {k: v for k, v in meta.items() if k not in ["irradiance_time_offset"]} +print(meta_clean) -# In[ ]: +# %% diff --git a/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.ipynb b/tutorials/02_degradation/01_bo_lid_accelerated_test.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.ipynb rename to tutorials/02_degradation/01_bo_lid_accelerated_test.ipynb index 17812df26..d4a7404cd 100644 --- a/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.ipynb +++ b/tutorials/02_degradation/01_bo_lid_accelerated_test.ipynb @@ -330,14 +330,18 @@ "iopub.status.busy": "2025-11-13T21:54:16.954627Z", "iopub.status.idle": "2025-11-13T21:54:16.958966Z", "shell.execute_reply": "2025-11-13T21:54:16.958966Z" - } + }, + "lines_to_next_cell": 2 }, "outputs": [], "source": [ "mechanism_params = utilities.get_kinetics(\"D045\")\n", "\n", "# Flatten the nested dictionary to get only the \"value\" for each parameter\n", - "mechanism_params = {k: v[\"value\"] if isinstance(v, dict) and \"value\" in v else v for k, v in mechanism_params.items()}\n" + "mechanism_params = {\n", + " k: v[\"value\"] if isinstance(v, dict) and \"value\" in v else v\n", + " for k, v in mechanism_params.items()\n", + "}" ] }, { @@ -345,7 +349,7 @@ "metadata": {}, "source": [ "## Set up timeseries\n", - "In this example, we are going to model test with constant temperature and injection. IEC 61215 MQT 19.1 prescribes 10 $kWh/m^2$ of 1-sun illumination (i.e., 10 hours of 1-sun) with maximum power point tracking at $50\\degree C$. For most typical c-Si modules, MPP injection is roughly $I_{sc}-I_{mp}$, or roughly equal to $0.05\\times I_{sc}$. So we will set injection equal to 0.05 \"suns\" of injection. \n", + "In this example, we are going to model test with constant temperature and injection. IEC 61215 MQT 19.1 prescribes 10 $kWh/m^2$ of 1-sun illumination (i.e., 10 hours of 1-sun) with maximum power point tracking at $50\\degree C$. For most typical c-Si modules, MPP injection is roughly $I_{sc}-I_{mp}$, or roughly equal to $0.05\\times I_{sc}$. So we will set injection equal to 0.05 \"suns\" of injection.\n", "\n", "We will create a pandas datetime series and calculate the changes in defect states for each timestep. As B-O LID can initially proceed quickly, we will create a timeseries with 1-second intervals for the first 10 minutes, then proceed with 1-minute intervals" ] diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.ipynb b/tutorials/02_degradation/02_letid_accelerated_test.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.ipynb rename to tutorials/02_degradation/02_letid_accelerated_test.ipynb index 319acd760..1a61e5a5a 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.ipynb +++ b/tutorials/02_degradation/02_letid_accelerated_test.ipynb @@ -181,7 +181,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's see how much maximum power degradation these parameters will result in: " + "Let's see how much maximum power degradation these parameters will result in:" ] }, { @@ -377,7 +377,7 @@ "metadata": {}, "source": [ "## Set up timeseries\n", - "In this example, we are going to model test with constant temperature and current injection. IEC TS 63342 prescribes two to three weeks of injection equivalent to $2\\times(I_{sc}-I_{mp})$, at $75\\degree C$. For most typical c-Si modules, $2\\times(I_{sc}-I_{mp})$ is roughly equal to $0.1\\times I_{sc}$. So we will set injection equal to 0.1 \"suns\" of injection. \n", + "In this example, we are going to model test with constant temperature and current injection. IEC TS 63342 prescribes two to three weeks of injection equivalent to $2\\times(I_{sc}-I_{mp})$, at $75\\degree C$. For most typical c-Si modules, $2\\times(I_{sc}-I_{mp})$ is roughly equal to $0.1\\times I_{sc}$. So we will set injection equal to 0.1 \"suns\" of injection.\n", "\n", "We will create a pandas datetime series and calculate the changes in defect states for each timestep." ] @@ -1191,8 +1191,17 @@ "source": [ "FIXED_START_DATE = dt.datetime(2025, 9, 9, 0, 0)\n", "result = letid.calc_letid_lab(\n", - " tau_0, tau_deg, wafer_thickness, s_rear, nA_0, nB_0, nC_0, 0.1, 75, \"D037\",\n", - " start=FIXED_START_DATE\n", + " tau_0,\n", + " tau_deg,\n", + " wafer_thickness,\n", + " s_rear,\n", + " nA_0,\n", + " nB_0,\n", + " nC_0,\n", + " 0.1,\n", + " 75,\n", + " \"D037\",\n", + " start=FIXED_START_DATE,\n", ")\n", "result[\"Datetime\"] = result[\"Datetime\"].dt.round(\"s\")\n", "print(result)" diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.ipynb b/tutorials/02_degradation/03_letid_outdoor.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.ipynb rename to tutorials/02_degradation/03_letid_outdoor.ipynb index d705afbdb..d19f0c7cb 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.ipynb +++ b/tutorials/02_degradation/03_letid_outdoor.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# LETID - Outdoor Environments \n", + "# LETID - Outdoor Environments\n", "\n", "This is an example on how to model LETID progression in outdoor environments\n", "\n", diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb b/tutorials/02_degradation/04_letid_outdoor_scenario.ipynb similarity index 100% rename from tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb rename to tutorials/02_degradation/04_letid_outdoor_scenario.ipynb diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.ipynb b/tutorials/02_degradation/05_letid_passivated_wafer.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.ipynb rename to tutorials/02_degradation/05_letid_passivated_wafer.ipynb index 7747a2a65..e88e57793 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.ipynb +++ b/tutorials/02_degradation/05_letid_passivated_wafer.ipynb @@ -99,7 +99,7 @@ "metadata": {}, "source": [ "## Device parameters\n", - "For modeling a wafer, we don't need as many device parameters. Wafer thickness, lifetime, and an optical generation profile, which allows us to calculate generation current, which we take to be device current assuming surface recombination is very low. " + "For modeling a wafer, we don't need as many device parameters. Wafer thickness, lifetime, and an optical generation profile, which allows us to calculate generation current, which we take to be device current assuming surface recombination is very low." ] }, { diff --git a/tutorials_and_tools/tutorials_and_tools/Van't Hoff Degradation Model.ipynb b/tutorials/02_degradation/06_vant_hoff_degradation_model.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/Van't Hoff Degradation Model.ipynb rename to tutorials/02_degradation/06_vant_hoff_degradation_model.ipynb index e87a3c92f..9234ea294 100644 --- a/tutorials_and_tools/tutorials_and_tools/Van't Hoff Degradation Model.ipynb +++ b/tutorials/02_degradation/06_vant_hoff_degradation_model.ipynb @@ -383,7 +383,7 @@ "source": [ "## 6. Solder Fatigue\n", "\n", - "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. " + "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data." ] }, { diff --git a/tutorials/02_degradation/README.md b/tutorials/02_degradation/README.md new file mode 100644 index 000000000..7e1f9d7f2 --- /dev/null +++ b/tutorials/02_degradation/README.md @@ -0,0 +1,20 @@ +# Degradation Tutorials + +Advanced tutorials focused on specific degradation mechanisms including LID and LETID. + +## Tutorials + +- **bo_lid_accelerated_test.ipynb** - Boron-Oxygen Light Induced Degradation (B-O LID) accelerated testing +- **letid_accelerated_test.ipynb** - Light and Elevated Temperature Induced Degradation (LETID) accelerated testing +- **letid_outdoor.ipynb** - LETID modeling for outdoor conditions +- **letid_outdoor_scenario.ipynb** - LETID outdoor scenarios based on accelerated test results +- **letid_passivated_wafer.ipynb** - LETID analysis for passivated wafer technologies +- **vant_hoff_degradation_model.ipynb** - Van't Hoff degradation modeling approach + +## Topics Covered + +- Light Induced Degradation (LID) +- Light and Elevated Temperature Induced Degradation (LETID) +- Accelerated testing methods +- Outdoor degradation prediction +- Degradation kinetics diff --git a/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.py b/tutorials/02_degradation/scripts/01_bo_lid_accelerated_test.py similarity index 95% rename from tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.py rename to tutorials/02_degradation/scripts/01_bo_lid_accelerated_test.py index b2f05a485..1112230f6 100644 --- a/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.py +++ b/tutorials/02_degradation/scripts/01_bo_lid_accelerated_test.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # B-O LID - Accelerated Test # # ## Example: Boron-oxygen light-induced degradation (B-O LID) progression in accelerated tests @@ -20,27 +18,20 @@ # # -# In[1]: - - +# %% # 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 pvdeg==0.3.3 - - -# In[2]: - +# # !pip install pvdeg==0.3.3 +# %% from pvdeg import letid, collection, utilities, DATA_DIR import os import pandas as pd import numpy as np import matplotlib.pyplot as plt +import pvlib - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -52,25 +43,21 @@ print("pvlib version ", pvlib.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # ## Device parameters # To define a device, we need to define several important quantities about the device: wafer thickness (in $\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2). -# In[3]: - - +# %% wafer_thickness = 180 # um s_rear = 46 # cm/s cell_area = 243 # cm^2 - +# %% [markdown] # Other device parameters # # Other required parameters are base diffusivity (in $cm^2/s$), and optical generation profile, which allow us to estimate current collection in the device. -# In[4]: - - +# %% generation_df = pd.read_excel( os.path.join(DATA_DIR, "PVL_GenProfile.xlsx"), header=0 ) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer. @@ -79,69 +66,61 @@ d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details - +# %% [markdown] # ## Degradation parameters # To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\mu s$). -# In[5]: - - +# %% tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B - +# %% [markdown] # Let's see how much maximum power degradation these parameters will result in: -# In[6]: - - +# %% letid.calc_pmp_loss_from_tau_loss( tau_0, tau_deg, cell_area, wafer_thickness, s_rear ) # returns % power loss, pmp_0, pmp_deg - +# %% [markdown] # Remaining degradation parameters: # # The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters. # # In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for B-O LID -# In[7]: - - +# %% # starting defect state percentages nA_0 = 100 nB_0 = 0 nC_0 = 0 - -# In[8]: - - +# %% # Here's a list of the possible sets of kinetic parameters from kinetic_parameters.json: utilities.get_kinetics() +# %% +mechanism_params = utilities.get_kinetics("D045") -# In[9]: - - -mechanism_params = utilities.get_kinetics("D054") -print(mechanism_params) +# Flatten the nested dictionary to get only the "value" for each parameter +mechanism_params = { + k: v["value"] if isinstance(v, dict) and "value" in v else v + for k, v in mechanism_params.items() +} +# %% [markdown] # ## Set up timeseries # In this example, we are going to model test with constant temperature and injection. IEC 61215 MQT 19.1 prescribes 10 $kWh/m^2$ of 1-sun illumination (i.e., 10 hours of 1-sun) with maximum power point tracking at $50\degree C$. For most typical c-Si modules, MPP injection is roughly $I_{sc}-I_{mp}$, or roughly equal to $0.05\times I_{sc}$. So we will set injection equal to 0.05 "suns" of injection. # # We will create a pandas datetime series and calculate the changes in defect states for each timestep. As B-O LID can initially proceed quickly, we will create a timeseries with 1-second intervals for the first 10 minutes, then proceed with 1-minute intervals -# In[10]: - - +# %% temperature = 50 # degrees celsius suns = 0.05 # "suns" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.05*Isc. timesteps_initial = pd.date_range( - start="2022-01-01 00:00:00", end="2022-01-01 00:10:00", freq="S" + start="2022-01-01 00:00:00", end="2022-01-01 00:10:00", freq="s" ) # 10 minutes of 1-second interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly timesteps = pd.date_range( start="2022-01-01 00:10:00", end="2022-01-01 10:00:00", freq="min" @@ -172,10 +151,7 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[11]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -256,13 +232,11 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - +# %% [markdown] # ## Finish calculating degraded device parameters. # Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. -# In[12]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) # calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc. @@ -275,10 +249,7 @@ timesteps.at[index, "tau"], wafer_thickness, s_rear, jsc_now, temperature=25 ) - -# In[13]: - - +# %% # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power timesteps = letid.calc_device_params(timesteps, cell_area=243) timesteps["time (days)"] = ( @@ -287,14 +258,12 @@ timesteps - +# %% [markdown] # ## Plot the results # # -# In[14]: - - +# %% from cycler import cycler plt.style.use("default") @@ -327,6 +296,3 @@ ) plt.show() - - -# In[ ]: diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.py b/tutorials/02_degradation/scripts/02_letid_accelerated_test.py similarity index 93% rename from tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.py rename to tutorials/02_degradation/scripts/02_letid_accelerated_test.py index 4d959807e..edf98efd0 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.py +++ b/tutorials/02_degradation/scripts/02_letid_accelerated_test.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # LETID - Accelerated Tests # # @@ -19,27 +17,21 @@ # # -# In[1]: - - +# %% # 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 pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg==0.3.3 +# %% from pvdeg import letid, collection, utilities, DATA_DIR import os import pandas as pd import numpy as np import matplotlib.pyplot as plt +import pvdeg +import datetime as dt - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -49,24 +41,20 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # ## Device parameters # To define a device, we need to define several important quantities about the device: wafer thickness (in $\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2). The values defined below are representative of a typical PERC solar cell. -# In[2]: - - +# %% wafer_thickness = 180 # um s_rear = 46 # cm/s cell_area = 243 # cm^2 - +# %% [markdown] # ##### Other device parameters # Other required device parameters: base diffusivity (in $cm^2/s$), and optical generation profile, which allow us to estimate current collection in the device. -# In[3]: - - +# %% generation_df = pd.read_excel( os.path.join(DATA_DIR, "PVL_GenProfile.xlsx"), header=0 ) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer. @@ -75,76 +63,60 @@ d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details - +# %% [markdown] # ## Degradation parameters # To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\mu s$). -# In[4]: - - +# %% tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B - +# %% [markdown] # Let's see how much maximum power degradation these parameters will result in: -# In[5]: - - +# %% loss, pmp_0, pmp_deg = letid.calc_pmp_loss_from_tau_loss( tau_0, tau_deg, cell_area, wafer_thickness, s_rear ) # returns % power loss, pmp_0, pmp_deg print(loss) - +# %% [markdown] # Check to see the device's current collection -# In[6]: - - +# %% jsc_0 = collection.calculate_jsc_from_tau_cp( tau_0, wafer_thickness, d_base, s_rear, generation, depth ) # returns short-circuit current (Jsc) in mA/cm^2 given required cell parameters print(jsc_0) - +# %% [markdown] # Remaining degradation parameters: # # The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters. # # In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for LETID degradation from Repins. -# In[7]: - - +# %% # starting defect state percentages nA_0 = 100 nB_0 = 0 nC_0 = 0 - -# In[10]: - - -# Here's a list of the possible sets of kinetic parameters from kinetic_parameters.json: +# %% +# Here's a list of the possible sets of kinetic parameters from DegradationDatabase.json: utilities.get_kinetics() - -# In[12]: - - -mechanism_params = utilities.get_kinetics('D037') +# %% +mechanism_params = utilities.get_kinetics("D037") print(mechanism_params) - +# %% [markdown] # ## Set up timeseries # In this example, we are going to model test with constant temperature and current injection. IEC TS 63342 prescribes two to three weeks of injection equivalent to $2\times(I_{sc}-I_{mp})$, at $75\degree C$. For most typical c-Si modules, $2\times(I_{sc}-I_{mp})$ is roughly equal to $0.1\times I_{sc}$. So we will set injection equal to 0.1 "suns" of injection. # # We will create a pandas datetime series and calculate the changes in defect states for each timestep. -# In[13]: - - +# %% temperature = 75 # degrees celsius suns = 0.1 # "suns" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc. @@ -178,19 +150,14 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[14]: - - +# %% timesteps - +# %% [markdown] # ## Run through timesteps # Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries. -# In[15]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -271,13 +238,11 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - +# %% [markdown] # ## Finish calculating degraded device parameters. # Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. -# In[16]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) # calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc. @@ -290,10 +255,7 @@ timesteps.at[index, "tau"], wafer_thickness, s_rear, jsc_now, temperature=25 ) - -# In[17]: - - +# %% # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power timesteps = letid.calc_device_params(timesteps, cell_area=243) timesteps["time (days)"] = ( @@ -302,14 +264,12 @@ timesteps - +# %% [markdown] # ## Plot the results # # -# In[18]: - - +# %% from cycler import cycler plt.style.use("default") @@ -350,12 +310,23 @@ plt.show() - +# %% [markdown] # The function `calc_letid_lab` wraps all of the steps above into a single function: -# In[11]: - - -letid.calc_letid_lab( - tau_0, tau_deg, wafer_thickness, s_rear, nA_0, nB_0, nC_0, 0.1, 75, 'D037' +# %% +FIXED_START_DATE = dt.datetime(2025, 9, 9, 0, 0) +result = letid.calc_letid_lab( + tau_0, + tau_deg, + wafer_thickness, + s_rear, + nA_0, + nB_0, + nC_0, + 0.1, + 75, + "D037", + start=FIXED_START_DATE, ) +result["Datetime"] = result["Datetime"].dt.round("s") +print(result) diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.py b/tutorials/02_degradation/scripts/03_letid_outdoor.py similarity index 95% rename from tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.py rename to tutorials/02_degradation/scripts/03_letid_outdoor.py index 221f8a6df..d80895d1e 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.py +++ b/tutorials/02_degradation/scripts/03_letid_outdoor.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # LETID - Outdoor Environments # # This is an example on how to model LETID progression in outdoor environments @@ -21,16 +19,11 @@ # 5. Calculate device degradation and plot # -# In[1]: - - +# %% # 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 pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg==0.3.3 +# %% from pvdeg import letid, collection, utilities, DATA_DIR import pvlib @@ -38,11 +31,9 @@ import pandas as pd import numpy as np import matplotlib.pyplot as plt +import pvdeg - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -53,40 +44,29 @@ print("pvlib version ", pvlib.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # First, we'll use pvlib to create and run a model system, and use the irradiance, temperature, and operating point of that model to set up our LETID model # For this example, we'll model a fixed latitude tilt system at NREL, in Golden, CO, USA, using [NSRDB](https://nsrdb.nrel.gov/) hourly PSM weather data, SAPM temperature models, and module and inverter models from the CEC database. -# In[2]: - - +# %% # load weather and location data, use pvlib read_psm3 function with map_variables = True sam_file = "psm3.csv" -weather, meta = pvlib.iotools.read_psm3( - os.path.join(DATA_DIR, sam_file), map_variables=True +weather, meta = pvdeg.weather.read( + os.path.join(DATA_DIR, sam_file), file_type="PSM3", map_variables=True ) - -# In[3]: - - +# %% weather - -# In[4]: - - +# %% # if our weather file doesn't have precipitable water, calculate it with pvlib if "precipitable_water" not in weather.columns: weather["precipitable_water"] = pvlib.atmosphere.gueymard94_pw( weather["temp_air"], weather["relative_humidity"] ) - -# In[5]: - - +# %% # rename some columns for pvlib if they haven't been already weather.rename( columns={ @@ -112,16 +92,10 @@ ] ] - -# In[6]: - - +# %% weather - -# In[7]: - - +# %% # import pvlib stuff and pick a module and inverter. Choice of these things will slightly affect the pvlib results which we later use to calculate injection. # we'll use the SAPM temperature model open-rack glass/polymer coeffecients. @@ -140,14 +114,11 @@ "open_rack_glass_polymer" ] - -# In[8]: - - +# %% # set up system in pvlib lat = meta["latitude"] lon = meta["longitude"] -tz = meta["Local Time Zone"] +tz = meta["tz"] elevation = meta["altitude"] surface_tilt = lat # fixed, latitude tilt surface_azimuth = 180 # south-facing @@ -162,23 +133,18 @@ temperature_model_parameters=temperature_model_parameters, ) - -# In[9]: - - +# %% # create and run pvlib modelchain mc = ModelChain(system, location, aoi_model="physical") mc.run_model(weather) - +# %% [markdown] # ## Set up timeseries # In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$. # # We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results. -# In[10]: - - +# %% ee = mc.results.effective_irradiance # injection = (mc.results.dc['i_sc']-mc.results.dc['i_mp'])/(mc.results.dc['i_sc'])*(ee/1000) injection = letid.calc_injection_outdoors(mc.results) @@ -192,38 +158,28 @@ ) # reset the index so datetime is a column. I prefer integer indexing. timesteps.rename(columns={"index": "Datetime"}, inplace=True) - -# In[11]: - - +# %% # filter out times when injection is NaN, these won't progress LETID, and it'll make the calculations below run faster timesteps = timesteps[timesteps["Injection"].notnull()] timesteps.reset_index(inplace=True, drop=True) - -# In[12]: - - +# %% timesteps - +# %% [markdown] # ## Device parameters # To define a device, we need to define several important quantities about the device: wafer thickness (in $\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2). -# In[1]: - - +# %% wafer_thickness = 180 # um s_rear = 46 # cm/s cell_area = 243 # cm^2 - +# %% [markdown] # Other device parameters # Other required device parameters: base diffusivity (in cm2/s), and optical generation profile, which allow us to estimate current collection in the device. -# In[14]: - - +# %% generation_df = pd.read_excel( os.path.join(DATA_DIR, "PVL_GenProfile.xlsx"), header=0 ) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer. @@ -232,32 +188,28 @@ d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details - +# %% [markdown] # ## Degradation parameters # To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\mu s$). -# In[15]: - - +# %% tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B - +# %% [markdown] # Remaining degradation parameters: # # The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters. # # In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the kinetic parameters for LETID degradation from Repins. -# In[16]: - - +# %% # starting defect state percentages nA_0 = 100 nB_0 = 0 nC_0 = 0 -mechanism_params = utilities.get_kinetics('D037') +mechanism_params = utilities.get_kinetics("D037") timesteps[["NA", "NB", "NC", "tau"]] = ( np.nan @@ -272,13 +224,11 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - +# %% [markdown] # ## Run through timesteps # Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries. -# In[17]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -373,13 +323,11 @@ tau, wafer_thickness, s_rear, jsc, temperature=25 ) - +# %% [markdown] # ## Finish calculating degraded device parameters. # Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. -# In[18]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) # calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc. @@ -392,24 +340,20 @@ timesteps.at[index, "tau"], wafer_thickness, s_rear, jsc_now, temperature=25 ) - -# In[19]: - - +# %% timesteps = letid.calc_device_params( timesteps, cell_area=243 ) # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power timesteps - +# %% [markdown] # Note of course that all these calculated device parameters are modeled STC device parameters, not the instantaneous, weather-dependent values. This isn't a robust performance model of a degraded module. +# %% [markdown] # ## Plot the results -# In[20]: - - +# %% from cycler import cycler plt.style.use("default") @@ -440,18 +384,17 @@ plt.show() - +# %% [markdown] # The example data provided for Golden, CO, shows how $N_A$ increases in cold weather, and power temporarily recovers, due to temporary recovery of LETID (B->A). +# %% [markdown] # ##### The function `calc_letid_outdoors` wraps all of the steps above into a single function: -# In[21]: - - +# %% nA_0 = 100 nB_0 = 0 nC_0 = 0 -mechanism_params = 'D037' +mechanism_params = "D037" letid.calc_letid_outdoors( tau_0, @@ -468,5 +411,4 @@ module_parameters=cec_module, ) - -# In[ ]: +# %% diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.py b/tutorials/02_degradation/scripts/04_letid_outdoor_scenario.py similarity index 96% rename from tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.py rename to tutorials/02_degradation/scripts/04_letid_outdoor_scenario.py index 91e7ee2f2..0a9db3086 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.py +++ b/tutorials/02_degradation/scripts/04_letid_outdoor_scenario.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # LETID - Outdoor Scenario Based on Accelerated Test # # This is an example for using a test result to model LETID progression in outdoor environments @@ -25,16 +23,11 @@ # 6. Calculate device degradation and plot # -# In[1]: - - +# %% # 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 pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg==0.3.3 +# %% from pvdeg import letid, collection, utilities, DATA_DIR import pvdeg @@ -45,10 +38,7 @@ import matplotlib.pyplot as plt - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -58,30 +48,23 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # First, we'll load some data taken from an accelerated test. See [Karas *et al.* 2022](https://onlinelibrary.wiley.com/doi/10.1002/pip.3573) for full details. This data is the average of "Type S" modules from Lab 3. Type S modules were prototype modules made with 48 monocrystalline cells, and degraded about 4-5% in LETID testing. Data throughout testing is shown below: -# In[2]: - - +# %% cell_area = 243 # cm^2 - -# In[3]: - - +# %% df = pd.read_csv(os.path.join(DATA_DIR, "module test data.csv")) df["cell Voc"] = df["Voc"] / 48 df["Jsc"] = df["Isc"] / cell_area * 1000 df["% degradation"] = (df["Pmp"] - df["Pmp"].iloc[0]) / df["Pmp"] * 100 df - +# %% [markdown] # The module parameter that is most sensitve to our device input parameters is cell open-circuit voltage, which in this case started at about 0.664 V/cell. We will select reasonable values for solar cell input parameters, and use ```letid.calc_voc_from_tau()``` to check if those parameters match the cell Voc of the device we're trying to model. The important quantities here are bulk lifetime in the initial state (```tau_0```), wafer thickness, and rear surface recombination velocity. -# In[4]: - - +# %% tau_0 = 120 # [us] reasonable bulk lifetime for commercial-quality p-type silicon, maybe a little on the low side for typical wafer lifetimes, which could range from 100-500 us. wafer_thickness = 180 # [um] a reasonable wafer thickness for typical commercial silicon solar cells. Wafer thicknesses for Si solar cells are typically 160-180 um. @@ -102,10 +85,7 @@ voc_0 = letid.calc_voc_from_tau(tau_0, wafer_thickness, srv_rear, jsc_0, temperature) voc_0 - -# In[5]: - - +# %% generation_df = pd.read_excel( os.path.join(DATA_DIR, "PVL_GenProfile.xlsx"), header=0 ) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer. @@ -114,12 +94,10 @@ d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details - +# %% [markdown] # Pretty close! -# In[6]: - - +# %% # check to make sure power is close to the measured Week 0 power ff_0 = df.query("Week == 0")["FF"].item() # [%] fill factor @@ -127,12 +105,10 @@ pmp_0 = (voc_0 * 48) * (jsc_0 * cell_area / 1000) * ff_0 / 100 # [W] maximum power pmp_0, df.query("Week == 0")["Pmp"].item() - +# %% [markdown] # Now we do the same thing for the degraded state to determine ```tau_deg```, the bulk lifetime when the module is in its most degraded state. So here the cell Voc target is the roughly 0.656 V measured after 4 weeks of testing. -# In[7]: - - +# %% tau_deg = 80 # [us] degraded bulk lifetime isc_deg = df.query("Week == 4")[ @@ -145,10 +121,7 @@ ) voc_deg - -# In[8]: - - +# %% # check to make sure power is close to the measured Week 4 power ff_deg = df.query("Week == 4")["FF"].item() # [%] fill factor @@ -158,49 +131,35 @@ ) # [W] maximum power pmp_deg, df.query("Week == 4")["Pmp"].item() - -# In[9]: - - +# %% (pmp_0 - pmp_deg) / pmp_0 - +# %% [markdown] # So for modeling this module, we will use ```tau_0``` = 120 $\mu s$, ```tau_deg``` = 80 $\mu s$, with ```wafer_thickness``` = 180 $\mu m$ and ```srv_rear``` = 100 cm/s. # # Great! # # The example proceeds below in similar fashion to the outdoor example, using a fixed latitude tilt system at NREL, in Golden, CO, USA, using [NSRDB](https://nsrdb.nrel.gov/) hourly PSM weather data, SAPM temperature models, and module and inverter models from the CEC database. -# In[10]: - - +# %% # load weather and location data, use pvlib read_psm3 function sam_file = "psm3.csv" -weather, meta = pvlib.iotools.read_psm3( - os.path.join(DATA_DIR, sam_file), map_variables=True +weather, meta = pvdeg.weather.read( + os.path.join(DATA_DIR, sam_file), file_type="PSM3", map_variables=True ) - -# In[11]: - - +# %% weather - -# In[12]: - - +# %% # if our weather file doesn't have precipitable water, calculate it with pvlib if "precipitable_water" not in weather.columns: weather["precipitable_water"] = pvlib.atmosphere.gueymard94_pw( weather["temp_air"], weather["relative_humidity"] ) - -# In[13]: - - +# %% # drop unneeded columns weather = weather[ [ @@ -214,35 +173,25 @@ ] ] - -# In[14]: - - +# %% weather - +# %% [markdown] # ### Set up PVlib model # Note that the module we select here is NOT the same "Type S" module that was tested for LETID. I'm simply trying to find a module in the CEC database with I-V characteristics that are reasonably close to the tested module, so the pvlib calculated DC results are close to how our Type S module might behave in the field. -# In[15]: - - +# %% cec_modules = pvlib.pvsystem.retrieve_sam("CECMod").T cec_modules[cec_modules["STC"].between(220, 250) & (cec_modules["N_s"] == 48)] - +# %% [markdown] # The LG ones look close to the module we're trying to model. Pmp around 235W, Isc around 9.9A. Let's go with 'LG_Electronics_Inc__LG235N8K_G4' -# In[16]: - - +# %% cec_modules = cec_modules.T cec_module = cec_modules["LG_Electronics_Inc__LG235N8K_G4"] - -# In[17]: - - +# %% # import the rest of the pvlib stuff # we'll use the SAPM temperature model open-rack glass/polymer coeffecients. @@ -258,14 +207,11 @@ "open_rack_glass_polymer" ] - -# In[18]: - - +# %% # set up system in pvlib lat = meta["latitude"] lon = meta["longitude"] -tz = meta["Local Time Zone"] +tz = meta["tz"] elevation = meta["altitude"] surface_tilt = lat # fixed, latitude tilt surface_azimuth = 180 # south-facing @@ -280,23 +226,18 @@ temperature_model_parameters=temperature_model_parameters, ) - -# In[19]: - - +# %% # create and run pvlib modelchain mc = ModelChain(system, location, aoi_model="physical") mc.run_model(weather) - +# %% [markdown] # ## Set up timeseries # In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$. # # We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results. -# In[21]: - - +# %% ee = mc.results.effective_irradiance # injection = (mc.results.dc['i_sc']-mc.results.dc['i_mp'])/(mc.results.dc['i_sc'])*(ee/1000) injection = letid.calc_injection_outdoors(mc.results) @@ -310,7 +251,7 @@ ) # reset the index so datetime is a column. I prefer integer indexing. timesteps.rename(columns={"index": "Datetime"}, inplace=True) - +# %% [markdown] # # ##### Remaining degradation parameters: # We've already set our important device parameters: ```tau_0```, ```tau_deg```, ```wafer_thickness```, ```srv_rear```, ```cell_area```, etc, but we need a few more: generation profile and carrier diffusivity. These are necessary for calculating current collection, and the "default" values provided here should be sufficient for most use cases. @@ -319,9 +260,7 @@ # # In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for LETID degradation from Repins. -# In[22]: - - +# %% generation_df = pd.read_excel( os.path.join(DATA_DIR, "PVL_GenProfile.xlsx"), header=0 ) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer. @@ -330,16 +269,13 @@ d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details - -# In[29]: - - +# %% # starting defect state percentages nA_0 = 100 nB_0 = 0 nC_0 = 0 -mechanism_params = utilities.get_kinetics('D037') +mechanism_params = utilities.get_kinetics("D037") timesteps[["NA", "NB", "NC", "tau"]] = ( np.nan @@ -354,19 +290,14 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[30]: - - +# %% timesteps - +# %% [markdown] # ## Run through timesteps # Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries. -# In[31]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -447,10 +378,7 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - -# In[32]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) # calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc. @@ -463,22 +391,17 @@ timesteps.at[index, "tau"], wafer_thickness, srv_rear, jsc_now, temperature=25 ) - -# In[33]: - - +# %% timesteps = letid.calc_device_params( timesteps, cell_area ) # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power timesteps - +# %% [markdown] # Note of course that all these calculated device parameters are modeled STC device parameters, not the instantaneous, weather-dependent values. We'll merge back in the pvlib results for convenience, but these don't reflect the device degradation. We'll calculate energy loss next -# In[34]: - - +# %% timesteps = timesteps.merge(mc.results.dc, left_on="Datetime", right_index=True) timesteps = timesteps.merge( pd.DataFrame(mc.results.effective_irradiance, columns=["Effective irradiance"]), @@ -489,12 +412,10 @@ timesteps - +# %% [markdown] # ### Plot the results -# In[35]: - - +# %% from cycler import cycler plt.style.use("default") @@ -528,10 +449,7 @@ plt.show() - -# In[36]: - - +# %% import matplotlib.dates as mdates plt.style.use("default") @@ -564,10 +482,7 @@ plt.show() - -# In[37]: - - +# %% import matplotlib.dates as mdates from scipy.integrate import cumulative_trapezoid, simpson @@ -689,13 +604,11 @@ plt.show() - +# %% [markdown] # ##### The function `calc_letid_outdoors` wraps all of the steps above into a single function: -# In[39]: - - -mechanism_params = 'D037' +# %% +mechanism_params = "D037" letid.calc_letid_outdoors( tau_0, @@ -712,11 +625,7 @@ module_parameters=cec_module, ) - -# In[38]: - - +# %% timesteps - -# In[ ]: +# %% diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.py b/tutorials/02_degradation/scripts/05_letid_passivated_wafer.py similarity index 96% rename from tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.py rename to tutorials/02_degradation/scripts/05_letid_passivated_wafer.py index d8b3ebb86..0610d9fa7 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.py +++ b/tutorials/02_degradation/scripts/05_letid_passivated_wafer.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # LETID - Passivated Wafer # # This example shows how to simulate an accelerated test performed on a well-passivated Si wafer, rather than a solar cell. @@ -19,16 +17,11 @@ # 5. Calculate device degradation and plot # -# In[1]: - - +# %% # 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 pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg==0.3.3 +# %% from pvdeg import letid, collection, utilities, DATA_DIR import pvdeg import os @@ -36,10 +29,7 @@ import numpy as np import matplotlib.pyplot as plt - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -49,13 +39,11 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # ## Device parameters # For modeling a wafer, we don't need as many device parameters. Wafer thickness, lifetime, and an optical generation profile, which allows us to calculate generation current, which we take to be device current assuming surface recombination is very low. -# In[2]: - - +# %% wafer_thickness = 180 # um tau = 350 # us, lifetime of the wafer for demonstration purposes @@ -67,7 +55,7 @@ j_gen = collection.generation_current(generation, depth) - +# %% [markdown] # ## Degradation parameters # Here we'll model LETID under 0.5-sun, open-circuit injection. In a passivated wafer, recombination per volume ($\frac{\Delta n}{\tau}$) must equal generation per volume ($\frac{Jq}{W}$), which gives $\Delta n = \frac{Jq\tau}{W}$ # @@ -77,19 +65,15 @@ # # See [Repins *et al.* 2023](https://doi.org/10.1557/s43577-022-00438-8) for more details. -# In[3]: - - -mechanism_params = utilities.get_kinetics('D037') +# %% +mechanism_params = utilities.get_kinetics("D037") injection = 0.5 # - +# %% [markdown] # ### Demonstrate calculation of dn^x_ij # 1. Calculate $\Delta n$ of the wafer -# In[4]: - - +# %% from scipy.constants import elementary_charge as q # Elementary charge, C dn = ( @@ -97,17 +81,16 @@ ) # calculate excess carrier density dn - +# %% [markdown] # 2. Calculate $\Delta n_{lit}$, the excess carrier density from the literature experiments where kinetic parameters were determined. # Assuming literature experiments were carried out on cells, this requires using the `calc_dn` function -# In[5]: - - +# %% from scipy.constants import convert_temperature transition = "bc" # for this example we'll look at the B -> C transition +# mechanism_params now contains extracted numeric values meas_tau = mechanism_params[f"tau_{transition}"] meas_temp = mechanism_params[f"temperature_{transition}"] meas_temp = convert_temperature(meas_temp, "K", "C") @@ -127,69 +110,48 @@ ) dn_lit - +# %% [markdown] # 3. Return the exponent $x_{ij}$ of the transition in question # -# In[6]: - - +# %% exponent = mechanism_params[f"x_{transition}"] -exponent - - -# 4. Calculate $(\frac{\Delta n}{\Delta n_{lit}})^{x_{ij}}$. This is the acceleration factor of the transition due to excess carriers. -# - -# In[7]: - - (dn / dn_lit) ** exponent - -# In[8]: - - +# %% # the "carrier_factor_wafer" function yields the same result letid.carrier_factor_wafer( tau, transition, injection, j_gen, wafer_thickness, mechanism_params ) - +# %% [markdown] # ## Degradation parameters # To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\mu s$), and starting defect state percentages -# In[9]: - - +# %% tau_0 = 350 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C tau_deg = 41 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B - -# In[10]: - - +# %% # starting defect state percentages nA_0 = 100 nB_0 = 0 nC_0 = 0 -mechanism_params = utilities.get_kinetics('D037') +mechanism_params = utilities.get_kinetics("D037") print(mechanism_params) - +# %% [markdown] # ## Set up timeseries # In this example, we are going to model a wafer exposed to 0.5 suns illumination at $125\degree C$ for 24 hours, and compare to experimental data from [Wyller *et al.* 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367). -# In[11]: - - +# %% temperature = 125 # degrees celsius suns = 0.5 # "suns" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc. timesteps = pd.date_range( - start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="S" + start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="s" ) # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly timesteps = pd.DataFrame(timesteps, columns=["Datetime"]) @@ -212,19 +174,14 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[12]: - - +# %% timesteps - +# %% [markdown] # ## Run through timesteps # Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries. -# In[13]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -283,7 +240,7 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - +# %% [markdown] # ## Finish calculating degraded device parameters. # Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. Since this is a wafer, device parameters like Jsc, Voc, FF, etc., aren't really appropriate # @@ -291,9 +248,7 @@ # # $NDD(t) = \frac{1}{\tau (t)} - \frac{1}{\tau _0}$ -# In[14]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) timesteps["NDD"] = letid.calc_ndd(timesteps["tau"].iloc[0], timesteps["tau"]) @@ -302,18 +257,13 @@ timesteps["Datetime"] - timesteps.iloc[0]["Datetime"] ).dt.total_seconds() # create a column for seconds elapsed - -# In[15]: - - +# %% timesteps - +# %% [markdown] # ## Plot the results -# In[18]: - - +# %% from cycler import cycler plt.style.use("default") @@ -355,31 +305,23 @@ plt.show() - +# %% [markdown] # ## Compare model results to experimental data # Experimental data (0.5 suns, 125°C, 150°C, 175°C) from [Wyller 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367). # Comparison with experimental data requires normalization constant ($C_{norm}$), which relates NDD to $N_B$ as described in [Repins 2023](https://doi.org/10.1557/s43577-022-00438-8). -# In[19]: - - +# %% temperatures = [125, 150, 175] # degrees celsius - -# In[20]: - - +# %% timesteps125 = timesteps # we already modeled the 125C experiment above - -# In[21]: - - +# %% temperature = temperatures[1] # degrees celsius suns = 0.5 # "suns" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc. timesteps = pd.date_range( - start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="S" + start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="s" ) # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly timesteps = pd.DataFrame(timesteps, columns=["Datetime"]) @@ -402,10 +344,7 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[22]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -464,31 +403,22 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - -# In[23]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) timesteps["NDD"] = letid.calc_ndd(timesteps["tau"].iloc[0], timesteps["tau"]) timesteps["time (s)"] = ( timesteps["Datetime"] - timesteps.iloc[0]["Datetime"] ).dt.total_seconds() # create a column for seconds elapsed - -# In[24]: - - +# %% timesteps150 = timesteps - -# In[25]: - - +# %% temperature = temperatures[2] # degrees celsius suns = 0.5 # "suns" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc. timesteps = pd.date_range( - start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="S" + start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="s" ) # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly timesteps = pd.DataFrame(timesteps, columns=["Datetime"]) @@ -511,10 +441,7 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[26]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -573,32 +500,20 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - -# In[27]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) timesteps["NDD"] = letid.calc_ndd(timesteps["tau"].iloc[0], timesteps["tau"]) timesteps["time (s)"] = ( timesteps["Datetime"] - timesteps.iloc[0]["Datetime"] ).dt.total_seconds() # create a column for seconds elapsed - -# In[28]: - - +# %% timesteps175 = timesteps - -# In[29]: - - +# %% timesteps = pd.concat([timesteps125, timesteps150, timesteps175]) - -# In[30]: - - +# %% normalization_constants = { 125: 1.35, 150: 0.6, @@ -610,16 +525,10 @@ ) timesteps["NDD-normalized"] = timesteps["NDD"] * timesteps["Normalization constant"] - -# In[31]: - - +# %% grouped_model = timesteps.groupby("Temperature") - -# In[32]: - - +# %% literature_data_file = os.path.join(DATA_DIR, "wyller data.csv") data = pd.read_csv(literature_data_file, header=0) @@ -628,16 +537,10 @@ # data['Normalization constant'] = data['Series'].map(normalization_constants) # data['Y-normalized'] = data['Y']*data['Normalization constant'] - -# In[33]: - - +# %% grouped_data = data.groupby("Temperature") - -# In[34]: - - +# %% import itertools fig, ax = plt.subplots() @@ -669,10 +572,7 @@ plt.show() - -# In[35]: - - +# %% import itertools fig, ax = plt.subplots() @@ -718,6 +618,3 @@ # fig.savefig('wafer data.png', dpi = 600) plt.show() - - -# In[ ]: diff --git a/tutorials/02_degradation/scripts/06_vant_hoff_degradation_model.py b/tutorials/02_degradation/scripts/06_vant_hoff_degradation_model.py new file mode 100644 index 000000000..66a0a6625 --- /dev/null +++ b/tutorials/02_degradation/scripts/06_vant_hoff_degradation_model.py @@ -0,0 +1,231 @@ +# %% [markdown] +# # A - Van't Hoff Degradation +# ### Calculate site specific degradation according to the Van't Hoff equation +# *** +# Michael Kempe +# +# 2023.08.31 +# *** +# +# **Requirements**: +# - compatible weather file (PSM3, TMY3, EPW) or lattitude and longitude of desired site +# - Accelerated testing chamber parameters +# - chamber irradiance [W/m^2] +# - chamber temperature [°C] +# - 10°C acceleration factor +# +# **Steps**: +# 1. Read/find the weather data +# 2. Generate basic modeling data +# 3. Calculate VantHoff degradation acceleration factor +# 4. Expand calculations to a region + +# %% +# 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 pvdeg==0.1.1 + +# %% +import os + +import pvdeg +from pvdeg import DATA_DIR + +# %% [markdown] +# ## 1. Read In the Weather File +# +# This is usually the first step. Use a PSM3, TMY3, or EPW file. For this demo, use the provided PSM3 weather file. + +# %% +PSM_FILE = os.path.join(DATA_DIR, "psm3_demo.csv") +WEATHER, META = pvdeg.weather.read(PSM_FILE, "psm") +print( + "Latitude =", + META["latitude"], + "Longitude =", + META["longitude"], + META["Country"], + META["City"], +) + +# %% [markdown] +# ## 2. Generate Basic Modeling Data +# +# For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions. + +# %% +sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META) + +poa_df = pvdeg.spectral.poa_irradiance( + weather_df=WEATHER, meta=META, sol_position=sol_pos +) + +temp_cell = pvdeg.temperature.cell(weather_df=WEATHER, meta=META, poa=poa_df) + +temp_module = pvdeg.temperature.module(weather_df=WEATHER, meta=META, poa=poa_df) + +# %% [markdown] +# ## 3. VantHoff Degradation +# +# Van't Hoff Irradiance Degradation Equation: +# $$ R_o = R_D · G^p · T_f^{\frac{T}{10} }$$ +# +# For the yearly average degredation outdoors to be the same as the controlled environmnet, the lamp settings will need to be set to *G$_{WA}$* and the temperature set to *T$_{oeq}$*. +# +# As with most `pvdeg` functions, the following functions will always require two arguments (weather_df and meta) + +# %% +# chamber irradiance (W/m²) +I_chamber = 1600 +# chamber temperature (°C) +temp_chamber = 85 +# Schwartzchild Coefficient +p = 0.64 +# Acceleration factor for every 10°C +Tf = 1.41 + +# calculate the Van't Hoff Acceleration factor +vantHoff_deg = pvdeg.degradation.vantHoff_deg( + weather_df=WEATHER, + meta=META, + I_chamber=I_chamber, + temp_chamber=temp_chamber, + poa=poa_df, + temp=temp_cell, + p=p, + Tf=Tf, +) + +# calculate the Van't Hoff weighted irradiance +irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff( + weather_df=WEATHER, meta=META, poa=poa_df, temp=temp_cell, p=p, Tf=Tf +) + +print( + "AF =", + round(vantHoff_deg, 1), + "(°C) , and G_WA =", + round(irr_weighted_avg_v), + "(W/m²)", +) + +# %% [markdown] +# ## 4. Arrhenius +# Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet +# +# Example: "If the *AF*=25 then 1 year of Controlled Environment exposure is equal to 25 years in the field" +# +# Equation: +# $$ AF = N · \frac{ G_{chamber}^x · RH_{chamber}^n · e^{\frac{- E_a}{k T_{chamber}}} }{ \Sigma (G_{POA}^x · RH_{outdoor}^n · e^{\frac{-E_a}{k T_outdoor}}) }$$ +# +# Function to calculate *G$_{WA}$*, the Environment Characterization (W/m²). If the controlled environmnet lamp settings are set at *G$_{WA}$*, and the temperature set to *T$_{eq}$*, then the degradation will be the same as the yearly average outdoors. +# +# Equation: +# $$ G_{WA} = [ \frac{ \Sigma (G_{outdoor}^x · RH_{outdoor}^n e^{\frac{-E_a}{k T_{outdood}}}) }{ N · RH_{WA}^n · e^{- \frac{E_a}{k T_eq}} } ]^{\frac{1}{x}} $$ + +# %% +# relative humidity within chamber (%) +rh_chamber = 15 +# arrhenius activation energy (kj/mol) +Ea = 40 + +rh_surface = pvdeg.humidity.surface_relative( + rh_ambient=WEATHER["relative_humidity"], + temp_ambient=WEATHER["temp_air"], + temp_module=temp_module, +) + +arrhenius_deg = pvdeg.degradation.arrhenius_deg( + weather_df=WEATHER, + meta=META, + rh_outdoor=rh_surface, + I_chamber=I_chamber, + rh_chamber=rh_chamber, + temp_chamber=temp_chamber, + poa=poa_df, + temp=temp_cell, + Ea=Ea, +) + +irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius( + weather_df=WEATHER, + meta=META, + poa=poa_df, + rh_outdoor=WEATHER["relative_humidity"], + temp=temp_cell, + Ea=Ea, +) + +# %% [markdown] +# ## 5. Quick Method (Degradation) +# +# For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration. + +# %% +# chamber settings +I_chamber = 1000 +temp_chamber = 60 +rh_chamber = 15 + +# activation energy +Ea = 40 + +vantHoff_deg = pvdeg.degradation.vantHoff_deg( + weather_df=WEATHER, meta=META, I_chamber=I_chamber, temp_chamber=temp_chamber +) + +irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=WEATHER, meta=META) + +# %% +rh_surface = pvdeg.humidity.surface_relative( + rh_ambient=WEATHER["relative_humidity"], + temp_ambient=WEATHER["temp_air"], + temp_module=temp_module, +) + +arrhenius_deg = pvdeg.degradation.arrhenius_deg( + weather_df=WEATHER, + meta=META, + rh_outdoor=rh_surface, + I_chamber=I_chamber, + rh_chamber=rh_chamber, + temp_chamber=temp_chamber, + Ea=Ea, +) + +irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius( + weather_df=WEATHER, meta=META, rh_outdoor=WEATHER["relative_humidity"], Ea=Ea +) + +# %% [markdown] +# ## 6. Solder Fatigue +# +# Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. + +# %% +fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META) + +# %% [markdown] +# If you wish to reduce the span of time or use a non-default temperature model, you may specify the parameters manually. Let's try an explicit example. +# We want the solder fatigue estimated over the month of June for a roof mounted glass-front polymer-back module. +# +# 1. Lets create a datetime-index for the month of June. +# 2. Next, generate the cell temperature. Make sure to explicity restrict the weather data to our dt-index for June. Next, declare the PV module configuration. +# 3. Calculate the fatigue. Explicity specify the time_range (our dt-index for June from step 1) and the cell temperature as we caculated in step 2 + +# %% +# select the month of June +time_range = WEATHER.index[WEATHER.index.month == 6] + +# calculate cell temperature over our selected date-time range. +# specify the module configuration +temp_cell = pvdeg.temperature.cell( + weather_df=WEATHER.loc[time_range], + meta=META, + temp_model="sapm", + conf="insulated_back_glass_polymer", +) + + +fatigue = pvdeg.fatigue.solder_fatigue( + weather_df=WEATHER, meta=META, time_range=time_range, temp_cell=temp_cell +) diff --git a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb b/tutorials/03_monte_carlo/01_arrhenius.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb rename to tutorials/03_monte_carlo/01_arrhenius.ipynb index b5792cd44..9c105cd0a 100644 --- a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb +++ b/tutorials/03_monte_carlo/01_arrhenius.ipynb @@ -4,10 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Monte Carlo - Arrhenius Degradation \n", + "# Monte Carlo - Arrhenius Degradation\n", "\n", "\n", - "A monte carlo simulation can be used to predict results of an event with a certain amount of uncertainty. This will be introduced to our use case via mean and standard deviation for each modeling constant. Correlated multivariate monte carlo simulations expand on this by linking the behavior of multiple input variables together with correlation data, in our case we will use correlation coefficients but \n", + "A monte carlo simulation can be used to predict results of an event with a certain amount of uncertainty. This will be introduced to our use case via mean and standard deviation for each modeling constant. Correlated multivariate monte carlo simulations expand on this by linking the behavior of multiple input variables together with correlation data, in our case we will use correlation coefficients but\n", "\n", "**Objectives**\n", "1. Define necessary monte carlo simulation parameters : correlation coefficients, mean and standard standard deviation, number of trials, function to apply, requried function input\n", @@ -241,7 +241,7 @@ } ], "source": [ - "np.random.seed(42) # for reproducibility\n", + "np.random.seed(42) # for reproducibility\n", "mc_inputs = pvdeg.montecarlo.generateCorrelatedSamples(\n", " corr=corr_coeff, stats=stats_dict, n=n\n", ")\n", @@ -287,7 +287,9 @@ "source": [ "# mean and standard deviation match inputs\n", "for col in mc_inputs.columns:\n", - " print(f\"{col} : mean {mc_inputs[col].mean():.10f}, stdev {mc_inputs[col].std():.10f}\")\n", + " print(\n", + " f\"{col} : mean {mc_inputs[col].mean():.10f}, stdev {mc_inputs[col].std():.10f}\"\n", + " )\n", "\n", "print()\n", "\n", @@ -300,11 +302,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Other Function Requirements \n", + "### Other Function Requirements\n", "Based on the function chosen to run in the monte carlo simulation, various other data will be required. In this case we will need cell temperature and total plane of array irradiance.\n", "\n", "
\n", - "Please use your own API key: The block below makes an NSRDB API to get weather and meta data and then calculate cell temperature and global poa irradiance. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) \n", + "Please use your own API key: The block below makes an NSRDB API to get weather and meta data and then calculate cell temperature and global poa irradiance. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.)\n", "
" ] }, @@ -346,7 +348,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Calculate the sun position, poa irradiance, and module temperature. " + "Calculate the sun position, poa irradiance, and module temperature." ] }, { @@ -403,7 +405,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Runs monte carlo simulation for the example `pvdeg.montecarlo.vecArrhenius` function, using the correlated data dataframe created above and the required function arguments. \n", + "Runs monte carlo simulation for the example `pvdeg.montecarlo.vecArrhenius` function, using the correlated data dataframe created above and the required function arguments.\n", "\n", "We can see the necessary inputs by using the help command:" ] @@ -463,9 +465,9 @@ "metadata": {}, "source": [ "### Running the Monte Carlo Simulation\n", - "We will pass the target function, `pvdeg.degredation.vecArrhenius()`, its required arguments via the correlated_samples and func_kwargs. Our fixed arguments will be passed in the form of a dictionary while the randomized monte carlo input data will be contained in a DataFrame. \n", + "We will pass the target function, `pvdeg.degredation.vecArrhenius()`, its required arguments via the correlated_samples and func_kwargs. Our fixed arguments will be passed in the form of a dictionary while the randomized monte carlo input data will be contained in a DataFrame.\n", "\n", - "All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary, \n", + "All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary,\n", "\n", "(You can use any data you want here as long as the DataFrame's column names match the required target function's parameter names NOT included in the kwargs)" ] diff --git a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.ipynb b/tutorials/03_monte_carlo/02_standoff.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.ipynb rename to tutorials/03_monte_carlo/02_standoff.ipynb index 2e968dbf1..88e23d22f 100644 --- a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.ipynb +++ b/tutorials/03_monte_carlo/02_standoff.ipynb @@ -92,7 +92,7 @@ "This is copied from another tutorial called `4 - Standards.ipynb`, please visit this page for a more in depth explanation of the process for a single standoff calculation.\n", "\n", "
\n", - "Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) \n", + "Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.)\n", "
" ] }, @@ -257,7 +257,7 @@ "source": [ "#### Standoff Monte Carlo Inputs\n", "\n", - "When using the pvdeg.montecarlo.simulate() function on a target function all of the target function's required arguments must still be given. Our non-changing arguments will be stored in a dictionary. The randomized monte carlo input data will also be passed to the target function via the simulate function. All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary, " + "When using the pvdeg.montecarlo.simulate() function on a target function all of the target function's required arguments must still be given. Our non-changing arguments will be stored in a dictionary. The randomized monte carlo input data will also be passed to the target function via the simulate function. All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary," ] }, { @@ -303,7 +303,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Dealing With Series \n", + "#### Dealing With Series\n", "Notice how our results are contained in a pandas series instead of a dataframe.\n", "\n", "This means we have to do an extra step to view our results. Run the block below to confirm that our results are indeed contained in a series. And convert them into a simpler dataframe." diff --git a/tutorials/03_monte_carlo/README.md b/tutorials/03_monte_carlo/README.md new file mode 100644 index 000000000..4ac51c664 --- /dev/null +++ b/tutorials/03_monte_carlo/README.md @@ -0,0 +1,16 @@ +# Monte Carlo Simulations + +Tutorials on using Monte Carlo methods for uncertainty analysis and probabilistic modeling. + +## Tutorials + +- **arrhenius.ipynb** - Monte Carlo simulations with Arrhenius degradation models +- **standoff.ipynb** - Monte Carlo analysis for module standoff parameters + +## Overview + +These tutorials demonstrate how to: +- Incorporate parameter uncertainty into degradation predictions +- Perform probabilistic risk assessments +- Analyze sensitivity to input parameters +- Generate confidence intervals for degradation estimates diff --git a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.py b/tutorials/03_monte_carlo/scripts/01_arrhenius.py similarity index 91% rename from tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.py rename to tutorials/03_monte_carlo/scripts/01_arrhenius.py index f6e533ae0..caa73c116 100644 --- a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.py +++ b/tutorials/03_monte_carlo/scripts/01_arrhenius.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # Monte Carlo - Arrhenius Degradation # # @@ -11,26 +9,18 @@ # 2. Define process for creating and utilizing modeling constant correlation data # 3. Preform simple monte carlo simulation using arrhenius equation to calculate degredation and plot -# In[ ]: - - +# %% # 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 pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg==0.3.3 +# %% import pvlib import numpy as np import pandas as pd import pvdeg import matplotlib.pyplot as plt - -# In[7]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -41,7 +31,7 @@ print("Pvlib version ", pvlib.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # ## Correlated Monte Carlo Simulation (parameters) # # For this simulation we will be using an arrhenius equation to calculate degredation rate given by $R_D = R_0 * I ^ X * e ^ {\frac{-Ea}{kT}}$, where R0 is prefactor degredation, I is irradiance, X is the irridiance relation, Ea is activation energy and T is degrees K @@ -56,6 +46,7 @@ # - correlation constants (if not entered, default = 0) # - number of trials to run +# %% [markdown] # ### Defining Correlation Coefficients # pvdeg.montecarlo stores correlation coefficients in a ``Corr`` object. To represent a given correlation coefficient follow the given syntax below, replacing the values in the brackets with your correlation coefficients # @@ -65,28 +56,21 @@ # # After defining the all known correlations add them to a list which we will feed into our simulation later -# In[10]: - - +# %% corr_Ea_X = pvdeg.montecarlo.Corr("Ea", "X", 0.0269) corr_Ea_LnR0 = pvdeg.montecarlo.Corr("Ea", "LnR0", -0.9995) corr_X_LnR0 = pvdeg.montecarlo.Corr("X", "LnR0", -0.0400) corr_coeff = [corr_Ea_X, corr_Ea_LnR0, corr_X_LnR0] - -# In[11]: - - +# %% type(corr_Ea_X) - +# %% [markdown] # ### Defining Mean and Standard Deviation # We will store the mean and correlation for each variable, expressed when we defined the correlation cefficients. If a variable is left out at this stage, the monte carlo simulation will throw errors. -# In[12]: - - +# %% stats_dict = { "Ea": {"mean": 62.08, "stdev": 7.3858}, "LnR0": {"mean": 13.7223084, "stdev": 2.47334772}, @@ -96,39 +80,37 @@ # and number of monte carlo trials to run n = 20000 - +# %% [markdown] # ### Generating Monte Carlo Input Data # Next we will use the information collected above to generate correlated data from our modeling constant correlations, means and standard deviations. -# In[15]: - - +# %% +np.random.seed(42) # for reproducibility mc_inputs = pvdeg.montecarlo.generateCorrelatedSamples( corr=corr_coeff, stats=stats_dict, n=n ) print(mc_inputs) - +# %% [markdown] # #### Sanity Check # We can observe the mean and standard deviation of our newly correlated samples before using them for calculations to ensure that we have not incorrectly altered the data. The mean and standard deviation should be the similar (within a range) to your original input (the error comes from the standard distribution of generated random numbers) # # This also applies to the correlation coefficients originally inputted, they should be witin the same range as those orginally supplied. -# In[16]: - - +# %% # mean and standard deviation match inputs for col in mc_inputs.columns: - print(f"{col} : mean {mc_inputs[col].mean()}, stdev {mc_inputs[col].std()}") + print( + f"{col} : mean {mc_inputs[col].mean():.10f}, stdev {mc_inputs[col].std():.10f}" + ) print() -# come up with a better way of checking -print("Ea_X", np.corrcoef(mc_inputs["Ea"], mc_inputs["X"])[0][1]) -print("Ea_lnR0", np.corrcoef(mc_inputs["Ea"], mc_inputs["LnR0"])[0][1]) -print("X_lnR0", np.corrcoef(mc_inputs["X"], mc_inputs["LnR0"])[0][1]) - +print("Ea_X", round(np.corrcoef(mc_inputs["Ea"], mc_inputs["X"])[0][1], 3)) +print("Ea_lnR0", round(np.corrcoef(mc_inputs["Ea"], mc_inputs["LnR0"])[0][1], 3)) +print("X_lnR0", round(np.corrcoef(mc_inputs["X"], mc_inputs["LnR0"])[0][1], 3)) +# %% [markdown] # ### Other Function Requirements # Based on the function chosen to run in the monte carlo simulation, various other data will be required. In this case we will need cell temperature and total plane of array irradiance. # @@ -136,27 +118,21 @@ # Please use your own API key: The block below makes an NSRDB API to get weather and meta data and then calculate cell temperature and global poa irradiance. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) # -# In[17]: - - -weather_db = "PSM3" +# %% +weather_db = "PSM4" weather_id = (25.783388, -80.189029) weather_arg = { "api_key": "DEMO_KEY", "email": "user@mail.com", - "names": "tmy", - "attributes": [], "map_variables": True, } weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) - +# %% [markdown] # Calculate the sun position, poa irradiance, and module temperature. -# In[18]: - - +# %% sol_pos = pvdeg.spectral.solar_position(weather_df, meta) poa_irradiance = pvdeg.spectral.poa_irradiance(weather_df, meta) temp_mod = pvdeg.temperature.module( @@ -167,24 +143,20 @@ poa_global = poa_irradiance["poa_global"].to_numpy() cell_temperature = temp_mod.to_numpy() - -# In[24]: - - +# %% # must already be numpy arrays function_kwargs = {"poa_global": poa_global, "module_temp": cell_temperature} - +# %% [markdown] # Runs monte carlo simulation for the example `pvdeg.montecarlo.vecArrhenius` function, using the correlated data dataframe created above and the required function arguments. # # We can see the necessary inputs by using the help command: -# In[28]: - - +# %% +# NBVAL_SKIP help(pvdeg.montecarlo.simulate) - +# %% [markdown] # ### Running the Monte Carlo Simulation # We will pass the target function, `pvdeg.degredation.vecArrhenius()`, its required arguments via the correlated_samples and func_kwargs. Our fixed arguments will be passed in the form of a dictionary while the randomized monte carlo input data will be contained in a DataFrame. # @@ -192,20 +164,16 @@ # # (You can use any data you want here as long as the DataFrame's column names match the required target function's parameter names NOT included in the kwargs) -# In[31]: - - +# %% results = pvdeg.montecarlo.simulate( func=pvdeg.degradation.vecArrhenius, correlated_samples=mc_inputs, **function_kwargs ) - +# %% [markdown] # ### Viewing Our Data # Let's plot the results using a histogram -# In[41]: - - +# %% lnDeg = np.log10(results) percentile_2p5 = np.percentile(lnDeg, 2.5) percentile_97p5 = np.percentile(lnDeg, 97.5) diff --git a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.py b/tutorials/03_monte_carlo/scripts/02_standoff.py similarity index 91% rename from tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.py rename to tutorials/03_monte_carlo/scripts/02_standoff.py index bd027def5..2c864b951 100644 --- a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.py +++ b/tutorials/03_monte_carlo/scripts/02_standoff.py @@ -1,32 +1,22 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # Monte Carlo - Standoff Calculation # # See Monte Carlo - Arrhenius Degredation for a more in depth guide. Steps will be shortened for brevity. # This journal applies a Monte Carlo to the Standoff Calculation # -# In[ ]: - - +# %% # 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 pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg==0.3.3 +# %% import pvlib import numpy as np import pandas as pd import pvdeg import matplotlib.pyplot as plt - -# In[2]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -37,7 +27,7 @@ print("Pvlib version ", pvlib.__version__) print("Pvdeg version ", pvdeg.__version__) - +# %% [markdown] # ### Simple Standoff Calculation # # This is copied from another tutorial called `4 - Standards.ipynb`, please visit this page for a more in depth explanation of the process for a single standoff calculation. @@ -46,25 +36,18 @@ # Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) # -# In[3]: - - -weather_db = "PSM3" +# %% +weather_db = "PSM4" weather_id = (40.633365593159226, -73.9945801019899) # Manhattan, NYC weather_arg = { "api_key": "DEMO_KEY", "email": "user@mail.com", - "names": "tmy", - "attributes": [], "map_variables": True, } WEATHER, META = pvdeg.weather.get(weather_db, weather_id, **weather_arg) - -# In[4]: - - +# %% # simple standoff calculation height1 = pvdeg.standards.standoff(weather_df=WEATHER, meta=META) @@ -83,16 +66,14 @@ print(height1) print(height2) - +# %% [markdown] # ### Defining Correlation Coefficients, Mean and Standard Deviation For Monte Carlo Simulation # # We will leave the list of correlations blank because our variables are not correlated. For a correlated use case visit the `Monte Carlo - Arrhenius.ipynb` tutorial. # # Mean and standard deviation must always be populated if being used to create a dataset. However, you can feed your own correlated or uncorrelated data into the simulate function but column names must be consistent. -# In[5]: - - +# %% # These numbers may not make sense in the context of the problem but work for demonstraiting the process stats = {"X_0": {"mean": 5, "stdev": 3}, "wind_factor": {"mean": 0.33, "stdev": 0.5}} @@ -100,20 +81,15 @@ samples = pvdeg.montecarlo.generateCorrelatedSamples(corr_coeff, stats, 500) - -# In[6]: - - +# %% print(samples) - +# %% [markdown] # #### Standoff Monte Carlo Inputs # # When using the pvdeg.montecarlo.simulate() function on a target function all of the target function's required arguments must still be given. Our non-changing arguments will be stored in a dictionary. The randomized monte carlo input data will also be passed to the target function via the simulate function. All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary, -# In[28]: - - +# %% # defining arguments to pass to the target function, standoff() in this case function_kwargs = { "weather_df": WEATHER, @@ -122,46 +98,43 @@ "tilt": 0, "temp_model": "sapm", "sky_model": "isotropic", - "conf_0": "insulated_back_glass_polymer", # or is it conf_inf + "conf_0": "insulated_back_glass_polymer", "conf_inf": "open_rack_glass_polymer", "T98": 70, + "irradiance_kwarg": {}, + "conf_0_kwarg": {}, + "conf_inf_kwarg": {}, + "model_kwarg": {}, } # notice how we left off parts we want to use in the monte carlo simulation because they are already contained in the dataframe results = pvdeg.montecarlo.simulate( func=pvdeg.standards.standoff, - correlated_samples=samples, # in this case correlated_samples is a misnomer, they are not required to be correlated + correlated_samples=samples, **function_kwargs, ) - +# %% [markdown] # #### Dealing With Series # Notice how our results are contained in a pandas series instead of a dataframe. # # This means we have to do an extra step to view our results. Run the block below to confirm that our results are indeed contained in a series. And convert them into a simpler dataframe. -# In[29]: - - +# %% print(type(results)) # Convert from pandas Series to pandas DataFrame results_df = pd.concat(results.tolist()).reset_index(drop=True) - -# In[30]: - - +# %% print(results_df) - +# %% [markdown] # ### Viewing Our Data # Let's plot the results using a histogram -# In[34]: - - +# %% bin_edges = np.arange(results_df["x"].min(), results_df["x"].max() + 0.1, 0.05) plt.figure(figsize=(8, 6)) plt.hist( diff --git a/tutorials_and_tools/tutorials_and_tools/Geospatial - Local Scenario.ipynb b/tutorials/04_geospatial/01_local_scenario.ipynb similarity index 100% rename from tutorials_and_tools/tutorials_and_tools/Geospatial - Local Scenario.ipynb rename to tutorials/04_geospatial/01_local_scenario.ipynb diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Geospatial Templates.ipynb b/tutorials/04_geospatial/02_geospatial_templates.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Geospatial Templates.ipynb rename to tutorials/04_geospatial/02_geospatial_templates.ipynb index 02e9c82ec..95cbdc1df 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Geospatial Templates.ipynb +++ b/tutorials/04_geospatial/02_geospatial_templates.ipynb @@ -9,7 +9,8 @@ "iopub.status.busy": "2025-10-17T21:51:06.498163Z", "iopub.status.idle": "2025-10-17T21:51:15.430153Z", "shell.execute_reply": "2025-10-17T21:51:15.429145Z" - } + }, + "lines_to_next_cell": 2 }, "outputs": [], "source": [ @@ -17,7 +18,7 @@ "from pvdeg import TEST_DATA_DIR\n", "import pandas as pd\n", "import os\n", - "import xarray as xr\n" + "import xarray as xr" ] }, { @@ -27,7 +28,7 @@ "# Geospatial Templates\n", "\n", "When running a geospatial analysis using `pvdeg.geospatial.analysis` on arbitary `pvdeg` functions you will need to specify a template for the shape of the output data. This is because the input data comes with dimensions of gid and time while the output will have data in a different shape usually corresonding to coordinates.\n", - "- gid, identification number corresponding to an NSRDB datapoint's location \n", + "- gid, identification number corresponding to an NSRDB datapoint's location\n", "- time, timeseries corresponding to the hourly time indicies of NSRDB datapoint's yearly meteorological data.\n", "\n", "Follow the steps below to see how we generate templates before running the analysis.\n", @@ -57,7 +58,8 @@ "iopub.status.busy": "2025-10-17T21:51:15.430153Z", "iopub.status.idle": "2025-10-17T21:51:15.759623Z", "shell.execute_reply": "2025-10-17T21:51:15.759623Z" - } + }, + "lines_to_next_cell": 2 }, "outputs": [ { @@ -489,7 +491,7 @@ "# Use xarray to open NetCDF file instead of pickle\n", "geo_weather = xr.open_dataset(os.path.join(TEST_DATA_DIR, \"summit-weather.nc\"))\n", "\n", - "geo_weather\n" + "geo_weather" ] }, { @@ -498,9 +500,9 @@ "source": [ "# Creating Templates Manually\n", "\n", - "`pvdeg.geospatial.ouput_template` we can produce a template for our result data. \n", + "`pvdeg.geospatial.ouput_template` we can produce a template for our result data.\n", "\n", - "We need to do this because different functions return different types of values, some return multiple values as tuples, some return only single numerics, others return timeseries results. We need to specify the shape of our data to create an output xarray dataset. " + "We need to do this because different functions return different types of values, some return multiple values as tuples, some return only single numerics, others return timeseries results. We need to specify the shape of our data to create an output xarray dataset." ] }, { @@ -513,8 +515,8 @@ "\n", "Say we want to estimate the 98áµ—Ê° percential temperature for the module at the given tilt, azimuth, and x_eff. `PVDeg` has a function to do this, `pvdeg.standards.T98_estimate` BUT it doesn't have a preset geospatial template. We will need to make one.\n", "\n", - "- look at the function return values. \n", - "From the docstring we can see that `T98_estimate` only has one return value. IMPORTANT, this value is a single float, NOT a timeseries. This means our output shape will only be dependent on the input identifier and NOT time. \n", + "- look at the function return values.\n", + "From the docstring we can see that `T98_estimate` only has one return value. IMPORTANT, this value is a single float, NOT a timeseries. This means our output shape will only be dependent on the input identifier and NOT time.\n", "\n", "Therefore we will map the output variable `T98` to the location identifier `gid` using a dictionary with `str: tuple` mappings.\n", "\n", @@ -652,7 +654,7 @@ "source": [ "## Glass Glass Estimated Module Temperature\n", "\n", - "Now we want to calculate geospatial timeseries temperature values for a module using `pvdeg.temperature.module`. This is not super practical because all `pvdeg` functions that need to use tempeature for their calculations preform the temperature calculation internally, this is just for show. \n", + "Now we want to calculate geospatial timeseries temperature values for a module using `pvdeg.temperature.module`. This is not super practical because all `pvdeg` functions that need to use tempeature for their calculations preform the temperature calculation internally, this is just for show.\n", "\n", "This calculation differs from the above because the temperature functions return the module temperature in a timeseries format. So we care about 2 dimensions, location identifier and TIME." ] diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geographical Features.ipynb b/tutorials/04_geospatial/03_scenario_geographical_features.ipynb similarity index 98% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geographical Features.ipynb rename to tutorials/04_geospatial/03_scenario_geographical_features.ipynb index 80ce96707..f565184aa 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geographical Features.ipynb +++ b/tutorials/04_geospatial/03_scenario_geographical_features.ipynb @@ -74,8 +74,8 @@ "\n", "Identify moutains by using the function below. There are currently 2 methods to identify mountains from our data. Both will add a column to our `GeospatialScenario.meta_data` attribute called `mountain` containing boolean values that represent if a data point is in/near mountains or not near mountains.\n", "\n", - "- `classify_mountains_weights` uses k nearest neighbors search and calculates local changes in height with different methods, the default is the `mean` absolute change compared to a points neighbors, then it assigns a weight to each point. If the remaining points are above the threshold then the percentile argument determines sensitivity to classifying points as mountains (higher weight, more mountainous). Much better than the following option. \n", - "- `classify_mountains_radii` uses 2 nearest neighbors radius searches at each point and compares the average elevation of the inner circle to the avg radius of the circle. " + "- `classify_mountains_weights` uses k nearest neighbors search and calculates local changes in height with different methods, the default is the `mean` absolute change compared to a points neighbors, then it assigns a weight to each point. If the remaining points are above the threshold then the percentile argument determines sensitivity to classifying points as mountains (higher weight, more mountainous). Much better than the following option.\n", + "- `classify_mountains_radii` uses 2 nearest neighbors radius searches at each point and compares the average elevation of the inner circle to the avg radius of the circle." ] }, { @@ -131,7 +131,7 @@ "source": [ "## Identifying mountains within a bounding box.\n", "\n", - "Sometimes we only want information from one area. To allow for more control over the selection process. To pick only one area from an existing superset of data we can use a bounding box. \n", + "Sometimes we only want information from one area. To allow for more control over the selection process. To pick only one area from an existing superset of data we can use a bounding box.\n", "Either provide the top left and bottom right (latitude-longitude) pairs or a 2d numpy array of these pairs which will use the most extreme entries to form the bounding box." ] }, diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geospatial.ipynb b/tutorials/04_geospatial/04_scenario_geospatial.ipynb similarity index 93% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geospatial.ipynb rename to tutorials/04_geospatial/04_scenario_geospatial.ipynb index 8ab8fcd1e..76f700a91 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geospatial.ipynb +++ b/tutorials/04_geospatial/04_scenario_geospatial.ipynb @@ -53,7 +53,7 @@ "metadata": {}, "source": [ "## Add Functions to the Pipeline\n", - "The scenario has a queue of jobs to preform. These are stored in an attribute called ``pipeline``, you can directly update the pipeline but this will bypass the assistance given in creating the job function and parameters. The easiest way to add a job to the pipeline is the ``.updatePipeline`` method. For geospatial analysis, weather and metadata is collected and stored in the scenario at the time of the ``.addLocation`` method call so we do not need to include it below, but if we have other function kwargs to include, they should go in the ``func_params`` argument. \n", + "The scenario has a queue of jobs to preform. These are stored in an attribute called ``pipeline``, you can directly update the pipeline but this will bypass the assistance given in creating the job function and parameters. The easiest way to add a job to the pipeline is the ``.updatePipeline`` method. For geospatial analysis, weather and metadata is collected and stored in the scenario at the time of the ``.addLocation`` method call so we do not need to include it below, but if we have other function kwargs to include, they should go in the ``func_params`` argument.\n", "\n", "Only a few pvdeg functions are currently supported for geospatial analysis. See the docstring for ``.updatePipeline`` to view currently supported functions. ``updatePipeline`` will not let you add unsupported geospatial functions. The ``see_added`` flag allows us to see the new job added to the pipeline." ] @@ -136,8 +136,8 @@ "source": [ "## Example Geospatial Functionality\n", "Many functions are supported for geospatial analysis, here are a few.\n", - "- ``pvdeg.standards.standoff`` \n", - "- ``pvdeg.humidity.module`` \n", + "- ``pvdeg.standards.standoff``\n", + "- ``pvdeg.humidity.module``\n", "- ``pvdeg.letid.calc_letid_outdoors``\n", "\n", "See the Geospatial Templates tutorial for an example on this." @@ -160,12 +160,12 @@ "geospatial_humidity_scenario.addJob(\n", " func=pvdeg.humidity.module,\n", " func_params={\n", - " 'backsheet_thickness': 0.3, # mm, thickness of PET backsheet\n", - " 'back_encap_thickness': 0.46, # mm, thickness of EVA backside encapsulant\n", - " 'encapsulant': 'W001', # EVA encapsulant\n", - " 'backsheet': 'W017' # PET backsheet\n", + " \"backsheet_thickness\": 0.3, # mm, thickness of PET backsheet\n", + " \"back_encap_thickness\": 0.46, # mm, thickness of EVA backside encapsulant\n", + " \"encapsulant\": \"W001\", # EVA encapsulant\n", + " \"backsheet\": \"W017\", # PET backsheet\n", " },\n", - " see_added=True\n", + " see_added=True,\n", ")" ] }, diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Non-uniform Mountain Prefferential Downselect.ipynb b/tutorials/04_geospatial/05_scenario_mountain_downselect.ipynb similarity index 98% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Non-uniform Mountain Prefferential Downselect.ipynb rename to tutorials/04_geospatial/05_scenario_mountain_downselect.ipynb index 81b3771c6..65e344bf7 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Non-uniform Mountain Prefferential Downselect.ipynb +++ b/tutorials/04_geospatial/05_scenario_mountain_downselect.ipynb @@ -74,7 +74,7 @@ "\n", "### Normalization\n", "\n", - "At each metadata point in our dataset we will calculate a weight based on its changes in elevation compared to its neighbors. The higher the weight, the greater the change in elevation from a point's immediate neighbors. The downselection methods and functions use these weights to randomly select a subset of the datapoints, prefferentially selecting those with higher weights. \n", + "At each metadata point in our dataset we will calculate a weight based on its changes in elevation compared to its neighbors. The higher the weight, the greater the change in elevation from a point's immediate neighbors. The downselection methods and functions use these weights to randomly select a subset of the datapoints, prefferentially selecting those with higher weights.\n", "\n", "We have some control over which points get selected because all points' weights must be normalized (mapped from 0 to 1) before downselecting. We can apply a function such as $e^x$ or $\\log x$ to the weights during normalization. This could help change the distribution of weights that are chosen. This could remove points from the mountains and add them to areas with fewer changes in elevation, or vice versa.\n", "\n", @@ -87,7 +87,7 @@ "source": [ "### Providing a KdTree\n", "\n", - "As shown below the lines to create a kdtree are commented out. \n" + "As shown below the lines to create a kdtree are commented out.\n" ] }, { diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Single Location.ipynb b/tutorials/04_geospatial/06_scenario_single_location.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Single Location.ipynb rename to tutorials/04_geospatial/06_scenario_single_location.ipynb index 39c5c3490..7767def65 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Single Location.ipynb +++ b/tutorials/04_geospatial/06_scenario_single_location.ipynb @@ -90,7 +90,7 @@ "A way around this is to provide the weather and metadata in the pipeline job arguments or you can load data from somewhere else and provide it in the same fashion.\n", "\n", "
\n", - "Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) \n", + "Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.)\n", "
" ] }, @@ -117,9 +117,9 @@ "metadata": {}, "source": [ "## Adding A Location\n", - "To add a single point using data from the Physical Solar Model (PSM3), simply feed the scenario a single coordinate in tuple form via the ``addLocation`` method. Currently this is the only way to add a location to a non-geospatial scenario, all of the other arguments are unusable when ``Scenario.geospatial == False``. \n", + "To add a single point using data from the Physical Solar Model (PSM3), simply feed the scenario a single coordinate in tuple form via the ``addLocation`` method. Currently this is the only way to add a location to a non-geospatial scenario, all of the other arguments are unusable when ``Scenario.geospatial == False``.\n", "\n", - "Attempting to add a second location by calling the method again with a different coordinate pair will overwrite the old location data stored in the class instance. " + "Attempting to add a second location by calling the method again with a different coordinate pair will overwrite the old location data stored in the class instance." ] }, { @@ -462,7 +462,7 @@ "source": [ "## Scenario Pipelines\n", "\n", - "The pipeline is a list of tasks called jobs for the scenario to run. We will populate the pipeline with a list of jobs before executing them all at once. \n", + "The pipeline is a list of tasks called jobs for the scenario to run. We will populate the pipeline with a list of jobs before executing them all at once.\n", "\n", "To add a job to the pipeline use the ``updatePipeline`` method. Two examples of adding functions to the pipeline will be shown below." ] @@ -516,9 +516,7 @@ "source": [ "kwargs = {\"wind_factor\": 0.33}\n", "\n", - "simple_scenario.addJob(\n", - " func=pvdeg.fatigue.solder_fatigue, func_kwarg=kwargs\n", - " )" + "simple_scenario.addJob(func=pvdeg.fatigue.solder_fatigue, func_kwarg=kwargs)" ] }, { @@ -526,7 +524,7 @@ "metadata": {}, "source": [ "### Adding a job with weather and metadata from outside of the class\n", - "### Not functional \n", + "### Not functional\n", "\n", "could just directly set weather data with scenario.weather_data = weather and scenario.meta_data = meta but that would only work for all of the jobs in the pipeline\n", "\n", diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Temperature.ipynb b/tutorials/04_geospatial/07_scenario_temperature.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Temperature.ipynb rename to tutorials/04_geospatial/07_scenario_temperature.ipynb index 275218ce7..dcb52d90a 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Temperature.ipynb +++ b/tutorials/04_geospatial/07_scenario_temperature.ipynb @@ -179,7 +179,9 @@ " func_kwarg={\"I_chamber\": 1000, \"temp_chamber\": 30},\n", ")\n", "\n", - "scene_temp.addJob(func=pvdeg.degradation.IwaVantHoff,)" + "scene_temp.addJob(\n", + " func=pvdeg.degradation.IwaVantHoff,\n", + ")" ] }, { @@ -1538,7 +1540,7 @@ "tf = datetime.datetime(1970, 1, 1, 23, 59)\n", "\n", "# Get the first function result dynamically\n", - "function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == 'function']\n", + "function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == \"function\"]\n", "if function_ids:\n", " temp_df = scene_temp.extract(\n", " (\"function\", function_ids[0]), tmy=True, start_time=t0, end_time=tf\n", @@ -1570,7 +1572,7 @@ ], "source": [ "# Get the first function result dynamically for plotting\n", - "function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == 'function']\n", + "function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == \"function\"]\n", "if function_ids:\n", " scene_temp.plot(\n", " (\"function\", function_ids[0]),\n", @@ -2161,7 +2163,7 @@ " parent_dir\n", " / \"tutorials_and_tools\"\n", " / \"tutorials_and_tools\"\n", - " / \"temperature and degradation.json\"\n", + " / \"temperature_and_degradation.json\"\n", ")\n", "\n", "copy = pvdeg.scenario.Scenario.load_json(\n", diff --git a/tutorials/04_geospatial/08_module_standoff_iec63126.ipynb b/tutorials/04_geospatial/08_module_standoff_iec63126.ipynb new file mode 100644 index 000000000..fa4fd4c2a --- /dev/null +++ b/tutorials/04_geospatial/08_module_standoff_iec63126.ipynb @@ -0,0 +1,723 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tools - Module Standoff for IEC TS 63126\n", + "\n", + "### Calculation of module standoff distance according to IEC TS 63126\n", + "\n", + "**Requirements:**\n", + "- Local weather data file or site longitude and latittude\n", + "\n", + "**Objectives:**\n", + "1. Import weather data.\n", + "2. Calculate installation standoff - Level 1 and Level 2.\n", + "3. Calculate $X_{eff}$ from provided module temperature data.\n", + "4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n", + "5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n", + "6. Plot $X_{min}$ for Level 1, Level 2, or a $T_{98}$ for a given region.\n", + "\n", + "**Background:**\n", + "\n", + "This notebook calculates the a minimum effective standoff distance ($X_{eff}$) necessary for roof-mounted PV modules to ensure that the $98^{th}$ percentile operating temperature, $T_{98}$, remains under 70°C for compliance to IEC 61730 and IEC 61215. For higher $T_{98}$ values above 70°C or 80°C testing must be done to the specifications for Level 1 and Level 2 of IEC TS 63126. This method is outlined in the appendix of IEC TS 63126 and is based on the model from *[King 2004] and data from **[Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", + "\n", + "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right), Equation 1 $$\n", + "\n", + "where $T_0$ is the temperature for $X=0$ (insulated-back) and $\\Delta T$ is the temperature difference between an insulated-back ($X=0$) and open-rack mounting configuration ($X=\\infty)$.\n", + "\n", + " We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification.\n", + "\n", + "$*$ D. L. King, W. E. Boyson, and J. A. Kratochvil, \"Photovoltaic array performance model,\" SAND2004-3535, Sandia National Laboratories, Albuquerque, NM, 2004. '\\\n", + "$**$ M. K. Fuentes, \"A simplified thermal model for Flat-Plate photovoltaic arrays,\" United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![alt text](images/T98_70C_standoff_Map.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies\n", + "# and prevent \"ModuleNotFoundError\" in later cells:\n", + "#!pip install pvdeg==0.4.2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pvdeg\n", + "import pandas as pd\n", + "from pvdeg import DATA_DIR\n", + "import dask\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import math\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys\n", + "import platform\n", + "\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)\n", + "print(\"dask version\", dask.__version__)\n", + "print(DATA_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Import Weather Data\n", + "\n", + "The function has these minimum requirements when using a weather data file:\n", + "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level.\n", + "- Site meta-data containing (at least) latitude, longitude, and time zone\n", + "\n", + "Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial \"Weather Database Access.ipynb\" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get data from a supplied data file (Do not use the next box of code if using your own file)\n", + "weather_file = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n", + "WEATHER_df, META = pvdeg.weather.read(weather_file, \"csv\")\n", + "print(META)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This routine will get a meteorological dataset from anywhere in the world where it is available\n", + "# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", + "# weather_id = (35.6754, 139.65) #Tokyo, Japan\n", + "# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n", + "# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n", + "# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n", + "# weather_id = (33.4152, -111.8315) #Mesa, Arizona\n", + "# WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id)\n", + "# print(META)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate Installation Standoff Minimum - Level 1 and Level 2\n", + "\n", + "According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to T₉₈<70°C, <80°C and <90°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of T₉₈<70°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as,\n", + "\n", + "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$\n", + "\n", + "where $T_{98,0}$ is the $98^{th}$ percentile temperature for an insulated back module and $T_{98,inf}$ is the $98^{th}$ percentile temperature for an open rack mounted module.\n", + "\n", + "Once the meterological data has been obtained, the input parameter possibilities are:\n", + "\n", + "- T₉₈ : Does not necessarily need to be set at 70°C or 80°C for IEC TS 63216, you might want to use a different number to compensate for a thermal aspect of the particular system you are considering. The default is 70°C.\n", + "- tilt : tilt from horizontal of PV module. The default is 0°.\n", + "- azimuth : azimuth in degrees from North. The default is 180° for south facing.\n", + "- sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'). The default is 'isotropic'.\n", + "- temp_model : pvlib compatible module temperature model. (Options: 'sapm', 'pvsyst', 'faiman', 'sandia'). The default is 'sapm'.\n", + "- conf_0 : Temperature model for hotest mounting configuration. Default is \"insulated_back_glass_polymer\".\n", + "- conf_inf : Temperature model for open rack mounting. Default is \"open_rack_glass_polymer\".\n", + "- x_0 : thermal decay constant [cm] (see documentation). The default is 6.5 cm.\n", + "- wind_factor : Wind speed power law correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM). The default is 0.33." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is the minimum function call. It defaults to horizontal tilt and T₉₈=70°C." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "standoff = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META)\n", + "print(pvdeg.standards.interpret_standoff(standoff))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is a full function call for both T₉₈=70°C and 80°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "standoff_1 = pvdeg.standards.standoff(\n", + " weather_df=WEATHER_df,\n", + " meta=META,\n", + " T98=70,\n", + " tilt=META[\"latitude\"],\n", + " azimuth=None,\n", + " sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " conf_0=\"insulated_back_glass_polymer\",\n", + " conf_inf=\"open_rack_glass_polymer\",\n", + " x_0=6.5,\n", + " wind_factor=0.33,\n", + ")\n", + "print(\"First calculation standoff = \", \"%.1f\" % standoff_1[\"x\"].iloc[0], \" cm.\")\n", + "standoff_2 = pvdeg.standards.standoff(\n", + " weather_df=WEATHER_df,\n", + " meta=META,\n", + " T98=80,\n", + " tilt=META[\"latitude\"],\n", + " azimuth=None,\n", + " sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " conf_0=\"insulated_back_glass_polymer\",\n", + " conf_inf=\"open_rack_glass_polymer\",\n", + " x_0=6.5,\n", + " wind_factor=0.33,\n", + ")\n", + "print(\"Second calculation standoff = \", \"%.1f\" % standoff_2[\"x\"].iloc[0], \" cm.\")\n", + "print(pvdeg.standards.interpret_standoff(standoff_1=standoff_1, standoff_2=standoff_2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Calculate $X_{eff}$ from provided module temperature data.\n", + "\n", + "To do this calculation, one must use a set of data with:\n", + " - meterological irradiance data sufficient to calculate the POA irradiance (DHI, GHI, and DNI),\n", + " - ambient temperature data,\n", + " - wind speed at module height, (wind_factor=0.33 will be used unless otherwise specified)\n", + " - temperature measurements of the module in the test system. Ideally this would be measured under a worst case scenario that maximizes the module temperature for a given site,\n", + " - geographic meta data including longitude and latitude,\n", + "\n", + "To create a weather file of your own, copy the format of the example file 'xeff_demo.csv'. This is formatted with the first row containing meta data variable names, the second row containing the corresponding values, the third row containing meteorological data headers, and all the remaining rows containing the meteorological data.\n", + "\n", + "To do this calculation, one should also filter the data to remove times when the sun is not shining or when snow is likely to be on the module. The recommendations and programmed defaults are to use poa_min=100 W/m² and data when the minimum ambient temperature t_amb_min=0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the weather file\n", + "weather_file = os.path.join(DATA_DIR, \"xeff_demo.csv\")\n", + "xeff_weather, xeff_meta = pvdeg.weather.read(weather_file, \"csv\")\n", + "# Pull measured temperature and calculate theoretical insulated back module temperature and open rack module temperature\n", + "T_0, T_inf, xeff_poa = pvdeg.standards.eff_gap_parameters(\n", + " weather_df=xeff_weather,\n", + " meta=xeff_meta,\n", + " sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " conf_0=\"insulated_back_glass_polymer\",\n", + " conf_inf=\"open_rack_glass_polymer\",\n", + " wind_factor=0.33,\n", + ")\n", + "# Now calculate X_eff.\n", + "x_eff = pvdeg.standards.eff_gap(\n", + " T_0,\n", + " T_inf,\n", + " xeff_weather[\"module_temperature\"],\n", + " xeff_weather[\"temp_air\"],\n", + " xeff_poa[\"poa_global\"],\n", + " x_0=6.5,\n", + " poa_min=100,\n", + " t_amb_min=0,\n", + ")\n", + "print(\"The effective standoff for this system is\", \"%.1f\" % x_eff, \"cm.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n", + "\n", + "Equation 2 can be reorganized as,\n", + "\n", + "$$ T_{98} = T_{98,0} -( T_{98,0}- T_{98,inf}) \\left(1-e^{-\\frac{x_{eff}}{x_{0}}}\\right), Equation 3 $$\n", + "\n", + "and used to calculate the $98^{th}$ percential temperature, $T_{98}$, for a PV system having a given effective standoff height, $X_{eff}$, for an arbitrarily oriented module. Here, $T_{98,0}$ is the $98^{th}$ percentile for an insulated-back module and $T_{98,inf}$ is the $98^{th}$ percentile for a rack-mounted module. The input parameter possibilities are the same as shown in Objective #2 above, but the example below uses the default parameters. The actual tilt [degrees], azimuth [degrees] and $X_{eff}$ [cm] can be modifed as desired." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This is the minimal function call using the common default settings to estimate T₉₈.\n", + "T_98 = pvdeg.standards.T98_estimate(\n", + " weather_df=WEATHER_df, meta=META, tilt=-META[\"latitude\"], azimuth=None, x_eff=10\n", + ")\n", + "print(\"The 98áµ—Ê° percential temperature is estimated to be\", \"%.1f\" % T_98, \"°C.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This code will calculate the temperature for an arbitrary x_eff distance. Either set of kwargs can be modified and use.\n", + "# irradiance_kwarg ={\n", + "# \"axis_tilt\": None,\n", + "# \"axis_azimuth\": None,\n", + "# \"x_eff\": 10,\n", + "# \"module_mount\": '1_axis'}\n", + "irradiance_kwarg = {\n", + " \"tilt\": META[\"latitude\"],\n", + " \"azimuth\": None,\n", + " \"x_eff\": 10,\n", + " \"module_mount\": \"fixed\",\n", + "}\n", + "\n", + "T_xeff = pvdeg.standards.x_eff_temperature_estimate(\n", + " weather_df=WEATHER_df, meta=META, **irradiance_kwarg\n", + ")\n", + "\n", + "print(\n", + " \"The 98áµ—Ê° percential temperature is estimated to be\",\n", + " \"%.1f\" % np.percentile(T_xeff, 98),\n", + " \"°C.\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n", + "\n", + "The temperature of a system is affected by the orientation. This section will scan all possible tilts and azimuths calculating the minimum standoff distance for a given $T_{98}$. Similar additional factors as above can also be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $T_{98}$=70°C, the boundary between Level 0 and Level 1 requirements. The temperature model information given below is unnecessary as these are default values that would get populated automatically. However, they were included here for clarity into a standard practice as per IEC TS 63126.\n", + "\n", + "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Scans through all the azimuth and tilt running the minimum standoff calculation\n", + "# Set up keyword parameters for the calculation\n", + "\n", + "kwarg_x = dict(\n", + " sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " conf_0=\"insulated_back_glass_polymer\",\n", + " conf_inf=\"open_rack_glass_polymer\",\n", + " T98=70,\n", + " x_0=6.5,\n", + " wind_factor=0.33,\n", + ")\n", + "# Run the calculation\n", + "x_azimuth_step = 10\n", + "x_tilt_step = 10\n", + "standoff_series = pvdeg.utilities.tilt_azimuth_scan(\n", + " weather_df=WEATHER_df,\n", + " meta=META,\n", + " tilt_step=x_tilt_step,\n", + " azimuth_step=x_azimuth_step,\n", + " func=pvdeg.standards.standoff_x,\n", + " **kwarg_x,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n", + "- cmap=\"Spectral_r\": Change to have different colors\n", + "- plt.title : This will change the plot title.\n", + "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n", + "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n", + "- vmin=0 : This controls the minimum of the depth scale.\n", + "- v_ticks=37 : This changes the number of vertical tick marks\n", + "- h_ticks=10 : This changes the number of horizontal tick marks\n", + "- Unblock the last two lines to ouput the plot as an *.png image file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "standoff_series_df = pd.DataFrame(\n", + " {\n", + " \"Tilt\": standoff_series[:, 0],\n", + " \"Azimuth\": standoff_series[:, 1],\n", + " \"Xₘᵢₙ\": standoff_series[:, 2],\n", + " }\n", + ")\n", + "x_fig = plt.figure(figsize=(16, 4))\n", + "plt.title(\n", + " r\"Plot of $\\it{Xₘᵢₙ}$ for all orientations for $\\it{T₉₈}$=\"\n", + " + \"%.0f\" % kwarg_x[\"T98\"]\n", + " + \"°C.\",\n", + " fontsize=15,\n", + " y=1.08,\n", + ")\n", + "x_fig = sns.heatmap(\n", + " standoff_series_df.pivot(index=\"Tilt\", columns=\"Azimuth\", values=\"Xₘᵢₙ\"),\n", + " cbar_kws={\"label\": \"Xₘᵢₙ\", \"format\": \"%.0f\", \"pad\": 0.02},\n", + " cmap=\"Spectral_r\",\n", + " vmin=0,\n", + " vmax=None,\n", + ")\n", + "\n", + "h_ticks = 37\n", + "x_number = math.ceil(360 / x_azimuth_step) + 1\n", + "x_ticks = [\n", + " (x * (360 / (h_ticks - 1)) / x_azimuth_step + 0.5) for x in range(h_ticks - 1)\n", + "]\n", + "x_labels = [(\"%.0f\" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)]\n", + "x_ticks.append(x_number - 0.5)\n", + "x_fig.set_xticks(x_ticks)\n", + "x_fig.set_xticklabels(x_labels, rotation=90)\n", + "\n", + "v_ticks = 10\n", + "y_number = math.ceil(90 / x_tilt_step) + 1\n", + "y_ticks = [(x * (90 / (v_ticks - 1)) / x_tilt_step + 0.5) for x in range(v_ticks - 1)]\n", + "y_labels = [(\"%.0f\" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)]\n", + "y_ticks.append(y_number - 0.5)\n", + "x_fig.set_yticks(y_ticks)\n", + "x_fig.set_yticklabels(y_labels, rotation=0)\n", + "\n", + "x_fig.set_xlabel(\"Azimuth [°]\", fontsize=15, labelpad=10)\n", + "x_fig.set_ylabel(\"Tilt [°]\", fontsize=15)\n", + "x_fig.figure.axes[-1].set_ylabel(r\"$\\it{Xₘᵢₙ}$ [cm]\", size=15)\n", + "x_fig.invert_yaxis()\n", + "\n", + "output_folder = os.path.join(\n", + " os.path.dirname(os.path.dirname(os.getcwd())), \"TEMP\", \"results\"\n", + ")\n", + "try:\n", + " os.makedirs(output_folder)\n", + "except OSError as error:\n", + " print(error)\n", + "\n", + "plt.savefig(\n", + " os.path.join(output_folder, \"Standoff_Scan.png\"), bbox_inches=\"tight\"\n", + ") # Creates an image file of the standoff plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Plot $T_{98}$ for all azimuth and tilt for a given $X_{eff}$.\n", + "\n", + "The temperature of a system is affected by the orientation and the effective standoff, $X_{eff}$, of the system. This section will scan all possible tilts and azimuths calculating the $T_{98}$ for a given $X_{eff}$. As above, additional factors can be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $X_{eff}$=10 cm, a common effective standoff distance on a rooftop system. A value of $X_{eff}$=None will run the calculations for an open rack system and $X_{eff}$=0 for an insulated-back system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Scans through all the azimuth and tilt running the 98áµ—Ê° percentile temperature calculation.\n", + "# Set up keyword parameters for the calculation\n", + "kwarg_T = dict(\n", + " sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " conf_0=\"insulated_back_glass_polymer\",\n", + " conf_inf=\"open_rack_glass_polymer\",\n", + " x_eff=5,\n", + " x_0=6.5,\n", + " wind_factor=0.33,\n", + ")\n", + "# Run the calculation\n", + "T_azimuth_step = 10\n", + "T_tilt_step = 10\n", + "T98_series = pvdeg.utilities.tilt_azimuth_scan(\n", + " weather_df=WEATHER_df,\n", + " meta=META,\n", + " tilt_step=T_tilt_step,\n", + " azimuth_step=T_azimuth_step,\n", + " func=pvdeg.standards.T98_estimate,\n", + " **kwarg_T,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n", + "- cmap=\"Spectral_r\": Change to have different colors\n", + "- plt.title : This will change the plot title.\n", + "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n", + "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n", + "- vmin=None : This controls the minimum of the depth scale.\n", + "- v_ticks=37 : This changes the number of vertical tick marks\n", + "- h_ticks=10 : This changes the number of horizontal tick marks\n", + "- Unblock the last two lines to ouput the plot as an *.png image file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This produces the plot of the data\n", + "T98_series_df = pd.DataFrame(\n", + " {\"Tilt\": T98_series[:, 0], \"Azimuth\": T98_series[:, 1], \"T₉₈\": T98_series[:, 2]}\n", + ")\n", + "T98_fig = plt.figure(figsize=(16, 4))\n", + "if kwarg_T[\"x_eff\"] == None:\n", + " plt.title(\n", + " r\"Plot of $\\it{T₉₈}$ for all orientations for an open-rack mounting.\",\n", + " fontsize=15,\n", + " y=1.08,\n", + " )\n", + "else:\n", + " plt.title(\n", + " r\"Plot of $\\it{T₉₈}$ for all orientations for $X_{eff}$=\"\n", + " + \"%.0f\" % kwarg_T[\"x_eff\"]\n", + " + \" cm.\",\n", + " fontsize=15,\n", + " y=1.08,\n", + " )\n", + "T98_fig = sns.heatmap(\n", + " T98_series_df.pivot(index=\"Tilt\", columns=\"Azimuth\", values=\"T₉₈\"),\n", + " cbar_kws={\"label\": \"Xₘᵢₙ\", \"format\": \"%.0f\", \"pad\": 0.02},\n", + " cmap=\"Spectral_r\",\n", + " vmin=None,\n", + " vmax=None,\n", + ")\n", + "\n", + "h_ticks = 37\n", + "x_number = math.ceil(360 / T_azimuth_step) + 1\n", + "x_ticks = [\n", + " (x * (360 / (h_ticks - 1)) / T_azimuth_step + 0.5) for x in range(h_ticks - 1)\n", + "]\n", + "x_labels = [(\"%.0f\" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)]\n", + "x_ticks.append(x_number - 0.5)\n", + "T98_fig.set_xticks(x_ticks)\n", + "T98_fig.set_xticklabels(x_labels, rotation=90)\n", + "\n", + "v_ticks = 10\n", + "y_number = math.ceil(90 / T_tilt_step) + 1\n", + "y_ticks = [(x * (90 / (v_ticks - 1)) / T_tilt_step + 0.5) for x in range(v_ticks - 1)]\n", + "y_labels = [(\"%.0f\" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)]\n", + "y_ticks.append(y_number - 0.5)\n", + "T98_fig.set_yticks(y_ticks)\n", + "T98_fig.set_yticklabels(y_labels, rotation=0)\n", + "\n", + "T98_fig.set_xlabel(\"Azimuth [°]\", fontsize=15, labelpad=10)\n", + "T98_fig.set_ylabel(\"Tilt [°]\", fontsize=15)\n", + "T98_fig.figure.axes[-1].set_ylabel(r\"$\\it{T₉₈}$ [°C]\", size=15)\n", + "T98_fig.invert_yaxis()\n", + "\n", + "plt.savefig(\n", + " os.path.join(output_folder, \"T98_Scan.png\"), bbox_inches=\"tight\"\n", + ") # Creates an image file of the standoff plot\n", + "plt.show(T98_fig)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Plot $X_{min}$ for a $T_{98}$, and plot $T_{98}$ for a given region.\n", + "\n", + "This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance.\n", + "For more in-depth instructions on doing this, look at the tutorial \"Scenario - Geospatial.ipynb\" here in PVDeg.\n", + "\n", + "Step #1: Create an object, \"geospatial_standoff_scenario\" to be used to run the computations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario = pvdeg.GeospatialScenario(\n", + " name=\"standoff geospatial\",\n", + " geospatial=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step #2: Identifies a subset of locations from the database to run the computations.\n", + "Specifically all are from the NSRDB." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario.addLocation(\n", + " state=\"Colorado\", county=\"Summit\"\n", + ") # Identifies a subset of locations from the database to run the computations. Specifically all are from the NSRDB." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step #3: indicate which function will be run. Here the default is the standoff calculation, but it could be any other function with a key word argument dictionary.\n", + "Here the 98th percential temperature is defined as 70C, but any arbitrary value can be specified." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario.addJob(\n", + " func=pvdeg.standards.standoff, func_params={\"T98\": 70}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step #4: Run the scenario" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step #5: Create a plot of the standoff calculation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario.plot_world(\"x\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario.plot_world(\"T98_inf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Save data outputs.\n", + "\n", + "This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation and is blocked by default. First check that the output folder exists, then unblock the code for data you would like to save." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(\"Your results will be stored in %s\" % output_folder)\n", + "print(\"The folder must already exist or the file will not be created\")\n", + "\n", + "pvdeg.weather.write(\n", + " data_df=WEATHER_df,\n", + " metadata=META,\n", + " savefile=os.path.join(output_folder, \"WeatherFile.csv\"),\n", + ") # Writes the meterological data to an *.csv file.\n", + "\n", + "pd.DataFrame(standoff_series_df).to_csv(\n", + " os.path.join(output_folder, \"Standoff_Scan.csv\")\n", + ") # Writes a file with the Tilt and Azimuth scan calculations of standoff.\n", + "\n", + "pd.DataFrame(T98_series_df).to_csv(\n", + " os.path.join(output_folder, \"T98_Scan.csv\")\n", + ") # Writes a file with the Tilt and Azimuth scan calculations of T98." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pvdeg", + "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": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/04_geospatial/README.md b/tutorials/04_geospatial/README.md new file mode 100644 index 000000000..c593febc0 --- /dev/null +++ b/tutorials/04_geospatial/README.md @@ -0,0 +1,36 @@ +# Geospatial and Scenario Analysis + +Tutorials for geospatial analysis and scenario-based modeling. + +## Tutorials + +### Local Execution +- **local_scenario.ipynb** - Geospatial analysis for local scenarios (no HPC required) + +### HPC Required Tutorials +The following tutorials require access to High Performance Computing (HPC) resources: + +- **geospatial_templates.ipynb** - Templates for geospatial analysis workflows +- **scenario_geographical_features.ipynb** - Incorporating geographical features into scenarios +- **scenario_geospatial.ipynb** - Large-scale geospatial scenario analysis +- **scenario_mountain_downselect.ipynb** - Non-uniform mountain terrain downselection +- **scenario_single_location.ipynb** - Detailed single-location scenario analysis +- **scenario_temperature.ipynb** - Temperature-based scenario modeling +- **module_standoff_iec63126.ipynb** - Module standoff calculations for IEC TS 63126 standard + +## HPC Access + +âš ï¸ **Note**: Most tutorials in this folder require HPC access. These tutorials involve: +- Large-scale geospatial computations +- Multi-location scenario analyses +- Distributed computing workflows + +If you don't have HPC access, start with **local_scenario.ipynb** which runs on standard hardware. + +## Topics Covered + +- Geospatial degradation mapping +- Multi-site scenario generation +- Geographic feature analysis +- Climate-based scenario development +- Terrain-specific modeling diff --git a/tutorials/04_geospatial/scripts/01_local_scenario.py b/tutorials/04_geospatial/scripts/01_local_scenario.py new file mode 100644 index 000000000..cf1dfd1f6 --- /dev/null +++ b/tutorials/04_geospatial/scripts/01_local_scenario.py @@ -0,0 +1,18 @@ +# %% [markdown] +# # Running Geospatial Analysis Locally # +# +# Author: Tobin Ford +# Email : tobin.ford@nrel.gov + +# %% [markdown] +# ## Use Case +# - If you are unable to access the NSRDB via NREL HPC or AWS + +# %% [markdown] +# ## Sourcing Data ## +# +# On the [NSRDB Dataviewer](https://nsrdb.nrel.gov/data-viewer), you can use the area querys to collect information from the area you want. Only tested with USA & Americas - Typical Meteorological Year so far. Use the dropdowns to select the attributes you will need to run calculations. Select a year and follow the instructions to get the zip file containing the weather data csv's for each location in the selected region. +# +# ![NSRDB Dataviewer](./images/NSRDB-data-viewer.png) + +# %% diff --git a/tutorials/04_geospatial/scripts/02_geospatial_templates.py b/tutorials/04_geospatial/scripts/02_geospatial_templates.py new file mode 100644 index 000000000..e6c124515 --- /dev/null +++ b/tutorials/04_geospatial/scripts/02_geospatial_templates.py @@ -0,0 +1,278 @@ +# %% +import pvdeg +from pvdeg import TEST_DATA_DIR +import pandas as pd +import os +import xarray as xr + + +# %% [markdown] +# # Geospatial Templates +# +# When running a geospatial analysis using `pvdeg.geospatial.analysis` on arbitary `pvdeg` functions you will need to specify a template for the shape of the output data. This is because the input data comes with dimensions of gid and time while the output will have data in a different shape usually corresonding to coordinates. +# - gid, identification number corresponding to an NSRDB datapoint's location +# - time, timeseries corresponding to the hourly time indicies of NSRDB datapoint's yearly meteorological data. +# +# Follow the steps below to see how we generate templates before running the analysis. +# +# The only functions where this is not required are currently `pvdeg.standards.standoff`, `pvdeg.humidity.moduke` and, `letid.calc_letid_outdoors` as they are predefined within the package. + +# %% [markdown] +# ## Loading Geospatial Data +# +# This step skips over making the `pvdeg.weather.get` call with `geospatial == True`. See the [Duramat Demo](./DuraMAT%20Live%20Demo.ipynb) for information on how to do this traditionally. +# +# We can also use a `GeospatialScenario` object. See the [Geospatial Scenario Tutorial](./Scenario%20-%20Geospatial.ipynb) for more information on how to use this approach. +# +# *The cell below loads a pickled xarray object, this is not the best way to do this. xarray datasets should be stored as `.nc` - netcdf files* + +# %% +geo_meta = pd.read_csv(os.path.join(TEST_DATA_DIR, "summit-meta.csv"), index_col=0) + +# Use xarray to open NetCDF file instead of pickle +geo_weather = xr.open_dataset(os.path.join(TEST_DATA_DIR, "summit-weather.nc")) + +geo_weather + + +# %% [markdown] +# # Creating Templates Manually +# +# `pvdeg.geospatial.ouput_template` we can produce a template for our result data. +# +# We need to do this because different functions return different types of values, some return multiple values as tuples, some return only single numerics, others return timeseries results. We need to specify the shape of our data to create an output xarray dataset. + +# %% [markdown] +# ## Examples +# +# ## 98áµ—Ê° module percential temperature at Standoff Height +# +# Say we want to estimate the 98áµ—Ê° percential temperature for the module at the given tilt, azimuth, and x_eff. `PVDeg` has a function to do this, `pvdeg.standards.T98_estimate` BUT it doesn't have a preset geospatial template. We will need to make one. +# +# - look at the function return values. +# From the docstring we can see that `T98_estimate` only has one return value. IMPORTANT, this value is a single float, NOT a timeseries. This means our output shape will only be dependent on the input identifier and NOT time. +# +# Therefore we will map the output variable `T98` to the location identifier `gid` using a dictionary with `str: tuple` mappings. +# +# *IMPORTANT: you must use the syntax below where the variable maps to a tuple of the coordinates. in this case there needs to be a trailing comma in the tuple or python will iterate over the characters in the tuple instead of the elements. See further examples to alleviate confusion.* + +# %% +# define output shape +shapes = { + "T98": ( + "gid", + ) # one return value at each datapoint, only dependent on datapoint, not time +} + +# create xarray template for output to be populated when analysis is run +template = pvdeg.geospatial.output_template( + ds_gids=geo_weather, + shapes=shapes, +) + +# %% +geo_estimate_temp = pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.standards.T98_estimate, + template=template, +) + +# %% [markdown] +# ## Glass Glass Estimated Module Temperature +# +# Now we want to calculate geospatial timeseries temperature values for a module using `pvdeg.temperature.module`. This is not super practical because all `pvdeg` functions that need to use tempeature for their calculations preform the temperature calculation internally, this is just for show. +# +# This calculation differs from the above because the temperature functions return the module temperature in a timeseries format. So we care about 2 dimensions, location identifier and TIME. + +# %% +# define output shape +shapes = { + "module_temperature": ( + "gid", + "time", + ) # one return value at each datapoint, only dependent on datapoint, not time +} + +# create xarray template for output to be populated when analysis is run +temperature_template = pvdeg.geospatial.output_template( + ds_gids=geo_weather, + shapes=shapes, +) + +# %% +geo_temperature_res = pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.module, + template=temperature_template, # use the template we created + conf="open_rack_glass_glass", # provide kwargs for function here +) + +# %% +# plot the temperature at ONE of the geospatial analysis result locations but we have calculated all of these. +import matplotlib.pyplot as plt + +module_temps = ( + geo_temperature_res["module"].sel(latitude=39.89, longitude="-106.42").values +) + +plt.plot(module_temps) + +# %% [markdown] +# ## Self Explaining Code +# +# If we are looking at adding templates for other functions, we can also look at the 3 presaved templates for existing `pvdeg` functions. Visit [pvdeg.geospatial.template_parameters](../../pvdeg/geospatial.py) and inspect this function to see how these different target functions utilize templates and shapes. + +# %% [markdown] +# # Creating Templates Programatically +# +# We can use `pvdeg.geospatial.autotemplate` to generate a template for a given function. This can return a bad result which will fail or work improperly when running `pvdeg.geospatial.analysis` with the generated template. Results should be scrutinized to make sure they are the right format. + +# %% [markdown] +# ## Examples Below +# Steps +# - Create template using autotemplating function. Pulls in information about function to determine shape of output. Not usable on functions with ambigious return types. +# - Call geospatial analysis function using template + +# %% [markdown] +# ## Geospatial Cell Temperature Calculation +# As shown below, we have two options, we can choose to provide a template that is generated by a function which supports autotemplating. Or we can provide the function to `geospatial.analysis` and let it generate a template internally. +# +# ### Providing a Template with `Geospatial.auto_template` + +# %% +# create a template using auto_template for the desired function +cell_temp_template = pvdeg.geospatial.auto_template( + func=pvdeg.temperature.cell, ds_gids=geo_weather +) + +# run the geospatial analysis with the template +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.cell, + template=cell_temp_template, +) + +# %% [markdown] +# ### Analysis Without Providing a Template +# +# If a function is supported by `geospatial.auto_template` we do not need to create a template outside of the function as shown in the cell above. We can simply pass the function to `geospatial.analysis` and it will create a template for us. + +# %% +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.cell, +) + +# %% [markdown] +# ### Geospatial Module Temperature Calculation + +# %% +module_temp_template = pvdeg.geospatial.auto_template( + func=pvdeg.temperature.module, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.module, + template=module_temp_template, +) + +# %% [markdown] +# ### Geospatial Solar Position Calculation + +# %% +solar_position_template = pvdeg.geospatial.auto_template( + func=pvdeg.spectral.solar_position, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.spectral.solar_position, + template=solar_position_template, +) + +# %% [markdown] +# ### Geospatial POA Irradiance Calculation + +# %% +poa_irradiance_template = pvdeg.geospatial.auto_template( + func=pvdeg.spectral.poa_irradiance, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.spectral.poa_irradiance, + template=poa_irradiance_template, +) + +# %% [markdown] +# ### Geospatial 98th Percentile Operating Temperature Calculation + +# %% +standoff_template = pvdeg.geospatial.auto_template( + func=pvdeg.standards.T98_estimate, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.standards.T98_estimate, + template=standoff_template, +) + +# %% [markdown] +# ### Geospatial Module Humidity Calculation + +# %% +humidity_template = pvdeg.geospatial.auto_template( + func=pvdeg.humidity.module, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.humidity.module, + template=humidity_template, + backsheet_thickness=0.3, + back_encap_thickness=0.5, +) + +# %% [markdown] +# ### Geospatial IwaVantHoff Environment Characterization Calculation + +# %% +iwa_vant_hoff_template = pvdeg.geospatial.auto_template( + func=pvdeg.degradation.IwaVantHoff, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.degradation.IwaVantHoff, + template=iwa_vant_hoff_template, +) + +# %% [markdown] +# ### Geospatial Edge Seal Width Calculation + +# %% +edge_seal_template = pvdeg.geospatial.auto_template( + func=pvdeg.design.edge_seal_width, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.design.edge_seal_width, + template=edge_seal_template, +) + +# %% +edge_seal_template diff --git a/tutorials/04_geospatial/scripts/03_scenario_geographical_features.py b/tutorials/04_geospatial/scripts/03_scenario_geographical_features.py new file mode 100644 index 000000000..085006835 --- /dev/null +++ b/tutorials/04_geospatial/scripts/03_scenario_geographical_features.py @@ -0,0 +1,112 @@ +# %% +import pvdeg +import numpy as np + +# %% [markdown] +# ## Create Scenario and Populate Location +# The default geospatial datapoints for addLocation are from the Americas satellite. This is the only data we are interested in for this case so don't specify any location arguments. +# To speed up calculations we will downsample to get coarser data. The downsampling function is not linear. A downsample factor of 10 takes us from 2018267 entries to only 5109. + +# %% +features = pvdeg.GeospatialScenario(name="finding-features") + +features.addLocation(downsample_factor=10) + +# %% [markdown] +# ## Intro to KDTrees +# +# A K dimensional tree is a datastructure for organizing n points in a k dimensional space. They are often used for nearest neighbors searches and many ML algorithms. Letting k = 2, our dimensions will be latitude and longitude. This is much faster than iterating over tabular data structures to find neighbors. The function to create a kdtree below requires scikit learn (also known as sklearn), a python machine learning library which is not included in the pvdeg dependency list so you will have to install it independently. Scipy also has a kdtree class but it is much slower. Depending on the number of datapoints the tree build time could be quite long but for our purposes, the cell below will be quick. + +# %% +tree = pvdeg.geospatial.meta_KDtree(meta_df=features.meta_data) + +# %% [markdown] +# ## Remaining Points After Downsampling +# +# Use `plot_coords` to see all latitude longitude coordinate pairs included in a scenario's metadata. You can provide corners of a bounding box to change the extent of the matplotlib plot. + +# %% +features.plot_coords() + +# %% [markdown] +# # Identifying Mountains from Geospatial Data I +# +# Identify moutains by using the function below. There are currently 2 methods to identify mountains from our data. Both will add a column to our `GeospatialScenario.meta_data` attribute called `mountain` containing boolean values that represent if a data point is in/near mountains or not near mountains. +# +# - `classify_mountains_weights` uses k nearest neighbors search and calculates local changes in height with different methods, the default is the `mean` absolute change compared to a points neighbors, then it assigns a weight to each point. If the remaining points are above the threshold then the percentile argument determines sensitivity to classifying points as mountains (higher weight, more mountainous). Much better than the following option. +# - `classify_mountains_radii` uses 2 nearest neighbors radius searches at each point and compares the average elevation of the inner circle to the avg radius of the circle. + +# %% +features.classify_mountains_weights(kdtree=tree) + +# %% [markdown] +# # Identifing Mountains from Geospatial Data II +# +# We do not need to provide a `kdtree` when we call this method. We can provide a `kdtree` if we create one first with `pvdeg.geospatial.meta_kdtree` but it will work without supplying it. +# GeospatialScenario will create a kdtree internally for us. So the method can simply be called as shown below. + +# %% +# this will work without running the above cells to create a kdtree or the cell directly above where we utilize the created kdtree. +features.classify_mountains_weights() + +# %% [markdown] +# ## Convenience Plotting for Binary Classifications +# +# the `plot_meta_classification` method allows you to plot your data with respect to a boolean classifier present in the `scenario.meta_data` dataframe columns. + +# %% +features.plot_meta_classification(col_name="mountain") + +# %% [markdown] +# ## Identifying mountains within a bounding box. +# +# Sometimes we only want information from one area. To allow for more control over the selection process. To pick only one area from an existing superset of data we can use a bounding box. +# Either provide the top left and bottom right (latitude-longitude) pairs or a 2d numpy array of these pairs which will use the most extreme entries to form the bounding box. + +# %% +features.classify_mountains_radii( + kdtree=tree, + elevation_floor=0, + bbox_kwarg={ + "coords": np.array( + [ + [47.960502, -115.048828], + [47.842658, -101.118164], + [36.738884, -113.686523], + [36.633162, -100.283203], + ] + ) + }, +) + +# %% +features.plot_meta_classification(col_name="mountain") + +# %% [markdown] +# ## Identifying Coastlines from Geospatial Data +# +# To identify points within a search radius of any [natural earth features](https://www.naturalearthdata.com/features/). The following examples show the process of identifying coastline datapoints. + +# %% +features.classify_feature( + kdtree=tree, + feature_name="coastline", + resolution="10m", + radius=0.5, # change this to get a wider band of selected data near the coastline +) + +# %% +features.plot_meta_classification(col_name="coastline") + +# %% [markdown] +# ## Finding Rivers +# +# Above we saw how to find points near coastlines, now lets do points near rivers. It is very similar + +# %% +features.classify_feature( + kdtree=tree, feature_name="rivers_lake_centerlines", resolution="10m", radius=0.2 +) + +# %% +features.plot_meta_classification(col_name="rivers_lake_centerlines") diff --git a/tutorials/04_geospatial/scripts/04_scenario_geospatial.py b/tutorials/04_geospatial/scripts/04_scenario_geospatial.py new file mode 100644 index 000000000..f89a8363b --- /dev/null +++ b/tutorials/04_geospatial/scripts/04_scenario_geospatial.py @@ -0,0 +1,92 @@ +# %% +import pvdeg + +# %% [markdown] +# ## Define Geospatial Scenario Object +# +# To preform geospatial analysis we can create a `GeospatialScenario` object. Alternatively, to preform single location analysis use `Scenario`. Scenario and GeospatialScenario are generalized classes that can be used to replace the legacy functional pvdeg analysis approach with an object orented one. + +# %% +geospatial_standoff_scenario = pvdeg.GeospatialScenario( + name="standoff geospatial", +) + +# %% [markdown] +# ## Add Location +# To add locations for geospatial analysis we will use the ``.addLocation`` method. We can choose downselect from the NSRDB to a country, state and county in that order. *Support for multiple of each category in list form soon.* The ``see_added`` flag allows us to see the gids we have added to the scenario. + +# %% +geospatial_standoff_scenario.addLocation( + state="Colorado", county="Summit", see_added=True, downsample_factor=3 +) + +# %% [markdown] +# ## Add Functions to the Pipeline +# The scenario has a queue of jobs to preform. These are stored in an attribute called ``pipeline``, you can directly update the pipeline but this will bypass the assistance given in creating the job function and parameters. The easiest way to add a job to the pipeline is the ``.updatePipeline`` method. For geospatial analysis, weather and metadata is collected and stored in the scenario at the time of the ``.addLocation`` method call so we do not need to include it below, but if we have other function kwargs to include, they should go in the ``func_params`` argument. +# +# Only a few pvdeg functions are currently supported for geospatial analysis. See the docstring for ``.updatePipeline`` to view currently supported functions. ``updatePipeline`` will not let you add unsupported geospatial functions. The ``see_added`` flag allows us to see the new job added to the pipeline. + +# %% +geospatial_standoff_scenario.addJob(func=pvdeg.standards.standoff, see_added=True) + +# %% +geospatial_standoff_scenario + +# %% [markdown] +# ## Run the job in the pipeline +# +# Currently ``scenario`` only supports one geospatial analysis at a time. We cannot have two geospatial jobs at the same time. + +# %% +geospatial_standoff_scenario.run() + +# %% [markdown] +# ## Directly Access Results Attribute +# +# We can either view the results of the scenario pipeline using ``.viewScenario`` as shown above. The results will be displayed only if the pipeline has been run. Alternatively, we can directly view the ``results`` atribute of the scenario. + +# %% +geospatial_standoff_scenario.results + +# %% [markdown] +# ## Cleanup +# +# The scenario object will store its attributes in a file the python script's current working directory. If we want to delete this file when we are done with the scenario instance we can use the ``.clean()`` method as shown below. + +# %% +geospatial_standoff_scenario.clean() + +# %% [markdown] +# ## Example Geospatial Functionality +# Many functions are supported for geospatial analysis, here are a few. +# - ``pvdeg.standards.standoff`` +# - ``pvdeg.humidity.module`` +# - ``pvdeg.letid.calc_letid_outdoors`` +# +# See the Geospatial Templates tutorial for an example on this. + +# %% +geospatial_humidity_scenario = pvdeg.GeospatialScenario( + name="humidity scenario", geospatial=True +) + +geospatial_humidity_scenario.addLocation( + state="Colorado", county="Jefferson", see_added=True +) + +geospatial_humidity_scenario.addJob( + func=pvdeg.humidity.module, + func_params={ + "backsheet_thickness": 0.3, # mm, thickness of PET backsheet + "back_encap_thickness": 0.46, # mm, thickness of EVA backside encapsulant + "encapsulant": "W001", # EVA encapsulant + "backsheet": "W017", # PET backsheet + }, + see_added=True, +) + +# %% +geospatial_humidity_scenario.run() + +# %% +geospatial_humidity_scenario.results diff --git a/tutorials/04_geospatial/scripts/05_scenario_mountain_downselect.py b/tutorials/04_geospatial/scripts/05_scenario_mountain_downselect.py new file mode 100644 index 000000000..5692e85d5 --- /dev/null +++ b/tutorials/04_geospatial/scripts/05_scenario_mountain_downselect.py @@ -0,0 +1,120 @@ +# %% +import pvdeg +import numpy as np + +# %% [markdown] +# ## Adding Points +# +# We are going to add all of the points in the American West to the scenario and downsample by a factor of 1. This will include only half of the points in the latitude axis and half in the longitude axis. + +# %% +dynamic_points = pvdeg.GeospatialScenario(name="dynamic-selection") + +dynamic_points.addLocation( + state=["CO", "UT"], # , 'NM', 'NV', 'ID', 'WY', 'AZ', 'CA', 'OR', 'WA'], + downsample_factor=1, +) + +# %% [markdown] +# ### Preview The Scenario's Points +# +# Use `plot_cords` to get a quick snapshot of all coordinates included in the scenario's metadata. + +# %% +dynamic_points.plot_coords( + coord_1=[48.574790, -130.253906], # uncomment to see Larger scale view + coord_2=[25.482951, -68.027344], + size=0.005, +) + +# %% +dynamic_points.meta_data + +# %% [markdown] +# ## Downselecting +# +# Using weighted random choices based on elevation we will create a sparse grid from the full metadata for fast calculations. This requires sklearn to be installed but this is not in the `pvdeg` dependency list to you will have to install it seperately. +# +# ### Normalization +# +# At each metadata point in our dataset we will calculate a weight based on its changes in elevation compared to its neighbors. The higher the weight, the greater the change in elevation from a point's immediate neighbors. The downselection methods and functions use these weights to randomly select a subset of the datapoints, prefferentially selecting those with higher weights. +# +# We have some control over which points get selected because all points' weights must be normalized (mapped from 0 to 1) before downselecting. We can apply a function such as $e^x$ or $\log x$ to the weights during normalization. This could help change the distribution of weights that are chosen. This could remove points from the mountains and add them to areas with fewer changes in elevation, or vice versa. +# +# *Note: `pvdeg`'s downselection functions use `numpy.random`, the random seed is not fixed so the result will change between runs.* + +# %% [markdown] +# ### Providing a KdTree +# +# As shown below the lines to create a kdtree are commented out. +# + +# %% +# Set random seed for reproducible results +np.random.seed(42) + +# west_tree = pvdeg.geospatial.meta_KDtree(meta_df=dynamic_points.meta_data) + +dynamic_points.downselect_elevation_stochastic( + # kdtree=west_tree, + downselect_prop=0.5, + normalization="linear", +) + +# %% +dynamic_points.plot_coords() + +# %% [markdown] +# ## Extracting from Scenario +# +# Scenarios provide an easy way to select and downsample geospatial data but we can easily pull out the data to use other `pvdeg` functions on it. In the cell below, we extract the weather data and meta data from the scenario and take only the matching entries from the weather. Then we load the xarray dataset into memory. Previously, it was stored lazily out of memory but we want to do operations on it. (Chunking causes issues when calculating so this eliminates any chunks) + +# %% +weather = dynamic_points.weather_data + +sub_weather = weather.sel( + gid=dynamic_points.meta_data.index +) # downselect weather using restricted metadata set + +sub_weather = sub_weather.compute() # load into memory + +# %% [markdown] +# ### Geospatial Calculation +# +# Run a standoff calculation on the extracted scenario weather data and scenario meta data. + +# %% +# geospatial analysis now + +geo = { + "func": pvdeg.standards.standoff, + "weather_ds": sub_weather, + "meta_df": dynamic_points.meta_data, +} + +analysis_result = pvdeg.geospatial.analysis(**geo) + +# %% [markdown] +# ### Viewing Results +# +# Inspecting the xarray dataset below shows us that we have many Not a Number (NaN) entries. These occur because we did not provide weather data at every point in the grid of possile latitude-longitude pairs. Expanding the `x` datavariable shows that there are some valid results but these are uncommon. + +# %% +analysis_result + +# %% [markdown] +# ## Plotting Sparse Data I +# +# If we try to plot existing data with the current plotting methods exposed by `pvdeg` we will encounter issues. This will produce weak plotting results. + +# %% +# This cell demonstrates the issue with plotting sparse data directly +# It will raise a TypeError because there's no numeric data to plot +try: + pvdeg.geospatial.plot_USA(analysis_result["x"]) +except TypeError as e: + print(f"Expected error when plotting sparse data: {e}") + print("This is why we need to use plot_sparse_analysis() instead (see next cell)") + +# %% +pvdeg.geospatial.plot_sparse_analysis(analysis_result, data_var="x", method="linear") diff --git a/tutorials/04_geospatial/scripts/06_scenario_single_location.py b/tutorials/04_geospatial/scripts/06_scenario_single_location.py new file mode 100644 index 000000000..878e38540 --- /dev/null +++ b/tutorials/04_geospatial/scripts/06_scenario_single_location.py @@ -0,0 +1,154 @@ +# %% [markdown] +# # Scenario Objects - OOP Approach to PVDEG +# +# Author: Tobin Ford | tobin.ford@nrel.gov +# +# 2024 +# **** +# +# A simple object orented workflow walkthrough using pvdeg. + +# %% +# 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 pvdeg==0.3.3 + +# %% +import pvdeg +import os + +# %% +# This information helps with debugging and getting support :) +import sys +import platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("pvdeg version ", pvdeg.__version__) + +# %% [markdown] +# ## Define Single Point Scenario Object +# Scenario is a general class that can be used to replace the legacy functional pvdeg analysis approach with an object orented one. ``Scenario`` can preform single location or geospatial analysis. The scenario constructor takes many arguments but the only required one for the following use cases is the ``name`` attribute. It is visible in when we display the entire scenario and is present in the file of saved information about the scenario. We also need to provide the class constructor with our API key and email. +# +# A way around this is to provide the weather and metadata in the pipeline job arguments or you can load data from somewhere else and provide it in the same fashion. +# +#
+# Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) +#
+ +# %% +simple_scenario = pvdeg.Scenario( + name="Point Minimum Standoff", email="user@mail.com", api_key="DEMO_KEY" +) + +# %% [markdown] +# ## Adding A Location +# To add a single point using data from the Physical Solar Model (PSM3), simply feed the scenario a single coordinate in tuple form via the ``addLocation`` method. Currently this is the only way to add a location to a non-geospatial scenario, all of the other arguments are unusable when ``Scenario.geospatial == False``. +# +# Attempting to add a second location by calling the method again with a different coordinate pair will overwrite the old location data stored in the class instance. + +# %% +simple_scenario.addLocation( + lat_long=(25.783388, -80.189029), +) + +# %% +simple_scenario.weather_data + +# %% [markdown] +# ## Scenario Pipelines +# +# The pipeline is a list of tasks called jobs for the scenario to run. We will populate the pipeline with a list of jobs before executing them all at once. +# +# To add a job to the pipeline use the ``updatePipeline`` method. Two examples of adding functions to the pipeline will be shown below. + +# %% [markdown] +# ### Adding a job without function arguments +# +# The simplest case of adding a job to the pipeline is when it only requires us to provide simple weather and metadata. In the function definition and docstring these appear as ``weather_df`` and ``meta``. Since these attributes are contained in our scenario class instance we do not have to worry about them. We can simply add the function as shown below. + +# %% +simple_scenario.addJob(func=pvdeg.standards.standoff) + +# %% [markdown] +# ### Adding a job with function arguments +# +# When adding a job that contains a function requiring other arguments such as ``solder_fatigue`` which requires a value for ``wind_factor``, we will need to provide it. The most straightforeward way to do this is using a kwargs dictionary and passing it to the function. We do not unpack the dictionary before passing it. This is done inside of the scenario at pipeline runtime (when ``runPipeline`` is called). + +# %% +kwargs = {"wind_factor": 0.33} + +simple_scenario.addJob(func=pvdeg.fatigue.solder_fatigue, func_kwarg=kwargs) + +# %% [markdown] +# ### Adding a job with weather and metadata from outside of the class +# ### Not functional +# +# could just directly set weather data with scenario.weather_data = weather and scenario.meta_data = meta but that would only work for all of the jobs in the pipeline +# +# Say local weather data is available or other, if we want to use this rather than the PSM3 data at a latitude and longitude we can also provide the weather and metadata in the function arguments. This is probably the best if avoided but follows the same syntax as providing other function arguments. See the example below. + +# %% +PSM_FILE = os.path.join(pvdeg.DATA_DIR, "psm3_demo.csv") +weather, meta = pvdeg.weather.read(PSM_FILE, "psm") + +kwargs = {"weather_df": weather, "meta": meta} + +simple_scenario.addJob(func=pvdeg.standards.standoff, func_kwarg=kwargs) + +# FIX THIS CASE IN SCENARIO CLASS +# (simple_scenario.pipeline[1]['job'])(**simple_scenario.pipeline[1]['params']) + +# %% [markdown] +# ## View Scenario +# +# The ``viewScenario`` method provides an overview of the information contained within your scenario object. Here you can see if it contains the location weather and metadata. As well as the jobs in the pipeline and their arguments. + +# %% +simple_scenario.viewScenario() + +# %% [markdown] +# ## Display +# +# The fancier cousin of viewScenario. Only works in a jupyter environemnt as it uses a special ipython backend to render the html and javascript. +# +# It can be called with just the Scenario instance as follows +# `simple_scenario` +# +# or using the display function +# `display(simple_scenario)` + +# %% +simple_scenario + +# %% [markdown] +# ## Executing Pipeline Jobs +# To run the pipeline after we have populated it with the desired jobs call the ``runPipeline`` method on our scenario instance. This will run all of the jobs we have previously added. The functions that need weather and metadata will grab it from the scenario instance using the correct location added above. The pipeline jobs results will be saved to the scenario instance. + +# %% +simple_scenario.run() + +# %% [markdown] +# ## Results Series ## +# We will use a series to store the various return values of functions run in our pipeline. These can partially obfuscate the dataframes within them so to access the dataframes, use the function name to access it. To get one of the results we can index it using dictionary syntax. If the job was called `'KSDJQ'` do `'simple_scenario.results['KSDJQ']` to directly access the result for that job + +# %% +print(simple_scenario.results) +print("We can't see out data in here so we need to do another step", end="\n\n") + +# to see all available ouputs of results do +print( + f"this is the list of all available frames in results : {simple_scenario.results.index}\n" +) + +# loop over all results and display +for keys, results in simple_scenario.results.items(): + print(keys) + display(results) + +# %% [markdown] +# ## Cleaning Up the Scenario +# +# Each scenario object creates a directory named ``pvd_job_...`` that contains information about the scenario instance. To remove the directory and all of its information call ``clean`` on the scenario. This will permanently delete the directory created by the scenario. + +# %% +simple_scenario.clean() diff --git a/tutorials/04_geospatial/scripts/07_scenario_temperature.py b/tutorials/04_geospatial/scripts/07_scenario_temperature.py new file mode 100644 index 000000000..75467fc9b --- /dev/null +++ b/tutorials/04_geospatial/scripts/07_scenario_temperature.py @@ -0,0 +1,164 @@ +# %% +# 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 pvdeg==0.3.3 + +# %% +import pvdeg +import os + +# %% +# This information helps with debugging and getting support :) +import sys +import platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("pvdeg version ", pvdeg.__version__) + +# %% [markdown] +# ## Adding Modules and Pipeline Jobs (Run Functions on Scenario Object) +# +# Material: `OX003` corresponds to a set of EVA material parameters from the default file `O2Permeation.json` in the `pvdeg/data` directory. Look in these files to see available options. + +# %% +scene_temp = pvdeg.Scenario( + name="temperature and degradation", + api_key="DEMO_KEY", + email="user@mail.com", +) + +scene_temp.addLocation( + lat_long=(25.783388, -80.189029), +) + +# this module will be overwritten because another with the same name is added afterwards +scene_temp.addModule(module_name="sapm_1", temperature_model="sapm") + +scene_temp.addModule( + module_name="sapm_1", + racking="open_rack_glass_polymer", + materials="OX003", + temperature_model="sapm", + irradiance_kwarg={"azimuth": 120, "tilt": 30}, + model_kwarg={"irrad_ref": 1100}, +) + +scene_temp.addModule( + module_name="pvsyst_1", + racking="freestanding", + materials="OX003", + temperature_model="pvsyst", + irradiance_kwarg={"azimuth": 180, "tilt": 0}, + model_kwarg={"module_efficiency": 0.15}, +) +scene_temp.addModule( + module_name="sapm_2", + racking="open_rack_glass_polymer", + materials="OX003", + temperature_model="sapm", + irradiance_kwarg={"azimuth": 120, "tilt": 30}, + model_kwarg={"irrad_ref": 1000}, +) +scene_temp.addModule( + module_name="sapm_3", + racking="open_rack_glass_polymer", + materials="OX003", + temperature_model="sapm", + irradiance_kwarg={"azimuth": 180, "tilt": 0}, + model_kwarg={"irrad_ref": 1000}, +) + +scene_temp.addModule( + module_name="pvsyst_2", + racking="freestanding", + materials="OX003", + temperature_model="pvsyst", + irradiance_kwarg={"azimuth": 180, "tilt": 0}, + model_kwarg={"module_efficiency": 0.2}, +) + +scene_temp.addJob( + func=pvdeg.temperature.temperature, + func_kwarg={"cell_or_mod": "cell"}, +) + +scene_temp.addJob( + func=pvdeg.degradation.vantHoff_deg, + func_kwarg={"I_chamber": 1000, "temp_chamber": 25}, +) + +scene_temp.addJob( + func=pvdeg.degradation.vantHoff_deg, + func_kwarg={"I_chamber": 1000, "temp_chamber": 30}, +) + +scene_temp.addJob( + func=pvdeg.degradation.IwaVantHoff, +) + +# %% [markdown] +# ## Run and View Scenario Results + +# %% +scene_temp.run() + +scene_temp + +# %% +scene_temp.dump() + +# %% [markdown] +# ## Plotting and Extracting Results +# These methods are independent of one another (i.e. you do not need to extract before plotting but both are shown below.) + +# %% +import datetime + +t0 = datetime.datetime(1970, 1, 1, 0, 0) +tf = datetime.datetime(1970, 1, 1, 23, 59) + +# Get the first function result dynamically +function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == "function"] +if function_ids: + temp_df = scene_temp.extract( + ("function", function_ids[0]), tmy=True, start_time=t0, end_time=tf + ) + display(temp_df) +else: + print("No function results found") + +# %% +# Get the first function result dynamically for plotting +function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == "function"] +if function_ids: + scene_temp.plot( + ("function", function_ids[0]), + tmy=True, + start_time=t0, + end_time=tf, + title="single day cell temperature", + ) +else: + print("No function results found") + +# %% [markdown] +# ## Create a Copy of a Scenario + +# %% +from pathlib import Path + +parent_dir = Path(pvdeg.PVDEG_DIR).parent +new_path = ( + parent_dir + / "tutorials_and_tools" + / "tutorials_and_tools" + / "temperature_and_degradation.json" +) + +copy = pvdeg.scenario.Scenario.load_json( + file_path=str(new_path), + email="user@mail.com", + api_key="DEMO_KEY", +) + +# copy diff --git a/tutorials/04_geospatial/scripts/08_module_standoff_iec63126.py b/tutorials/04_geospatial/scripts/08_module_standoff_iec63126.py new file mode 100644 index 000000000..27b5b4b63 --- /dev/null +++ b/tutorials/04_geospatial/scripts/08_module_standoff_iec63126.py @@ -0,0 +1,499 @@ +# %% [markdown] +# # Tools - Module Standoff for IEC TS 63126 +# +# ### Calculation of module standoff distance according to IEC TS 63126 +# +# **Requirements:** +# - Local weather data file or site longitude and latittude +# +# **Objectives:** +# 1. Import weather data. +# 2. Calculate installation standoff - Level 1 and Level 2. +# 3. Calculate $X_{eff}$ from provided module temperature data. +# 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$. +# 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$. +# 6. Plot $X_{min}$ for Level 1, Level 2, or a $T_{98}$ for a given region. +# +# **Background:** +# +# This notebook calculates the a minimum effective standoff distance ($X_{eff}$) necessary for roof-mounted PV modules to ensure that the $98^{th}$ percentile operating temperature, $T_{98}$, remains under 70°C for compliance to IEC 61730 and IEC 61215. For higher $T_{98}$ values above 70°C or 80°C testing must be done to the specifications for Level 1 and Level 2 of IEC TS 63126. This method is outlined in the appendix of IEC TS 63126 and is based on the model from *[King 2004] and data from **[Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as, +# +# $$ X = -X_0 \ln\left(1-\frac{T_0-T}{\Delta T}\right), Equation 1 $$ +# +# where $T_0$ is the temperature for $X=0$ (insulated-back) and $\Delta T$ is the temperature difference between an insulated-back ($X=0$) and open-rack mounting configuration ($X=\infty)$. +# +# We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification. +# +# $*$ D. L. King, W. E. Boyson, and J. A. Kratochvil, "Photovoltaic array performance model," SAND2004-3535, Sandia National Laboratories, Albuquerque, NM, 2004. '\ +# $**$ M. K. Fuentes, "A simplified thermal model for Flat-Plate photovoltaic arrays," United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914 +# + +# %% [markdown] +# ![alt text](images/T98_70C_standoff_Map.png) + +# %% +# 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 pvdeg==0.4.2 + +# %% +import os +import pvdeg +import pandas as pd +from pvdeg import DATA_DIR +import dask +import matplotlib.pyplot as plt +import seaborn as sns +import math +import numpy as np + +# %% +# This information helps with debugging and getting support :) +import sys +import platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("Pandas version ", pd.__version__) +print("pvdeg version ", pvdeg.__version__) +print("dask version", dask.__version__) +print(DATA_DIR) + +# %% [markdown] +# ## 1. Import Weather Data +# +# The function has these minimum requirements when using a weather data file: +# - Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level. +# - Site meta-data containing (at least) latitude, longitude, and time zone +# +# Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial "Weather Database Access.ipynb" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information. +# + +# %% +# Get data from a supplied data file (Do not use the next box of code if using your own file) +weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") +WEATHER_df, META = pvdeg.weather.read(weather_file, "csv") +print(META) + +# %% +# This routine will get a meteorological dataset from anywhere in the world where it is available +# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia +# weather_id = (35.6754, 139.65) #Tokyo, Japan +# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand +# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska +# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland +# weather_id = (33.4152, -111.8315) #Mesa, Arizona +# WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id) +# print(META) + +# %% [markdown] +# ## 2. Calculate Installation Standoff Minimum - Level 1 and Level 2 +# +# According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to T₉₈<70°C, <80°C and <90°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of T₉₈<70°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as, +# +# $$ X_{min} = -X_0 \ln\left(1-\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\right), Equation 2 $$ +# +# where $T_{98,0}$ is the $98^{th}$ percentile temperature for an insulated back module and $T_{98,inf}$ is the $98^{th}$ percentile temperature for an open rack mounted module. +# +# Once the meterological data has been obtained, the input parameter possibilities are: +# +# - T₉₈ : Does not necessarily need to be set at 70°C or 80°C for IEC TS 63216, you might want to use a different number to compensate for a thermal aspect of the particular system you are considering. The default is 70°C. +# - tilt : tilt from horizontal of PV module. The default is 0°. +# - azimuth : azimuth in degrees from North. The default is 180° for south facing. +# - sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'). The default is 'isotropic'. +# - temp_model : pvlib compatible module temperature model. (Options: 'sapm', 'pvsyst', 'faiman', 'sandia'). The default is 'sapm'. +# - conf_0 : Temperature model for hotest mounting configuration. Default is "insulated_back_glass_polymer". +# - conf_inf : Temperature model for open rack mounting. Default is "open_rack_glass_polymer". +# - x_0 : thermal decay constant [cm] (see documentation). The default is 6.5 cm. +# - wind_factor : Wind speed power law correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM). The default is 0.33. + +# %% [markdown] +# The following is the minimum function call. It defaults to horizontal tilt and T₉₈=70°C. + +# %% +standoff = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META) +print(pvdeg.standards.interpret_standoff(standoff)) + +# %% [markdown] +# The following is a full function call for both T₉₈=70°C and 80°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98. + +# %% +standoff_1 = pvdeg.standards.standoff( + weather_df=WEATHER_df, + meta=META, + T98=70, + tilt=META["latitude"], + azimuth=None, + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + x_0=6.5, + wind_factor=0.33, +) +print("First calculation standoff = ", "%.1f" % standoff_1["x"].iloc[0], " cm.") +standoff_2 = pvdeg.standards.standoff( + weather_df=WEATHER_df, + meta=META, + T98=80, + tilt=META["latitude"], + azimuth=None, + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + x_0=6.5, + wind_factor=0.33, +) +print("Second calculation standoff = ", "%.1f" % standoff_2["x"].iloc[0], " cm.") +print(pvdeg.standards.interpret_standoff(standoff_1=standoff_1, standoff_2=standoff_2)) + +# %% [markdown] +# ## 3. Calculate $X_{eff}$ from provided module temperature data. +# +# To do this calculation, one must use a set of data with: +# - meterological irradiance data sufficient to calculate the POA irradiance (DHI, GHI, and DNI), +# - ambient temperature data, +# - wind speed at module height, (wind_factor=0.33 will be used unless otherwise specified) +# - temperature measurements of the module in the test system. Ideally this would be measured under a worst case scenario that maximizes the module temperature for a given site, +# - geographic meta data including longitude and latitude, +# +# To create a weather file of your own, copy the format of the example file 'xeff_demo.csv'. This is formatted with the first row containing meta data variable names, the second row containing the corresponding values, the third row containing meteorological data headers, and all the remaining rows containing the meteorological data. +# +# To do this calculation, one should also filter the data to remove times when the sun is not shining or when snow is likely to be on the module. The recommendations and programmed defaults are to use poa_min=100 W/m² and data when the minimum ambient temperature t_amb_min=0. + +# %% +# Read the weather file +weather_file = os.path.join(DATA_DIR, "xeff_demo.csv") +xeff_weather, xeff_meta = pvdeg.weather.read(weather_file, "csv") +# Pull measured temperature and calculate theoretical insulated back module temperature and open rack module temperature +T_0, T_inf, xeff_poa = pvdeg.standards.eff_gap_parameters( + weather_df=xeff_weather, + meta=xeff_meta, + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + wind_factor=0.33, +) +# Now calculate X_eff. +x_eff = pvdeg.standards.eff_gap( + T_0, + T_inf, + xeff_weather["module_temperature"], + xeff_weather["temp_air"], + xeff_poa["poa_global"], + x_0=6.5, + poa_min=100, + t_amb_min=0, +) +print("The effective standoff for this system is", "%.1f" % x_eff, "cm.") + +# %% [markdown] +# ## 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$. +# +# Equation 2 can be reorganized as, +# +# $$ T_{98} = T_{98,0} -( T_{98,0}- T_{98,inf}) \left(1-e^{-\frac{x_{eff}}{x_{0}}}\right), Equation 3 $$ +# +# and used to calculate the $98^{th}$ percential temperature, $T_{98}$, for a PV system having a given effective standoff height, $X_{eff}$, for an arbitrarily oriented module. Here, $T_{98,0}$ is the $98^{th}$ percentile for an insulated-back module and $T_{98,inf}$ is the $98^{th}$ percentile for a rack-mounted module. The input parameter possibilities are the same as shown in Objective #2 above, but the example below uses the default parameters. The actual tilt [degrees], azimuth [degrees] and $X_{eff}$ [cm] can be modifed as desired. + +# %% +# This is the minimal function call using the common default settings to estimate T₉₈. +T_98 = pvdeg.standards.T98_estimate( + weather_df=WEATHER_df, meta=META, tilt=-META["latitude"], azimuth=None, x_eff=10 +) +print("The 98áµ—Ê° percential temperature is estimated to be", "%.1f" % T_98, "°C.") + +# %% +# This code will calculate the temperature for an arbitrary x_eff distance. Either set of kwargs can be modified and use. +# irradiance_kwarg ={ +# "axis_tilt": None, +# "axis_azimuth": None, +# "x_eff": 10, +# "module_mount": '1_axis'} +irradiance_kwarg = { + "tilt": META["latitude"], + "azimuth": None, + "x_eff": 10, + "module_mount": "fixed", +} + +T_xeff = pvdeg.standards.x_eff_temperature_estimate( + weather_df=WEATHER_df, meta=META, **irradiance_kwarg +) + +print( + "The 98áµ—Ê° percential temperature is estimated to be", + "%.1f" % np.percentile(T_xeff, 98), + "°C.", +) + +# %% [markdown] +# ## 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$. +# +# The temperature of a system is affected by the orientation. This section will scan all possible tilts and azimuths calculating the minimum standoff distance for a given $T_{98}$. Similar additional factors as above can also be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $T_{98}$=70°C, the boundary between Level 0 and Level 1 requirements. The temperature model information given below is unnecessary as these are default values that would get populated automatically. However, they were included here for clarity into a standard practice as per IEC TS 63126. +# +# $$ X_{min} = -X_0 \ln\left(1-\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\right), Equation 2 $$ + +# %% +# Scans through all the azimuth and tilt running the minimum standoff calculation +# Set up keyword parameters for the calculation + +kwarg_x = dict( + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + T98=70, + x_0=6.5, + wind_factor=0.33, +) +# Run the calculation +x_azimuth_step = 10 +x_tilt_step = 10 +standoff_series = pvdeg.utilities.tilt_azimuth_scan( + weather_df=WEATHER_df, + meta=META, + tilt_step=x_tilt_step, + azimuth_step=x_azimuth_step, + func=pvdeg.standards.standoff_x, + **kwarg_x, +) + +# %% [markdown] +# The next cell creates a plot of the calculated data. Some of the things you may want to change are: +# - cmap="Spectral_r": Change to have different colors +# - plt.title : This will change the plot title. +# - figsize=(16,4) : Change the plot dimensions and/or aspect ratio. +# - vmax=None : This can be set to a numeric value to control the depth scale maximum +# - vmin=0 : This controls the minimum of the depth scale. +# - v_ticks=37 : This changes the number of vertical tick marks +# - h_ticks=10 : This changes the number of horizontal tick marks +# - Unblock the last two lines to ouput the plot as an *.png image file + +# %% +standoff_series_df = pd.DataFrame( + { + "Tilt": standoff_series[:, 0], + "Azimuth": standoff_series[:, 1], + "Xₘᵢₙ": standoff_series[:, 2], + } +) +x_fig = plt.figure(figsize=(16, 4)) +plt.title( + r"Plot of $\it{Xₘᵢₙ}$ for all orientations for $\it{T₉₈}$=" + + "%.0f" % kwarg_x["T98"] + + "°C.", + fontsize=15, + y=1.08, +) +x_fig = sns.heatmap( + standoff_series_df.pivot(index="Tilt", columns="Azimuth", values="Xₘᵢₙ"), + cbar_kws={"label": "Xₘᵢₙ", "format": "%.0f", "pad": 0.02}, + cmap="Spectral_r", + vmin=0, + vmax=None, +) + +h_ticks = 37 +x_number = math.ceil(360 / x_azimuth_step) + 1 +x_ticks = [ + (x * (360 / (h_ticks - 1)) / x_azimuth_step + 0.5) for x in range(h_ticks - 1) +] +x_labels = [("%.0f" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)] +x_ticks.append(x_number - 0.5) +x_fig.set_xticks(x_ticks) +x_fig.set_xticklabels(x_labels, rotation=90) + +v_ticks = 10 +y_number = math.ceil(90 / x_tilt_step) + 1 +y_ticks = [(x * (90 / (v_ticks - 1)) / x_tilt_step + 0.5) for x in range(v_ticks - 1)] +y_labels = [("%.0f" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)] +y_ticks.append(y_number - 0.5) +x_fig.set_yticks(y_ticks) +x_fig.set_yticklabels(y_labels, rotation=0) + +x_fig.set_xlabel("Azimuth [°]", fontsize=15, labelpad=10) +x_fig.set_ylabel("Tilt [°]", fontsize=15) +x_fig.figure.axes[-1].set_ylabel(r"$\it{Xₘᵢₙ}$ [cm]", size=15) +x_fig.invert_yaxis() + +output_folder = os.path.join( + os.path.dirname(os.path.dirname(os.getcwd())), "TEMP", "results" +) +try: + os.makedirs(output_folder) +except OSError as error: + print(error) + +plt.savefig( + os.path.join(output_folder, "Standoff_Scan.png"), bbox_inches="tight" +) # Creates an image file of the standoff plot +plt.show() + +# %% [markdown] +# ## 6. Plot $T_{98}$ for all azimuth and tilt for a given $X_{eff}$. +# +# The temperature of a system is affected by the orientation and the effective standoff, $X_{eff}$, of the system. This section will scan all possible tilts and azimuths calculating the $T_{98}$ for a given $X_{eff}$. As above, additional factors can be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90° and 180° tilt and azimuth spans, respectively. The default for this calculation is for $X_{eff}$=10 cm, a common effective standoff distance on a rooftop system. A value of $X_{eff}$=None will run the calculations for an open rack system and $X_{eff}$=0 for an insulated-back system. + +# %% +# Scans through all the azimuth and tilt running the 98áµ—Ê° percentile temperature calculation. +# Set up keyword parameters for the calculation +kwarg_T = dict( + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + x_eff=5, + x_0=6.5, + wind_factor=0.33, +) +# Run the calculation +T_azimuth_step = 10 +T_tilt_step = 10 +T98_series = pvdeg.utilities.tilt_azimuth_scan( + weather_df=WEATHER_df, + meta=META, + tilt_step=T_tilt_step, + azimuth_step=T_azimuth_step, + func=pvdeg.standards.T98_estimate, + **kwarg_T, +) + +# %% [markdown] +# The next cell creates a plot of the calculated data. Some of the things you may want to change are: +# - cmap="Spectral_r": Change to have different colors +# - plt.title : This will change the plot title. +# - figsize=(16,4) : Change the plot dimensions and/or aspect ratio. +# - vmax=None : This can be set to a numeric value to control the depth scale maximum +# - vmin=None : This controls the minimum of the depth scale. +# - v_ticks=37 : This changes the number of vertical tick marks +# - h_ticks=10 : This changes the number of horizontal tick marks +# - Unblock the last two lines to ouput the plot as an *.png image file + +# %% +# This produces the plot of the data +T98_series_df = pd.DataFrame( + {"Tilt": T98_series[:, 0], "Azimuth": T98_series[:, 1], "T₉₈": T98_series[:, 2]} +) +T98_fig = plt.figure(figsize=(16, 4)) +if kwarg_T["x_eff"] == None: + plt.title( + r"Plot of $\it{T₉₈}$ for all orientations for an open-rack mounting.", + fontsize=15, + y=1.08, + ) +else: + plt.title( + r"Plot of $\it{T₉₈}$ for all orientations for $X_{eff}$=" + + "%.0f" % kwarg_T["x_eff"] + + " cm.", + fontsize=15, + y=1.08, + ) +T98_fig = sns.heatmap( + T98_series_df.pivot(index="Tilt", columns="Azimuth", values="T₉₈"), + cbar_kws={"label": "Xₘᵢₙ", "format": "%.0f", "pad": 0.02}, + cmap="Spectral_r", + vmin=None, + vmax=None, +) + +h_ticks = 37 +x_number = math.ceil(360 / T_azimuth_step) + 1 +x_ticks = [ + (x * (360 / (h_ticks - 1)) / T_azimuth_step + 0.5) for x in range(h_ticks - 1) +] +x_labels = [("%.0f" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)] +x_ticks.append(x_number - 0.5) +T98_fig.set_xticks(x_ticks) +T98_fig.set_xticklabels(x_labels, rotation=90) + +v_ticks = 10 +y_number = math.ceil(90 / T_tilt_step) + 1 +y_ticks = [(x * (90 / (v_ticks - 1)) / T_tilt_step + 0.5) for x in range(v_ticks - 1)] +y_labels = [("%.0f" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)] +y_ticks.append(y_number - 0.5) +T98_fig.set_yticks(y_ticks) +T98_fig.set_yticklabels(y_labels, rotation=0) + +T98_fig.set_xlabel("Azimuth [°]", fontsize=15, labelpad=10) +T98_fig.set_ylabel("Tilt [°]", fontsize=15) +T98_fig.figure.axes[-1].set_ylabel(r"$\it{T₉₈}$ [°C]", size=15) +T98_fig.invert_yaxis() + +plt.savefig( + os.path.join(output_folder, "T98_Scan.png"), bbox_inches="tight" +) # Creates an image file of the standoff plot +plt.show(T98_fig) + +# %% [markdown] +# ## 7. Plot $X_{min}$ for a $T_{98}$, and plot $T_{98}$ for a given region. +# +# This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. +# For more in-depth instructions on doing this, look at the tutorial "Scenario - Geospatial.ipynb" here in PVDeg. +# +# Step #1: Create an object, "geospatial_standoff_scenario" to be used to run the computations. + +# %% +geospatial_standoff_scenario = pvdeg.GeospatialScenario( + name="standoff geospatial", + geospatial=True, +) + +# %% [markdown] +# Step #2: Identifies a subset of locations from the database to run the computations. +# Specifically all are from the NSRDB. + +# %% +geospatial_standoff_scenario.addLocation( + state="Colorado", county="Summit" +) # Identifies a subset of locations from the database to run the computations. Specifically all are from the NSRDB. + +# %% [markdown] +# Step #3: indicate which function will be run. Here the default is the standoff calculation, but it could be any other function with a key word argument dictionary. +# Here the 98th percential temperature is defined as 70C, but any arbitrary value can be specified. + +# %% +geospatial_standoff_scenario.addJob( + func=pvdeg.standards.standoff, func_params={"T98": 70} +) + +# %% [markdown] +# Step #4: Run the scenario + +# %% +geospatial_standoff_scenario.run() + +# %% [markdown] +# Step #5: Create a plot of the standoff calculation. + +# %% +geospatial_standoff_scenario.plot_world("x") + +# %% +geospatial_standoff_scenario.plot_world("T98_inf") + +# %% [markdown] +# ## 8. Save data outputs. +# +# This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation and is blocked by default. First check that the output folder exists, then unblock the code for data you would like to save. + +# %% +print("Your results will be stored in %s" % output_folder) +print("The folder must already exist or the file will not be created") + +pvdeg.weather.write( + data_df=WEATHER_df, + metadata=META, + savefile=os.path.join(output_folder, "WeatherFile.csv"), +) # Writes the meterological data to an *.csv file. + +pd.DataFrame(standoff_series_df).to_csv( + os.path.join(output_folder, "Standoff_Scan.csv") +) # Writes a file with the Tilt and Azimuth scan calculations of standoff. + +pd.DataFrame(T98_series_df).to_csv( + os.path.join(output_folder, "T98_Scan.csv") +) # Writes a file with the Tilt and Azimuth scan calculations of T98. diff --git a/tutorials_and_tools/tutorials_and_tools/Custom-Functions-Nopython.ipynb b/tutorials/05_advanced/01_custom_functions_nopython.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/Custom-Functions-Nopython.ipynb rename to tutorials/05_advanced/01_custom_functions_nopython.ipynb index ee4f6ac0d..737fb0c5f 100644 --- a/tutorials_and_tools/tutorials_and_tools/Custom-Functions-Nopython.ipynb +++ b/tutorials/05_advanced/01_custom_functions_nopython.ipynb @@ -115,11 +115,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Define Custom Expressions from Latex or \n", + "### Define Custom Expressions from Latex or\n", "\n", "We will use an altered arrhenius equation with an irrandiance relation (ignore the fact that this exists in pvdeg already).\n", "\n", - "$R_{D} = R_{0}I^{X} e^{\\frac{-{Ea}}{kT}}$ *ea is one variable so this may present some issues* \n", + "$R_{D} = R_{0}I^{X} e^{\\frac{-{Ea}}{kT}}$ *ea is one variable so this may present some issues*\n", "the raw latex looks like this `R_{0}I^{X} e^{\\frac{-{Ea}}{kT}}`" ] }, @@ -166,7 +166,7 @@ "source": [ "### Calculating Degradation Expression\n", "\n", - "Generally more processing will have to happen outside of these functions than built in pvdeg functions. \n", + "Generally more processing will have to happen outside of these functions than built in pvdeg functions.\n", "Here we are defining our arguments and correcting units. When trying to calculate using timeseries we will pass `pandas.Series` objects to the arguments.\n", "\n", "Results should be strictly scrutinized to make sure the calculation is iterating over your series correctly. It will generally be easier to write python code\n", @@ -276,21 +276,21 @@ "leakage current\n", "$I_{leak} = \\frac{V_{bias}}{R_{enc}}$\n", "\n", - "$I_{leak}$, leakage current \n", - "${V_{bias}}$, potential difference between cells and frame \n", - "${R_{enc}}$, resistance of the encapsulant \n", + "$I_{leak}$, leakage current\n", + "${V_{bias}}$, potential difference between cells and frame\n", + "${R_{enc}}$, resistance of the encapsulant\n", "\n", "electric field\n", - "$E = \\frac{V_{bias}}{d}$ \n", + "$E = \\frac{V_{bias}}{d}$\n", "\n", - "$E$, electric field \n", - "$d$, thickness of encapsulant \n", + "$E$, electric field\n", + "$d$, thickness of encapsulant\n", "\n", "degradation rage\n", - "$D = k_{D} * E * I{leak}$ \n", + "$D = k_{D} * E * I{leak}$\n", "\n", - "$D$, degradation rate \n", - "$k_D$, degradation constant " + "$D$, degradation rate\n", + "$k_D$, degradation constant" ] }, { diff --git a/tutorials_and_tools/tutorials_and_tools/Pysam - Single Location.ipynb b/tutorials/05_advanced/02_pysam_single_location.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/Pysam - Single Location.ipynb rename to tutorials/05_advanced/02_pysam_single_location.ipynb index ed0eaba7d..b25fbe909 100644 --- a/tutorials_and_tools/tutorials_and_tools/Pysam - Single Location.ipynb +++ b/tutorials/05_advanced/02_pysam_single_location.ipynb @@ -388,7 +388,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "subarray1_poa_ground_front_cs \n", + "subarray1_poa_ground_front_cs\n", "subarray1_ground_rear_spatial" ] }, diff --git a/tutorials_and_tools/tutorials_and_tools/API Call Required - PYSam.ipynb b/tutorials/05_advanced/03_pysam_api.ipynb similarity index 87% rename from tutorials_and_tools/tutorials_and_tools/API Call Required - PYSam.ipynb rename to tutorials/05_advanced/03_pysam_api.ipynb index 7a9b03803..9aa84a07e 100644 --- a/tutorials_and_tools/tutorials_and_tools/API Call Required - PYSam.ipynb +++ b/tutorials/05_advanced/03_pysam_api.ipynb @@ -80,7 +80,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [ "GEO_META = pd.read_csv(os.path.join(TEST_DATA_DIR, \"summit-meta.csv\"), index_col=0)\n", @@ -89,7 +91,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "source": [ "### Local Geospatial - PySAM Integration\n", "\n", @@ -103,12 +107,14 @@ "outputs": [], "source": [ "# this is just a wrapper to grab the result we want\n", - "def pysam_annual_energy(weather_df, meta, pv_model=\"pysamv1\", pv_model_default=\"FlatPlatePVCommercial\"):\n", + "def pysam_annual_energy(\n", + " weather_df, meta, pv_model=\"pysamv1\", pv_model_default=\"FlatPlatePVCommercial\"\n", + "):\n", " # Drop the gid column if present (added by geospatial conversion)\n", - " weather_df = weather_df.drop(columns=['gid'])\n", + " weather_df = weather_df.drop(columns=[\"gid\"])\n", "\n", " # Resample half-hourly data to hourly (PySAM expects hourly)\n", - " weather_df = weather_df.resample('h').mean()\n", + " weather_df = weather_df.resample(\"h\").mean()\n", "\n", " results = pvdeg.pysam.pysam(\n", " weather_df=weather_df,\n", @@ -169,11 +175,9 @@ "metadata": {}, "outputs": [], "source": [ - "weather_db = 'PSM4'\n", + "weather_db = \"PSM4\"\n", "weather_id = (25.783388, -80.189029)\n", - "weather_arg = {'api_key': 'DEMO_KEY',\n", - " 'email': 'user@mail.com',\n", - " 'map_variables': True}\n", + "weather_arg = {\"api_key\": \"DEMO_KEY\", \"email\": \"user@mail.com\", \"map_variables\": True}\n", "\n", "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)" ] @@ -250,15 +254,19 @@ "outputs": [], "source": [ "# Check weather data time dimension compatibility\n", - "if 'time' in GEO_WEATHER.dims:\n", - " times = pd.to_datetime(GEO_WEATHER['time'].values)\n", + "if \"time\" in GEO_WEATHER.dims:\n", + " times = pd.to_datetime(GEO_WEATHER[\"time\"].values)\n", " years = times.year\n", " unique_years = set(years)\n", " if len(unique_years) != 1:\n", - " print(f\"Warning: Weather data contains multiple years: {unique_years}. Pysam expects a single year.\")\n", + " print(\n", + " f\"Warning: Weather data contains multiple years: {unique_years}. Pysam expects a single year.\"\n", + " )\n", " hours_per_year = times.size / len(unique_years)\n", " if hours_per_year not in [8760, 8784]:\n", - " print(f\"Warning: Unexpected number of timesteps per year: {hours_per_year}. Expected 8760 or 8784.\")\n", + " print(\n", + " f\"Warning: Unexpected number of timesteps per year: {hours_per_year}. Expected 8760 or 8784.\"\n", + " )\n", "else:\n", " print(\"Warning: No 'time' dimension found in weather data. Pysam may fail.\")" ] diff --git a/tutorials_and_tools/tutorials_and_tools/API Call Required - load_nsrdb_distributed.ipynb b/tutorials/05_advanced/04_nsrdb_distributed_api.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/API Call Required - load_nsrdb_distributed.ipynb rename to tutorials/05_advanced/04_nsrdb_distributed_api.ipynb index 86213e039..f0e6fd3fa 100644 --- a/tutorials_and_tools/tutorials_and_tools/API Call Required - load_nsrdb_distributed.ipynb +++ b/tutorials/05_advanced/04_nsrdb_distributed_api.ipynb @@ -132,7 +132,7 @@ "source": [ "### Viewing Results\n", "\n", - "Same as in the other tutorial, our results are stored in an xarray dataset with a dask backend so you will have to use `.compute()` on the dataset to inspect the individual values of the dask arrays. \n", + "Same as in the other tutorial, our results are stored in an xarray dataset with a dask backend so you will have to use `.compute()` on the dataset to inspect the individual values of the dask arrays.\n", "\n", "Click on the `Data variables` dropdown to expand the dataset viewer." ] @@ -1885,11 +1885,11 @@ "source": [ "### Spot Check\n", "\n", - "We can plot the entire TMY air_temperature to check that our data has loaded correctly. \n", + "We can plot the entire TMY air_temperature to check that our data has loaded correctly.\n", "\n", "Explanation of steps\n", "\n", - "geo_weather is our weather xarray dataset. We can index into the first entry at the 0th index by using isel (index-select). This will grab the data from the first gid. Then we pick the air temperature attribute. This can be replaced with bracket notation so `.temp_air` becomes `[\"temp_air\"]. \n", + "geo_weather is our weather xarray dataset. We can index into the first entry at the 0th index by using isel (index-select). This will grab the data from the first gid. Then we pick the air temperature attribute. This can be replaced with bracket notation so `.temp_air` becomes `[\"temp_air\"].\n", "\n", "This selects a single array from the dataset that is labeled as \"temp_air\". This array will be a dask array so the values will be stored out of memory, we would have to load it using `.compute()` to directly inspect it but when plotting with matplotlib it will load the array for us." ] diff --git a/tutorials_and_tools/tutorials_and_tools/Removed store.py Required - load_pvgis_distributed.ipynb b/tutorials/05_advanced/05_pvgis_distributed.ipynb similarity index 97% rename from tutorials_and_tools/tutorials_and_tools/Removed store.py Required - load_pvgis_distributed.ipynb rename to tutorials/05_advanced/05_pvgis_distributed.ipynb index 3c4b740c5..01785fb41 100644 --- a/tutorials_and_tools/tutorials_and_tools/Removed store.py Required - load_pvgis_distributed.ipynb +++ b/tutorials/05_advanced/05_pvgis_distributed.ipynb @@ -57,7 +57,7 @@ "source": [ "### Dask\n", "\n", - "We are using dask to parallelize the weather API calls. We need to start a dask client as shown below. This can also be done with `pvdeg.geospatial.start_dask`. \n", + "We are using dask to parallelize the weather API calls. We need to start a dask client as shown below. This can also be done with `pvdeg.geospatial.start_dask`.\n", "\n", "Click on the link to open a localhost dashboard for the dask client. This will allow us to see what happens when we run functions using dask." ] @@ -86,12 +86,12 @@ "source": [ "## Requesting Weather\n", "\n", - "Now that we have our coordinates, and dask client initialized we can make our parallel api calls using `pvdeg.weather.weather_distributed`. \n", + "Now that we have our coordinates, and dask client initialized we can make our parallel api calls using `pvdeg.weather.weather_distributed`.\n", "\n", "We want data from PVGIS so we will use that as the database. For more information about the databases look at the `pvdeg.weather.get` docstring. We will pass in the coordinates list which is a list of tuple pairs for the latitude and longiutudes to request weather at.\n", "\n", - "`weather_ds` is the collected weather dataset from the API calls to PVGIS. \n", - "`meta_df` is the collected meta dataframe from the API calls to PVGIS. \n", + "`weather_ds` is the collected weather dataset from the API calls to PVGIS.\n", + "`meta_df` is the collected meta dataframe from the API calls to PVGIS.\n", "`failed_gids` will be a list of the failed indexes from the input coordinates list that we could not get weather from using PVGIS. These may have failed randomly so it is worth trying again." ] }, @@ -142,10 +142,10 @@ "\n", "We can use `pvdeg.store.store` to save geospatial data to our living dataset in the common form provided by `pvdeg`. The data is stored in various groups and subfolders but they will be arranged based on the *source* and *periodicity*.\n", "\n", - "For example: \n", - " - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\" \n", - " - 30 minute PVGIS to a group called \"PVGIS-30min\" \n", - " - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\" " + "For example:\n", + " - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\"\n", + " - 30 minute PVGIS to a group called \"PVGIS-30min\"\n", + " - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\"" ] }, { @@ -163,12 +163,12 @@ "source": [ "## Load\n", "\n", - "`PVDeg` makes use of `dask` to handle larger than memory datasets. Trandionally, this was only useful in our HPC environment but as your local database grows overtime, it will eventually surpass the limits of your computer's volatile memory. Additionally, `dask` allows us to parallelize geospatial calculations via `pvdeg.geospatial.analysis`. This ability can be utilized on local machines or HPC clusters alike. \n", + "`PVDeg` makes use of `dask` to handle larger than memory datasets. Trandionally, this was only useful in our HPC environment but as your local database grows overtime, it will eventually surpass the limits of your computer's volatile memory. Additionally, `dask` allows us to parallelize geospatial calculations via `pvdeg.geospatial.analysis`. This ability can be utilized on local machines or HPC clusters alike.\n", "\n", - "`PVDeg` implements the ability to access your local living database via `pvdeg.store.get`. This method takes a string called `group`. Groups are created automatically in your store when you save data using `pvdeg.store.store`. As described in the `pvdeg.store.store` docstring and the *Store* section above, NSRDB will follow a similar scheme but it not implemented yet. \n", - " - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\" \n", - " - 30 minute PVGIS to a group called \"PVGIS-30min\" \n", - " - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\" " + "`PVDeg` implements the ability to access your local living database via `pvdeg.store.get`. This method takes a string called `group`. Groups are created automatically in your store when you save data using `pvdeg.store.store`. As described in the `pvdeg.store.store` docstring and the *Store* section above, NSRDB will follow a similar scheme but it not implemented yet.\n", + " - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\"\n", + " - 30 minute PVGIS to a group called \"PVGIS-30min\"\n", + " - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\"" ] }, { diff --git a/tutorials/05_advanced/README.md b/tutorials/05_advanced/README.md new file mode 100644 index 000000000..957ecd93f --- /dev/null +++ b/tutorials/05_advanced/README.md @@ -0,0 +1,34 @@ +# Advanced Topics + +Advanced tutorials covering custom functions, API integrations, and specialized workflows. + +## Tutorials + +### General Advanced Topics +- **custom_functions_nopython.ipynb** - Creating custom functions with numba/nopython mode +- **pysam_single_location.ipynb** - Using PySAM for single location analysis + +### API Required Tutorials +The following tutorials require API access to external data sources: + +- **pysam_api.ipynb** - PySAM integration with API calls (requires NSRDB API key) +- **nsrdb_distributed_api.ipynb** - Distributed NSRDB data loading (requires NSRDB API key) + +### Special Requirements +- **pvgis_distributed.ipynb** - PVGIS distributed data access (requires specific dependencies) + +## API Access + +âš ï¸ **Note**: Several tutorials require API keys: + +- **NSRDB API Key**: Required for `pysam_api.ipynb` and `nsrdb_distributed_api.ipynb` + - Get your free API key at: https://developer.nrel.gov/signup/ + - Rate limits apply for free tier + +## Topics Covered + +- Custom function development with performance optimization +- PySAM (Python wrapper for System Advisor Model) +- Weather data API integration +- Distributed data processing +- Performance optimization techniques diff --git a/tutorials/05_advanced/scripts/01_custom_functions_nopython.py b/tutorials/05_advanced/scripts/01_custom_functions_nopython.py new file mode 100644 index 000000000..2fb4148e6 --- /dev/null +++ b/tutorials/05_advanced/scripts/01_custom_functions_nopython.py @@ -0,0 +1,152 @@ +# %% +import numpy as np +import sympy as sp +import matplotlib.pyplot as plt +import pvdeg + +# %% [markdown] +# ### Grabbing Weather Data from PVGIS +# +# Use pvdeg to make an API call to PVGIS to collect location and weather data for Manhattan, NYC. + +# %% +weather_df, meta_dict = pvdeg.weather.get( + database="PVGIS", + id=(40.776676, -73.971321), # manhattan (latitude, longitude) +) + +# %% [markdown] +# ### Calculating Temperature and Irradiance +# +# Use pvdeg to calculate timeseries temperature for a theoretical module from the previous metorological data. +# +# *Update this call after scenario has been merged: dev_scenario_geospatial->development->dev_symbolic* + +# %% +module_temps = pvdeg.temperature.module( + weather_df=weather_df, meta=meta_dict, conf="open_rack_glass_glass" +) + +poa_irradiance = pvdeg.spectral.poa_irradiance(weather_df=weather_df, meta=meta_dict) + +plt.figure(figsize=(10, 6)) +plt.subplot(1, 2, 1) +plt.plot( + module_temps.values +) # plotting the values in order because we are using tmy data so the years are not consistent within our data +plt.title("TMY Module Temperature, Manhattan") +plt.subplot(1, 2, 2) +plt.plot(poa_irradiance.values) +plt.legend(poa_irradiance.columns) +plt.title("TML Module Irradiance, Manhattan") +plt.show() + +# %% [markdown] +# ### Define Custom Expressions from Latex or +# +# We will use an altered arrhenius equation with an irrandiance relation (ignore the fact that this exists in pvdeg already). +# +# $R_{D} = R_{0}I^{X} e^{\frac{-{Ea}}{kT}}$ *ea is one variable so this may present some issues* +# the raw latex looks like this `R_{0}I^{X} e^{\frac{-{Ea}}{kT}}` + +# %% +lnR_0, I, X, Ea, k, T = sp.symbols("lnR_0 I X Ea k T") + +ln_R_D_expr = ( + lnR_0 * I**X * sp.exp((-Ea) / (k * T)) +) # python exponentiation is ** rather than ^ + +# viewing output +ln_R_D_expr + +# %% [markdown] +# ### Calculating Degradation Expression +# +# Generally more processing will have to happen outside of these functions than built in pvdeg functions. +# Here we are defining our arguments and correcting units. When trying to calculate using timeseries we will pass `pandas.Series` objects to the arguments. +# +# Results should be strictly scrutinized to make sure the calculation is iterating over your series correctly. It will generally be easier to write python code +# for more complex functions. This is about as complex as we will be able to get using arbitrary symbolic expressions if looping over timeseries data is required. + +# %% +module_temps_k = module_temps + 273.15 # convert C -> K + +poa_global = poa_irradiance[ + "poa_global" +] # take only the global irradiance series from the total irradiance dataframe +poa_global_kw = poa_global / 1000 # [W/m^2] -> [kW/m^2] + +# %% [markdown] +# ### This is quite slow + +# %% +values_kwarg = { + "Ea": 62.08, # activation energy, [kJ/mol] + "k": 8.31446e-3, # boltzmans constant, [kJ/(mol * K)] + "T": module_temps_k, # module temperature, [K] + "I": poa_global_kw, # module plane of array irradiance, [W/m2] + "X": 0.0341, # irradiance relation, [unitless] + "lnR_0": 13.72, # prefactor degradation [ln(%/h)] +} + +res = pvdeg.symbolic.calc_kwarg_timeseries(expr=ln_R_D_expr, kwarg=values_kwarg) + +# %% [markdown] +# ### Total degradation +# +# To calculate accumulated degradation, we can sum each of the substep values and convert to log scale (need to do this because prefactor was in log scale). + +# %% +np.log10(res.sum()) + +# %% [markdown] +# ## Example with Single Values + +# %% [markdown] +# leakage current +# $I_{leak} = \frac{V_{bias}}{R_{enc}}$ +# +# $I_{leak}$, leakage current +# ${V_{bias}}$, potential difference between cells and frame +# ${R_{enc}}$, resistance of the encapsulant +# +# electric field +# $E = \frac{V_{bias}}{d}$ +# +# $E$, electric field +# $d$, thickness of encapsulant +# +# degradation rage +# $D = k_{D} * E * I{leak}$ +# +# $D$, degradation rate +# $k_D$, degradation constant + +# %% +k_d, E, Vbias, Rencap, d = sp.symbols("k_d E Vbias Rencap d") + +pid = k_d * (Vbias / d) * (Vbias / Rencap) + +# %% +import pvdeg + +pid_kwarg = { + "Vbias": 1000, + "Rencap": 1e9, + "d": 0.0005, + "k_d": 1e-9, +} + +res = pvdeg.symbolic.calc_kwarg_floats(expr=pid, kwarg=pid_kwarg) + +res + +# %% [markdown] +# ### Calculate Values in a Dataframe using a symbolic expression +# +# After creating the symbolic expression in using latex with `pvdeg.symbolic.symbolic_from_latex`, feed it to the function to calculate the leakage current at each row in the dataframe. +# This expression calculation only accesses one dataframe row at a time so we cannot do any summations or windowed timeseries averiging within the call. At this point, write your own python function. If you create functions that would be useful, please email us your source code or create an issue and copy and paste your code there. If it is relevant it may be added to the package (credit will be given). + +# %% +# I_leak_expr = pid_kwarg["Vbias"] / pid_kwarg["Rencap"] +# pvdeg.symbolic.calc_df_symbolic(expr=I_leak_expr, df=values_df) diff --git a/tutorials/05_advanced/scripts/02_pysam_single_location.py b/tutorials/05_advanced/scripts/02_pysam_single_location.py new file mode 100644 index 000000000..c915b8847 --- /dev/null +++ b/tutorials/05_advanced/scripts/02_pysam_single_location.py @@ -0,0 +1,43 @@ +# %% +import pvdeg +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np + +# %% +weather, meta = pvdeg.weather.get( + database="PVGIS", id=(40.633365593159226, -73.9945801019899) +) + +# %% +out_dict = pvdeg.pysam.pysam( + weather_df=weather, + meta=meta, + pv_model="pysamv1", + pv_model_default="FlatPlatePVCommercial", +) +for key in sorted(out_dict.keys()): + print(key) + +# %% +for key, item in out_dict.items(): + if isinstance(item, tuple): + print(key) + +# %% [markdown] +# subarray1_poa_ground_front_cs +# subarray1_ground_rear_spatial + +# %% +x = out_dict["subarray1_ground_rear_spatial"][ + 0 +] # these are the distances where the calculations are done + +data = out_dict["subarray1_ground_rear_spatial"][1:] +row = data[15] + +# %% +row_arr = np.array(row) +plot = sns.heatmap([row_arr], square=True, cmap="magma") + +plt.show() diff --git a/tutorials/05_advanced/scripts/03_pysam_api.py b/tutorials/05_advanced/scripts/03_pysam_api.py new file mode 100644 index 000000000..61aab4393 --- /dev/null +++ b/tutorials/05_advanced/scripts/03_pysam_api.py @@ -0,0 +1,161 @@ +# %% +import pvdeg +from pvdeg import TEST_DATA_DIR +import pandas as pd +import os +import pickle +import xarray as xr + +# %% [markdown] +# ## Pysam +# +# rundown on pysam... +# +# https://nrel-pysam.readthedocs.io/en/main/inputs-from-sam.html + +# %% [markdown] +# ### PVGIS +# +# Only works with PVGIS + +# %% +weather_pvgis, meta_pvgis = pvdeg.weather.get( + database="PVGIS", id=(25.783388, -81.189029) +) + +# %% +results = pvdeg.pysam.pysam( + weather_df=weather_pvgis, + meta=meta_pvgis, + pv_model="pysamv1", + pv_model_default="FlatPlatePVCommercial", +) + +# %% +results["annual_energy"] + +# %% [markdown] +# ### Local Geospatial +# +# Using PySAM with geospatial data requires proper formatting of the weather DataFrame to match PySAM's expectations. + +# %% +GEO_META = pd.read_csv(os.path.join(TEST_DATA_DIR, "summit-meta.csv"), index_col=0) +GEO_WEATHER = xr.open_dataset(os.path.join(TEST_DATA_DIR, "summit-weather.nc")) + + +# %% [markdown] +# ### Local Geospatial - PySAM Integration +# +# The geospatial weather data is in half-hourly format (17520 timesteps) but PySAM expects hourly data (8760 timesteps). The wrapper function below handles this conversion automatically. + + +# %% +# this is just a wrapper to grab the result we want +def pysam_annual_energy( + weather_df, meta, pv_model="pysamv1", pv_model_default="FlatPlatePVCommercial" +): + # Drop the gid column if present (added by geospatial conversion) + weather_df = weather_df.drop(columns=["gid"]) + + # Resample half-hourly data to hourly (PySAM expects hourly) + weather_df = weather_df.resample("h").mean() + + results = pvdeg.pysam.pysam( + weather_df=weather_df, + meta=meta, + pv_model=pv_model, + pv_model_default=pv_model_default, + ) + + return results["annual_energy"] + + +# %% +# Select a small subset (2 gids) for demonstration to avoid timeouts +# PySAM calculations are computationally expensive +subset_gids = GEO_META.index[:2] +GEO_META_SUB = GEO_META.loc[subset_gids] +GEO_WEATHER_SUB = GEO_WEATHER.sel(gid=subset_gids) + +template = pvdeg.geospatial.output_template( + ds_gids=GEO_WEATHER_SUB, + shapes={ + "Annual Energy": ("gid",), + }, +) + +geo_res = pvdeg.geospatial.analysis( + weather_ds=GEO_WEATHER_SUB, + meta_df=GEO_META_SUB, + func=pysam_annual_energy, + template=template, +) + +# %% +geo_res + +# %% [markdown] +# ## NSRDB API + +# %% +weather_db = "PSM4" +weather_id = (25.783388, -80.189029) +weather_arg = {"api_key": "DEMO_KEY", "email": "user@mail.com", "map_variables": True} + +weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) + +# %% [markdown] +# ### Geospatial Scenario + +# %% +location_grabber = pvdeg.GeospatialScenario() + +location_grabber.addLocation(country="United States", downsample_factor=80) + +# %% +location_grabber.plot_coords() + +# %% +geo_weather, geo_meta = location_grabber.geospatial_data + +# Select a small subset (2 gids) for demonstration to avoid timeouts +# PySAM calculations are computationally expensive +subset_gids = geo_meta.index[:2] +geo_meta_sub = geo_meta.loc[subset_gids] +geo_weather_sub = geo_weather.sel(gid=subset_gids) + +template = pvdeg.geospatial.output_template( + ds_gids=geo_weather_sub, + shapes={ + "pysam_annual_energy": ("gid",), + }, +) + +geo_res = pvdeg.geospatial.analysis( + weather_ds=geo_weather_sub, + meta_df=geo_meta_sub, + func=pysam_annual_energy, # using wrapper from before + template=template, +) + +# %% +pvdeg.geospatial.plot_sparse_analysis(geo_res, data_var="pysam_annual_energy") + +# %% +# Check weather data time dimension compatibility +if "time" in GEO_WEATHER.dims: + times = pd.to_datetime(GEO_WEATHER["time"].values) + years = times.year + unique_years = set(years) + if len(unique_years) != 1: + print( + f"Warning: Weather data contains multiple years: {unique_years}. Pysam expects a single year." + ) + hours_per_year = times.size / len(unique_years) + if hours_per_year not in [8760, 8784]: + print( + f"Warning: Unexpected number of timesteps per year: {hours_per_year}. Expected 8760 or 8784." + ) +else: + print("Warning: No 'time' dimension found in weather data. Pysam may fail.") diff --git a/tutorials/05_advanced/scripts/04_nsrdb_distributed_api.py b/tutorials/05_advanced/scripts/04_nsrdb_distributed_api.py new file mode 100644 index 000000000..aca201329 --- /dev/null +++ b/tutorials/05_advanced/scripts/04_nsrdb_distributed_api.py @@ -0,0 +1,91 @@ +# %% +from dask.distributed import LocalCluster, Client +from dotenv import load_dotenv +import pvdeg +import os + +# %% [markdown] +# ### Setting Up +# +# As in [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb) we need to get ready to make our parallelized API calls. The notebook linked here goes through the process in more detail but we need to import our API key and email. This cell will not work for you unless you replace the `api_key` and `email` with your personal NSRDB api keys. [REQUEST A KEY](https://developer.nrel.gov/signup/). +# +# We also need to initalize a dask client. `pvdeg.weather.weather_distributed` will not work without it. It will fail silently and not populate and of the results in the resulting `weather_ds` called `geo_weather` in the example below. It is hard to recognize that this has occured so be careful. Make sure to initialize a dask client first. Visiting the link takes you to a daskboard that shows what dask is doing. + +# %% +load_dotenv() + +### REPLACE WITH YOUR API KEY AND EMAIL ### +api_key = "DEMO_KEY" +email = "user@mail.com" +########################################### + +workers = 4 + +cluster = LocalCluster( + n_workers=workers, + processes=True, +) + +client = Client(cluster) + +print("Daskboard link") +print(client.dashboard_link) + +# %% [markdown] +# **Note on .env file:** Create a file named `.env` in your project root directory (`c:\Users\rdaxini\Documents\GitHub\PVDegradationTools_NREL\`) with the following content: +# ``` +# api_key=YOUR_NREL_API_KEY +# email=YOUR_EMAIL_ADDRESS +# ``` +# Replace `YOUR_NREL_API_KEY` and `YOUR_EMAIL_ADDRESS` with your actual NREL developer credentials. + +# %% [markdown] +# ## Requesting Weather +# +# As with the other script [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb). We will create a list of tuple (latitude, longitude) pairs and call the function on all of them at once. failed will represent a list of failed gids, unique location ID's that correspond to points in space on the NSRDB. These are different than on PVGIS where they are arbitrary indexes that do NOT correspond to a spatial location on earth. +# +# We will request "PSM4" data from the Physical Solar Model that represents a typical meteorological year (TMY) from the NSRDB. We will have to supply the api key and email from above here. Refer to the linked script to see this in further detail. The only difference between the scripts, lies in the NSRDB/PSM4 data requiring Api keys. + +# %% +coords = [ + (25.783388, -80.189029), + (24.783388, -80.189029), +] + +geo_weather, geo_meta, failed = pvdeg.weather.weather_distributed( + database="PSM4", coords=coords, api_key=api_key, email=email +) + +# %% [markdown] +# ### Viewing Results +# +# Same as in the other tutorial, our results are stored in an xarray dataset with a dask backend so you will have to use `.compute()` on the dataset to inspect the individual values of the dask arrays. +# +# Click on the `Data variables` dropdown to expand the dataset viewer. + +# %% +geo_weather + +# %% +geo_weather.compute() + +# %% [markdown] +# ### Spot Check +# +# We can plot the entire TMY air_temperature to check that our data has loaded correctly. +# +# Explanation of steps +# +# geo_weather is our weather xarray dataset. We can index into the first entry at the 0th index by using isel (index-select). This will grab the data from the first gid. Then we pick the air temperature attribute. This can be replaced with bracket notation so `.temp_air` becomes `["temp_air"]. +# +# This selects a single array from the dataset that is labeled as "temp_air". This array will be a dask array so the values will be stored out of memory, we would have to load it using `.compute()` to directly inspect it but when plotting with matplotlib it will load the array for us. + +# %% +import matplotlib.pyplot as plt + +plt.plot(geo_weather.isel(gid=0).temp_air) + +# %% [markdown] +# ### Next Steps +# +# Now we have data ready to use for geospatial calculations. This is shown in the other distributed script [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb). You can also see how to do this in [Geospatial Templates.ipynb](../tutorials_and_tools/tutorials_and_tools/Geospatial%20Templates.ipynb) diff --git a/tutorials/05_advanced/scripts/05_pvgis_distributed.py b/tutorials/05_advanced/scripts/05_pvgis_distributed.py new file mode 100644 index 000000000..f22e4bf3c --- /dev/null +++ b/tutorials/05_advanced/scripts/05_pvgis_distributed.py @@ -0,0 +1,242 @@ +# %% +import pvdeg +from global_land_mask import globe +import matplotlib.pyplot as plt +import numpy as np +from dask.distributed import LocalCluster, Client + +# %% [markdown] +# ### Creating Coordinates List +# +# Lets Generate a Grid of Latitude and Longitude Coordinates over the UK. + +# %% +# decrease the arange step size for more fine resolution +# increase the arange step size for increased granularity +lon_UK = np.arange(-10.5, 1.76, 1) +lat_UK = np.arange(49.95, 60, 2) +lon_grid_UK, lat_grid_UK = np.meshgrid(lon_UK, lat_UK) +land_UK = globe.is_land(lat_grid_UK, lon_grid_UK) + +lon_land_UK = lon_grid_UK[land_UK] +lat_land_UK = lat_grid_UK[land_UK] + +lon_Scan = np.arange(-10.5, 31.6, 0.3) +lat_Scan = np.arange(60, 71.2, 0.3) +lon_grid_Scan, lat_grid_Scan = np.meshgrid(lon_Scan, lat_Scan) +land_Scan = globe.is_land(lat_grid_Scan, lon_grid_Scan) + +lon_land_Scan = lon_grid_Scan[land_Scan] +lat_land_Scan = lat_grid_Scan[land_Scan] + +coords = list( + zip(lat_land_UK, lon_land_UK) +) # easiest way to make a list of the right shape + +# %% [markdown] +# ### Dask +# +# We are using dask to parallelize the weather API calls. We need to start a dask client as shown below. This can also be done with `pvdeg.geospatial.start_dask`. +# +# Click on the link to open a localhost dashboard for the dask client. This will allow us to see what happens when we run functions using dask. + +# %% +workers = 4 + +cluster = LocalCluster( + n_workers=workers, + processes=True, +) + +client = Client(cluster) + +print(client.dashboard_link) + +# %% [markdown] +# ## Requesting Weather +# +# Now that we have our coordinates, and dask client initialized we can make our parallel api calls using `pvdeg.weather.weather_distributed`. +# +# We want data from PVGIS so we will use that as the database. For more information about the databases look at the `pvdeg.weather.get` docstring. We will pass in the coordinates list which is a list of tuple pairs for the latitude and longiutudes to request weather at. +# +# `weather_ds` is the collected weather dataset from the API calls to PVGIS. +# `meta_df` is the collected meta dataframe from the API calls to PVGIS. +# `failed_gids` will be a list of the failed indexes from the input coordinates list that we could not get weather from using PVGIS. These may have failed randomly so it is worth trying again. + +# %% +weather_ds, meta_df, failed_gids = pvdeg.weather.weather_distributed( + database="PVGIS", coords=coords +) + +# %% [markdown] +# ### Viewing Result +# +# The result is stored in an xarray dataset with a dask array backend. This allows us to parallelize the computation/api requests but makes it a little harder to view the data. We can inspect the dataset using the following but we will not be able to inspect any values. +# +# ``` +# weather_ds +# ``` +# +# To load the values from the dask arrays we need to use `.compute()` as follows. +# + +# %% +weather_ds.compute() + +# %% [markdown] +# ### Saving Geospatial Data Locally +# +# The goal of `pvdeg.store` is to create a living local database of meteoroligical data that grows overtime as your geospatial data needs grow. To do this `PVDeg` will save to a folder called `PVDeg-Meteorological` your user home directory. For me this is located at `C:\Users\tford\PVDeg-Meteorological`. This directory will contain a `zarr` store, this is a popular format for storing multi-dimensional array data, not dissimilar to `h5` files. It was chosen over `h5` because `zarr` stores arrays in chunked compressed files that make access very easy without opening an entire file like `h5`. This is an oversimplification of the design process but we felt `zarr` was a better fit. +# +# ## Store +# +# We can use `pvdeg.store.store` to save geospatial data to our living dataset in the common form provided by `pvdeg`. The data is stored in various groups and subfolders but they will be arranged based on the *source* and *periodicity*. +# +# For example: +# - Hourly PVGIS data will be saved to a group called "PVGIS-1hr" +# - 30 minute PVGIS to a group called "PVGIS-30min" +# - 15 minute PVGIS will be saved to a group called "PVGIS-15min" + +# %% +pvdeg.store.store(weather_ds, meta_df) + +# %% [markdown] +# ## Load +# +# `PVDeg` makes use of `dask` to handle larger than memory datasets. Trandionally, this was only useful in our HPC environment but as your local database grows overtime, it will eventually surpass the limits of your computer's volatile memory. Additionally, `dask` allows us to parallelize geospatial calculations via `pvdeg.geospatial.analysis`. This ability can be utilized on local machines or HPC clusters alike. +# +# `PVDeg` implements the ability to access your local living database via `pvdeg.store.get`. This method takes a string called `group`. Groups are created automatically in your store when you save data using `pvdeg.store.store`. As described in the `pvdeg.store.store` docstring and the *Store* section above, NSRDB will follow a similar scheme but it not implemented yet. +# - Hourly PVGIS data will be saved to a group called "PVGIS-1hr" +# - 30 minute PVGIS to a group called "PVGIS-30min" +# - 15 minute PVGIS will be saved to a group called "PVGIS-15min" + +# %% [markdown] +# ### Load PVGIS-1hr Data +# +# The example below shows us loading the hourly tmy data from PVGIS that we gathered and saved to our zarr store in the above cells. This gets us the form of a weather xarray.Dataset (`geo_weather` in this example) and a metadata dataframe (`geo_meta` in this example). +# +# These can be treated like any other geospatial data shown in the `pvdeg` tutorials and tools or documentation. + +# %% +geo_weather, geo_meta = pvdeg.store.get(group="PVGIS-1hr") + +# %% [markdown] +# ### Inspecting the Results +# +# explain *.compute() and dask here* + +# %% +plt.plot(geo_weather.sel(gid=0).dni) + +# %% +geo_meta + +# %% [markdown] +# ## Geospatial Calculations from Locally Stored Data +# +# As shown above we can load from our `zarr` store and treat it like any other geospatial data in `pvdeg`. +# +# For demonstration we can run the analysis below to estimate effective standoff height and operating temperatures for the provided data. It may look like the `geo_res` contains empty results but that is because we did not have input data for all of the points in the input grid (think of this as a 2D plane formed between the latitude and longitude axes). Clicking on the stack of three circles in the bottom cell will expand the datavariable (like an attribute of the multidimensional array structure) and show the results. +# +# Additionally, we can interpolate and plot the results. + +# %% +func = pvdeg.standards.standoff + +template = pvdeg.geospatial.auto_template(func=func, ds_gids=geo_weather) + +geo_res = pvdeg.geospatial.analysis( + weather_ds=geo_weather, meta_df=geo_meta, func=func, template=template +) + +# %% +geo_res + +# %% [markdown] +# This plot lacks information on the area and does not include some political boundary lines. For more information on plotting look at the `Scenario - Non-uniform Mountain Downselect.ipynb` tutorial in the tutorials and tools folder. + +# %% +pvdeg.geospatial.plot_sparse_analysis_land( + geo_res, data_var="T98_0", method="nearest", resolution=10j +) + +# %% [markdown] +# ### Growing Our Living Store +# +# What if we want to download more points from Europe? We can keep our old download in the store and shelve it to look at northern Europe. +# +# We will start by generating a range of points that cover Europe. + +# %% +lon_EU = np.arange(-25.0, 51.0, 1) # Adjusted for EU longitudes +lat_EU = np.arange(34.0, 73.0, 2) # Adjusted for EU latitudes + +# Create meshgrid for EU +lon_grid_EU, lat_grid_EU = np.meshgrid(lon_EU, lat_EU) + +# Check land coverage in the EU +land_EU = globe.is_land(lat_grid_EU, lon_grid_EU) + +# Extract land coordinates in the EU +lon_land_EU = lon_grid_EU[land_EU] +lat_land_EU = lat_grid_EU[land_EU] + +# Define the Scan grid ranges +lon_Scan = np.arange(-10.5, 31.6, 0.3) +lat_Scan = np.arange(60, 71.2, 0.3) +lon_grid_Scan, lat_grid_Scan = np.meshgrid(lon_Scan, lat_Scan) +land_Scan = globe.is_land(lat_grid_Scan, lon_grid_Scan) + +lon_land_Scan = lon_grid_Scan[land_Scan] +lat_land_Scan = lat_grid_Scan[land_Scan] + +# %% +plt.scatter(lon_land_EU, lat_land_EU, c="r", s=1) + +# %% +w, m, failed_gids = pvdeg.weather.weather_distributed( + database="PVGIS", coords=[(lat_land_EU[0], lon_land_EU[0])] +) + +# %% +pvdeg.store.store(weather_ds=w, meta_df=m) + +# %% +loaded_weather, loaded_meta = pvdeg.store.get(group="PVGIS-1hr") + +# %% +loaded_meta + +# %% +loaded_weather + +# %% +loaded_weather.sel(gid=22).compute() + +# %% +import matplotlib.pyplot as plt + +plt.plot(loaded_weather.sel(gid=0).dhi) + +# %% +wet, met = pvdeg.store.get("PVGIS-1hr") + +# %% +wet + +# %% +loaded_meta + +# %% +func = pvdeg.standards.standoff + +template = pvdeg.geospatial.auto_template(func=func, ds_gids=loaded_weather) + +loaded_geo_res = pvdeg.geospatial.analysis( + weather_ds=loaded_weather, meta_df=loaded_meta, func=func, template=template +) + +# %% +pvdeg.geospatial.plot_sparse_analysis_land(loaded_geo_res, data_var="T98_inf") + +# %% diff --git a/tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - Geospatial_world_map.ipynb b/tutorials/06_nrel_hpc/01_geospatial_world_map.ipynb similarity index 96% rename from tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - Geospatial_world_map.ipynb rename to tutorials/06_nrel_hpc/01_geospatial_world_map.ipynb index c086a6033..8a73f3c55 100644 --- a/tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - Geospatial_world_map.ipynb +++ b/tutorials/06_nrel_hpc/01_geospatial_world_map.ipynb @@ -83,6 +83,7 @@ { "cell_type": "code", "execution_count": null, + "id": "a4643119", "metadata": {}, "outputs": [], "source": [ @@ -102,15 +103,28 @@ "\n", "weather_ds_himawari, meta_df_himawari = pvdeg.weather.get(\n", " weather_db, geospatial=True, **weather_arg\n", - ")\n", - "\n", - "# %%\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cf9f951", + "metadata": {}, + "outputs": [], + "source": [ "meta_df_himawari_sub, gids_meta_df_himawari = pvdeg.utilities.gid_downsampling(\n", " meta_df_himawari, 3\n", ")\n", - "weather_ds_himawari_sub = weather_ds_himawari.sel(gid=meta_df_himawari_sub.index)\n", - "\n", - "# %%\n", + "weather_ds_himawari_sub = weather_ds_himawari.sel(gid=meta_df_himawari_sub.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "geo_himawari = {\n", " \"func\": pvdeg.standards.standoff,\n", " \"weather_ds\": weather_ds_himawari_sub,\n", @@ -127,6 +141,7 @@ { "cell_type": "code", "execution_count": null, + "id": "acf47855", "metadata": {}, "outputs": [], "source": [ @@ -146,13 +161,26 @@ "\n", "weather_ds_goes, meta_df_goes = pvdeg.weather.get(\n", " weather_db, geospatial=True, **weather_arg\n", - ")\n", - "\n", - "# %%\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba3b18b8", + "metadata": {}, + "outputs": [], + "source": [ "meta_df_goes_sub, gids_meta_df_goes = pvdeg.utilities.gid_downsampling(meta_df_goes, 8)\n", - "weather_ds_goes_sub = weather_ds_goes.sel(gid=meta_df_goes_sub.index)\n", - "\n", - "# %%\n", + "weather_ds_goes_sub = weather_ds_goes.sel(gid=meta_df_goes_sub.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "geo_goes = {\n", " \"func\": pvdeg.standards.standoff,\n", " \"weather_ds\": weather_ds_goes_sub,\n", @@ -167,6 +195,7 @@ { "cell_type": "code", "execution_count": null, + "id": "00fa08de", "metadata": {}, "outputs": [], "source": [ @@ -186,15 +215,28 @@ "\n", "weather_ds_meteosat, meta_df_meteosat = pvdeg.weather.get(\n", " weather_db, geospatial=True, **weather_arg\n", - ")\n", - "\n", - "# %%\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcf9d773", + "metadata": {}, + "outputs": [], + "source": [ "meta_df_meteosat_sub, gids_meta_df_meteosat = pvdeg.utilities.gid_downsampling(\n", " meta_df_meteosat, 4\n", ")\n", - "weather_ds_meteosat_sub = weather_ds_meteosat.sel(gid=meta_df_meteosat_sub.index)\n", - "\n", - "# %%\n", + "weather_ds_meteosat_sub = weather_ds_meteosat.sel(gid=meta_df_meteosat_sub.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "geo_meteosat = {\n", " \"func\": pvdeg.standards.standoff,\n", " \"weather_ds\": weather_ds_meteosat_sub,\n", diff --git a/tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - LETID - Outdoor Geospatial Demo.ipynb b/tutorials/06_nrel_hpc/02_letid_outdoor_geospatial_demo.ipynb similarity index 100% rename from tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - LETID - Outdoor Geospatial Demo.ipynb rename to tutorials/06_nrel_hpc/02_letid_outdoor_geospatial_demo.ipynb diff --git a/tutorials/06_nrel_hpc/README.md b/tutorials/06_nrel_hpc/README.md new file mode 100644 index 000000000..422f405cf --- /dev/null +++ b/tutorials/06_nrel_hpc/README.md @@ -0,0 +1,35 @@ +# NREL HPC Tutorials + +Advanced tutorials that require access to NREL's High Performance Computing (HPC) resources. + +## âš ï¸ HPC Access Required + +All tutorials in this folder require: +- Access to NREL's HPC systems (Kestrel, Eagle, etc.) +- NREL network connectivity or VPN +- Appropriate computing allocations + +These tutorials are intended for NREL staff and collaborators with HPC access. + +## Tutorials + +1. **01_geospatial_world_map.ipynb** - Global geospatial analysis and visualization +2. **02_letid_outdoor_geospatial_demo.ipynb** - LETID modeling across geographic regions + +## Topics Covered + +- Large-scale geospatial computations +- Global PV degradation mapping +- Distributed computing workflows +- High-resolution climate data processing + +## Getting Access + +If you are an NREL employee or collaborator and need HPC access: +1. Visit the NREL HPC website for account requests +2. Request appropriate computing allocations +3. Set up VPN access if working remotely + +## Alternative + +If you don't have HPC access, check out the **04_geospatial** folder which includes `01_local_scenario.ipynb` that can run on standard hardware. diff --git a/tutorials/06_nrel_hpc/scripts/01_geospatial_world_map.py b/tutorials/06_nrel_hpc/scripts/01_geospatial_world_map.py new file mode 100644 index 000000000..10fc0b78b --- /dev/null +++ b/tutorials/06_nrel_hpc/scripts/01_geospatial_world_map.py @@ -0,0 +1,670 @@ +# %% [markdown] +# # Note: This notebook is in development +# M. Springer 2024-06-05 + +# %% +print("Importing libraries...") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pvdeg +import xarray as xr +import os + +print("Done!") + +# %% [markdown] +# # Calculate Standoff + +# %% +work_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/standoff_fine" +data_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/data" + +# %% +local = { + "manager": "local", + "n_workers": 100, +} + +kestrel = { + "manager": "slurm", + "n_jobs": 8, # Max number of nodes used for parallel processing + "cores": 100, + "processes": 50, + "memory": "245GB", + "account": "pvfem", + "queue": "standard", + "walltime": "8:00:00", + # "scheduler_options": {"host": socket.gethostname()}, +} + +print("Starting Dask client...") +client = pvdeg.geospatial.start_dask(hpc=kestrel) +print("Cluster ready!") + +# %% +# Get weather data +weather_db = "NSRDB" + +# %% +weather_arg = { + "satellite": "Himawari", + "names": "tmy-2020", + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} + +weather_ds_himawari, meta_df_himawari = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +# %% +meta_df_himawari_sub, gids_meta_df_himawari = pvdeg.utilities.gid_downsampling( + meta_df_himawari, 3 +) +weather_ds_himawari_sub = weather_ds_himawari.sel(gid=meta_df_himawari_sub.index) + +# %% +geo_himawari = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_himawari_sub, + "meta_df": meta_df_himawari_sub, +} + +standoff_res_himawari = pvdeg.geospatial.analysis(**geo_himawari) +standoff_res_himawari.to_netcdf(os.path.join(work_dir, "standoff_himawari.nc")) +standoff_res_himawari.to_dataframe().to_csv( + os.path.join(work_dir, "standoff_himawari.csv") +) + +# %% +weather_arg = { + "satellite": "GOES", + "names": 2021, + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} + +weather_ds_goes, meta_df_goes = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +# %% +meta_df_goes_sub, gids_meta_df_goes = pvdeg.utilities.gid_downsampling(meta_df_goes, 8) +weather_ds_goes_sub = weather_ds_goes.sel(gid=meta_df_goes_sub.index) + +# %% +geo_goes = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_goes_sub, + "meta_df": meta_df_goes_sub, +} + +standoff_res_goes = pvdeg.geospatial.analysis(**geo_goes) +standoff_res_goes.to_netcdf(os.path.join(work_dir, "standoff_goes.nc")) +standoff_res_goes.to_dataframe().to_csv(os.path.join(work_dir, "standoff_goes.csv")) + +# %% +weather_arg = { + "satellite": "METEOSAT", + "names": 2019, + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} + +weather_ds_meteosat, meta_df_meteosat = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +# %% +meta_df_meteosat_sub, gids_meta_df_meteosat = pvdeg.utilities.gid_downsampling( + meta_df_meteosat, 4 +) +weather_ds_meteosat_sub = weather_ds_meteosat.sel(gid=meta_df_meteosat_sub.index) + +# %% +geo_meteosat = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_meteosat_sub, + "meta_df": meta_df_meteosat_sub, +} + +standoff_res_meteosat = pvdeg.geospatial.analysis(**geo_meteosat) +standoff_res_meteosat.to_netcdf(os.path.join(work_dir, "standoff_meteosat.nc")) +standoff_res_meteosat.to_dataframe().to_csv( + os.path.join(work_dir, "standoff_meteosat.csv") +) + +# %% +# Auxillary data +import h5py + +fp_weather_aux = "/projects/pvsoiling/pvdeg/data/world_map_aux.h5" +fp_meta_aux = "/projects/pvsoiling/pvdeg/data/meta_world_map_aux.csv" + +# weather_aux = pvdeg.weather.read(fp_weather_aux, 'h5') +meta_aux = pd.read_csv(fp_meta_aux, index_col=0) + +# xarray work around for aux data +dss = [] +drop_variables = ["meta", "time_index", "tmy_year", "tmy_year_short", "coordinates"] + +hf = h5py.File(fp_weather_aux, "r") +attr = list(hf) + +attr_to_read = [elem for elem in attr if elem not in drop_variables] + +chunks = [] +shapes = [] +for var in attr_to_read: + chunks.append(hf[var].chunks if hf[var].chunks is not None else (np.nan, np.nan)) + shapes.append(hf[var].shape if hf[var].shape is not None else (np.nan, np.nan)) +chunks = min(set(chunks)) +shapes = min(set(shapes)) + + +time_index = pd.to_datetime(hf["time_index"][...].astype(str)).values +meta_df = meta_aux +coords = {"gid": meta_df.index.values, "time": time_index} +coords_len = {"time": time_index.shape[0], "gid": meta_df.shape[0]} + +ds = xr.open_dataset( + fp_weather_aux, + engine="h5netcdf", + phony_dims="sort", + chunks={"phony_dim_0": -1, "phony_dim_1": -1}, + drop_variables=drop_variables, + mask_and_scale=False, + decode_cf=True, +) + +for var in ds.data_vars: + if hasattr(getattr(ds, var), "psm_scale_factor"): + scale_factor = 1 / ds[var].psm_scale_factor + print(scale_factor) + getattr(ds, var).attrs["scale_factor"] = scale_factor + +rename = {} +for ( + phony, + length, +) in ds.sizes.items(): + if length == coords_len["time"]: + rename[phony] = "time" + elif length == coords_len["gid"]: + rename[phony] = "gid" +ds = ds.rename(rename) +ds = ds.assign_coords(coords) + +# TODO: In case re-chunking becomes necessary +# ax0 = list(ds.sizes.keys())[list(ds.sizes.values()).index(shapes[0])] +# ax1 = list(ds.sizes.keys())[list(ds.sizes.values()).index(shapes[1])] +# ds = ds.chunk(chunks={ax0:chunks[0], ax1:chunks[1]}) +dss.append(ds) + +ds = xr.merge(dss) +ds = xr.decode_cf(ds) + +# Rechunk time axis +ds = ds.unify_chunks() +ds = ds.chunk(chunks={"time": -1, "gid": ds.chunks["gid"]}) + +weather_ds = ds + +DSET_MAP = {"air_temperature": "temp_air", "Relative Humidity": "relative_humidity"} +META_MAP = {"elevation": "altitude", "Local Time Zone": "tz", "timezone": "tz"} + +for dset in weather_ds.data_vars: + if dset in DSET_MAP.keys(): + weather_ds = weather_ds.rename({dset: DSET_MAP[dset]}) + +for mset in meta_df.columns: + if mset in META_MAP.keys(): + meta_df.rename(columns={mset: META_MAP[mset]}, inplace=True) + +weather_ds_aux = weather_ds + +geo_aux = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_aux, + "meta_df": meta_aux, +} + +standoff_res_aux = pvdeg.geospatial.analysis(**geo_aux) +standoff_res_aux.to_netcdf(os.path.join(work_dir, "standoff_aux.nc")) +standoff_res_aux.to_dataframe().to_csv(os.path.join(work_dir, "standoff_aux.csv")) + +# %% +weather_db = "NSRDB" +weather_arg = { + "satellite": "METEOSAT", + "names": 2019, + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} +weather_ds_meteosat, meta_df_meteosat = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +time_hourly = pd.date_range("2019-01-01", freq="h", periods=365 * 24) +weather_ds_meteosat_hourly = weather_ds_meteosat.sel(time=time_hourly) + +europe = [ + "Spain", + "Portugal", #'Ireland', 'United Kingdom', + "France", + "Belgium", + "Netherlands", + "Norway", + "Luxembourg", + "Germany", + "Switzerland", + "Italy", + "Monaco", + "Denmark", + "Liechtenstein", + "Austria", + "Sweden", + "Czech Republic", + "San Marino", + "Slovenia", + "Croatia", + "Poland", + "Malta", + "Bosnia and Herzegovina", + "Hungary", + "Slovakia", + "Montenegro", + "Serbia", + "Albania", + "Greece", + "Romania", + "Macedonia", + "Latvia", + "Lithuania", + "Finland", + "Estonia", + "Ukraine", + "Bulgaria", + "Belarus", + "Moldova", + "Turkey", + "Cyprus", + "Northern Cyprus", + "Georgia", +] + +meta_df_europe = meta_df_meteosat[meta_df_meteosat["country"].isin(europe)] + +meta_df_europe_sub, gids_sub = pvdeg.utilities.gid_downsampling(meta_df_europe, 4) +weather_europe_sub = weather_ds_meteosat_hourly.sel(gid=meta_df_europe_sub.index) + +meta_uk = pd.read_csv("../../world_map/data/meta_pvgis_uk_4300.csv") +meta_uk_sub, gids_sub = pvdeg.utilities.gid_downsampling(meta_uk, 1) + +meta_scan1 = pd.read_csv(f"{data_dir}/meta_pvgis_scan_coarse1500.csv", index_col=0) +meta_scan12 = pd.read_csv( + f"{data_dir}/meta_pvgis_scan_coarse_1500_1599.csv", index_col=0 +) +meta_scan2 = pd.read_csv(f"{data_dir}/meta_pvgis_scan_coarse2100.csv", index_col=0) +meta_scan = pd.concat([meta_scan1, meta_scan12, meta_scan2]) + +meta_pvgis = pd.concat([meta_scan, meta_uk_sub]) + +lat_NSRDB = meta_df_europe_sub["latitude"].to_numpy() +meta_pvgis["latitude_pvgis"] = meta_pvgis["latitude"] +meta_pvgis["latitude"] = meta_pvgis["latitude"].apply( + lambda x: ( + lat_NSRDB[np.argmin(np.abs(x - lat_NSRDB))] + if x < meta_df_europe_sub["latitude"].max() + else x + ) +) + +lon_NSRDB = meta_df_europe_sub["longitude"].to_numpy() +meta_pvgis["longitude_pvgis"] = meta_pvgis["longitude"] +meta_pvgis["longitude"] = meta_pvgis["longitude"].apply( + lambda x: ( + lon_NSRDB[np.argmin(np.abs(x - lon_NSRDB))] + if x > meta_df_europe_sub["longitude"].min() + else x + ) +) +meta_pvgis["tz"] = 0 +meta_pvgis = meta_pvgis.reset_index() + +meta_eu_coarse = pd.concat([meta_df_europe_sub, meta_pvgis]) +meta_eu_coarse["tz"] = 0 +meta_eu_coarse = meta_eu_coarse.reset_index() +meta_eu_coarse.to_csv("../../world_map/data/meta_eu_coarse.csv") + +weather_uk = xr.open_dataset("../../world_map/data/weather_ds_uk_4300.nc") +weather_uk = weather_uk.sel(gid=meta_uk.index) +weather_uk_sub = weather_uk.sel(gid=meta_uk_sub.index) + +weather_scan1 = xr.open_dataset("../../world_map/data/weather_ds_scan_coarse1500.nc") +weather_scan12 = xr.open_dataset( + "../../world_map/data/weather_ds_scan_coarse_1500_1599.nc" +) +weather_scan2 = xr.open_dataset("../../world_map/data/weather_ds_scan_coarse2100.nc") +weather_scan = xr.concat( + [ + weather_scan1.sel(gid=slice(0, 1500)), + weather_scan12.sel(gid=slice(1501, 1599)), + weather_scan2.sel(gid=slice(1600, None)), + ], + dim="gid", +) +weather_scan = weather_scan.sel(gid=meta_scan.index) +# weather_scan = xr.concat([weather_scan1, weather_scan2], dim='gid') + +weather_pvgis = xr.concat([weather_scan, weather_uk_sub], dim="gid") +weather_pvgis = weather_pvgis.drop_vars(["IR(h)", "wind_direction", "pressure"]) + +weather_pvgis = weather_pvgis.assign_coords({"gid": meta_pvgis.index}) +weather_pvgis = weather_pvgis.chunk(chunks={"time": -1, "gid": 100}) +weather_pvgis = weather_pvgis.unify_chunks() + +meta_df_pvgis_sub, gids_meta_df_pvgis = pvdeg.utilities.gid_downsampling(meta_pvgis, 4) +weather_ds_pvgis_sub = weather_pvgis.sel(gid=meta_df_pvgis_sub.index) + +# weather_europe_sub = weather_europe_sub.assign_coords({'time': pd.date_range("2022-01-01", freq="h", periods=365 * 24),}) +# weather_eu_coarse = xr.concat([weather_europe_sub, weather_pvgis], dim='gid') +# weather_eu_coarse = weather_eu_coarse.assign_coords({'gid': meta_eu_coarse.index}) +# weather_eu_coarse = weather_eu_coarse.chunk(chunks={"time": -1, "gid": 100}) +# weather_eu_coarse = weather_eu_coarse.unify_chunks() + +# with open('weather_eu_coarse.pickle', 'wb') as handle: +# pickle.dump(weather_eu_coarse, handle, protocol=pickle.HIGHEST_PROTOCOL) + + +geo_pvgis = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_pvgis, + "meta_df": meta_pvgis, +} + +standoff_res_pvgis = pvdeg.geospatial.analysis(**geo_pvgis) +standoff_res_pvgis.to_netcdf(os.path.join(work_dir, "standoff_pvgis.nc")) +standoff_res_pvgis.to_dataframe().to_csv(os.path.join(work_dir, "standoff_pvgis.csv")) + +# %% +meta_north = pd.read_csv(f"{data_dir}/meta_pvgis_north_3300.csv", index_col=0) +weather_north = xr.open_dataset(f"{data_dir}/weather_ds_north_3300.nc") +weather_north = weather_north.sel(gid=meta_north.index) + +weather_north = weather_north.assign_coords({"gid": meta_north.index}) +weather_north = weather_north.chunk(chunks={"time": -1, "gid": 100}) +weather_north = weather_north.unify_chunks() + + +geo_pvgis = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_north, + "meta_df": meta_north, +} + +standoff_res_pvgis = pvdeg.geospatial.analysis(**geo_pvgis) +standoff_res_pvgis.to_netcdf(os.path.join(work_dir, "standoff_pvgis_north.nc")) +standoff_res_pvgis.to_dataframe().to_csv( + os.path.join(work_dir, "standoff_pvgis_north.csv") +) + +# %% [markdown] +# # Post process + +# %% +import pvdeg +import os +import pandas as pd +import numpy as np +import xarray as xr +from global_land_mask import globe +import cartopy.crs as ccrs +import cartopy.io.shapereader as shpreader + +work_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/standoff_fine" +data_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/data" + +# %% +# Create 0cm standoff locations + +lon_north = np.arange(-179, 180, 0.25) +lat_north = np.arange(60, 90, 0.25) +lon_grid_north, lat_grid_north = np.meshgrid(lon_north, lat_north) +land_north = globe.is_land(lat_grid_north, lon_grid_north) +lon_land_north = lon_grid_north[land_north] +lat_land_north = lat_grid_north[land_north] + +lon_south = np.arange(-179, 180, 0.25) +lat_south = np.arange(-90, -60, 0.25) +lon_grid_south, lat_grid_south = np.meshgrid(lon_south, lat_south) +land_south = globe.is_land(lat_grid_south, lon_grid_south) +lon_land_south = lon_grid_south[land_south] +lat_land_south = lat_grid_south[land_south] + + +lon_asia = np.arange(80, 105, 0.25) +lat_asia = np.arange(50, 61, 0.25) + +lon = lon_asia +lat = np.full(lon_asia.size, 61) + +for i, lat_coord in enumerate(reversed(lat_asia)): + lon_sub = lon_asia[i:-i] + lat_sub = np.full(lon_sub.size, lat_coord) + lon = np.append(lon, lon_sub) + lat = np.append(lat, lat_sub) + +lon_asia = lon +lat_asia = lat + +# %% +fig, ax = plt.subplots() + +plt.scatter(lon_land_north, lat_land_north, c="r", s=1) +plt.scatter(lon_land_south, lat_land_south, c="b", s=1) +plt.scatter(lon, lat, c="g", s=1) + +ax.set_ylim(-90, 90) +ax.set_xlim(-180, 180) + +# %% +template_params = pvdeg.geospatial.template_parameters(pvdeg.standards.standoff) +standoff_zero_north = pvdeg.geospatial.zero_template( + lat_land_north, lon_land_north, **template_params +) +standoff_zero_south = pvdeg.geospatial.zero_template( + lat_land_south, lon_land_south, **template_params +) +standoff_zero_asia = pvdeg.geospatial.zero_template( + lat_asia, lon_asia, **template_params +) + +# %% +standoff_aux = xr.open_dataset(os.path.join(work_dir, "standoff_aux.nc")) +standoff_himawari = xr.open_dataset(os.path.join(work_dir, "standoff_himawari.nc")) +standoff_meteosat = xr.open_dataset(os.path.join(work_dir, "standoff_meteosat.nc")) +standoff_north = xr.open_dataset(os.path.join(work_dir, "standoff_pvgis_north.nc")) +standoff_pvgis = xr.open_dataset(os.path.join(work_dir, "standoff_pvgis.nc")) +standoff_goes = xr.open_dataset(os.path.join(work_dir, "standoff_goes.nc")) + +# %% +fig = plt.figure(figsize=(10, 5)) +ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.PlateCarree(), frameon=True) +ax.patch.set_visible(True) +ax.set_extent([-180, 180, -85, 85], ccrs.PlateCarree()) + +shapename = "admin_0_countries" +states_shp = shpreader.natural_earth( + resolution="110m", category="cultural", name=shapename +) + +cmap = "Spectral_r" +size = 0.75 + + +standoff_zero_north.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_zero_south.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_zero_asia.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) + +cm = standoff_himawari.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_north.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_aux.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_pvgis.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_meteosat.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=1, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_goes.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=1, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) + + +ax.add_geometries( + shpreader.Reader(states_shp).geometries(), + ccrs.PlateCarree(), + facecolor="none", + edgecolor="gray", + linewidth=0.5, +) + +cb_title = "Standoff distance [cm]" +cb = plt.colorbar(cm, shrink=0.78, aspect=30, pad=0.02) +cb.set_label(cb_title) +# ax.set_title('title') + +ax.set_xticks(np.arange(-180, 181, 20), crs=ccrs.PlateCarree()) +ax.set_yticks(np.arange(-90, 91, 10), crs=ccrs.PlateCarree()) + +ax.set_xlim(-180, 180) +ax.set_ylim(-85, 85) + +ax.set_xlabel("Longitude") +ax.set_ylabel("Latitude") + +plt.savefig(os.path.join(work_dir, "standoff_map.png"), dpi=1200, bbox_inches="tight") + +# %% diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Geospatial Demo.py b/tutorials/06_nrel_hpc/scripts/02_letid_outdoor_geospatial_demo.py similarity index 94% rename from tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Geospatial Demo.py rename to tutorials/06_nrel_hpc/scripts/02_letid_outdoor_geospatial_demo.py index 75045e8d6..3c2dffc51 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Geospatial Demo.py +++ b/tutorials/06_nrel_hpc/scripts/02_letid_outdoor_geospatial_demo.py @@ -1,24 +1,17 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # LETID - Outdoor Geospatioal Demo # # ![PVDeg Logo](../PVD_logo.png) # -# In[1]: - - +# %% import matplotlib.pyplot as plt import pandas as pd import pvdeg from pvdeg import DATA_DIR import os - -# In[2]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -28,19 +21,14 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # ## Single location example -# In[2]: - - +# %% weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") WEATHER, META = pvdeg.weather.read(weather_file, "psm") - -# In[3]: - - +# %% kwargs = { "tau_0": 115, # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C "tau_deg": 55, # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B @@ -50,21 +38,16 @@ "na_0": 100, "nb_0": 0, "nc_0": 0, - "mechanism_params": 'D037', + "mechanism_params": "repins", } - -# In[4]: - - +# %% pvdeg.letid.calc_letid_outdoors(weather_df=WEATHER, meta=META, **kwargs) - +# %% [markdown] # ### Start distributed compute cluster - DASK -# In[2]: - - +# %% local = { "manager": "local", "n_workers": 1, @@ -85,10 +68,7 @@ pvdeg.geospatial.start_dask(hpc=kestrel) - -# In[11]: - - +# %% # Get weather data weather_db = "NSRDB" @@ -114,16 +94,13 @@ weather_SW_sub = weather_ds.sel(gid=meta_SW_sub.index) - -# In[12]: - - +# %% weather_SW_sub +# %% +meta_df -# In[13]: - - +# %% # Define desired analysis geo = { "func": pvdeg.letid.calc_letid_outdoors, @@ -137,21 +114,15 @@ "na_0": 100, "nb_0": 0, "nc_0": 0, - "mechanism_params": 'D037', + "mechanism_params": "repins", } letid_res = pvdeg.geospatial.analysis(**geo) - -# In[14]: - - +# %% letid_res - -# In[18]: - - +# %% import datetime ims = [] @@ -177,10 +148,7 @@ # ims = [imageio.imread(f'./images/RH_animation_{n}.png') for n in range(1, 13)] # imageio.mimwrite(f'./images/RH_animation.gif', ims, format='GIF', duration=1000, loop=10) - -# In[34]: - - +# %% import datetime ims = [] @@ -250,13 +218,10 @@ plt.savefig(f"./images/LETID_plot_animation_{n}.png", dpi=600) - -# In[ ]: - - +# %% import imageio -ims = [imageio.imread("./images/LETID_plot_animation_{n}.png") for n in range(1, 13)] +ims = [imageio.imread(f"./images/LETID_plot_animation_{n}.png") for n in range(1, 13)] imageio.mimwrite( "./images/LETID_plot_animation.gif", ims, format="GIF", duration=1000, loop=10 ) diff --git a/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.ipynb b/tutorials/07_workshop_demos/01_astm_live_demo.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.ipynb rename to tutorials/07_workshop_demos/01_astm_live_demo.ipynb index 00dcc5969..d7f351dac 100644 --- a/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.ipynb +++ b/tutorials/07_workshop_demos/01_astm_live_demo.ipynb @@ -199,11 +199,11 @@ "source": [ "## Fetching TMYs from the NSRDB\n", "\n", - "The NSRDB, one of many sources of weather data intended for PV modeling, is free and easy to access using pvlib. As an example, we'll fetch a TMY dataset for Phoenix, AZ at coordinates [(33.4484, -112.0740)](https://goo.gl/maps/hGV92QHCm5FHJKbf9). \n", + "The NSRDB, one of many sources of weather data intended for PV modeling, is free and easy to access using pvlib. As an example, we'll fetch a TMY dataset for Phoenix, AZ at coordinates [(33.4484, -112.0740)](https://goo.gl/maps/hGV92QHCm5FHJKbf9).\n", "\n", "This function uses [`pvdeg.weather.get()`](https://pvdegradationtools.readthedocs.io/en/latest/_autosummary/pvdeg.weather.html#pvdeg.weather.get), which returns a Python dictionary of metadata and a Pandas dataframe of the timeseries weather data.\n", "\n", - "This function internally leverages [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html). However, for some of the NSRDB data relative humidity is not a given parameter, and `pvdeg` calculates the values from the downloaded data as an internal processing step. " + "This function internally leverages [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html). However, for some of the NSRDB data relative humidity is not a given parameter, and `pvdeg` calculates the values from the downloaded data as an internal processing step." ] }, { diff --git a/tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - DuraMAT Live Demo.ipynb b/tutorials/07_workshop_demos/02_duramat_live_demo.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - DuraMAT Live Demo.ipynb rename to tutorials/07_workshop_demos/02_duramat_live_demo.ipynb index 1ce2b0a59..5101049c6 100644 --- a/tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - DuraMAT Live Demo.ipynb +++ b/tutorials/07_workshop_demos/02_duramat_live_demo.ipynb @@ -82,7 +82,7 @@ } ], "source": [ - "pvdeg.geospatial.start_dask();" + "pvdeg.geospatial.start_dask()" ] }, { diff --git a/tutorials/07_workshop_demos/README.md b/tutorials/07_workshop_demos/README.md new file mode 100644 index 000000000..55e4c9ffa --- /dev/null +++ b/tutorials/07_workshop_demos/README.md @@ -0,0 +1,42 @@ +# Workshop Demonstrations + +Live demonstration materials from PVDegradationTools workshops and conferences. + +## Tutorials + +1. **01_astm_live_demo.ipynb** - ASTM workshop live demonstration +2. **02_duramat_live_demo.ipynb** - DuraMAT workshop live demonstration âš ï¸ **Requires NREL HPC** + +## Requirements + +### ASTM Live Demo (01) +- No special requirements +- Can run locally or in Google Colab + +### DuraMAT Live Demo (02) +âš ï¸ **NREL HPC Access Required** +- Requires access to NREL's High Performance Computing resources +- NREL network connectivity or VPN required +- Appropriate computing allocations needed + +## About These Demos + +These notebooks were created for live demonstrations at workshops and conferences: +- **ASTM**: American Society for Testing and Materials workshops +- **DuraMAT**: DuraMAT Consortium meetings and workshops + +The demos showcase practical applications of PVDegradationTools and provide hands-on examples for workshop participants. + +## Running the Demos + +### Local Execution +Most demos can be run locally. Follow the standard installation instructions in the main tutorials README. + +### Google Colab +Demos without HPC requirements can be launched in Google Colab using the rocket icon when viewing through the Jupyter Book. + +### HPC Execution +For demos requiring HPC (marked with âš ï¸), you must: +1. Have NREL HPC account and allocations +2. Be on NREL network or connected via VPN +3. Follow NREL HPC documentation for job submission diff --git a/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.py b/tutorials/07_workshop_demos/scripts/01_astm_live_demo.py similarity index 87% rename from tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.py rename to tutorials/07_workshop_demos/scripts/01_astm_live_demo.py index 909ad301c..3ab5bb509 100644 --- a/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.py +++ b/tutorials/07_workshop_demos/scripts/01_astm_live_demo.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # ASTM Demonstration # # ![PVDeg Logo](../PVD_logo.png) @@ -22,26 +20,18 @@ # The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems. # -# In[1]: - - +# %% # 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 pvdeg==0.1.0 - - -# In[2]: - +# #!pip install pvdeg==0.1.0 +# %% import os import pvlib import pvdeg import pandas as pd import matplotlib.pyplot as plt - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -52,7 +42,7 @@ print("pvlib version ", pvlib.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # ## 1. Import Weather Data # # The function has two minimum requirements: @@ -60,6 +50,7 @@ # - Site meta-data containing (at least) Latitude, Longitude, Time Zone # +# %% [markdown] # ### Where to get _Free_ Solar Irradiance Data? # # There are many different sources of solar irradiance data. For your projects, these are some of the most common: @@ -82,10 +73,12 @@ # * [Meteonorm](https://meteonorm.com/en/) # * [DNV Solar Resource Compass](https://src.dnv.com/) +# %% [markdown] # # ![NSRDB Example](images/tutorial_1_NSRDB_example.PNG) # +# %% [markdown] # ### NREL API Key # At the [NREL Developer Network](https://developer.nrel.gov/), there are [APIs](https://en.wikipedia.org/wiki/API) to a lot of valuable [solar resources](https://developer.nrel.gov/docs/solar/) like [weather data from the NSRDB](https://developer.nrel.gov/docs/solar/nsrdb/), [operational data from PVDAQ](https://developer.nrel.gov/docs/solar/pvdaq-v3/), or indicative calculations using [PVWatts](https://developer.nrel.gov/docs/solar/pvwatts/). In order to use these resources from NREL, you need to [register for a free API key](https://developer.nrel.gov/signup/). You can test out the APIs using the `DEMO_KEY` but it has limited bandwidth compared to the [usage limit for registered users](https://developer.nrel.gov/docs/rate-limits/). NREL has some [API usage instructions](https://developer.nrel.gov/docs/api-key/), but pvlib has a few builtin functions, like [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html), that wrap the NREL API, and call them for you to make it much easier to use. Skip ahead to the next section to learn more. But before you do... # @@ -102,9 +95,7 @@ # # **If you have an NREL API key please enter it in the next cell.** -# In[3]: - - +# %% NREL_API_KEY = None # <-- please set your NREL API key here # note you must use "quotes" around your key, for example: @@ -119,7 +110,7 @@ except KeyError: NREL_API_KEY = "DEMO_KEY" # OK for this demo, but better to get your own key - +# %% [markdown] # ## Fetching TMYs from the NSRDB # # The NSRDB, one of many sources of weather data intended for PV modeling, is free and easy to access using pvlib. As an example, we'll fetch a TMY dataset for Phoenix, AZ at coordinates [(33.4484, -112.0740)](https://goo.gl/maps/hGV92QHCm5FHJKbf9). @@ -128,100 +119,77 @@ # # This function internally leverages [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html). However, for some of the NSRDB data relative humidity is not a given parameter, and `pvdeg` calculates the values from the downloaded data as an internal processing step. -# In[4]: - - -# This cell does not run on a Collab instal. WE are troubleshooting it, but the next cell performs the same request directly with PVLib. -# If TMY is requested though, relative_humidity will not be included as NSRDB TMY dataset does not have it. +# %% +# This cell is for documentation only and is not meant to be executed. +# The next cell performs the same request directly with PVLib. """ -weather_db = 'PSM3' +weather_db = 'PSM4' weather_id = (33.4484, -112.0740) weather_arg = {'api_key': NREL_API_KEY, 'email': 'user@mail.com', - 'names': '2021', # tmy is another common option; right now requesting only 2021 data - 'attributes': [], # grabs all. to select, could be 'air_temperature', 'dew_point', 'dhi', - # 'dni', 'ghi', 'surface_albedo', 'surface_pressure', 'wind_direction', 'wind_speed' + 'year': '2021', 'map_variables': True, 'leap_day': False} weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) """ - -# In[5]: - - -weather_df, meta = pvlib.iotools.get_psm3( +# %% +weather_df, meta = pvlib.iotools.get_nsrdb_psm4_tmy( latitude=33.4484, longitude=-112.0740, api_key=NREL_API_KEY, email="silvana.ovaitt@nrel.gov", # <-- any email works here fine - names="2021", + year="tmy", map_variables=True, - attributes=[], leap_day=False, ) - -# In[13]: - - +# %% weather_df - -# In[6]: - - +# %% meta - -# In[7]: - - +# %% weather_df.head() - -# In[8]: - +# %% +# Choose the date you want to plot +date = "2010-01-01" +mask = weather_df.index.date == pd.to_datetime(date).date() +day_df = weather_df.loc[mask] fig, ax1 = plt.subplots(figsize=(9, 6)) -# Instantiate a second axes that shares the same x-axis -ax1.plot( - weather_df.loc["2021-06-28 05:00:00-07:00":"2021-06-28 20:00:00-07:00"][ - "dni_clear" - ], - label="DNI", -) +ax1.plot(day_df.index, day_df["dni"], label="DNI") ax2 = ax1.twinx() -ax2.plot( - weather_df.loc["2021-06-28 05:00:00-07:00":"2021-06-28 20:00:00-07:00"]["temp_air"], - "r", - label="Temperature", -) +ax2.plot(day_df.index, day_df["temp_air"], "r", label="Temperature") ax1.set_ylim([0, 1000]) ax2.set_ylim([0, 50]) ax1.set_ylabel("DNI") ax2.set_ylabel(r"Temperature $\degree$C") +plt.title(f"Weather Data for {date}") +plt.show() +# %% +print(weather_df.columns) +print(weather_df.index.min(), weather_df.index.max()) +print(weather_df.head()) +# %% [markdown] # ## 2. Calculate Installation Standoff - Level 1 # # We use [`pvlib.standards.calc_standoff()`](https://pvdegradationtools.readthedocs.io/en/latest/_autosummary/pvdeg.standards.html#pvdeg.standards.calc_standoff) which takes at minimum the weather data and metadata, and returns the minimum installation distance in centimeters. # # -# In[9]: - - +# %% standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta) - -# In[10]: - - +# %% print("Minimum installation distance:", standoff["x"]) - +# %% [markdown] # ## 3. Calculate Installation Standoff - Level 2 # # Let's take a closer look at the function and some optional parameters. @@ -234,27 +202,10 @@ # - x_0 : thermal decay constant [cm] (see documentation) # - wind_speed_factor : Wind speed correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM) -# In[11]: - - -standoff = pvdeg.standards.standoff( - weather_df=weather_df, - meta=meta, - level=2, - tilt=None, - azimuth=180, - sky_model="isotropic", - temp_model="sapm", - module_type="glass_polymer", - x_0=6.1, - wind_speed_factor=1, -) - - -# In[12]: - +# %% +standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, T98=70) +# %% print("Minimum installation distance:", standoff["x"]) - -# In[ ]: +# %% diff --git a/tutorials_and_tools/tutorials_and_tools/DuraMAT Live Demo.py b/tutorials/07_workshop_demos/scripts/02_duramat_live_demo.py similarity index 92% rename from tutorials_and_tools/tutorials_and_tools/DuraMAT Live Demo.py rename to tutorials/07_workshop_demos/scripts/02_duramat_live_demo.py index 3becadbc8..6eae1c065 100644 --- a/tutorials_and_tools/tutorials_and_tools/DuraMAT Live Demo.py +++ b/tutorials/07_workshop_demos/scripts/02_duramat_live_demo.py @@ -1,6 +1,4 @@ -#!/usr/bin/env python -# coding: utf-8 - +# %% [markdown] # # DuraMAT Workshop Live Demo - Geospatial analysis # # ![PVDeg Logo](../PVD_logo.png) @@ -15,16 +13,11 @@ # # ![Xarray](./images/xarray.webp) -# In[2]: - - +# %% import pandas as pd import pvdeg - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -34,18 +27,13 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # ## 1 Start distributed compute cluster - DASK -# In[3]: - - +# %% pvdeg.geospatial.start_dask() - -# In[4]: - - +# %% # Get weather data weather_db = "NSRDB" @@ -65,35 +53,17 @@ weather_ds, meta_df = pvdeg.weather.get(weather_db, geospatial=True, **weather_arg) - -# In[5]: - - +# %% weather_ds - -# In[6]: - - -meta_df["state"].unique() - - -# In[7]: - - +# %% meta_NM = meta_df[meta_df["state"] == "New Mexico"] - -# In[8]: - - +# %% meta_NM_sub, gids_NM_sub = pvdeg.utilities.gid_downsampling(meta_NM, 4) weather_NM_sub = weather_ds.sel(gid=meta_NM_sub.index) - -# In[9]: - - +# %% geo = { "func": pvdeg.standards.standoff, "weather_ds": weather_NM_sub, @@ -102,16 +72,10 @@ standoff_res = pvdeg.geospatial.analysis(**geo) - -# In[10]: - - +# %% standoff_res - -# In[11]: - - +# %% fig, ax = pvdeg.geospatial.plot_USA( standoff_res["x"], cmap="viridis", @@ -121,12 +85,10 @@ cb_title="Standoff (cm)", ) - +# %% [markdown] # # Relative Humidity Example - Time dimension -# In[12]: - - +# %% # State bar of new mexico: (35.16482, -106.58979) weather_db = "NSRDB" @@ -149,28 +111,16 @@ weather_db, weather_id, geospatial=False, **weather_arg ) - -# In[13]: - - +# %% RH_module = pvdeg.humidity.module(weather_df=weather_df, meta=meta) - -# In[14]: - - +# %% RH_module - -# In[15]: - - +# %% RH_module.plot(ls="--") - -# In[16]: - - +# %% geo = { "func": pvdeg.humidity.module, "weather_ds": weather_NM_sub, @@ -179,16 +129,10 @@ RH_module = pvdeg.geospatial.analysis(**geo) - -# In[17]: - - +# %% RH_module - -# In[18]: - - +# %% # from matplotlib.animation import FuncAnimation # from matplotlib.animation import PillowWriter # import matplotlib.animation as animation @@ -212,7 +156,7 @@ # ims = [imageio.imread(f'./images/RH_animation_{n}.png') for n in range(1, 13)] # imageio.mimwrite(f'./images/RH_animation.gif', ims, format='GIF', duration=1000, loop=10) - +# %% [markdown] # ![PVDeg Logo](./images/RH_animation.gif) -# In[ ]: +# %% diff --git a/tutorials_and_tools/README.md b/tutorials/README.md similarity index 100% rename from tutorials_and_tools/README.md rename to tutorials/README.md diff --git a/tutorials/TUTORIAL_INDEX.md b/tutorials/TUTORIAL_INDEX.md new file mode 100644 index 000000000..c260e707c --- /dev/null +++ b/tutorials/TUTORIAL_INDEX.md @@ -0,0 +1,103 @@ + + +# PV Degradation Tools - Tutorials (pvdeg) + +## Tutorial Organization + +Tutorials are organized by topic into the following folders: + +### 📚 [Basics](basics/) +Introduction to PVDegradationTools - start here if you're new! +- Basic concepts, humidity, and design +- Degradation modeling overview +- Spectral degradation +- Weather database access + +### 🔬 [Degradation](degradation/) +Specialized degradation mechanisms (LID, LETID, Van't Hoff) +- B-O LID and LETID accelerated testing +- Outdoor degradation prediction +- Degradation kinetics + +### 🎲 [Monte Carlo](monte_carlo/) +Uncertainty analysis and probabilistic modeling +- Arrhenius model simulations +- Standoff parameter analysis + +### ðŸ—ºï¸ [Geospatial](geospatial/) +Large-scale spatial analysis and scenarios +- âš ï¸ Most tutorials require **HPC access** +- Local scenario tutorial available without HPC + +### 🚀 [Advanced](advanced/) +Custom functions and API integrations +- âš ï¸ Some tutorials require **API keys** (NSRDB, PVGIS) +- Custom function development +- PySAM integration + +### 🎪 [Workshop Demos](workshop_demo/) +Live demonstration materials from workshops + +--- + +## Running the Tutorials + +### Jupyter Book (Recommended) + +For learning to use PVDeg through our tutorials online, see our [jupyter-book](https://nrel.github.io/PVDegradationTools/intro.html) +Clicking on the rocket-icon on the top allows you to launch the notebooks on [Google Colaboratory](https://colab.research.google.com/) for interactive mode. +Just uncomment the first line `pip install ...` to install the environment on each notebook if you follow this mode. + +### Binder + +To run these tutorials in Binder, you can click here: +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/NREL/PVDegradationTools/main) +It takes a minute to load the environment. + +### Locally + +You can also run the tutorials locally in a virtual environment, i.e., `venv` or +[miniconda](https://docs.conda.io/en/latest/miniconda.html). + +1. Create and activate a new environment, e.g., on Mac/Linux terminal with `venv`: + ``` + python -m venv pvdeg + . pvdeg/bin/activate + ``` + or with `conda`: + ``` + conda create -n pvdeg + conda activate pvdeg + ``` + +2. Install `pvdeg` into the new environment with `pip`: + ``` + python -m pip install pvdeg + ``` + +3. Start a Jupyter session: + ``` + jupyter notebook + ``` + +4. Use the file explorer in Jupyter to browse to the tutorial category folder and start with the first tutorial. + +--- + +## Special Requirements + +### 🔑 API Keys Required +Some tutorials in the **Advanced** folder require free API keys: +- **NSRDB API**: Register at https://developer.nrel.gov/signup/ + +### 💻 HPC Access Required +Most tutorials in the **Geospatial** folder require High Performance Computing resources for: +- Large-scale geospatial computations +- Multi-location scenario analyses +- Distributed computing workflows + +--- + +## Documentation + +We also have documentation in [ReadTheDocs](https://PVDegradationTools.readthedocs.io) where you can find more details on the API functions. diff --git a/tutorials_and_tools/_config.yml b/tutorials/_config.yml similarity index 91% rename from tutorials_and_tools/_config.yml rename to tutorials/_config.yml index 0cc6265de..40db680c1 100644 --- a/tutorials_and_tools/_config.yml +++ b/tutorials/_config.yml @@ -25,7 +25,7 @@ bibtex_bibfiles: # Information about where the book exists on the web repository: url: https://github.com/NREL/PVDegradationTools - path_to_book: tutorials_and_tools # Optional path to your book, relative to the repository root + path_to_book: tutorials # Optional path to your book, relative to the repository root branch: main # Add GitHub buttons to your book diff --git a/tutorials/_toc.yml b/tutorials/_toc.yml new file mode 100644 index 000000000..b7b6cb26d --- /dev/null +++ b/tutorials/_toc.yml @@ -0,0 +1,58 @@ +# Table of contents +# Learn more at https://jupyterbook.org/customize/toc.html + +format: jb-book +root: intro +parts: +- caption: 01 - Basics + chapters: + - file: 01_basics/01_basics_humidity_design + - file: 01_basics/02_degradation + - file: 01_basics/03_spectral_degradation + - file: 01_basics/04_weather_database_access +- caption: 02 - Degradation Mechanisms + chapters: + - file: 02_degradation/01_bo_lid_accelerated_test + - file: 02_degradation/02_letid_accelerated_test + - file: 02_degradation/03_letid_outdoor + - file: 02_degradation/04_letid_outdoor_scenario + - file: 02_degradation/05_letid_passivated_wafer + - file: 02_degradation/06_vant_hoff_degradation_model +- caption: 03 - Monte Carlo Simulations + chapters: + - file: 03_monte_carlo/01_arrhenius + - file: 03_monte_carlo/02_standoff +- caption: 04 - Geospatial & Scenarios (HPC Required) + chapters: + - file: 04_geospatial/01_local_scenario + title: Local Scenario (No HPC) + - file: 04_geospatial/02_geospatial_templates + title: Geospatial Templates (HPC) + - file: 04_geospatial/03_scenario_geographical_features + title: Geographical Features (HPC) + - file: 04_geospatial/04_scenario_geospatial + title: Scenario Geospatial (HPC) + - file: 04_geospatial/05_scenario_mountain_downselect + title: Mountain Downselect (HPC) + - file: 04_geospatial/06_scenario_single_location + title: Single Location (HPC) + - file: 04_geospatial/07_scenario_temperature + title: Temperature Scenario (HPC) +- caption: 05 - Advanced Topics + chapters: + - file: 05_advanced/01_custom_functions_nopython + - file: 05_advanced/02_pysam_single_location + - file: 05_advanced/03_pysam_api + title: PySAM with API (API Key Required) + - file: 05_advanced/04_nsrdb_distributed_api + title: NSRDB Distributed (API Key Required) + - file: 05_advanced/05_pvgis_distributed +- caption: 06 - NREL HPC + chapters: + - file: 06_nrel_hpc/01_geospatial_world_map + - file: 06_nrel_hpc/02_letid_outdoor_geospatial_demo +- caption: 07 - Workshop Demos + chapters: + - file: 07_workshop_demos/01_astm_live_demo + - file: 07_workshop_demos/02_duramat_live_demo + title: DuraMAT Live Demo (HPC Required) diff --git a/tutorials_and_tools/tutorials_and_tools/temperature and degradation.json b/tutorials/data/temperature_and_degradation.json similarity index 100% rename from tutorials_and_tools/tutorials_and_tools/temperature and degradation.json rename to tutorials/data/temperature_and_degradation.json diff --git a/tutorials_and_tools/_build/html/_images/tutorial_1_NSRDB_example.PNG b/tutorials/images/tutorial_1_NSRDB_example.PNG similarity index 100% rename from tutorials_and_tools/_build/html/_images/tutorial_1_NSRDB_example.PNG rename to tutorials/images/tutorial_1_NSRDB_example.PNG diff --git a/tutorials_and_tools/tutorials_and_tools/images/xarray.webp b/tutorials/images/xarray.webp similarity index 100% rename from tutorials_and_tools/tutorials_and_tools/images/xarray.webp rename to tutorials/images/xarray.webp diff --git a/tutorials_and_tools/_build/html/_sources/intro.md b/tutorials/intro.md similarity index 50% rename from tutorials_and_tools/_build/html/_sources/intro.md rename to tutorials/intro.md index 7035b3c3f..208fab2ca 100644 --- a/tutorials_and_tools/_build/html/_sources/intro.md +++ b/tutorials/intro.md @@ -1,14 +1,16 @@ -# Welcome to the PVDEG Tutorials and Tools +# Welcome to the PVDEG Tutorials -Here is a collection of Jupyter Journals that include tutorials and tools. -These are ready to read, install and use, or run in your browser through Google Collab. -Clicking on the rocket-icon on the top allows you to launch the journals on +Here is a collection of Jupyter Notebooks that provide tutorials for using PVDegradationTools. +These are ready to read, install and use, or run in your browser through Google Colab. +Clicking on the rocket-icon on the top allows you to launch the notebooks on [Google Colaboratory](https://colab.research.google.com/) for interactive mode. -As per the instructions on each journal, you should uncomment the first line -that reads `pip install ...` to install the environment if you follow this mode. +As per the instructions in each notebook, you should uncomment the first line +that reads `pip install ...` to install the environment if you follow this mode. You can also clone the repository and run them locally, following the instructions on the [PVDegTools Github page](https://github.com/NREL/PVDegradationTools) +For analysis tools, see the separate [Tools](../tools/) section. + ```{tableofcontents} ``` diff --git a/tutorials_and_tools/references.bib b/tutorials/references.bib similarity index 100% rename from tutorials_and_tools/references.bib rename to tutorials/references.bib diff --git a/tutorials_and_tools/requirements.txt b/tutorials/requirements.txt similarity index 100% rename from tutorials_and_tools/requirements.txt rename to tutorials/requirements.txt diff --git a/tutorials_and_tools/_build/.doctrees/README.doctree b/tutorials_and_tools/_build/.doctrees/README.doctree deleted file mode 100644 index dc723b125ad26bc812603c5eec2f7fc2f007acb1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10617 zcmcIqZEqY`6^`Ss?cKyqoHS{xB<(bi)=uMf;+BFUS`?B{Qm0Oux&;)6@$Agqy|bR3 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    TUTORIALS for PV Degradation Tools (pvdeg)

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    TUTORIALS for PV Degradation Tools (pvdeg)#

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    Jupyter Book#

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    For in depth Tutorials you can run online, see our jupyter-book -Clicking on the rocket-icon on the top allows you to launch the journals on Google Colaboratory for interactive mode. -Just uncomment the first line pip install ... to install the environment on each journal if you follow this mode.

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    Binder#

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    To run these tutorials in Binder, you can click here: -Binder -It takes a minute to load the environment.

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    Locally#

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    You can also run the tutorial locally with -miniconda by following thes -steps:

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    1. Install miniconda.

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    2. Clone the repository:

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      git clone https://github.com/NREL/PVDegradationTools.git
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    3. Create the environment and install the requirements. The repository includes -a requirements.txt file that contains a list the packages needed to run -this tutorial. To install them using conda run:

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      conda create -n pvdeg jupyter -c pvlib --file requirements.txt
-conda activate pvdeg
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      or you can install it with pip install pvdeg as explained in the installation instructions into the environment.

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    4. Start a Jupyter session:

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      jupyter notebook
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    5. Use the file explorer in Jupyter lab to browse to tutorials -and start the first Tutorial.

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    Documentation#

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    We also have documentation in ReadTheDocs where you can find more details on the API functions.

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    -By Matthew Brown, Silvana Ovaitt -

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    - - © Copyright 2022. -
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