def wind_apply_feedinlib(coastDat_years, reference_data):
coastDat2 = {
'dhi': 0,
'dirhi': 0,
'pressure': 0,
'temp_air': 2,
'v_wind': 10,
'Z0': 0
}
basic_path = os.path.join(os.path.expanduser("~"), '.oemof')
# iterate over passed reference data dict
wind_feedin_annual = {}
for unit in list(reference_data.keys()):
wind_feedin_annual[unit] = {}
years = [int(y) for y in reference_data[unit]['generation']]
wind_model = plants.WindPowerPlant(
model=models.SimpleWindTurbine,
**{
'h_hub': reference_data[unit]['h_hub'],
'd_rotor': reference_data[unit]['d_rotor'],
'wind_conv_type': reference_data[unit]['wind_conv_type'],
'data_height': coastDat2
})
for year in set(years).intersection(coastDat_years):
# get weather data
file = 'weather_' + unit + '_' + str(year) + '.csv'
filename = os.path.join(basic_path, file)
if not os.path.isfile(filename):
fetch_test_data_file(file, basic_path)
my_weather_df = read_test_data(filename)
my_weather = weather.FeedinWeather(
data=my_weather_df,
timezone=reference_data[unit]['tz'],
latitude=reference_data[unit]['location']['lon'],
longitude=reference_data[unit]['location']['lat'],
data_height=coastDat2)
if reference_data[unit].get('number') is not None:
wind_feedin_annual[unit][year] = wind_model.feedin(
weather=my_weather,
number=reference_data[unit]['number']).sum() / 1e6
elif reference_data[unit].get('capacity') is not None:
wind_feedin_annual[unit][year] = wind_model.feedin(
weather=my_weather,
installed_capacity=reference_data[unit]
['capacity']).sum() / 1e6
else:
print('at least provide `number` or `capacity`')
return wind_feedin_annual
def setUpClass(self):
self.required_parameter = {}
self.required_parameter['wind_model'] = {
'h_hub': 'height of the hub in meters',
'd_rotor': 'diameter of the rotor in meters',
'wind_conv_type':
'wind converter according to the list in the csv file.'}
self.required_parameter['pv_model'] = {
'azimuth': 'Azimuth angle of the pv module',
'tilt': 'Tilt angle of the pv module',
'module_name': 'According to the sandia module library.',
'albedo': 'Albedo value'}
self.height_of_measurement = {
'dhi': 0,
'dirhi': 0,
'pressure': 0,
'temp_air': 2,
'v_wind': 10,
'Z0': 0}
self.site = {
'module_name': 'Yingli_YL210__2008__E__',
'azimuth': 180,
'tilt': 30,
'albedo': 0.2,
'h_hub': 135,
'd_rotor': 127,
'wind_conv_type': 'ENERCON E 126 7500'}
timezone = 'Europe/Berlin'
n = 876
self.weather_df = pandas.DataFrame(index=pandas.date_range(
pandas.datetime(2010, 1, 1, 0), periods=n, freq='H',
tz=timezone))
self.weather_df['temp_air'] = 280.5 * numpy.ones(n)
self.weather_df['pressure'] = 100168 * numpy.ones(n)
self.weather_df['dirhi'] = 111 * numpy.ones(n)
self.weather_df['dhi'] = 111 * numpy.ones(n)
self.weather_df['v_wind'] = 4.8 * numpy.ones(n)
self.weather_df['z0'] = 0.15 * numpy.ones(n)
self.weather = weather.FeedinWeather(
data=self.weather_df,
timezone=timezone,
latitude=52,
longitude=12,
data_height=self.height_of_measurement)
def create_single_weather(df, rename_dc):
"""Create an oemof weather object for the given geometry"""
my_weather = weather.FeedinWeather()
data_height = {}
name = None
# Create a pandas.DataFrame with the time series of the weather data set
weather_df = pd.DataFrame(index=df.time_series.iloc[0].index)
for row in df.iterrows():
key = rename_dc[row[1].type]
weather_df[key] = row[1].time_series
data_height[key] = row[1].height if not np.isnan(row[1].height) else 0
name = row[1].gid
my_weather.data = weather_df
my_weather.timezone = weather_df.index.tz
my_weather.longitude = df.geom_point.iloc[0].x
my_weather.latitude = df.geom_point.iloc[0].y
my_weather.geometry = df.geom_point.iloc[0]
my_weather.data_height = data_height
my_weather.name = name
return my_weather
def create_single_weather(df, geo, rename_dc):
''
my_weather = weather.FeedinWeather()
data_height = {}
# Create a pandas.DataFrame with the time series of the weather data set
weather_df = pd.DataFrame(index=df.time_series.iloc[0].index)
for row in df.iterrows():
key = rename_dc[row[1].type]
weather_df[key] = row[1].time_series
data_height[key] = row[1].height if not np.isnan(row[1].height) else 0
my_weather.data = weather_df
my_weather.timezone = weather_df.index.tz
if geo.geom_type == 'Point':
my_weather.longitude = geo.x
my_weather.latitude = geo.y
else:
my_weather.longitude = geo.centroid.x
my_weather.latitude = geo.centroid.y
my_weather.geometry = geo
my_weather.data_height = data_height
my_weather.name = row[1].gid
return my_weather
def test_load_feedinlib(self):
my_weather = weather.FeedinWeather()
basic_path = os.path.dirname(os.path.abspath(__file__))
filename = os.path.join(basic_path, 'test_weather.csv')
my_weather.read_feedinlib_csv(filename=filename)
def test_csv_weather_file(self):
my_weather = weather.FeedinWeather()
my_weather.read_feedinlib_csv(filename='')
def pv_apply_feedinlib(coastDat_years, reference_data=None):
# get reference data
if reference_data is None:
reference_data = pv_generation_reference_data()
coastDat2 = {
'dhi': 0,
'dirhi': 0,
'pressure': 0,
'temp_air': 2,
'v_wind': 10,
'Z0': 0
}
pv_feedin_annual = {}
basic_path = os.path.join(os.path.expanduser("~"), '.oemof')
# iterate over passed reference data dict
for unit in list(reference_data.keys()):
pv_feedin_annual[unit] = {}
# update reference data with default values if values are missing
if reference_data[unit]['azimuth'] is None:
logging.warning('Azimuth unknown... overwrite with 0')
reference_data[unit]['azimuth'] = 180
if reference_data[unit]['tilt'] is None:
logging.warning('Tilt unknown... overwrite with 30')
reference_data[unit]['tilt'] = 30
years = [int(y) for y in reference_data[unit]['generation']]
# choose module type and add location
module_type = {
'module_name': reference_data[unit]['module_name'],
'azimuth': reference_data[unit]['azimuth'],
'tilt': reference_data[unit]['tilt'],
'albedo': 0.2,
'latitude': reference_data[unit]['location']['lat'],
'longitude': reference_data[unit]['location']['lon'],
'tz': reference_data[unit]['tz']
}
pv_module = plants.Photovoltaic(model=models.PvlibBased, **module_type)
for year in set(years).intersection(coastDat_years):
# get weather data
file = 'weather_' + unit + '_' + str(year) + '.csv'
filename = os.path.join(basic_path, file)
if not os.path.isfile(filename):
fetch_test_data_file(file, basic_path)
my_weather_df = read_test_data(filename)
my_weather = weather.FeedinWeather(
data=my_weather_df,
timezone=reference_data[unit]['tz'],
latitude=reference_data[unit]['location']['lon'],
longitude=reference_data[unit]['location']['lat'],
data_height=coastDat2)
if reference_data[unit].get('module_number') is not None:
pv_feedin_annual[unit][year] = pv_module.feedin(
weather=my_weather,
number=reference_data[unit]['module_number']).sum() / 1e3
elif reference_data[unit].get('capacity') is not None:
pv_feedin_annual[unit][year] = pv_module.feedin(
weather=my_weather,
peak_power=reference_data[unit]['capacity']).sum() / 1e3
else:
print('at least supply `module_number` or `capacity`')
return pv_feedin_annual
def __init__(self, required=['steps']):
self.required = required
def feedin(self, **kwargs):
mydata = kwargs["weather"].data.v_wind * kwargs["steps"]
return pd.Series(data=mydata,
index=kwargs['weather'].data.index,
name='my_feedin_wind_series')
# Writing your own model and passing it to the feedinlib is usefull for testing
# purpose. If you have a good alternative model we request you to publish it
# within the feedinlib or anywhere else, so that we can link to it.
# Loading weather data from csv-file.
my_weather = weather.FeedinWeather()
my_weather.read_feedinlib_csv(filename='weather_wittenberg.csv')
# Initialise your own model and apply it.
mymodel = MyModel(required=['steps'])
myplant = plants.WindPowerPlant(model=mymodel, steps=3)
myfeedin = myplant.feedin(weather=my_weather, number=2)
# Plot
if plot_fkt:
myfeedin.plot()
plt.show()
else:
print(myfeedin)
def ready_example_data(filename, datetime_column='Unnamed: 0'):
df = pd.read_csv(filename)
return df.set_index(pd.to_datetime(df[datetime_column])).tz_localize(
'UTC').tz_convert('Europe/Berlin').drop(datetime_column, 1)
filename1 = 'weather.csv'
filename2 = 'weather_wittenberg.csv'
# Two Variants to create your weather object
# 1. Variant: Passing all data to the weather class
weather_df = ready_example_data(filename1)
my_weather_a = weather.FeedinWeather(data=weather_df,
timezone='Europe/Berlin',
latitude=52,
longitude=13,
data_height=coastDat2)
# 2. Variant: Loading a csv-file that has the feedinlib-csv-header (see docs)
my_weather_b = weather.FeedinWeather()
my_weather_b.read_feedinlib_csv(filename=filename2)
# Loading the weather data
my_weather = my_weather_b
# Initialise different power plants
# So far there is only one model available. So you do not have to pass a model
# (s. E126). If model is passed the default model is used.
# We hope that there will be different models in future versions. You can also
# write your own model an pass it to the powerplant.