def main():
windgenerator = collect_energymap_data()
capacity = collect_ego_turbines()
power_classes = get_power_classes(capacity)
selected_plants = get_plant_per_class(windgenerator, power_classes)
power_class_to_db(power_classes, selected_plants)
cfg = db.readcfg(config)
temp = {}
powerplants = {}
powerplants['solar'] = plants.Photovoltaic(
**{k: asnumber(v)
for k, v in cfg.items('Photovoltaic')})
powerplants['wind_offshore'] = plants.WindPowerPlant(
**{k: asnumber(v)
for k, v in cfg.items('WindTurbineOffshore')})
print('Calculating feedins...')
for coastdat_id, type_of_generation, geom in points:
try:
weather = coastdat.get_weather(conn, geom, weather_year)
except IndexError:
print('Geometry cannot be handled: %s, %s' % (geom.x, geom.y))
continue
if type_of_generation == 'wind_offshore':
feedin = correction_offshore * powerplants[type_of_generation].\
feedin(weather=weather, installed_capacity=1)
power_class = 0
temp[(coastdat_id, type_of_generation,
power_class)] = feedin.values
elif type_of_generation == 'wind_onshore':
power_class = 1
for index, row in selected_plants.iterrows():
plant = wind_dict(row)
feedin = correction_onshore * plants.WindPowerPlant(**plant).\
feedin(weather=weather, installed_capacity=1)
temp[(coastdat_id, type_of_generation,
power_class)] = feedin.values
power_class += 1
elif type_of_generation == 'solar':
feedin = correction_solar * powerplants[type_of_generation].\
feedin(weather=weather, peak_power=1)
power_class = 0
temp[(coastdat_id, type_of_generation,
power_class)] = feedin.values
else:
continue
#temp[(coastdat_id, type_of_generation)] = feedin.values
df = pd.DataFrame(temp)
df_to_renewable_feedin(df, weather_year, weather_scenario_id)
print('Done!')
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
'azimuth': 180,
'tilt': 30,
'albedo': 0.2
}
#wind turbine
enerconE126 = {
'h_hub': 135,
'd_rotor': 127,
'wind_conv_type': 'ENERCON E 126 7500',
'data_height': coastDat2
}
#
E126_power_plant = plants.WindPowerPlant(**enerconE126)
yingli_module = plants.Photovoltaic(**yingli210)
wind_feedin = E126_power_plant.feedin(weather=my_weather, installed_capacity=1)
pv_feedin = yingli_module.feedin(weather=my_weather, peak_power=1)
#conn = db.connection()
#pol = c.next()
#multi_weather = coastdat.get_weather(conn, germany_u['geom'][0], year)
def optimise_storage_size(filename="storage_invest.csv",
solvername='cbc',
debug=True,
number_timesteps=8760,
tee_switch=True):
def wind_result_test(self):
wind_model = model.SimpleWindTurbine(
required=list(self.required_parameter['wind_model'].keys()))
wind_power_plant = plant.WindPowerPlant(model=wind_model, **self.site)
wka_feedin = wind_power_plant.feedin(weather=self.weather)
nt.eq_(round(wka_feedin.sum() / 1000), 1523340.0)
def test_wind_model(self):
plant.WindPowerPlant(model=model.SimplewindTurbine(["missing"]))
}
year = 2010
conn = db.connection()
my_weather_single = coastdat.get_weather(
conn, geopy.Point(loc_berlin['longitude'], loc_berlin['latitude']), year)
geo = geopy.Polygon([(12.2, 52.2), (12.2, 51.6), (13.2, 51.6), (13.2, 52.2)])
multi_weather = coastdat.get_weather(conn, geo, year)
my_weather = multi_weather[0]
# my_weather = my_weather_single
# Initialise different power plants
E126_power_plant = plants.WindPowerPlant(**enerconE126)
V90_power_plant = plants.WindPowerPlant(**vestasV90)
# Create a feedin series for a specific powerplant under specific weather
# conditions. One can define the number of turbines or the over all capacity.
# If no multiplier is set, the time series will be for one turbine.
E126_feedin = E126_power_plant.feedin(weather=my_weather, number=2)
V90_feedin = V90_power_plant.feedin(weather=my_weather,
installed_capacity=(15 * 10**6))
E126_feedin.name = 'E126'
V90_feedin.name = 'V90'
if plt:
E126_feedin.plot(legend=True)
V90_feedin.plot(legend=True)
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)
logging.info('Done!')
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.
E126_power_plant = plants.WindPowerPlant(**enerconE126)
V90_power_plant = plants.WindPowerPlant(model=models.SimpleWindTurbine,
**vestasV90)
# Create a feedin series for a specific powerplant under specific weather
# conditions. One can define the number of turbines or the over all capacity.
# If no multiplier is set, the time series will be for one turbine.
E126_feedin = E126_power_plant.feedin(weather=my_weather, number=2)
V90_feedin = V90_power_plant.feedin(weather=my_weather,
installed_capacity=15 * 10**6)
E126_feedin.name = 'E126'
V90_feedin.name = 'V90'
if plt:
E126_feedin.plot(legend=True)
def get_timeseries(self, conn, **kwargs):
''
weather = coastdat.get_weather(conn, kwargs['geometry'],
kwargs['year'])
pv_df = 0
pv_cap = {}
wind_df = 0
wind_cap = {}
if not isinstance(weather, list):
weather = [weather]
for w_cell in weather:
ee_pps = pg_pp.get_energymap_pps(conn,
geometry1=w_cell.geometry,
geometry2=kwargs['geometry'])
# Find type of wind turbine and its parameters according to the
# windzone.
wz = tools.get_windzone(conn, w_cell.geometry)
kwargs['wind_conv_type'] = (kwargs['wka_model_dc'].get(
wz, kwargs['wka_model']))
kwargs['d_rotor'] = (kwargs['d_rotor_dc'].get(
wz, kwargs['d_rotor']))
kwargs['h_hub'] = (kwargs['h_hub_dc'].get(wz, kwargs['h_hub']))
# Determine the feedin time series for the weather cell
# Wind energy
wind_peak_power = ee_pps[ee_pps.type == 'wind_power'].cap.sum()
wind_power_plant = pp.WindPowerPlant(**kwargs)
wind_series = wind_power_plant.feedin(
weather=w_cell, installed_capacity=wind_peak_power)
wind_series.name = w_cell.name
wind_cap[w_cell.name] = wind_peak_power
# PV
pv_peak_power = ee_pps[ee_pps.type == 'solar_power'].cap.sum()
pv_plant = pp.Photovoltaic(**kwargs)
pv_series = pv_plant.feedin(weather=w_cell,
peak_power=pv_peak_power)
pv_series.name = w_cell.name
pv_cap[w_cell.name] = pv_peak_power
# Combine the results to a DataFrame
try:
pv_df = pd.concat([pv_df, pv_series], axis=1)
wind_df = pd.concat([wind_df, wind_series], axis=1)
except:
pv_df = pv_series.to_frame()
wind_df = wind_series.to_frame()
# Write capacity into a dataframe
capw = pd.Series(pd.DataFrame.from_dict(wind_cap, orient='index')[0])
capw.name = 'wind_pwr'
cappv = pd.Series(pd.DataFrame.from_dict(pv_cap, orient='index')[0])
cappv.name = 'pv_pwr'
cap = pd.concat([capw, cappv], axis=1)
return pv_df, wind_df, cap
#geom = [geopy.Polygon(
#[(12.2, 52.2), (12.2, 51.6), (13.2, 51.6), (13.2, 52.2)])]
# -------------------------- Get weather objects ---------------------------- #
print(' ')
print('Collecting weather objects...')
multi_weather = get_data(conn=conn,
year=year,
geom=geom[0],
pickle_load=load_multi_weather,
filename='multiweather_pickle_{0}.p'.format(year),
data_type='multi_weather')
# ------------------------------ Feedin data -------------------------------- #
if (energy_source == 'Wind' or energy_source == 'Wind_PV'):
turbine = plants.WindPowerPlant(**enerconE126)
feedin = get_data(power_plant=turbine,
multi_weather=multi_weather,
pickle_load=load_wind_feedin,
filename='windfeedin_pickle_{0}.p'.format(year),
data_type='wind_feedin')
if (energy_source == 'PV' or energy_source == 'Wind_PV'):
module = plants.Photovoltaic(**advent210)
feedin = get_data(power_plant=module,
multi_weather=multi_weather,
pickle_load=load_pv_feedin,
filename='pv_feedin_pickle_{0}.p'.format(year),
data_type='pv_feedin')
# TODO: total sum of feedins for PV + Wind (feedin: Dictionary, keys: gids)
# -------------------- Calms: Calculations and Geoplots --------------------- #
# Calculate calms
