The Pvlib model can be used to determine the feed-in of a photovoltaic module using the pvlib.
The pvlib is a python library for simulating the performance of photovoltaic energy systems. For more information check out the documentation of the pvlib.
The following example shows you how to use the Pvlib model.
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from feedinlib import Photovoltaic
# suppress warnings
import warnings
warnings.filterwarnings("ignore")
To set up a Photovoltaic system you have to provide all PV system parameters required by the PVlib model. The required parameters can be looked up in the model's documentation.
For the Pvlib model these are the azimuth and tilt of the module as well as the albedo or surface type. Furthermore, the name of the module and inverter are needed to obtain technical parameters from the provided module and inverter databases. For an overview of the provided modules and inverters you can use the function get_power_plant_data().
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from feedinlib import get_power_plant_data
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# get modules
module_df = get_power_plant_data(dataset='sandiamod')
# print the first four modules
module_df.iloc[:, 1:5]
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# get inverter data
inverter_df = get_power_plant_data(dataset='cecinverter')
# print the first four inverters
inverter_df.iloc[:, 1:5]
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Now you can set up a PV system to calculate feed-in for, using for example the first module and converter in the databases:
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system_data = {
'module_name': 'Advent_Solar_Ventura_210___2008_', # module name as in database
'inverter_name': 'ABB__MICRO_0_25_I_OUTD_US_208__208V_', # inverter name as in database
'azimuth': 180,
'tilt': 30,
'albedo': 0.2}
pv_system = Photovoltaic(**system_data)
Optional power plant parameters
Besides the required PV system parameters you can provide optional parameters such as the number of modules per string, etc. Optional PV system parameters are specific to the used model and how to find out about the possible optional parameters is documented in the model's feedin method under power_plant_parameters. In case of the Pvlib model see here.
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system_data['modules_per_string'] = 2
pv_system_with_optional_parameters = Photovoltaic(**system_data)
Besides setting up your PV system you have to provide weather data the feed-in is calculated with. This example uses open_FRED weather data. For more information on the data and download see the load_open_fred_weather_data Notebook.
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from feedinlib.open_FRED import Weather
from feedinlib.open_FRED import defaultdb
from shapely.geometry import Point
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# specify latitude and longitude of PV system location
lat = 52.4
lon = 13.5
location = Point(lon, lat)
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# download weather data for June 2017
open_FRED_weather_data = Weather(
start='2017-06-01', stop='2017-07-01',
locations=[location],
variables="pvlib",
**defaultdb())
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# get weather data in pvlib format
weather_df = open_FRED_weather_data.df(location=location, lib="pvlib")
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# plot irradiance
import matplotlib.pyplot as plt
%matplotlib inline
weather_df.loc[:, ['dhi', 'ghi']].plot(title='Irradiance')
plt.xlabel('Time')
plt.ylabel('Irradiance in $W/m^2$');
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feedin = pv_system.feedin(
weather=weather_df,
location=(lat, lon))
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# plot calculated feed-in
import matplotlib.pyplot as plt
%matplotlib inline
feedin.plot(title='PV feed-in')
plt.xlabel('Time')
plt.ylabel('Power in W');
Scaled feed-in
The PV feed-in can also be automatically scaled by the PV system's area or peak power. The following example shows how to scale feed-in by area.
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feedin_scaled = pv_system.feedin(
weather=weather_df,
location=(lat, lon),
scaling='area')
To scale by the peak power use scaling=peak_power.
The PV system area and peak power can be retrieved as follows:
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pv_system.area
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pv_system.peak_power
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# plot calculated feed-in
import matplotlib.pyplot as plt
%matplotlib inline
feedin_scaled.plot(title='Scaled PV feed-in')
plt.xlabel('Time')
plt.ylabel('Power in W');
Feed-in for PV system with optional parameters
In the following example the feed-in is calculated for the PV system with optional system parameters (with 2 modules per string, instead of 1, which is the default). It was chosen to demonstrate the importantance of choosing a suitable converter.
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feedin_ac = pv_system_with_optional_parameters.feedin(
weather=weather_df,
location=(lat, lon))
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# plot calculated feed-in
import matplotlib.pyplot as plt
%matplotlib inline
feedin_ac.plot(title='PV feed-in')
plt.xlabel('Time')
plt.ylabel('Power in W');
As the above plot shows the feed-in is cut off at 250 W. That is because it is limited by the inverter. So while the area is as expected two times greater as for the PV system without optional parameters, the peak power is only around 1.2 times higher.
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pv_system_with_optional_parameters.peak_power / pv_system.peak_power
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pv_system_with_optional_parameters.area / pv_system.area
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If you are only interested in the modules power output without the inverter losses you can have the Pvlib model return the DC feed-in. This is done as follows:
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feedin_dc = pv_system_with_optional_parameters.feedin(
weather=weather_df,
location=(lat, lon),
mode='dc')
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# plot calculated feed-in
import matplotlib.pyplot as plt
%matplotlib inline
feedin_dc.plot(label='DC', title='AC and DC PV feed-in', legend=True)
feedin_ac.plot(label='AC', legend=True)
plt.xlabel('Time')
plt.ylabel('Power in W');
Feed-in with optional model parameters
In order to change the default calculation configurations of the Pvlib model to e.g. choose a different model to calculate losses or the solar position you can pass further parameters to the feedin method. An overview of which further parameters may be provided is documented under the feedin method's kwargs.
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feedin_no_loss = pv_system.feedin(
weather=weather_df,
location=(lat, lon),
aoi_model='no_loss')
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# plot calculated feed-in
import matplotlib.pyplot as plt
%matplotlib inline
feedin_no_loss.iloc[0:96].plot(label='aoi_model = no_loss', legend=True)
feedin.iloc[0:96].plot(label='aoi_model = sapm_aoi_loss', legend=True)
plt.xlabel('Time')
plt.ylabel('Power in W');
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