def system_power(request, system_location, naive_times):
tilt = request.param[0]
azimuth = request.param[1]
local_time = naive_times.tz_localize(system_location.tz)
clearsky = system_location.get_clearsky(local_time,
model='simplified_solis')
solar_position = system_location.get_solarposition(local_time)
poa = irradiance.get_total_irradiance(tilt, azimuth,
solar_position['zenith'],
solar_position['azimuth'],
**clearsky)
temp_cell = pvlib.temperature.sapm_cell(
poa['poa_global'], 25, 0,
**pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']
['open_rack_glass_glass'])
pdc = pvsystem.pvwatts_dc(poa['poa_global'], temp_cell, 100, -0.002)
pac = pvsystem.inverter.pvwatts(pdc, 120)
return {
'location': system_location,
'tilt': tilt,
'azimuth': azimuth,
'clearsky': clearsky,
'solar_position': solar_position,
'ac': pac
}
def test_pvwatts_dc_arrays():
irrad_trans = np.array([np.nan, 900, 900])
temp_cell = np.array([30, np.nan, 30])
irrad_trans, temp_cell = np.meshgrid(irrad_trans, temp_cell)
expected = np.array([[nan, 88.65, 88.65], [nan, nan, nan],
[nan, 88.65, 88.65]])
out = pvsystem.pvwatts_dc(irrad_trans, temp_cell, 100, -0.003)
assert_allclose(out, expected, equal_nan=True)
def test_pvwatts_dc_arrays():
irrad_trans = np.array([np.nan, 900, 900])
temp_cell = np.array([30, np.nan, 30])
irrad_trans, temp_cell = np.meshgrid(irrad_trans, temp_cell)
expected = np.array([[ nan, 88.65, 88.65],
[ nan, nan, nan],
[ nan, 88.65, 88.65]])
out = pvsystem.pvwatts_dc(irrad_trans, temp_cell, 100, -0.003)
assert_allclose(expected, out, equal_nan=True)
def test_orientation_fit_pvwatts_missing_data(naive_times):
tilt = 30
azimuth = 100
system_location = location.Location(35, -106)
local_time = naive_times.tz_localize('MST')
clearsky = system_location.get_clearsky(local_time,
model='simplified_solis')
clearsky.loc['3/1/2020':'3/15/2020'] = np.nan
solar_position = system_location.get_solarposition(clearsky.index)
solar_position.loc['3/1/2020':'3/15/2020'] = np.nan
poa = irradiance.get_total_irradiance(tilt, azimuth,
solar_position['zenith'],
solar_position['azimuth'],
**clearsky)
temp_cell = pvlib.temperature.sapm_cell(
poa['poa_global'], 25, 0,
**pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']
['open_rack_glass_glass'])
pdc = pvsystem.pvwatts_dc(poa['poa_global'], temp_cell, 100, -0.002)
pac = pvsystem.inverter.pvwatts(pdc, 120)
solar_position.dropna(inplace=True)
with pytest.raises(ValueError,
match=".* must not contain undefined values"):
system.infer_orientation_fit_pvwatts(
pac,
solar_zenith=solar_position['zenith'],
solar_azimuth=solar_position['azimuth'],
**clearsky)
pac.dropna(inplace=True)
with pytest.raises(ValueError,
match=".* must not contain undefined values"):
system.infer_orientation_fit_pvwatts(
pac,
solar_zenith=solar_position['zenith'],
solar_azimuth=solar_position['azimuth'],
**clearsky)
clearsky.dropna(inplace=True)
tilt_out, azimuth_out, rsquared = system.infer_orientation_fit_pvwatts(
pac,
solar_zenith=solar_position['zenith'],
solar_azimuth=solar_position['azimuth'],
**clearsky)
assert rsquared > 0.9
assert tilt_out == pytest.approx(tilt, abs=10)
assert azimuth_out == pytest.approx(azimuth, abs=10)
def power_pvwatts(request, clearsky_july, solarposition_july):
pdc0 = 100
pdc0_inverter = 110
tilt = 30
azimuth = 180
pdc0_marker = request.node.get_closest_marker("pdc0_inverter")
if pdc0_marker is not None:
pdc0_inverter = pdc0_marker.args[0]
poa = irradiance.get_total_irradiance(
tilt, azimuth,
solarposition_july['zenith'], solarposition_july['azimuth'],
**clearsky_july
)
cell_temp = temperature.sapm_cell(
poa['poa_global'], 25, 0,
**TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']
)
dc = pvsystem.pvwatts_dc(poa['poa_global'], cell_temp, pdc0, -0.004)
return inverter.pvwatts(dc, pdc0_inverter)
def test_pvwatts_dc_series():
irrad_trans = pd.Series([np.nan, 900, 900])
temp_cell = pd.Series([30, np.nan, 30])
expected = pd.Series(np.array([ nan, nan, 88.65]))
out = pvsystem.pvwatts_dc(irrad_trans, temp_cell, 100, -0.003)
assert_series_equal(expected, out)
def test_pvwatts_dc_scalars():
expected = 88.65
out = pvsystem.pvwatts_dc(900, 30, 100, -0.003)
assert_allclose(out, expected)
def test_pvwatts_dc_series():
irrad_trans = pd.Series([np.nan, 900, 900])
temp_cell = pd.Series([30, np.nan, 30])
expected = pd.Series(np.array([ nan, nan, 88.65]))
out = pvsystem.pvwatts_dc(irrad_trans, temp_cell, 100, -0.003)
assert_series_equal(expected, out)
def test_pvwatts_dc_scalars():
expected = 88.65
out = pvsystem.pvwatts_dc(900, 30, 100, -0.003)
assert_allclose(expected, out)
irrad = pd.concat(irrad, axis=1)
# using bifaciality factor and pvfactors results, create effective irradiance
bifaciality = 0.75
effective_irrad_bifi = irrad['total_abs_front'] + (irrad['total_abs_back'] *
bifaciality)
# get cell temperature using the Faiman model
temp_cell = temperature.faiman(effective_irrad_bifi, temp_air=25, wind_speed=1)
# using the pvwatts_dc model and parameters detailed above,
# set pdc0 and return DC power for both bifacial and monofacial
pdc0 = 1
gamma_pdc = -0.0043
pdc_bifi = pvsystem.pvwatts_dc(effective_irrad_bifi,
temp_cell,
pdc0,
gamma_pdc=gamma_pdc).fillna(0)
pdc_bifi.plot(title='Bifacial Simulation on June Solstice', ylabel='DC Power')
# %%
# For illustration, perform monofacial simulation using pvfactors front-side
# irradiance (AOI-corrected), and plot along with bifacial results.
effective_irrad_mono = irrad['total_abs_front']
pdc_mono = pvsystem.pvwatts_dc(effective_irrad_mono,
temp_cell,
pdc0,
gamma_pdc=gamma_pdc).fillna(0)
# plot monofacial results
plt.figure()
def test_orientation_fit_pvwatts_temp_wind_as_series(naive_times):
tilt = 30
azimuth = 100
system_location = location.Location(35, -106)
local_time = naive_times.tz_localize('MST')
clearsky = system_location.get_clearsky(local_time,
model='simplified_solis')
solar_position = system_location.get_solarposition(clearsky.index)
poa = irradiance.get_total_irradiance(tilt, azimuth,
solar_position['zenith'],
solar_position['azimuth'],
**clearsky)
temp_cell = pvlib.temperature.sapm_cell(
poa['poa_global'], 25, 1,
**pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']
['open_rack_glass_glass'])
temperature = pd.Series(25, index=clearsky.index)
wind_speed = pd.Series(1, index=clearsky.index)
temperature_missing = temperature.copy()
temperature_missing.loc['4/5/2020':'4/10/2020'] = np.nan
wind_speed_missing = wind_speed.copy()
wind_speed_missing.loc['5/5/2020':'5/15/2020'] = np.nan
pdc = pvsystem.pvwatts_dc(poa['poa_global'], temp_cell, 100, -0.002)
pac = pvsystem.inverter.pvwatts(pdc, 120)
with pytest.raises(ValueError,
match=".* must not contain undefined values"):
system.infer_orientation_fit_pvwatts(
pac,
solar_zenith=solar_position['zenith'],
solar_azimuth=solar_position['azimuth'],
temperature=temperature_missing,
wind_speed=wind_speed_missing,
**clearsky)
with pytest.raises(ValueError,
match="temperature must not contain undefined values"):
system.infer_orientation_fit_pvwatts(
pac,
solar_zenith=solar_position['zenith'],
solar_azimuth=solar_position['azimuth'],
temperature=temperature_missing,
wind_speed=wind_speed,
**clearsky)
with pytest.raises(ValueError,
match="wind_speed must not contain undefined values"):
system.infer_orientation_fit_pvwatts(
pac,
solar_zenith=solar_position['zenith'],
solar_azimuth=solar_position['azimuth'],
temperature=temperature,
wind_speed=wind_speed_missing,
**clearsky)
# ValueError if indices don't match
with pytest.raises(ValueError):
system.infer_orientation_fit_pvwatts(
pac,
solar_zenith=solar_position['zenith'],
solar_azimuth=solar_position['azimuth'],
temperature=temperature_missing.dropna(),
wind_speed=wind_speed_missing.dropna(),
**clearsky)
tilt_out, azimuth_out, rsquared = system.infer_orientation_fit_pvwatts(
pac,
solar_zenith=solar_position['zenith'],
solar_azimuth=solar_position['azimuth'],
**clearsky)
assert rsquared > 0.9
assert tilt_out == pytest.approx(tilt, abs=10)
assert azimuth_out == pytest.approx(azimuth, abs=10)
def performance_ratio_nrel(poa_global,
temp_air,
wind_speed,
pac,
pdc0,
a=-3.56,
b=-0.075,
deltaT=3,
gamma_pdc=-0.00433):
r"""
Calculate NREL Performance Ratio.
See equation [5] in Weather-Corrected Performance Ratio [1]_ for details
on the weighted method for Tref.
Parameters
----------
poa_global : numeric
Total incident irradiance [W/m^2].
temp_air : numeric
Ambient dry bulb temperature [C].
wind_speed : numeric
Wind speed at a height of 10 meters [m/s].
pac : float
AC power [kW].
pdc0 : float
Power of the modules at 1000 W/m2 and cell reference temperature [kW].
a : float
Parameter :math:`a` in SAPM model [unitless].
b : float
Parameter :math:`b` in in SAPM model [s/m].
deltaT : float
Parameter :math:`\Delta T` in SAPM model [C].
gamma_pdc : float
The temperature coefficient in units of 1/C. Typically -0.002 to
-0.005 per degree C [1/C].
Returns
-------
performance_ratio: float
Performance Ratio of data.
References
----------
.. [1] Dierauf et al. "Weather-Corrected Performance Ratio". NREL, 2013.
https://www.nrel.gov/docs/fy13osti/57991.pdf
"""
cell_temperature = sapm_cell(poa_global, temp_air, wind_speed, a, b,
deltaT)
tcell_poa_global = poa_global * cell_temperature
tref = tcell_poa_global.sum() / poa_global.sum()
pdc = pvwatts_dc(poa_global,
cell_temperature,
pdc0,
gamma_pdc,
temp_ref=tref)
performance_ratio = pac.sum() / pdc.sum()
return performance_ratio
