Add gallery example for OEDI system 9068

docs/examples/system-models/README.rst

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+System Models
+-------------

docs/examples/system-models/plot_oedi_9068.py

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+"""
+4.7 MW CdTe single-axis tracking (OEDI System 9068)
+===================================================
+
+A basic model of a 4.7 MW single-axis tracking CdTe system located in
+Colorado, United States.
+"""
+
+# %%
+# This example model uses satellite-based solar resource data from the
+# NSRDB PSM3. This approach is useful for pre-construction energy modeling
+# and in retrospective analyses where the system’s own irradiance
+# measurements are not present or unreliable.
+#
+# The system has public monitoring data available at the Open Energy Data
+# Initiative (OEDI) under `System ID
+# 9068 <https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pvdaq%2F2023-solar-data-prize%2F9068_OEDI%2F>`__.
+# For more information about the system, see its `OEDI
+# page <https://openei.org/wiki/PVDAQ/Sites/SR_CO>`__.
+
+import pvlib
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# %%
+# System parameters
+# -----------------
+#
+# The system description on the OEDI provides some high-level system
+# information, but unfortunately we have to make some guesses about other
+# aspects of the system’s configuration.
+#
+# The cells below define the system parameter values required in the
+# simulation.
+
+# information provided by system description on OEDI
+
+latitude = 40.3864
+longitude = -104.5512
+
+# the inverters have identical PV array topologies:
+modules_per_string = 15
+strings_per_inverter = 1344
+
+# %%
+
+# "unofficial" information
+
+# We know the system uses 117.5 W CdTe modules.  Based on the system vintage
+# (data begins in 2017), it seems likely that the array uses First Solar
+# Series 4 modules (FS-4117).
+cec_module_db = pvlib.pvsystem.retrieve_sam('cecmod')
+module_parameters = cec_module_db['First_Solar__Inc__FS_4117_3']
+# ensure that correct spectral correction is applied
+module_parameters['Technology'] = 'CdTe'
+
+# default Faiman model parameters:
+temperature_model_parameters = dict(u0=25.0, u1=6.84)
+module_unit_mass = 12 / 0.72  # kg/m^2, taken from datasheet values
+
+# The OEDI metadata says the inverters have AC capacities of 1910 kW,
+# but the clipping level in the measured inverter output is more like 1840 kW.
+# It's not clear what specific model is installed, so let's just assume
+# this inverter, which the CEC database lists as having a nominal AC
+# capacity of 1833 kW:
+cec_inverter_db = pvlib.pvsystem.retrieve_sam('cecinverter')
+inverter_parameters = cec_inverter_db['TMEIC__PVL_L1833GRM']
+
+# We'll use the PVWatts v5 losses model.  Set shading to zero as it is
+# accounted for elsewhere in the model, and disable availability loss since
+# we want a "clean" simulation.
+# Leaving the other pvwatts loss types (mismatch, wiring, etc) unspecified
+# causes them to take their default values.
+losses_parameters = dict(shading=0, availability=0)
+
+# Google Street View images show that each row is four modules high, in
+# landscape orientation.  Assuming the modules are First Solar Series 4,
+# each of them is 600 mm wide.
+# Assume ~1 centimeter gap between modules (three gaps total).
+# And from Google Earth, the array's pitch is estimated to be about 7.0 meters.
+# From these we calculate the ground coverage ratio (GCR):
+pitch = 7  # meters
+gcr = (4 * 0.6 + 3 * 0.01) / pitch
+
+# The tracker rotation measurements reveal that the tracker rotation limits
+# are +/- 60 degrees, and backtracking is not enabled:
+max_angle = 60  # degrees
+backtrack = False
+
+# Google Earth shows that the tracker axes are very close to north-south:
+axis_azimuth = 180  # degrees
+
+# Estimated from Google Street View images
+axis_height = 1.5  # meters
+
+# %%
+# Create system objects
+# ---------------------
+#
+# The system has two inverters which seem to have identical specifications
+# and arrays. To save some code and computation repetition, we will just
+# model one inverter.
+
+location = pvlib.location.Location(latitude, longitude)
+mount = pvlib.pvsystem.SingleAxisTrackerMount(
+    gcr=gcr,
+    backtrack=backtrack,
+    max_angle=max_angle,
+    axis_azimuth=axis_azimuth
+)
+array = pvlib.pvsystem.Array(
+    mount,
+    module_parameters=module_parameters,
+    modules_per_string=modules_per_string,
+    temperature_model_parameters=temperature_model_parameters,
+    strings=strings_per_inverter
+)
+system = pvlib.pvsystem.PVSystem(
+    array,
+    inverter_parameters=inverter_parameters,
+    losses_parameters=losses_parameters
+)
+
+model = pvlib.modelchain.ModelChain(
+    system,
+    location,
+    spectral_model='first_solar',
+    aoi_model='physical',
+    losses_model='pvwatts'
+)
+
+# %%
+# Fetch weather data
+# ------------------
+#
+# The system does have measured plane-of-array irradiance data, but the
+# measurements suffer from row-to-row shading and tracker stalls. In this
+# example, we will use weather data taken from the NSRDB PSM3 for the year
+# 2019.
+
+api_key = 'DEMO_KEY'
+email = 'your_email@domain.com'
+
+keys = ['ghi', 'dni', 'dhi', 'temp_air', 'wind_speed',
+        'albedo', 'precipitable_water']
+psm3, psm3_metadata = pvlib.iotools.get_psm3(latitude, longitude, api_key,
+                                             email, interval=5, names=2019,
+                                             map_variables=True, leap_day=True,
+                                             attributes=keys)
+
+# %%
+# Pre-generate some model inputs
+# ------------------------------
+#
+# This system’s trackers are configured to not backtrack, meaning the
+# array shades itself when the sun is low in the sky. pvlib’s
+# ``ModelChain`` currently has no shade modeling ability, so we will model
+# it separately.
+#
+# Since this system uses thin-film modules, oriented in such a way that
+# row-to-row shadows affect each cell in the module equally, we can assume
+# that the effect of shading is linear with the reduction in incident beam
+# irradiance. That means we can use pvlib’s infinite sheds model, which
+# penalizes incident beam irradiance according to the calculated shaded
+# module fraction and returns the average irradiance over the total module
+# surface.
+
+solar_position = location.get_solarposition(psm3.index, latitude, longitude)
+tracker_angles = mount.get_orientation(
+    solar_position['apparent_zenith'],
+    solar_position['azimuth']
+)
+dni_extra = pvlib.irradiance.get_extra_radiation(psm3.index)
+
+# note: this system is monofacial, so only calculate irradiance for the
+# front side:
+averaged_irradiance = pvlib.bifacial.infinite_sheds.get_irradiance_poa(
+    tracker_angles['surface_tilt'], tracker_angles['surface_azimuth'],
+    solar_position['apparent_zenith'], solar_position['azimuth'],
+    gcr, axis_height, pitch,
+    psm3['ghi'], psm3['dhi'], psm3['dni'], psm3['albedo'],
+    model='haydavies', dni_extra=dni_extra,
+)
+
+# %%
+# ``ModelChain`` does not consider thermal transience either, so since we
+# are using 5-minute weather data, we will precalculate the cell
+# temperature as well:
+
+cell_temperature_steady_state = pvlib.temperature.faiman(
+    poa_global=averaged_irradiance['poa_global'],
+    temp_air=psm3['temp_air'],
+    wind_speed=psm3['wind_speed'],
+    **temperature_model_parameters,
+)
+
+cell_temperature = pvlib.temperature.prilliman(
+    cell_temperature_steady_state,
+    psm3['wind_speed'],
+    unit_mass=module_unit_mass
+)
+
+# %%
+# Run the model
+# -------------
+#
+# Finally, we are ready to run the rest of the system model. Since we want
+# to use pre-calculated plane-of-array irradiance, we will use
+# :py:meth:`~pvlib.modelchain.ModelChain.run_model_from_poa`:
+
+weather_inputs = pd.DataFrame({
+    'poa_global': averaged_irradiance['poa_global'],
+    'poa_direct': averaged_irradiance['poa_direct'],
+    'poa_diffuse': averaged_irradiance['poa_diffuse'],
+    'cell_temperature': cell_temperature,
+    'precipitable_water': psm3['precipitable_water'],  # for the spectral model
+})
+model.run_model_from_poa(weather_inputs)
+
+
+# %%
+# Compare with measured production
+# --------------------------------
+#
+# Now, let’s compare our modeled AC power with the system’s actual
+# inverter-level AC power measurements:
+
+fn = r"path/to/9068_ac_power_data.csv"
+df_inverter_measured = pd.read_csv(fn, index_col=0, parse_dates=True)
+df_inverter_measured = df_inverter_measured.tz_localize('US/Mountain',
+                                                        ambiguous='NaT',
+                                                        nonexistent='NaT')
+# convert to standard time to match the NSRDB-based simulation
+df_inverter_measred = df_inverter_measured.tz_convert('Etc/GMT+7')
+
+# %%
+
+inverter_ac_powers = [
+    'inverter_1_ac_power_(kw)_inv_150143',
+    'inverter_2_ac_power_(kw)_inv_150144'
+]
+df = df_inverter_measured.loc['2019', inverter_ac_powers]
+df['model'] = model.results.ac / 1000  # convert W to kW
+
+# %%
+
+for column_name in inverter_ac_powers:
+    fig, axes = plt.subplots(1, 3, figsize=(12, 4))
+    df.plot.scatter('model', column_name, ax=axes[0], s=1, alpha=0.1)
+    axes[0].axline((0, 0), slope=1, c='k')
+    axes[0].set_ylabel('Measured 5-min power [kW]')
+    axes[0].set_xlabel('Modeled 5-min power [kW]')
+
+    hourly_average = df.resample('h').mean()
+    hourly_average.plot.scatter('model', column_name, ax=axes[1], s=2)
+    axes[1].axline((0, 0), slope=1, c='k')
+    axes[1].set_ylabel('Measured hourly energy [kWh]')
+    axes[1].set_xlabel('Modeled hourly energy [kWh]')
+
+    daily_total = hourly_average.resample('d').sum()
+    daily_total.plot.scatter('model', column_name, ax=axes[2], s=5)
+    axes[2].axline((0, 0), slope=1, c='k')
+    axes[2].set_ylabel('Measured daily energy [kWh]')
+    axes[2].set_xlabel('Modeled daily energy [kWh]')
+
+    fig.suptitle(column_name)
+    fig.tight_layout()
+
+
+# %%
+# .. image:: ../../_images/OEDI_9068_inverter1_comparison.png
+#
+# .. image:: ../../_images/OEDI_9068_inverter2_comparison.png
+
+fig, ax = plt.subplots(figsize=(12, 4))
+daily_energy = df.clip(lower=0).resample('h').mean().resample('d').sum()
+daily_energy.plot(ax=ax)
+plt.ylim(bottom=0)
+plt.ylabel('Daily Production [kWh]')
+plt.tight_layout()
+
+# %%
+# .. image:: ../../_images/OEDI_9068_daily_timeseries.png

docs/sphinx/source/conf.py

@@ -357,8 +357,8 @@ def setup(app):
 sphinx_gallery_conf = {
     'examples_dirs': ['../../examples'],  # location of gallery scripts
     'gallery_dirs': ['gallery'],  # location of generated output
-    # sphinx-gallery only shows plots from plot_*.py files by default:
-    # 'filename_pattern': '*.py',
+    # execute all scripts except for ones in the "system-models" directory:
+    'filename_pattern': '^((?!system-models).)*$',
 
     # directory where function/class granular galleries are stored
     'backreferences_dir': 'reference/generated/gallery_backreferences',

docs/sphinx/source/whatsnew/v0.10.4.rst

@@ -40,6 +40,7 @@ Documentation
 * Improved references and description for :py:func:`~pvlib.irradiance.get_ground_diffuse`. (:pull:`1953`)
 * Fixed broken URLs in various places. (:pull:`1957`, :pull:`1960`)
 * Clarified documentation for :py:func:`~pvlib.irradiance.get_ground_diffuse`. (:pull:`1883`)
+* Added a gallery example with a model for OEDI system 9068. (:pull:`1985`)
 
 
 Requirements