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Mount gallery examples (#1266)
* create discontinuous tracking example * create dual-axis example * create mixed-orientation example * create seasonal tilt example * stickler * whatsnew * bsrn -> clear-sky sim * stickler, d'oh
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‎docs/examples/plot_discontinuous_tracking.py‎

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"""
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Discontinuous Tracking
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======================
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Example of a custom Mount class.
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"""
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# %%
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# Many real-world tracking arrays adjust their position in discrete steps
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# rather than through continuous movement. This example shows how to model
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# this discontinuous tracking by implementing a custom Mount class.
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from pvlib import tracking, pvsystem, location, modelchain
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from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS
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import matplotlib.pyplot as plt
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import pandas as pd
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# %%
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# We'll define our custom Mount by extending
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# :py:class:`~pvlib.pvsystem.SingleAxisTrackerMount` for convenience.
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# Another approach would be to extend ``AbstractMount`` directly; see
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# the source code of :py:class:`~pvlib.pvsystem.SingleAxisTrackerMount`
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# and :py:class:`~pvlib.pvsystem.FixedMount` for how that is done.
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class DiscontinuousTrackerMount(pvsystem.SingleAxisTrackerMount):
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# inherit from SingleAxisTrackerMount so that we get the
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# constructor and tracking attributes (axis_tilt etc) automatically
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def get_orientation(self, solar_zenith, solar_azimuth):
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# Different trackers update at different rates; in this example we'll
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# assume a relatively slow update interval of 15 minutes to make the
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# effect more visually apparent.
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zenith_subset = solar_zenith.resample('15min').first()
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azimuth_subset = solar_azimuth.resample('15min').first()
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tracking_data_15min = tracking.singleaxis(
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zenith_subset, azimuth_subset,
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self.axis_tilt, self.axis_azimuth,
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self.max_angle, self.backtrack,
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self.gcr, self.cross_axis_tilt
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)
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# propagate the 15-minute positions to 1-minute stair-stepped values:
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tracking_data_1min = tracking_data_15min.reindex(solar_zenith.index,
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method='ffill')
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return tracking_data_1min
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# %%
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# Let's take a look at the tracker rotation curve it produces:
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times = pd.date_range('2019-06-01', '2019-06-02', freq='1min', tz='US/Eastern')
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loc = location.Location(40, -80)
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solpos = loc.get_solarposition(times)
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mount = DiscontinuousTrackerMount(axis_azimuth=180, gcr=0.4)
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tracker_data = mount.get_orientation(solpos.apparent_zenith, solpos.azimuth)
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tracker_data['tracker_theta'].plot()
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plt.ylabel('Tracker Rotation [degree]')
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plt.show()
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# %%
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# With our custom tracking logic defined, we can create the corresponding
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# Array and PVSystem, and then run a ModelChain as usual:
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module_parameters = {'pdc0': 1, 'gamma_pdc': -0.004, 'b': 0.05}
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temp_params = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_polymer']
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array = pvsystem.Array(mount=mount, module_parameters=module_parameters,
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temperature_model_parameters=temp_params)
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system = pvsystem.PVSystem(arrays=[array], inverter_parameters={'pdc0': 1})
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mc = modelchain.ModelChain(system, loc, spectral_model='no_loss')
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# simple simulated weather, just to show the effect of discrete tracking
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weather = loc.get_clearsky(times)
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weather['temp_air'] = 25
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weather['wind_speed'] = 1
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mc.run_model(weather)
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fig, axes = plt.subplots(2, 1, sharex=True)
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mc.results.effective_irradiance.plot(ax=axes[0])
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axes[0].set_ylabel('Effective Irradiance [W/m^2]')
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mc.results.ac.plot(ax=axes[1])
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axes[1].set_ylabel('AC Power')
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fig.show()
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# %%
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# The effect of discontinuous tracking creates a "jagged" effect in the
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# simulated plane-of-array irradiance, which then propagates through to
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# the AC power output.

‎docs/examples/plot_dual_axis_tracking.py‎

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"""
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Dual-Axis Tracking
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==================
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Example of a custom Mount class.
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"""
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# %%
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# Dual-axis trackers can track the sun in two dimensions across the sky dome
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# instead of just one like single-axis trackers. This example shows how to
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# model a simple dual-axis tracking system using ModelChain with a custom
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# Mount class.
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from pvlib import pvsystem, location, modelchain
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import pandas as pd
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import matplotlib.pyplot as plt
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# %%
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# New Mount classes should extend ``pvlib.pvsystem.AbstractMount``
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# and must implement a ``get_orientation(solar_zenith, solar_azimuth)`` method:
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class DualAxisTrackerMount(pvsystem.AbstractMount):
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def get_orientation(self, solar_zenith, solar_azimuth):
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# no rotation limits, no backtracking
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return {'surface_tilt': solar_zenith, 'surface_azimuth': solar_azimuth}
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loc = location.Location(40, -80)
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array = pvsystem.Array(
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mount=DualAxisTrackerMount(),
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module_parameters=dict(pdc0=1, gamma_pdc=-0.004, b=0.05),
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temperature_model_parameters=dict(a=-3.56, b=-0.075, deltaT=3))
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system = pvsystem.PVSystem(arrays=[array], inverter_parameters=dict(pdc0=3))
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mc = modelchain.ModelChain(system, loc, spectral_model='no_loss')
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times = pd.date_range('2019-01-01 06:00', '2019-01-01 18:00', freq='5min',
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tz='Etc/GMT+5')
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weather = loc.get_clearsky(times)
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mc.run_model(weather)
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mc.results.ac.plot()
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plt.ylabel('Output Power')
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plt.show()

‎docs/examples/plot_mixed_orientation.py‎

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"""
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Mixed Orientation
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=================
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Using multiple Arrays in a single PVSystem.
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"""
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# %%
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# Residential and Commercial systems often have fixed-tilt arrays
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# installed at different azimuths. This can be modeled by using
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# multiple :py:class:`~pvlib.pvsystem.Array` objects (one for each
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# orientation) with a single :py:class:`~pvlib.pvsystem.PVSystem` object.
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#
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# This particular example has one east-facing array (azimuth=90) and one
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# west-facing array (azimuth=270), which aside from orientation are identical.
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from pvlib import pvsystem, modelchain, location
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import pandas as pd
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import matplotlib.pyplot as plt
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array_kwargs = dict(
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module_parameters=dict(pdc0=1, gamma_pdc=-0.004),
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temperature_model_parameters=dict(a=-3.56, b=-0.075, deltaT=3)
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)
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arrays = [
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pvsystem.Array(pvsystem.FixedMount(30, 270), name='West-Facing Array',
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**array_kwargs),
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pvsystem.Array(pvsystem.FixedMount(30, 90), name='East-Facing Array',
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**array_kwargs),
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]
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loc = location.Location(40, -80)
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system = pvsystem.PVSystem(arrays=arrays, inverter_parameters=dict(pdc0=3))
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mc = modelchain.ModelChain(system, loc, aoi_model='physical',
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spectral_model='no_loss')
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times = pd.date_range('2019-01-01 06:00', '2019-01-01 18:00', freq='5min',
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tz='Etc/GMT+5')
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weather = loc.get_clearsky(times)
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mc.run_model(weather)
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fig, ax = plt.subplots()
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for array, pdc in zip(system.arrays, mc.results.dc):
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pdc.plot(label=f'{array.name}')
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mc.results.ac.plot(label='Inverter')
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plt.ylabel('System Output')
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plt.legend()
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plt.show()

‎docs/examples/plot_seasonal_tilt.py‎

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"""
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Seasonal Tilt
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=============
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Example of a custom Mount class.
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"""
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# %%
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# Some PV systems are built with the option to adjust the module
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# tilt to follow seasonal changes in solar position. For example,
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# SAM calls this strategy "Seasonal Tilt". This example shows how
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# to use a custom Mount class to use the Seasonal Tilt strategy
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# with :py:class:`~pvlib.modelchain.ModelChain`.
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import pvlib
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from pvlib import pvsystem, location, modelchain, iotools
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from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS
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import pandas as pd
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import pathlib
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import matplotlib.pyplot as plt
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from dataclasses import dataclass
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# %%
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# New Mount classes should extend ``pvlib.pvsystem.AbstractMount``
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# and must implement a ``get_orientation(solar_zenith, solar_azimuth)`` method:
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@dataclass
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class SeasonalTiltMount(pvsystem.AbstractMount):
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monthly_tilts: list # length 12, one tilt per calendar month
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surface_azimuth: float = 180.0
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def get_orientation(self, solar_zenith, solar_azimuth):
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tilts = [self.monthly_tilts[m-1] for m in solar_zenith.index.month]
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return pd.DataFrame({
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'surface_tilt': tilts,
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'surface_azimuth': self.surface_azimuth,
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}, index=solar_zenith.index)
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# %%
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# First let's grab some weather data and make sure our mount produces tilts
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# like we expect:
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DATA_DIR = pathlib.Path(pvlib.__file__).parent / 'data'
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tmy, metadata = iotools.read_tmy3(DATA_DIR / '723170TYA.CSV', coerce_year=1990)
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# shift from TMY3 right-labeled index to left-labeled index:
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tmy.index = tmy.index - pd.Timedelta(hours=1)
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weather = pd.DataFrame({
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'ghi': tmy['GHI'], 'dhi': tmy['DHI'], 'dni': tmy['DNI'],
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'temp_air': tmy['DryBulb'], 'wind_speed': tmy['Wspd'],
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})
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loc = location.Location.from_tmy(metadata)
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solpos = loc.get_solarposition(weather.index)
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# same default monthly tilts as SAM:
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tilts = [40, 40, 40, 20, 20, 20, 20, 20, 20, 40, 40, 40]
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mount = SeasonalTiltMount(monthly_tilts=tilts)
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orientation = mount.get_orientation(solpos.apparent_zenith, solpos.azimuth)
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orientation['surface_tilt'].plot()
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plt.ylabel('Surface Tilt [degrees]')
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plt.show()
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# %%
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# With our custom tilt strategy defined, we can create the corresponding
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# Array and PVSystem, and then run a ModelChain as usual:
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module_parameters = {'pdc0': 1, 'gamma_pdc': -0.004, 'b': 0.05}
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temp_params = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_polymer']
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array = pvsystem.Array(mount=mount, module_parameters=module_parameters,
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temperature_model_parameters=temp_params)
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system = pvsystem.PVSystem(arrays=[array], inverter_parameters={'pdc0': 1})
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mc = modelchain.ModelChain(system, loc, spectral_model='no_loss')
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_ = mc.run_model(weather)
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# %%
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# Now let's re-run the simulation assuming tilt=30 for the entire year:
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array2 = pvsystem.Array(mount=pvsystem.FixedMount(30, 180),
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module_parameters=module_parameters,
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temperature_model_parameters=temp_params)
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system2 = pvsystem.PVSystem(arrays=[array2], inverter_parameters={'pdc0': 1})
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mc2 = modelchain.ModelChain(system2, loc, spectral_model='no_loss')
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_ = mc2.run_model(weather)
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# %%
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# And finally, compare simulated monthly generation between the two tilt
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# strategies:
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# sphinx_gallery_thumbnail_number = 2
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results = pd.DataFrame({
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'Seasonal 20/40 Production': mc.results.ac,
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'Fixed 30 Production': mc2.results.ac,
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})
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results.resample('m').sum().plot()
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plt.ylabel('Monthly Production')
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plt.show()

‎docs/sphinx/source/whatsnew/v0.9.0.rst‎

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* Use ``Mount`` classes in ``introtutorial`` and ``pvsystem`` docs pages (:pull:`1267`)
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* Clarified how statistics are calculated for :py:func:`pvlib.clearsky.detect_clearsky`
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(:issue:`1070`, :pull:`1243`)
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* Add gallery examples using the new ``Mount`` classes (:pull:`1266`)
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Requirements
211212
~~~~~~~~~~~~

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