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ModelChain model variation example to system-models (pvlib#2691)
* created plot_modelchain_model_variations.py file * add ModelChain temperature comparison example to system-models gallery * fixing temperature model consistency and display figures * enable execution of system-models examples in Sphinx Gallery * fixing flake8 * changing conf.py to allow files to run * prevent oedi example from executing during docs build by renaming file * Apply suggestions from code review Co-authored-by: Cliff Hansen <cwhanse@sandia.gov> --------- Co-authored-by: Cliff Hansen <cwhanse@sandia.gov>
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docs/examples/system-models/plot_oedi_9068.py renamed to docs/examples/system-models/oedi_9068.py

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‎docs/examples/system-models/plot_modelchain_model_variations.py‎

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"""
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Varying Model Components in ModelChain
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======================================
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This example demonstrates how changing modeling components
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within ``pvlib.modelchain.ModelChain`` affects simulation results.
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Using the same PV system and weather data, we create two
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ModelChain instances that differ only in their temperature
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model. By comparing the resulting cell temperature and AC
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power output, we can see how changing a single modeling
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component affects overall system behavior.
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"""
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# %%
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# Varying ModelChain components
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# ------------------------------
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#
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# Below, we create two ModelChain objects with identical system
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# definitions and weather inputs. The only difference between them
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# is the selected temperature model. This highlights how individual
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# modeling components in ``ModelChain`` can be swapped while keeping
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# the overall workflow unchanged.
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import pvlib
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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# %%
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# Define location
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# ---------------
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#
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# We select Tucson, Arizona, a location frequently used in pvlib
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# examples due to its strong solar resource and available TMY data.
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latitude = 32.2
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longitude = -110.9
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location = pvlib.location.Location(latitude, longitude)
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# %%
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# Generate clear-sky weather data
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# --------------------------------
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#
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# We generate clear-sky irradiance using pvlib and create a
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# varying air temperature profile instead of using constant
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# values.
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times = pd.date_range(
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"2019-06-01 00:00",
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"2019-06-07 23:00",
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freq="1h",
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tz="Etc/GMT+7",
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)
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# Clear-sky irradiance
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clearsky = location.get_clearsky(times)
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# Create a simple daily temperature cycle
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temp_air = 20 + 10 * np.sin(2 * np.pi * (times.hour - 6) / 24)
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weather_subset = clearsky.copy()
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weather_subset["temp_air"] = temp_air
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weather_subset["wind_speed"] = 1
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# %%
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# Define a simple PV system
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# -------------------------
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#
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# To keep the focus on the temperature model comparison,
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# we define a minimal PV system using the PVWatts DC and AC models.
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# These models require only a few high-level parameters.
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#
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# The module DC rating (pdc0) represents the array capacity at
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# reference conditions, and gamma_pdc describes the power
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# temperature coefficient.
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#
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# For the temperature model parameters, we use the sapm values
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# for an open-rack, glass-glass module configuration. These
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# parameters describe how heat is transferred from the module
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# to the surrounding environment.
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module_parameters = dict(pdc0=5000, gamma_pdc=-0.003)
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inverter_parameters = dict(pdc0=4000)
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temperature_model_parameters = (
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pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS["sapm"]
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["open_rack_glass_glass"]
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)
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system = pvlib.pvsystem.PVSystem(
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surface_tilt=30,
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surface_azimuth=180,
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module_parameters=module_parameters,
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inverter_parameters=inverter_parameters,
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temperature_model_parameters=temperature_model_parameters,
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)
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# %%
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# ModelChain using the sapm temperature model
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# --------------------------------------------
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#
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# First, we construct a ModelChain that uses the sapm
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# temperature model. All other modeling components remain
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# identical between simulations.
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#
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# This ensures that any differences in the results arise
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# solely from the temperature model choice.
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temperature_model_parameters_sapm = (
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pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS["sapm"]
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["open_rack_glass_glass"]
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)
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system_sapm = pvlib.pvsystem.PVSystem(
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surface_tilt=30,
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surface_azimuth=180,
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module_parameters=module_parameters,
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inverter_parameters=inverter_parameters,
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temperature_model_parameters=temperature_model_parameters_sapm,
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)
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mc_sapm = pvlib.modelchain.ModelChain(
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system_sapm,
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location,
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dc_model="pvwatts",
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ac_model="pvwatts",
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temperature_model="sapm",
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aoi_model="no_loss",
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)
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mc_sapm.run_model(weather_subset)
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# %%
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# ModelChain using the Faiman temperature model
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# ----------------------------------------------
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#
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# Next, we repeat the same simulation but replace the
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# temperature model with the Faiman model.
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#
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# No other system or weather parameters are changed.
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# This illustrates how individual components within
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# ModelChain can be varied independently.
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temperature_model_parameters_faiman = dict(u0=25, u1=6.84)
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system_faiman = pvlib.pvsystem.PVSystem(
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surface_tilt=30,
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surface_azimuth=180,
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module_parameters=module_parameters,
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inverter_parameters=inverter_parameters,
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temperature_model_parameters=temperature_model_parameters_faiman,
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)
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mc_faiman = pvlib.modelchain.ModelChain(
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system_faiman,
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location,
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dc_model="pvwatts",
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ac_model="pvwatts",
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temperature_model="faiman",
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aoi_model="no_loss",
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)
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mc_faiman.run_model(weather_subset)
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# %%
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# Compare modeled cell temperature
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# ---------------------------------
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#
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# Since module temperature directly affects DC power
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# through the temperature coefficient, differences
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# between temperature models can propagate into
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# performance results.
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# %%
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fig, ax = plt.subplots(figsize=(10, 4))
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mc_sapm.results.cell_temperature.plot(ax=ax, label="SAPM")
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mc_faiman.results.cell_temperature.plot(ax=ax, label="Faiman")
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ax.set_ylabel("Cell Temperature (°C)")
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ax.set_title("Comparison of Cell Temperature")
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ax.legend()
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plt.tight_layout()
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# %%
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# Compare AC power output
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# ------------------------
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#
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# Finally, we compare the resulting AC power. In this case, the
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# differences in temperature modeling lead to small
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# differences in predicted energy production.
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# %%
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fig, ax = plt.subplots(figsize=(10, 4))
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mc_sapm.results.ac.plot(ax=ax, label="SAPM")
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mc_faiman.results.ac.plot(ax=ax, label="Faiman")
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ax.set_ylabel("AC Power (W)")
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ax.set_title("AC Output with Different Temperature Models")
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ax.legend()
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plt.tight_layout()

‎docs/sphinx/source/conf.py‎

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sphinx_gallery_conf = {
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'examples_dirs': ['../../examples'], # location of gallery scripts
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'gallery_dirs': ['gallery'], # location of generated output
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# execute all scripts except for ones in the "system-models" directory:
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'filename_pattern': '^((?!system-models).)*$',
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# execute only files starting with plot_
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'filename_pattern': 'plot_',
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# directory where function/class granular galleries are stored
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'backreferences_dir': 'reference/generated/gallery_backreferences',

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