def pv_timeseries(lon,
lat,
solar_data,
pv_panel,
weather_dir,
inverter_type,
scale=True):
"""Generate PV power feedin timeseries."""
# pv system parameters
system_data = {
'module_name': pv_panel,
'inverter_name': inverter_type,
'azimuth': 180,
'tilt': 30,
'albedo': 0.2
}
pv_system = Photovoltaic(**system_data)
if scale:
feedin_pv = pv_system.feedin(weather=solar_data,
location=(lat, lon),
scaling="peak_power")
else:
feedin_pv = pv_system.feedin(weather=solar_data, location=(lat, lon))
peak_power_pv = pv_system.peak_power
module_area = pv_system.area
where_nan = isnan(feedin_pv)
feedin_pv[where_nan] = 0
feedin_pv[feedin_pv < 0] = 0
pv_parameter = [peak_power_pv, module_area]
pvpower = feedin_pv.to_frame().rename(columns={0: "pv"})
pvpower.to_csv(weather_dir + "pv_power.csv")
logging.info("PV feedin timeseries generated.")
return pv_parameter
def test_powerplant_requirements(self, pvlib_pv_system, pvlib_weather):
"""
Test that attribute error is not raised in case a valid model is
specified when calling feedin method.
"""
test_module = Photovoltaic(**pvlib_pv_system)
feedin = test_module.feedin(weather=pvlib_weather,
model=Pvlib,
location=(52, 13))
assert 143.28844 == pytest.approx(feedin.values[0], 1e-5)
def test_pvlib_feedin_with_surface_type(self, pvlib_pv_system,
pvlib_weather):
"""
Test basic feedin calculation using pvlib and providing surface type
instead of albedo.
"""
del pvlib_pv_system['albedo']
pvlib_pv_system['surface_type'] = 'grass'
test_module = Photovoltaic(**pvlib_pv_system)
feedin = test_module.feedin(weather=pvlib_weather, location=(52, 13))
assert 143.28844 == pytest.approx(feedin.values[0], 1e-5)
def test_pvlib_feedin(self, pvlib_pv_system, pvlib_weather):
"""
Test basic feedin calculation using pvlib.
It is also tested if dictionary with PV system parameters remains the
same to make sure it could be further used to calculate feed-in with
a different model.
"""
test_copy = deepcopy(pvlib_pv_system)
test_module = Photovoltaic(**pvlib_pv_system)
feedin = test_module.feedin(weather=pvlib_weather, location=(52, 13))
assert 143.28844 == pytest.approx(feedin.values[0], 1e-5)
assert test_copy == pvlib_pv_system
def test_powerplant_requirements_2(self, pvlib_pv_system, pvlib_weather):
"""
Test that attribute error is raised in case required power plant
parameters are missing when feedin is called with a different model
than initially specified.
"""
test_module = Photovoltaic(**pvlib_pv_system)
msg = "The specified model 'windpowerlib_single_turbine' requires"
with pytest.raises(AttributeError, match=msg):
test_module.feedin(weather=pvlib_weather,
model=WindpowerlibTurbine,
location=(52, 13))
def test_pvlib_feedin_with_optional_pp_parameter(self, pvlib_pv_system,
pvlib_weather):
"""
Test basic feedin calculation using pvlib and providing an optional
PV system parameter.
"""
pvlib_pv_system['strings_per_inverter'] = 2
test_module = Photovoltaic(**pvlib_pv_system)
feedin = test_module.feedin(weather=pvlib_weather, location=(52, 13))
# power output is in this case limited by the inverter, which is why
# power output with 2 strings is not twice as high as power output of
# one string
assert 250.0 == pytest.approx(feedin.values[0], 1e-5)
def test_pv_feedin_scaling(self, pvlib_pv_system, pvlib_weather):
"""
Test that PV feedin timeseries are scaled correctly.
"""
test_module = Photovoltaic(**pvlib_pv_system)
feedin = test_module.feedin(weather=pvlib_weather,
location=(52, 13),
scaling='peak_power')
assert 0.67462 == pytest.approx(feedin.values[0], 1e-5)
feedin = test_module.feedin(weather=pvlib_weather,
location=(52, 13),
scaling='area')
assert 84.28732 == pytest.approx(feedin.values[0], 1e-5)
def test_pvlib_missing_powerplant_parameter(self, pvlib_pv_system):
"""
Test if initialization of powerplant fails in case of missing power
plant parameter.
"""
del (pvlib_pv_system['albedo'])
msg = "The specified model 'pvlib' requires"
with pytest.raises(AttributeError, match=msg):
Photovoltaic(**pvlib_pv_system)
weather_df_pv['ghi'] = weather_df_pv.dirhi + weather_df_pv.dhi
weather_df_pv.rename(columns={'v_wind': 'wind_speed'}, inplace=True)
# ######## Pvlib model #########
# specify pv system
yingli210 = {
'module_name': 'Yingli_YL210__2008__E__',
'inverter_name': 'ABB__PVI_3_0_OUTD_S_US_Z__277V__277V__CEC_2018_',
'azimuth': 180,
'tilt': 30,
'albedo': 0.2,
'modules_per_string': 4}
# instantiate feedinlib Photovoltaic object
yingli_module = Photovoltaic(**yingli210)
# calculate feedin
feedin = yingli_module.feedin(
weather=weather_df_pv[['wind_speed', 'temp_air', 'dhi', 'ghi']],
location=(52, 13), scaling='peak_power', scaling_value=10)
# plot
feedin.fillna(0).plot(title='PV feedin')
plt.xlabel('Time')
plt.ylabel('Power in W')
plt.show()
# ######## WindpowerlibTurbine model #########
# specify wind turbine