def test_run_timeseries_engine(fn_report_example, params_serial,
df_inputs_clearsky_8760):
df_inputs = df_inputs_clearsky_8760.iloc[:24, :]
n = df_inputs.shape[0]
# Get MET data
timestamps = df_inputs.index
dni = df_inputs.dni.values
dhi = df_inputs.dhi.values
solar_zenith = df_inputs.solar_zenith.values
solar_azimuth = df_inputs.solar_azimuth.values
surface_tilt = df_inputs.surface_tilt.values
surface_azimuth = df_inputs.surface_azimuth.values
report = run_timeseries_engine(fn_report_example, params_serial,
timestamps, dni, dhi, solar_zenith,
solar_azimuth, surface_tilt,
surface_azimuth,
params_serial['rho_ground'])
assert len(report['qinc_front']) == n
# Test value consistency
np.testing.assert_almost_equal(np.nansum(report['qinc_back']),
541.7115807694377)
np.testing.assert_almost_equal(np.nansum(report['iso_back']),
18.050083142438311)
# Check a couple values
np.testing.assert_almost_equal(report['qinc_back'][7], 11.160301350847325)
np.testing.assert_almost_equal(report['qinc_back'][-8], 8.642850754173368)
def test_params_irradiance_model():
"""Test that irradiance params are passed correctly in
run_timeseries_engine"""
mock_irradiance_model = mock.MagicMock()
mock_engine = mock.MagicMock()
mock_pvarray = mock.MagicMock()
irradiance_params = {'horizon_band_angle': 15.}
_ = run_timeseries_engine(None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
cls_engine=mock_engine,
cls_pvarray=mock_pvarray,
cls_irradiance=mock_irradiance_model,
irradiance_model_params=irradiance_params)
mock_irradiance_model.assert_called_once_with(
horizon_band_angle=irradiance_params['horizon_band_angle'])
def test_params_ghi_passed():
"""Test that GHI is passed correctly to run functions"""
mock_irradiance_model = mock.MagicMock()
mock_engine = mock.MagicMock()
mock_pvarray = mock.MagicMock()
ghi = [1000.]
_ = run_timeseries_engine(None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
cls_engine=mock_engine,
cls_pvarray=mock_pvarray,
cls_irradiance=mock_irradiance_model,
ghi=ghi)
mock_engine.return_value.fit.assert_called_with(None,
None,
None,
None,
None,
None,
None,
None,
ghi=ghi)
def test_run_timeseries_engine_fast_mode(fn_report_example, params_serial,
df_inputs_clearsky_8760):
"""Test that running timeseries engine with fast mode works consistently.
Values are supposed to be a little higher than with full mode"""
df_inputs = df_inputs_clearsky_8760.iloc[:24, :]
n = df_inputs.shape[0]
# Get MET data
timestamps = df_inputs.index
dni = df_inputs.dni.values
dhi = df_inputs.dhi.values
solar_zenith = df_inputs.solar_zenith.values
solar_azimuth = df_inputs.solar_azimuth.values
surface_tilt = df_inputs.surface_tilt.values
surface_azimuth = df_inputs.surface_azimuth.values
fast_mode_pvrow_index = 1
def fn_report(pvarray):
return {
'qinc_back': pvarray.ts_pvrows[1].back.get_param_weighted('qinc'),
'iso_back':
pvarray.ts_pvrows[1].back.get_param_weighted('isotropic')
}
report = run_timeseries_engine(fn_report,
params_serial,
timestamps,
dni,
dhi,
solar_zenith,
solar_azimuth,
surface_tilt,
surface_azimuth,
params_serial['rho_ground'],
fast_mode_pvrow_index=fast_mode_pvrow_index)
assert len(report['qinc_back']) == n
# Test value consistency
np.testing.assert_almost_equal(np.nansum(report['qinc_back']),
548.0011865481954)
np.testing.assert_almost_equal(np.nansum(report['iso_back']),
18.03732189070727)
# Check a couple values
np.testing.assert_almost_equal(report['qinc_back'][7], 11.304105184587364)
np.testing.assert_almost_equal(report['qinc_back'][-8], 8.743201975668212)
def pvfactors_timeseries(solar_azimuth,
solar_zenith,
surface_azimuth,
surface_tilt,
axis_azimuth,
timestamps,
dni,
dhi,
gcr,
pvrow_height,
pvrow_width,
albedo,
n_pvrows=3,
index_observed_pvrow=1,
rho_front_pvrow=0.03,
rho_back_pvrow=0.05,
horizon_band_angle=15.):
"""
Calculate front and back surface plane-of-array irradiance on
a fixed tilt or single-axis tracker PV array configuration, and using
the open-source "pvfactors" package. pvfactors implements the model
described in [1]_.
Please refer to pvfactors online documentation for more details:
https://sunpower.github.io/pvfactors/
Parameters
----------
solar_azimuth: numeric
Sun's azimuth angles using pvlib's azimuth convention (deg)
solar_zenith: numeric
Sun's zenith angles (deg)
surface_azimuth: numeric
Azimuth angle of the front surface of the PV modules, using pvlib's
convention (deg)
surface_tilt: numeric
Tilt angle of the PV modules, going from 0 to 180 (deg)
axis_azimuth: float
Azimuth angle of the rotation axis of the PV modules, using pvlib's
convention (deg). This is supposed to be fixed for all timestamps.
timestamps: datetime or DatetimeIndex
List of simulation timestamps
dni: numeric
Direct normal irradiance (W/m2)
dhi: numeric
Diffuse horizontal irradiance (W/m2)
gcr: float
Ground coverage ratio of the pv array
pvrow_height: float
Height of the pv rows, measured at their center (m)
pvrow_width: float
Width of the pv rows in the considered 2D plane (m)
albedo: float
Ground albedo
n_pvrows: int, default 3
Number of PV rows to consider in the PV array
index_observed_pvrow: int, default 1
Index of the PV row whose incident irradiance will be returned. Indices
of PV rows go from 0 to n_pvrows-1.
rho_front_pvrow: float, default 0.03
Front surface reflectivity of PV rows
rho_back_pvrow: float, default 0.05
Back surface reflectivity of PV rows
horizon_band_angle: float, default 15
Elevation angle of the sky dome's diffuse horizon band (deg)
Returns
-------
poa_front: numeric
Calculated incident irradiance on the front surface of the PV modules
(W/m2)
poa_back: numeric
Calculated incident irradiance on the back surface of the PV modules
(W/m2)
poa_front_absorbed: numeric
Calculated absorbed irradiance on the front surface of the PV modules
(W/m2), after AOI losses
poa_back_absorbed: numeric
Calculated absorbed irradiance on the back surface of the PV modules
(W/m2), after AOI losses
References
----------
.. [1] Anoma, Marc Abou, et al. "View Factor Model and Validation for
Bifacial PV and Diffuse Shade on Single-Axis Trackers." 44th IEEE
Photovoltaic Specialist Conference. 2017.
"""
# Convert pandas Series inputs (and some lists) to numpy arrays
if isinstance(solar_azimuth, pd.Series):
solar_azimuth = solar_azimuth.values
elif isinstance(solar_azimuth, list):
solar_azimuth = np.array(solar_azimuth)
if isinstance(solar_zenith, pd.Series):
solar_zenith = solar_zenith.values
elif isinstance(solar_zenith, list):
solar_zenith = np.array(solar_zenith)
if isinstance(surface_azimuth, pd.Series):
surface_azimuth = surface_azimuth.values
elif isinstance(surface_azimuth, list):
surface_azimuth = np.array(surface_azimuth)
if isinstance(surface_tilt, pd.Series):
surface_tilt = surface_tilt.values
elif isinstance(surface_tilt, list):
surface_tilt = np.array(surface_tilt)
if isinstance(dni, pd.Series):
dni = dni.values
elif isinstance(dni, list):
dni = np.array(dni)
if isinstance(dhi, pd.Series):
dhi = dhi.values
elif isinstance(dhi, list):
dhi = np.array(dhi)
# Import pvfactors functions for timeseries calculations.
from pvfactors.run import run_timeseries_engine
# Build up pv array configuration parameters
pvarray_parameters = {
'n_pvrows': n_pvrows,
'axis_azimuth': axis_azimuth,
'pvrow_height': pvrow_height,
'pvrow_width': pvrow_width,
'gcr': gcr
}
irradiance_model_params = {
'rho_front': rho_front_pvrow,
'rho_back': rho_back_pvrow,
'horizon_band_angle': horizon_band_angle
}
# Create report function
def fn_build_report(pvarray):
return {
'total_inc_back':
pvarray.ts_pvrows[index_observed_pvrow].back.get_param_weighted(
'qinc'),
'total_inc_front':
pvarray.ts_pvrows[index_observed_pvrow].front.get_param_weighted(
'qinc'),
'total_abs_back':
pvarray.ts_pvrows[index_observed_pvrow].back.get_param_weighted(
'qabs'),
'total_abs_front':
pvarray.ts_pvrows[index_observed_pvrow].front.get_param_weighted(
'qabs')
}
# Run pvfactors calculations
report = run_timeseries_engine(
fn_build_report,
pvarray_parameters,
timestamps,
dni,
dhi,
solar_zenith,
solar_azimuth,
surface_tilt,
surface_azimuth,
albedo,
irradiance_model_params=irradiance_model_params)
# Turn report into dataframe
df_report = pd.DataFrame(report, index=timestamps)
return (df_report.total_inc_front, df_report.total_inc_back,
df_report.total_abs_front, df_report.total_abs_back)
def get_effective_irradiance(self, weather, utc_offset):
'''
Transform GHI/DNI/DHI from weather dataframe into POA.
This is a custom method added to the modelchain class.
Modelchain instance must already be assigned bifacial_losses attribute.
Parameters:
self: pvlib.modelchain
a ModelChain instance
weather: pandas.DataFrame
time series weather data with columns ['ghi', 'dni', 'dhi', 'temp_air',
'wind_speed', 'surface_albedo']
utc_offset: int.
hour offset of local timezone (in weather DataFrame) from UTC
Returns:
pvlib.modelchain
'''
# Prepare weather DataFrame ----
# Must be datetimeindex aware
tz = 'Etc/GMT+' + str(-utc_offset)
# If pd.datetimeindex is naive, localize, otherwise convert
if (pd.to_datetime(weather.index).tz is None):
weather.index = pd.to_datetime(weather.index).tz_localize(tz)
else:
weather.index = pd.to_datetime(weather.index).tz_convert(tz)
# Check if I should override weather's albedo values
# (only canopy or rooftop systems will have self.system.albedo values)
try:
weather.albedo = self.system.albedo
except:
pass
# Run PVLib models ----
self.prepare_inputs(weather)
self.aoi_model()
self.spectral_model()
self.effective_irradiance_model()
self.effective_irradiance = self.effective_irradiance.fillna(0)
self.temperature_model()
# Use PVFactors viewshed model ----
# Surface Inputs calculated in prepare_inputs if tracking,
if isinstance(self.system, SingleAxisTracker):
surface_tilt = self.tracking.surface_tilt
surface_azimuth = self.tracking.surface_azimuth
# but must be manually calculated if fixed-tilt
else:
surface_tilt = np.repeat(self.system.surface_tilt,
len(self.weather.index))
surface_azimuth = np.repeat(self.system.surface_azimuth,
len(self.weather.index))
# Need a custom function to build the report of the PVFactors simulation
def pvfactor_build_report(pvarray):
return {
'total_inc_back':
pvarray.ts_pvrows[1].back.get_param_weighted('qinc').tolist(),
'total_inc_front':
pvarray.ts_pvrows[1].front.get_param_weighted('qinc').tolist()
}
pvarray_parameters = {
'n_pvrows': 3,
'axis_azimuth': self.system.axis_azimuth,
'pvrow_height': self.system.axis_height,
'pvrow_width': self.system.collector_width,
'gcr': self.system.gcr,
# front and back reflectivity
'rho_front_pvrow': 0.01,
'rho_back_pvrow': .03,
# sky dome's diffuse horizon band angle
'horizon_band_angle': 15
}
pvfactor_report = run_timeseries_engine(
pvfactor_build_report, pvarray_parameters, weather.index, weather.dni,
weather.dhi, self.solar_position.zenith, self.solar_position.azimuth,
surface_tilt, surface_azimuth, weather.surface_albedo)
# Save the PVFactor results
pvfactor_df = pd.DataFrame(pvfactor_report, index=weather.index)
setattr(self, 'back_irradiance', pvfactor_df.total_inc_back.fillna(0))
setattr(self, 'front_irradiance', pvfactor_df.total_inc_front.fillna(0))
# Calculate plane of array irradiance losses ----
# PVFactors doesn't account for backtracking, use frontside irradiance from
# PVLib instead. (self.effective_irradiance for backtracking systems instead
# of self.front_irradiance)
# Effective Irradiance = Frontside POA + Backside POA *
# (Bifaciality Reduced For Backside Losses)
if self.system.backtrack:
setattr(
self, 'effective_irradiance_bifacial',
self.effective_irradiance + self.back_irradiance *
(self.system.module_parameters['bifaciality'] *
(1 - self.bifacial_losses)))
else:
setattr(
self, 'effective_irradiance_bifacial',
self.front_irradiance + self.back_irradiance *
(self.system.module_parameters['bifaciality'] *
(1 - self.bifacial_losses)))
# Apply same soiling loss to front-and-backside POA (conservative approach)
setattr(self, 'soiling', weather.soiling)
setattr(self, 'effective_irradiance_soiled',
self.effective_irradiance_bifacial * (1 - self.soiling))
return self
def pvfactors_timeseries(solar_azimuth,
solar_zenith,
surface_azimuth,
surface_tilt,
axis_azimuth,
timestamps,
dni,
dhi,
gcr,
pvrow_height,
pvrow_width,
albedo,
n_pvrows=3,
index_observed_pvrow=1,
rho_front_pvrow=0.03,
rho_back_pvrow=0.05,
horizon_band_angle=15.,
run_parallel_calculations=True,
n_workers_for_parallel_calcs=2):
"""
Calculate front and back surface plane-of-array irradiance on
a fixed tilt or single-axis tracker PV array configuration, and using
the open-source "pvfactors" package. pvfactors implements the model
described in [1]_.
Please refer to pvfactors online documentation for more details:
https://sunpower.github.io/pvfactors/
Parameters
----------
solar_azimuth: numeric
Sun's azimuth angles using pvlib's azimuth convention (deg)
solar_zenith: numeric
Sun's zenith angles (deg)
surface_azimuth: numeric
Azimuth angle of the front surface of the PV modules, using pvlib's
convention (deg)
surface_tilt: numeric
Tilt angle of the PV modules, going from 0 to 180 (deg)
axis_azimuth: float
Azimuth angle of the rotation axis of the PV modules, using pvlib's
convention (deg). This is supposed to be fixed for all timestamps.
timestamps: datetime or DatetimeIndex
List of simulation timestamps
dni: numeric
Direct normal irradiance (W/m2)
dhi: numeric
Diffuse horizontal irradiance (W/m2)
gcr: float
Ground coverage ratio of the pv array
pvrow_height: float
Height of the pv rows, measured at their center (m)
pvrow_width: float
Width of the pv rows in the considered 2D plane (m)
albedo: float
Ground albedo
n_pvrows: int, default 3
Number of PV rows to consider in the PV array
index_observed_pvrow: int, default 1
Index of the PV row whose incident irradiance will be returned. Indices
of PV rows go from 0 to n_pvrows-1.
rho_front_pvrow: float, default 0.03
Front surface reflectivity of PV rows
rho_back_pvrow: float, default 0.05
Back surface reflectivity of PV rows
horizon_band_angle: float, default 15
Elevation angle of the sky dome's diffuse horizon band (deg)
run_parallel_calculations: bool, default True
pvfactors is capable of using multiprocessing. Use this flag to decide
to run calculations in parallel (recommended) or not.
n_workers_for_parallel_calcs: int, default 2
Number of workers to use in the case of parallel calculations. The
'-1' value will lead to using a value equal to the number
of CPU's on the machine running the model.
Returns
-------
front_poa_irradiance: numeric
Calculated incident irradiance on the front surface of the PV modules
(W/m2)
back_poa_irradiance: numeric
Calculated incident irradiance on the back surface of the PV modules
(W/m2)
df_registries: pandas DataFrame
DataFrame containing detailed outputs of the simulation; for
instance the shapely geometries, the irradiance components incident on
all surfaces of the PV array (for all timestamps), etc.
In the pvfactors documentation, this is refered to as the "surface
registry".
References
----------
.. [1] Anoma, Marc Abou, et al. "View Factor Model and Validation for
Bifacial PV and Diffuse Shade on Single-Axis Trackers." 44th IEEE
Photovoltaic Specialist Conference. 2017.
"""
# Convert pandas Series inputs (and some lists) to numpy arrays
if isinstance(solar_azimuth, pd.Series):
solar_azimuth = solar_azimuth.values
elif isinstance(solar_azimuth, list):
solar_azimuth = np.array(solar_azimuth)
if isinstance(solar_zenith, pd.Series):
solar_zenith = solar_zenith.values
if isinstance(surface_azimuth, pd.Series):
surface_azimuth = surface_azimuth.values
elif isinstance(surface_azimuth, list):
surface_azimuth = np.array(surface_azimuth)
if isinstance(surface_tilt, pd.Series):
surface_tilt = surface_tilt.values
if isinstance(dni, pd.Series):
dni = dni.values
if isinstance(dhi, pd.Series):
dhi = dhi.values
if isinstance(solar_azimuth, list):
solar_azimuth = np.array(solar_azimuth)
# Import pvfactors functions for timeseries calculations.
from pvfactors.run import (run_timeseries_engine, run_parallel_engine)
# Build up pv array configuration parameters
pvarray_parameters = {
'n_pvrows': n_pvrows,
'axis_azimuth': axis_azimuth,
'pvrow_height': pvrow_height,
'pvrow_width': pvrow_width,
'gcr': gcr,
'rho_front_pvrow': rho_front_pvrow,
'rho_back_pvrow': rho_back_pvrow,
'horizon_band_angle': horizon_band_angle
}
# Run pvfactors calculations: either in parallel or serially
if run_parallel_calculations:
report = run_parallel_engine(PVFactorsReportBuilder,
pvarray_parameters,
timestamps,
dni,
dhi,
solar_zenith,
solar_azimuth,
surface_tilt,
surface_azimuth,
albedo,
n_processes=n_workers_for_parallel_calcs)
else:
report = run_timeseries_engine(PVFactorsReportBuilder.build,
pvarray_parameters, timestamps, dni,
dhi, solar_zenith, solar_azimuth,
surface_tilt, surface_azimuth, albedo)
# Turn report into dataframe
df_report = pd.DataFrame(report, index=timestamps)
return df_report.total_inc_front, df_report.total_inc_back
def test_run_timeseries_faoi_fn(params_serial, pvmodule_canadian,
df_inputs_clearsky_8760):
"""Test that in run_timeseries function, faoi functions are used
correctly"""
# Prepare timeseries inputs
df_inputs = df_inputs_clearsky_8760.iloc[:24, :]
timestamps = df_inputs.index
dni = df_inputs.dni.values
dhi = df_inputs.dhi.values
solar_zenith = df_inputs.solar_zenith.values
solar_azimuth = df_inputs.solar_azimuth.values
surface_tilt = df_inputs.surface_tilt.values
surface_azimuth = df_inputs.surface_azimuth.values
expected_qinc_back = 542.018551
expected_qinc_front = 5452.858863
# --- Test without passing vf parameters
# report function with test in it
def report_fn_with_tests_no_faoi(pvarray):
vf_aoi_matrix = pvarray.ts_vf_aoi_matrix
pvrow = pvarray.ts_pvrows[0]
list_back_pvrow_idx = [
ts_surf.index for ts_surf in pvarray.ts_pvrows[0].all_ts_surfaces
]
# Check that sum of vf_aoi is equal to reflectivity values
# since no faoi_fn used
np.testing.assert_allclose(
vf_aoi_matrix[list_back_pvrow_idx, :, 12].sum(axis=1),
[0.99, 0., 0.97, 0.])
return {
'qinc_front': pvrow.front.get_param_weighted('qinc'),
'qabs_front': pvrow.front.get_param_weighted('qabs'),
'qinc_back': pvrow.back.get_param_weighted('qinc'),
'qabs_back': pvrow.back.get_param_weighted('qabs')
}
# create calculator
report = run_timeseries_engine(report_fn_with_tests_no_faoi,
params_serial,
timestamps,
dni,
dhi,
solar_zenith,
solar_azimuth,
surface_tilt,
surface_azimuth,
params_serial['rho_ground'],
vf_calculator_params=None,
irradiance_model_params=None)
np.testing.assert_allclose(np.nansum(report['qinc_back']),
expected_qinc_back)
np.testing.assert_allclose(np.nansum(report['qabs_back']), 525.757995)
np.testing.assert_allclose(np.nansum(report['qinc_front']),
expected_qinc_front)
np.testing.assert_allclose(np.nansum(report['qabs_front']), 5398.330275)
# --- Test when passing vf parameters
# Prepare vf calc params
faoi_fn = faoi_fn_from_pvlib_sandia(pvmodule_canadian)
# the following is a very high number to get agreement in
# integral sums between back and front surfaces
n_sections = 10000
vf_calc_params = {
'faoi_fn_front': faoi_fn,
'faoi_fn_back': faoi_fn,
'n_aoi_integral_sections': n_sections
}
irr_params = {'faoi_fn_front': faoi_fn, 'faoi_fn_back': faoi_fn}
def report_fn_with_tests_w_faoi(pvarray):
vf_aoi_matrix = pvarray.ts_vf_aoi_matrix
pvrow = pvarray.ts_pvrows[0]
list_back_pvrow_idx = [
ts_surf.index for ts_surf in pvrow.all_ts_surfaces
]
# Check that sum of vf_aoi is consistent
np.testing.assert_allclose(vf_aoi_matrix[list_back_pvrow_idx, :,
12].sum(axis=1),
[0.97102, 0., 0.971548, 0.],
atol=0,
rtol=1e-6)
return {
'qinc_front': pvrow.front.get_param_weighted('qinc'),
'qabs_front': pvrow.front.get_param_weighted('qabs'),
'qinc_back': pvrow.back.get_param_weighted('qinc'),
'qabs_back': pvrow.back.get_param_weighted('qabs')
}
# create calculator
report = run_timeseries_engine(report_fn_with_tests_w_faoi,
params_serial,
timestamps,
dni,
dhi,
solar_zenith,
solar_azimuth,
surface_tilt,
surface_azimuth,
params_serial['rho_ground'],
vf_calculator_params=vf_calc_params,
irradiance_model_params=irr_params)
np.testing.assert_allclose(np.nansum(report['qinc_back']),
expected_qinc_back)
np.testing.assert_allclose(np.nansum(report['qabs_back']), 520.892016)
np.testing.assert_allclose(np.nansum(report['qinc_front']),
expected_qinc_front)
np.testing.assert_allclose(np.nansum(report['qabs_front']), 5347.050682)