def main():
client = EntsoePandasClient(api_key=api_key)
(start, end) = get_start_end()
response = client.query_crossborder_flows(country_code_from,
country_code_to,
start=start,
end=end)
response_revert_country_flow = client.query_crossborder_flows(
country_code_to, country_code_from, start=start, end=end)
# normalize
result = response.to_frame() - response_revert_country_flow.to_frame()
result.columns = ['value']
if len(result):
print(result.to_markdown())
result.to_sql(db_table, db, if_exists='append')
class EntsoeClient:
def __init__(self, token):
self.client = EntsoePandasClient(api_key=token)
def get_unavailability_production(self, country_code, start_date,
end_date):
"""
Get unavailability of production units (whatever that is)
:param country_code: iso code of country
:param start_date: epoch time of start in ms
:param end_date: epoch time of end in ms
:return: pandas dataframe with the requested data
"""
start_date = pd.Timestamp(start_date, unit="ms", tz="CET")
end_date = pd.Timestamp(end_date, unit="ms", tz="CET")
return self.client.query_unavailability_of_production_units(
country_code.upper(), start=start_date, end=end_date)
def get_cross_border_flow(self, country_from, country_to, start_date,
end_date):
start_date = pd.Timestamp(start_date, unit="ms", tz="CET")
end_date = pd.Timestamp(end_date, unit="ms", tz="CET")
return self.client.query_crossborder_flows(
country_code_from=country_from.upper(),
country_code_to=country_to.upper(),
start=start_date,
end=end_date)
def get_forecasted_transfer(self, country_from, country_to, start_date,
end_date):
"""
Net capacity transfer for the day ahead between the 2 countries
:param country_from:
:param country_to:
:param start_date:
:param end_date:
:return:
"""
start_date = pd.Timestamp(start_date, unit="ms", tz="CET")
end_date = pd.Timestamp(end_date, unit="ms", tz="CET")
return self.client.query_net_transfer_capacity_dayahead(
country_code_from=country_from.upper(),
country_code_to=country_to.upper(),
start=start_date,
end=end_date)
country_code, start=start, end=end, psr_type=None)
df_wind_and_solar_forecast.to_csv('data_base/df_wind_and_solar_forecast.csv')
df_generation = client.query_generation(country_code,
start=start,
end=end,
psr_type=None)
df_generation.to_csv('data_base/df_generation.csv')
df_installed_generation_capacity = client.query_installed_generation_capacity(
country_code, start=start, end=end, psr_type=None)
df_installed_generation_capacity.to_csv(
'data_base/df_installed_generation_capacity.csv')
df_crossborder_flows = client.query_crossborder_flows('DE',
'DK',
start=start,
end=end)
df_crossborder_flows.to_csv('data_base/df_crossborder_flows.csv')
# df_imbalance_prices = client.query_imbalance_prices(country_code, start=start,end=end, psr_type=None)
# df_imbalance_prices.to_csv('data_base/df_imbalance_prices.csv')
# BadRequest:
# df_unavailability_of_generation_units = client.query_unavailability_of_generation_units(country_code, start=start,end=end, docstatus=None)
# BadRequest:
# df_withdrawn_unavailability_of_generation_units = client.query_withdrawn_unavailability_of_generation_units('DE', start=start,end=end)
# ts.to_csv('outfile.csv')
#"""
start=start,
end=end,
psr_type=None)
ts.to_csv(path + country_code + '-solar+PV_d-1.csv')
client.query_generation(country_code,
start=start,
end=end,
psr_type=None)
ts.to_csv(path + country_code + '-generacion_d.csv')
ts = client.query_installed_generation_capacity(country_code,
start=start,
end=end,
psr_type=None)
ts.to_csv(path + country_code + '-capacidad_instalada.csv')
ts = client.query_crossborder_flows(antiguo_country_code,
country_code,
start=start,
end=end)
ts.to_csv(path + country_code + '-flujo_fronterizo.csv')
ts = client.query_imbalance_prices(country_code,
start=start,
end=end,
psr_type=None)
ts.to_csv(path + country_code + '-desequilibrio_precios.csv')
ts = client.query_unavailability_of_generation_units(country_code,
start=start,
end=end,
docstatus=None)
ts.to_csv(path + country_code + '-incapacidad_unidades_generacion.csv')
ts = client.query_withdrawn_unavailability_of_generation_units(
country_code, start=start, end=end)
ts.to_csv(path + country_code +
neighbour_emission_factors = []
for neighbour in neighbour_production:
neighbour_emissions_df = pd.DataFrame(index=neighbour.index, columns=neighbour.keys())
for power_type in neighbour:
neighbour_emissions_df[power_type] = neighbour[power_type]
neighbour_emissions_df.loc[:,power_type] *= emission_production_matrix.loc[data_source, power_type]
neighbour_emission_factors.append(neighbour_emissions_df.sum(axis=1).divide(neighbour.sum(axis=1)))
import_cross_border_flows = [] # if the flows are power and not energy, change the resampling from sum to mean
for country in neighbour_countries:
# resampling to the hour with mean because quarter hourly values are given in MW not MWh
import_cross_border_flows.append(client.query_crossborder_flows(country, country_code, start=start_time, end=end_time).resample("H").mean())
#Initialization of tatal factor with total summation of emmissions of domestic production
total_emission_factor = domestic_emissions_df.sum(axis=1)
# print("Gelb")
# print(total_emission_factor)
# print("hier_gopfer")
# print(import_cross_border_flows[0])
# print("am gopfsten")
# print(neighbour_emission_factors[0])
# print("hier_gopf")
# print(import_cross_border_flows[0]*neighbour_emission_factors[0])
# print("hier werden die nefs geprinted:")
# print(neighbour_emission_factors)