def test_sapm():
modules = sam_data["sandiamod"]
module = modules.Canadian_Solar_CS5P_220M___2009_
sapm = pvsystem.sapm(module, irrad_data["dni"], irrad_data["dhi"], 25, am, aoi)
sapm = pvsystem.sapm(module.to_dict(), irrad_data["dni"], irrad_data["dhi"], 25, am, aoi)
def test_sapm():
modules = sam_data['sandiamod']
module_parameters = modules['Canadian_Solar_CS5P_220M___2009_']
times = pd.DatetimeIndex(start='2015-01-01', periods=2, freq='12H')
irrad_data = pd.DataFrame({'dni':[0,1000], 'ghi':[0,600], 'dhi':[0,100]},
index=times)
am = pd.Series([0, 2.25], index=times)
aoi = pd.Series([180, 30], index=times)
sapm = pvsystem.sapm(module_parameters, irrad_data['dni'],
irrad_data['dhi'], 25, am, aoi)
expected = pd.DataFrame(np.array(
[[ 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. ],
[ 5.74526799, 5.12194115, 59.67914031, 48.41924255,
248.00051089, 5.61787615, 3.52581308, 1.12848138]]),
columns=['i_sc', 'i_mp', 'v_oc', 'v_mp', 'p_mp', 'i_x', 'i_xx',
'effective_irradiance'],
index=times)
assert_frame_equal(sapm, expected)
# just make sure it works with a dict input
sapm = pvsystem.sapm(module_parameters.to_dict(), irrad_data['dni'],
irrad_data['dhi'], 25, am, aoi)
def test_sapm():
modules = sam_data['sandiamod']
module_parameters = modules['Canadian_Solar_CS5P_220M___2009_']
times = pd.DatetimeIndex(start='2015-01-01', periods=2, freq='12H')
irrad_data = pd.DataFrame(
{
'dni': [0, 1000],
'ghi': [0, 600],
'dhi': [0, 100]
}, index=times)
am = pd.Series([0, 2.25], index=times)
aoi = pd.Series([180, 30], index=times)
sapm = pvsystem.sapm(module_parameters, irrad_data['dni'],
irrad_data['dhi'], 25, am, aoi)
expected = pd.DataFrame(np.array([[0., 0., 0., 0., 0., 0., 0., 0.],
[
5.74526799, 5.12194115, 59.67914031,
48.41924255, 248.00051089,
5.61787615, 3.52581308, 1.12848138
]]),
columns=[
'i_sc', 'i_mp', 'v_oc', 'v_mp', 'p_mp', 'i_x',
'i_xx', 'effective_irradiance'
],
index=times)
assert_frame_equal(sapm, expected)
# just make sure it works with a dict input
sapm = pvsystem.sapm(module_parameters.to_dict(), irrad_data['dni'],
irrad_data['dhi'], 25, am, aoi)
def test_sapm():
modules = sam_data['sandiamod']
module = modules.Canadian_Solar_CS5P_220M___2009_
sapm = pvsystem.sapm(module, irrad_data.DNI, irrad_data.DHI, 25, am, aoi)
sapm = pvsystem.sapm(module.to_dict(), irrad_data.DNI,
irrad_data.DHI, 25, am, aoi)
def pvmodule_detail(request, pvmodule_id):
pvmod = get_object_or_404(PVModule, pk=pvmodule_id)
fieldnames = PVModule._meta.get_fields()
pvmod_dict = {k.name: getattr(pvmod, k.name) for k in fieldnames}
for k in ['IXO', 'IXXO', 'C4', 'C5', 'C6', 'C7']:
if pvmod_dict[k] is None:
pvmod_dict[k] = 0.
celltemps = np.linspace(0, 100, 5)
effirrad, celltemp = np.meshgrid(np.linspace(0.1, 1, 10), celltemps)
results = sapm(effirrad, celltemp, pvmod_dict)
eff = results['p_mp'] / effirrad / pvmod.Area * 100 / 1000
fig = figure(
x_axis_label='effective irradiance, Ee [suns]',
y_axis_label='efficiency [%]',
title=pvmod.Name,
plot_width=800, plot_height=600, sizing_mode='scale_width'
)
r = fig.multi_line(
effirrad.tolist(), eff.tolist(), color=cmap, line_width=4)
legend = Legend(items=[
LegendItem(label='{:d} [C]'.format(int(ct)), renderers=[r], index=n)
for n, ct in enumerate(celltemps)])
fig.add_layout(legend)
plot_script, plot_div = components(fig)
return render(
request, 'pvmodule_detail.html', {
'path': request.path, 'pvmod': pvmod, 'plot_script': plot_script,
'plot_div': plot_div, 'pvmod_dict': pvmod_dict})
def test_sapm(sapm_module_params):
times = pd.date_range(start='2015-01-01', periods=5, freq='12H')
effective_irradiance = pd.Series([-1000, 500, 1100, np.nan, 1000],
index=times)
temp_cell = pd.Series([10, 25, 50, 25, np.nan], index=times)
out = pvsystem.sapm(effective_irradiance, temp_cell, sapm_module_params)
expected = pd.DataFrame(np.array(
[[-5.0608322, -4.65037767, nan, nan, nan, -4.91119927, -4.15367716],
[
2.545575, 2.28773882, 56.86182059, 47.21121608, 108.00693168,
2.48357383, 1.71782772
],
[
5.65584763, 5.01709903, 54.1943277, 42.51861718, 213.32011294,
5.52987899, 3.48660728
], [nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan]]),
columns=[
'i_sc', 'i_mp', 'v_oc', 'v_mp', 'p_mp', 'i_x',
'i_xx'
],
index=times)
assert_frame_equal(out, expected, check_less_precise=4)
out = pvsystem.sapm(1000, 25, sapm_module_params)
expected = OrderedDict()
expected['i_sc'] = 5.09115
expected['i_mp'] = 4.5462909092579995
expected['v_oc'] = 59.260800000000003
expected['v_mp'] = 48.315600000000003
expected['p_mp'] = 219.65677305534581
expected['i_x'] = 4.9759899999999995
expected['i_xx'] = 3.1880204359100004
for k, v in expected.items():
assert_allclose(out[k], v, atol=1e-4)
# just make sure it works with Series input
pvsystem.sapm(effective_irradiance, temp_cell,
pd.Series(sapm_module_params))
def gen_iec_61853_from_sapm(pvmodule):
"""
Generate an IEC 61853 test from Sandia Array Performance Model (sapm).
:param pvmodule: PV module to be tested
:type pvmodule: dict
Module is a dictionary according to :def:`pvlib.pvsystem.sapm`.
"""
tc, irr = TEST_MAT
return sapm(irr / 1000.0, tc, pvmodule)
def test_sapm(sapm_module_params):
times = pd.DatetimeIndex(start='2015-01-01', periods=5, freq='12H')
effective_irradiance = pd.Series([-1, 0.5, 1.1, np.nan, 1], index=times)
temp_cell = pd.Series([10, 25, 50, 25, np.nan], index=times)
out = pvsystem.sapm(effective_irradiance, temp_cell, sapm_module_params)
expected = pd.DataFrame(np.array(
[[ -5.0608322 , -4.65037767, nan, nan,
nan, -4.91119927, -4.15367716],
[ 2.545575 , 2.28773882, 56.86182059, 47.21121608,
108.00693168, 2.48357383, 1.71782772],
[ 5.65584763, 5.01709903, 54.1943277 , 42.51861718,
213.32011294, 5.52987899, 3.48660728],
[ nan, nan, nan, nan,
nan, nan, nan],
[ nan, nan, nan, nan,
nan, nan, nan]]),
columns=['i_sc', 'i_mp', 'v_oc', 'v_mp', 'p_mp', 'i_x', 'i_xx'],
index=times)
assert_frame_equal(out, expected, check_less_precise=4)
out = pvsystem.sapm(1, 25, sapm_module_params)
expected = OrderedDict()
expected['i_sc'] = 5.09115
expected['i_mp'] = 4.5462909092579995
expected['v_oc'] = 59.260800000000003
expected['v_mp'] = 48.315600000000003
expected['p_mp'] = 219.65677305534581
expected['i_x'] = 4.9759899999999995
expected['i_xx'] = 3.1880204359100004
for k, v in expected.items():
assert_allclose(out[k], v, atol=1e-4)
# just make sure it works with a dict input
pvsystem.sapm(effective_irradiance, temp_cell,
sapm_module_params.to_dict())
def gen_iec_61853_from_sapm(pvmodule):
"""
Generate an IEC 61853 test from Sandia Array Performance Model (sapm).
:param dict pvmodule: PV module to be tested
:returns: a pandas dataframe with columns ``i_mp``, ``v_mp``, ``i_sc``, and
``v_oc`` and rows corresponding to the IEC61853 test conditions
Module is a dictionary according to ``pvlib.pvsystem.sapm``.
"""
tc, irr = TEST_MAT
return sapm(irr / 1000.0, tc, pvmodule)
def get_perfect_voltage_for_a_day(start, freq):
"""This method is used to build a pandas serie with voltage values.
This serie has DateTime index and contains a value for every "freq"
seconds during 24 hours starting from "start" date.
There are several assumptions:
1. Location is Munich
2. A battery is pointing to the south, amount of blocks is 20
3. Sandia Module database is used
4. pvlib library is heavily used
:param start: datetime. First timestamp in result series
:param freq: str. How often voltage should be sampled
:return: voltage : Series
"""
surface_tilt = _munich_location.latitude
surface_azimuth = 180 # pointing south
date_range = pd.date_range(start=start,
end=start + dt.timedelta(
hours=23, minutes=59, seconds=59),
freq=freq, tz=_munich_location.tz)
clearsky_estimations = _munich_location.get_clearsky(date_range)
dni_extra = irradiance.extraradiation(date_range)
solar_position = solarposition.get_solarposition(
date_range, _munich_location.latitude, _munich_location.longitude)
airmass = atmosphere.relativeairmass(solar_position['apparent_zenith'])
pressure = atmosphere.alt2pres(_munich_location.altitude)
am_abs = atmosphere.absoluteairmass(airmass, pressure)
total_irrad = irradiance.total_irrad(surface_tilt,
surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'],
clearsky_estimations['dni'],
clearsky_estimations['ghi'],
clearsky_estimations['dhi'],
dni_extra=dni_extra,
model='haydavies')
temps = pvsystem.sapm_celltemp(total_irrad['poa_global'], 0, 15)
aoi = irradiance.aoi(surface_tilt, surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'])
# add 0.0001 to avoid np.log(0) and warnings about that
effective_irradiance = pvsystem.sapm_effective_irradiance(
total_irrad['poa_direct'], total_irrad['poa_diffuse'], am_abs,
aoi, _sandia_module) + 0.0001
sapm = pvsystem.sapm(effective_irradiance, temps['temp_cell'],
_sandia_module)
return sapm['p_mp'] * _module_count
def DC_out(solpos, poa_irrad, aoi, pvtemps):
## First calculate airmass
airmass = atmosphere.relativeairmass(solpos['apparent_zenith'])
## Get list of modules
sandia_modules = pvsystem.retrieve_sam('SandiaMod')
## Choose model (e.g. CS5P_220M___2009)
sandia_module = sandia_modules.Canadian_Solar_CS5P_220M___2009_
## Run SAPM model
effective_irradiance = pvsystem.sapm_effective_irradiance(
poa_irrad.poa_direct, poa_irrad.poa_diffuse, airmass, aoi,
sandia_module)
p_dc = pvsystem.sapm(effective_irradiance, pvtemps['temp_cell'],
sandia_module)
return (p_dc)
def __init__(self, panel=None, forecast_length=7, forecast_model=None):
self.forecast_length = forecast_length
if panel == None:
self.panel = Panel()
else:
self.panel = panel
if forecast_model == None:
self.fm = GFS()
else:
self.fm = forecast_model
self.start = pd.Timestamp(datetime.date.today(),
tz=self.panel.tz) # today's date
self.end = self.start + pd.Timedelta(
days=forecast_length) # days from today
print(
"getting processed data with lat: %s, lng: %s, start:%s, end:%s" %
(self.panel.latitude, self.panel.longitude, self.start, self.end))
# get forecast data
forecast_data = self.fm.get_processed_data(self.panel.latitude,
self.panel.longitude,
self.start, self.end)
ghi = forecast_data['ghi']
# get solar position
time = forecast_data.index
a_point = self.fm.location
solpos = a_point.get_solarposition(time)
# get PV(photovoltaic device) modules
sandia_modules = pvsystem.retrieve_sam('SandiaMod')
sandia_module = sandia_modules.Canadian_Solar_CS5P_220M___2009_
dni_extra = irradiance.get_extra_radiation(
self.fm.time) # extra terrestrial radiation
airmass = atmosphere.get_relative_airmass(solpos['apparent_zenith'])
# POA: Plane Of Array: an image sensing device consisting of an array
# (typically rectangular) of light-sensing pixels at the focal plane of a lens.
# https://en.wikipedia.org/wiki/Staring_array
# Diffuse sky radiation is solar radiation reaching the Earth's surface after
# having been scattered from the direct solar beam by molecules or particulates
# in the atmosphere.
# https://en.wikipedia.org/wiki/Diffuse_sky_radiation
poa_sky_diffuse = irradiance.haydavies(self.panel.surface_tilt,
self.panel.surface_azimuth,
forecast_data['dhi'],
forecast_data['dni'], dni_extra,
solpos['apparent_zenith'],
solpos['azimuth'])
# Diffuse reflection is the reflection of light or other waves or particles
# from a surface such that a ray incident on the surface is scattered at many
# angles rather than at just one angle as in the case of specular reflection.
poa_ground_diffuse = irradiance.get_ground_diffuse(
self.panel.surface_tilt, ghi, albedo=self.panel.albedo)
# AOI: Angle Of Incidence
aoi = irradiance.aoi(self.panel.surface_tilt,
self.panel.surface_azimuth,
solpos['apparent_zenith'], solpos['azimuth'])
# irradiance is the radiant flux (power) received by a surface per unit area
# https://en.wikipedia.org/wiki/Irradiance
poa_irrad = irradiance.poa_components(aoi, forecast_data['dni'],
poa_sky_diffuse,
poa_ground_diffuse)
temperature = forecast_data['temp_air']
wnd_spd = forecast_data['wind_speed']
# pvtemps: pv temperature
pvtemps = pvsystem.sapm_celltemp(poa_irrad['poa_global'], wnd_spd,
temperature)
# irradiance actually used by PV
effective_irradiance = pvsystem.sapm_effective_irradiance(
poa_irrad.poa_direct, poa_irrad.poa_diffuse, airmass, aoi,
sandia_module)
# SAPM: Sandia PV Array Performance Model
# https://pvpmc.sandia.gov/modeling-steps/2-dc-module-iv/point-value-models/sandia-pv-array-performance-model/
self.sapm_out = pvsystem.sapm(effective_irradiance,
pvtemps['temp_cell'], sandia_module)
sapm_inverters = pvsystem.retrieve_sam('sandiainverter')
sapm_inverter = sapm_inverters[
'ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']
self.ac_power = pvsystem.snlinverter(self.sapm_out.v_mp,
self.sapm_out.p_mp, sapm_inverter)
def basic_chain(times,
latitude,
longitude,
module_parameters,
inverter_parameters,
irradiance=None,
weather=None,
surface_tilt=None,
surface_azimuth=None,
orientation_strategy=None,
transposition_model='haydavies',
solar_position_method='nrel_numpy',
airmass_model='kastenyoung1989',
altitude=None,
pressure=None,
**kwargs):
"""
An experimental function that computes all of the modeling steps
necessary for calculating power or energy for a PV system at a given
location.
Parameters
----------
times : DatetimeIndex
Times at which to evaluate the model.
latitude : float.
Positive is north of the equator.
Use decimal degrees notation.
longitude : float.
Positive is east of the prime meridian.
Use decimal degrees notation.
module_parameters : None, dict or Series
Module parameters as defined by the SAPM.
inverter_parameters : None, dict or Series
Inverter parameters as defined by the CEC.
irradiance : None or DataFrame, default None
If None, calculates clear sky data.
Columns must be 'dni', 'ghi', 'dhi'.
weather : None or DataFrame, default None
If None, assumes air temperature is 20 C and
wind speed is 0 m/s.
Columns must be 'wind_speed', 'temp_air'.
surface_tilt : None, float or Series, default None
Surface tilt angles in decimal degrees.
The tilt angle is defined as degrees from horizontal
(e.g. surface facing up = 0, surface facing horizon = 90)
surface_azimuth : None, float or Series, default None
Surface azimuth angles in decimal degrees.
The azimuth convention is defined
as degrees east of north
(North=0, South=180, East=90, West=270).
orientation_strategy : None or str, default None
The strategy for aligning the modules.
If not None, sets the ``surface_azimuth`` and ``surface_tilt``
properties of the ``system``. Allowed strategies include 'flat',
'south_at_latitude_tilt'. Ignored for SingleAxisTracker systems.
transposition_model : str, default 'haydavies'
Passed to system.get_irradiance.
solar_position_method : str, default 'nrel_numpy'
Passed to solarposition.get_solarposition.
airmass_model : str, default 'kastenyoung1989'
Passed to atmosphere.relativeairmass.
altitude : None or float, default None
If None, computed from pressure. Assumed to be 0 m
if pressure is also None.
pressure : None or float, default None
If None, computed from altitude. Assumed to be 101325 Pa
if altitude is also None.
**kwargs
Arbitrary keyword arguments.
See code for details.
Returns
-------
output : (dc, ac)
Tuple of DC power (with SAPM parameters) (DataFrame) and AC
power (Series).
"""
# use surface_tilt and surface_azimuth if provided,
# otherwise set them using the orientation_strategy
if surface_tilt is not None and surface_azimuth is not None:
pass
elif orientation_strategy is not None:
surface_tilt, surface_azimuth = \
get_orientation(orientation_strategy, latitude=latitude)
else:
raise ValueError('orientation_strategy or surface_tilt and '
'surface_azimuth must be provided')
times = times
if altitude is None and pressure is None:
altitude = 0.
pressure = 101325.
elif altitude is None:
altitude = atmosphere.pres2alt(pressure)
elif pressure is None:
pressure = atmosphere.alt2pres(altitude)
solar_position = solarposition.get_solarposition(
times,
latitude,
longitude,
altitude=altitude,
pressure=pressure,
method=solar_position_method,
**kwargs)
# possible error with using apparent zenith with some models
airmass = atmosphere.get_relative_airmass(
solar_position['apparent_zenith'], model=airmass_model)
airmass = atmosphere.get_absolute_airmass(airmass, pressure)
dni_extra = pvlib.irradiance.get_extra_radiation(solar_position.index)
aoi = pvlib.irradiance.aoi(surface_tilt, surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'])
if irradiance is None:
linke_turbidity = clearsky.lookup_linke_turbidity(
solar_position.index, latitude, longitude)
irradiance = clearsky.ineichen(solar_position['apparent_zenith'],
airmass,
linke_turbidity,
altitude=altitude,
dni_extra=dni_extra)
total_irrad = pvlib.irradiance.get_total_irradiance(
surface_tilt,
surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'],
irradiance['dni'],
irradiance['ghi'],
irradiance['dhi'],
model=transposition_model,
dni_extra=dni_extra)
if weather is None:
weather = {'wind_speed': 0, 'temp_air': 20}
temps = pvsystem.sapm_celltemp(total_irrad['poa_global'],
weather['wind_speed'], weather['temp_air'])
effective_irradiance = pvsystem.sapm_effective_irradiance(
total_irrad['poa_direct'], total_irrad['poa_diffuse'], airmass, aoi,
module_parameters)
dc = pvsystem.sapm(effective_irradiance, temps['temp_cell'],
module_parameters)
ac = pvsystem.snlinverter(dc['v_mp'], dc['p_mp'], inverter_parameters)
return dc, ac
def basic_chain(times, latitude, longitude,
module_parameters, inverter_parameters,
irradiance=None, weather=None,
surface_tilt=None, surface_azimuth=None,
orientation_strategy=None,
transposition_model='haydavies',
solar_position_method='nrel_numpy',
airmass_model='kastenyoung1989',
altitude=None, pressure=None,
**kwargs):
"""
An experimental function that computes all of the modeling steps
necessary for calculating power or energy for a PV system at a given
location.
Parameters
----------
times : DatetimeIndex
Times at which to evaluate the model.
latitude : float.
Positive is north of the equator.
Use decimal degrees notation.
longitude : float.
Positive is east of the prime meridian.
Use decimal degrees notation.
module_parameters : None, dict or Series
Module parameters as defined by the SAPM.
inverter_parameters : None, dict or Series
Inverter parameters as defined by the CEC.
irradiance : None or DataFrame
If None, calculates clear sky data.
Columns must be 'dni', 'ghi', 'dhi'.
weather : None or DataFrame
If None, assumes air temperature is 20 C and
wind speed is 0 m/s.
Columns must be 'wind_speed', 'temp_air'.
surface_tilt : float or Series
Surface tilt angles in decimal degrees.
The tilt angle is defined as degrees from horizontal
(e.g. surface facing up = 0, surface facing horizon = 90)
surface_azimuth : float or Series
Surface azimuth angles in decimal degrees.
The azimuth convention is defined
as degrees east of north
(North=0, South=180, East=90, West=270).
orientation_strategy : None or str
The strategy for aligning the modules.
If not None, sets the ``surface_azimuth`` and ``surface_tilt``
properties of the ``system``.
transposition_model : str
Passed to system.get_irradiance.
solar_position_method : str
Passed to location.get_solarposition.
airmass_model : str
Passed to location.get_airmass.
altitude : None or float
If None, computed from pressure. Assumed to be 0 m
if pressure is also None.
pressure : None or float
If None, computed from altitude. Assumed to be 101325 Pa
if altitude is also None.
**kwargs
Arbitrary keyword arguments.
See code for details.
Returns
-------
output : (dc, ac)
Tuple of DC power (with SAPM parameters) (DataFrame) and AC
power (Series).
"""
# use surface_tilt and surface_azimuth if provided,
# otherwise set them using the orientation_strategy
if surface_tilt is not None and surface_azimuth is not None:
pass
elif orientation_strategy is not None:
surface_tilt, surface_azimuth = \
get_orientation(orientation_strategy, latitude=latitude)
else:
raise ValueError('orientation_strategy or surface_tilt and ' +
'surface_azimuth must be provided')
times = times
if altitude is None and pressure is None:
altitude = 0.
pressure = 101325.
elif altitude is None:
altitude = atmosphere.pres2alt(pressure)
elif pressure is None:
pressure = atmosphere.alt2pres(altitude)
solar_position = solarposition.get_solarposition(times, latitude,
longitude,
altitude=altitude,
pressure=pressure,
**kwargs)
# possible error with using apparent zenith with some models
airmass = atmosphere.relativeairmass(solar_position['apparent_zenith'],
model=airmass_model)
airmass = atmosphere.absoluteairmass(airmass, pressure)
dni_extra = pvlib.irradiance.extraradiation(solar_position.index)
dni_extra = pd.Series(dni_extra, index=solar_position.index)
aoi = pvlib.irradiance.aoi(surface_tilt, surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'])
if irradiance is None:
irradiance = clearsky.ineichen(
solar_position.index,
latitude,
longitude,
zenith_data=solar_position['apparent_zenith'],
airmass_data=airmass,
altitude=altitude)
total_irrad = pvlib.irradiance.total_irrad(
surface_tilt,
surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'],
irradiance['dni'],
irradiance['ghi'],
irradiance['dhi'],
model=transposition_model,
dni_extra=dni_extra)
if weather is None:
weather = {'wind_speed': 0, 'temp_air': 20}
temps = pvsystem.sapm_celltemp(total_irrad['poa_global'],
weather['wind_speed'],
weather['temp_air'])
dc = pvsystem.sapm(module_parameters, total_irrad['poa_direct'],
total_irrad['poa_diffuse'],
temps['temp_cell'],
airmass,
aoi)
ac = pvsystem.snlinverter(inverter_parameters, dc['v_mp'], dc['p_mp'])
return dc, ac
def forecast_dc_power(poa_irrad, airmass, aoi, pvmodule, pvtemp):
# Run the SAPM using the parameters we calculated above.
effective_irradiance = pvsystem.sapm_effective_irradiance(poa_irrad.poa_direct, poa_irrad.poa_diffuse, airmass, aoi, pvmodule)
sapm_out = pvsystem.sapm(effective_irradiance, pvtemp, pvmodule)
return sapm_out
def simulate_power_by_station(station_index, surface_tilt, surface_azimuth,
pv_module, tcell_model_parameters, ghi, tamb,
wspd, albedo, days, lead_times, air_mass,
dni_extra, zenith, apparent_zenith, azimuth):
"""
This is the worker function for simulating power at a specified location. This function should be used inside
of `simulate_power` and direct usage is discouraged.
:param station_index: A station index
:param ghi: See `simulate_power`
:param tamb: See `simulate_power`
:param wspd: See `simulate_power`
:param albedo: See `simulate_power`
:param days: See `simulate_power`
:param lead_times: See `simulate_power`
:param air_mass: See `simulate_power`
:param dni_extra: See `simulate_power`
:param zenith: See `simulate_power`
:param apparent_zenith: See `simulate_power`
:param azimuth: See `simulate_power`
:param surface_tilt: See `simulate_power`
:param surface_azimuth: See `simulate_power`
:param pv_module: A PV module name
:param tcell_model_parameters: A cell module name
:return: A list with power, cell temperature, and the effective irradiance
"""
# Sanity check
assert 0 <= station_index < ghi.shape[3], 'Invalid station index'
# Determine the dimensions
num_analogs = ghi.shape[0]
num_lead_times = ghi.shape[1]
num_days = ghi.shape[2]
# Initialization
p_mp = np.zeros((num_analogs, num_lead_times, num_days))
tcell = np.zeros((num_analogs, num_lead_times, num_days))
effective_irradiance = np.zeros((num_analogs, num_lead_times, num_days))
pv_module = pvsystem.retrieve_sam("SandiaMod")[pv_module]
tcell_model_parameters = temperature.TEMPERATURE_MODEL_PARAMETERS["sapm"][
tcell_model_parameters]
for day_index in range(num_days):
for lead_time_index in range(num_lead_times):
# Determine the current time
current_posix = days[day_index] + lead_times[lead_time_index]
current_time = pd.Timestamp(current_posix, tz="UTC", unit='s')
for analog_index in range(num_analogs):
ghi_ = ghi[analog_index, lead_time_index, day_index,
station_index]
if ghi_ == 0:
continue
albedo_ = albedo[analog_index, lead_time_index, day_index,
station_index]
wspd_ = wspd[analog_index, lead_time_index, day_index,
station_index]
tamb_ = tamb[analog_index, lead_time_index, day_index,
station_index]
air_mass_ = air_mass[lead_time_index, day_index, station_index]
dni_extra_ = dni_extra[lead_time_index, day_index,
station_index]
zenith_ = zenith[lead_time_index, day_index, station_index]
apparent_zenith_ = apparent_zenith[lead_time_index, day_index,
station_index]
azimuth_ = azimuth[lead_time_index, day_index, station_index]
##########################################################################################
# #
# Core procedures of simulating power at one location #
# #
##########################################################################################
# Decompose DNI from GHI
dni_dict = irradiance.disc(ghi_, zenith_, current_time)
# Calculate POA sky diffuse
poa_sky_diffuse = irradiance.haydavies(
surface_tilt, surface_azimuth, ghi_, dni_dict["dni"],
dni_extra_, apparent_zenith_, azimuth_)
# Calculate POA ground diffuse
poa_ground_diffuse = irradiance.get_ground_diffuse(
surface_tilt, ghi_, albedo_)
# Calculate angle of incidence
aoi = irradiance.aoi(surface_tilt, surface_azimuth,
apparent_zenith_, azimuth_)
# Calculate POA total
poa_irradiance = irradiance.poa_components(
aoi, dni_dict["dni"], poa_sky_diffuse, poa_ground_diffuse)
# Calculate cell temperature
tcell[analog_index, lead_time_index,
day_index] = pvsystem.temperature.sapm_cell(
poa_irradiance['poa_global'], tamb_, wspd_,
tcell_model_parameters['a'],
tcell_model_parameters['b'],
tcell_model_parameters["deltaT"])
# Calculate effective irradiance
effective_irradiance[
analog_index, lead_time_index,
day_index] = pvsystem.sapm_effective_irradiance(
poa_irradiance['poa_direct'],
poa_irradiance['poa_diffuse'], air_mass_, aoi,
pv_module)
# Calculate power
sapm_out = pvsystem.sapm(
effective_irradiance[analog_index, lead_time_index,
day_index],
tcell[analog_index, lead_time_index, day_index], pv_module)
# Save output to numpy
p_mp[analog_index, lead_time_index,
day_index] = sapm_out["p_mp"]
return [p_mp, tcell, effective_irradiance]
forecast_data['dhi'], forecast_data['dni'], dni_extra,
solpos['apparent_zenith'], solpos['azimuth'])
poa_ground_diffuse = irradiance.get_ground_diffuse(surface_tilt, ghi, albedo=albedo)
aoi = irradiance.aoi(surface_tilt, surface_azimuth, solpos['apparent_zenith'], solpos['azimuth'])
poa_irrad = irradiance.poa_components(aoi, forecast_data['dni'], poa_sky_diffuse,
poa_ground_diffuse)
temperature = forecast_data['temp_air']
wnd_spd = forecast_data['wind_speed']
pvtemps = pvsystem.sapm_celltemp(poa_irrad['poa_global'], wnd_spd, temperature)
effective_irradiance = pvsystem.sapm_effective_irradiance(poa_irrad.poa_direct,
poa_irrad.poa_diffuse, airmass, aoi, sandia_module)
sapm_out = pvsystem.sapm(effective_irradiance, pvtemps['temp_cell'], sandia_module)
# print(sapm_out.head())
print(sapm_out['p_mp'])
plot = "pop"
# sapm_out[['p_mp']].plot()
# plt.ylabel('DC Power (W)')
# plot = plt.savefig('tmp.png')
# sapm_inverters = pvsystem.retrieve_sam('sandiainverter')
# sapm_inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']
# p_ac = pvsystem.snlinverter(sapm_out.v_mp, sapm_out.p_mp, sapm_inverter)
#
# p_ac.plot()
# plt.ylabel('AC Power (W)')
# plt.ylim(0, None)
