plot_modelchain_model_variations.py

"""
Varying Model Components in ModelChain
======================================

This example demonstrates how changing modeling components
within ``pvlib.modelchain.ModelChain`` affects simulation results.

Using the same PV system and weather data, we create two
ModelChain instances that differ only in their temperature
model. By comparing the resulting cell temperature and AC
power output, we can see how changing a single modeling
component affects overall system behavior.
"""

# %%
# Varying ModelChain components
# ------------------------------
#
# Below, we create two ModelChain objects with identical system
# definitions and weather inputs. The only difference between them
# is the selected temperature model. This highlights how individual
# modeling components in ``ModelChain`` can be swapped while keeping
# the overall workflow unchanged.

import pvlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# %%
# Define location
# ---------------
#
# We select Tucson, Arizona, a location frequently used in pvlib
# examples due to its strong solar resource and available TMY data.
latitude = 32.2
longitude = -110.9
location = pvlib.location.Location(latitude, longitude)

# %%
# Generate clear-sky weather data
# --------------------------------
#
# We generate clear-sky irradiance using pvlib and create a
# varying air temperature profile instead of using constant
# values.
times = pd.date_range(
    "2019-06-01 00:00",
    "2019-06-07 23:00",
    freq="1h",
    tz="Etc/GMT+7",
)

# Clear-sky irradiance
clearsky = location.get_clearsky(times)

# Create a simple daily temperature cycle
temp_air = 20 + 10 * np.sin(2 * np.pi * (times.hour - 6) / 24)

weather_subset = clearsky.copy()
weather_subset["temp_air"] = temp_air
weather_subset["wind_speed"] = 1

# %%
# Define a simple PV system
# -------------------------
#
# To keep the focus on the temperature model comparison,
# we define a minimal PV system using the PVWatts DC and AC models.
# These models require only a few high-level parameters.
#
# The module DC rating (pdc0) represents the array capacity at
# reference conditions, and gamma_pdc describes the power
# temperature coefficient.
#
# For the temperature model parameters, we use the sapm values
# for an open-rack, glass-glass module configuration. These
# parameters describe how heat is transferred from the module
# to the surrounding environment.
module_parameters = dict(pdc0=5000, gamma_pdc=-0.003)
inverter_parameters = dict(pdc0=4000)

temperature_model_parameters = (
    pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS["sapm"]
    ["open_rack_glass_glass"]
)

system = pvlib.pvsystem.PVSystem(
    surface_tilt=30,
    surface_azimuth=180,
    module_parameters=module_parameters,
    inverter_parameters=inverter_parameters,
    temperature_model_parameters=temperature_model_parameters,
)

# %%
# ModelChain using the sapm temperature model
# --------------------------------------------
#
# First, we construct a ModelChain that uses the sapm
# temperature model. All other modeling components remain
# identical between simulations.
#
# This ensures that any differences in the results arise
# solely from the temperature model choice.
temperature_model_parameters_sapm = (
    pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS["sapm"]
    ["open_rack_glass_glass"]
)

system_sapm = pvlib.pvsystem.PVSystem(
    surface_tilt=30,
    surface_azimuth=180,
    module_parameters=module_parameters,
    inverter_parameters=inverter_parameters,
    temperature_model_parameters=temperature_model_parameters_sapm,
)

mc_sapm = pvlib.modelchain.ModelChain(
    system_sapm,
    location,
    dc_model="pvwatts",
    ac_model="pvwatts",
    temperature_model="sapm",
    aoi_model="no_loss",
)

mc_sapm.run_model(weather_subset)

# %%
# ModelChain using the Faiman temperature model
# ----------------------------------------------
#
# Next, we repeat the same simulation but replace the
# temperature model with the Faiman model.
#
# No other system or weather parameters are changed.
# This illustrates how individual components within
# ModelChain can be varied independently.
temperature_model_parameters_faiman = dict(u0=25, u1=6.84)

system_faiman = pvlib.pvsystem.PVSystem(
    surface_tilt=30,
    surface_azimuth=180,
    module_parameters=module_parameters,
    inverter_parameters=inverter_parameters,
    temperature_model_parameters=temperature_model_parameters_faiman,
)

mc_faiman = pvlib.modelchain.ModelChain(
    system_faiman,
    location,
    dc_model="pvwatts",
    ac_model="pvwatts",
    temperature_model="faiman",
    aoi_model="no_loss",
)

mc_faiman.run_model(weather_subset)

# %%
# Compare modeled cell temperature
# ---------------------------------
#
# Since module temperature directly affects DC power
# through the temperature coefficient, differences
# between temperature models can propagate into
# performance results.

# %%
fig, ax = plt.subplots(figsize=(10, 4))
mc_sapm.results.cell_temperature.plot(ax=ax, label="SAPM")
mc_faiman.results.cell_temperature.plot(ax=ax, label="Faiman")

ax.set_ylabel("Cell Temperature (°C)")
ax.set_title("Comparison of Cell Temperature")
ax.legend()
plt.tight_layout()

# %%
# Compare AC power output
# ------------------------
#
# Finally, we compare the resulting AC power. In this case, the
# differences in temperature modeling lead to small
# differences in predicted energy production.

# %%
fig, ax = plt.subplots(figsize=(10, 4))
mc_sapm.results.ac.plot(ax=ax, label="SAPM")
mc_faiman.results.ac.plot(ax=ax, label="Faiman")

ax.set_ylabel("AC Power (W)")
ax.set_title("AC Output with Different Temperature Models")
ax.legend()
plt.tight_layout()