def test_plant_stats(self):
if os.path.isfile('C:/Users/user/Documents/plant_stats.csv'):
t0 = time.time()
plant_types = data_fetch_methods.static_table_xl('', '', 'Generators and Scheduled Loads', 'E:/raw_aemo_data',
select_columns=['DUID', 'Fuel Source - Primary',
'Region', 'Participant'])
plant_stats = custom_tables.plant_stats('2017/07/01 00:05:00', '2018/07/01 00:05:00', '', 'E:/raw_aemo_data')
plant_stats = pd.merge(plant_stats, plant_types, 'left', 'DUID')
plant_stats['TRADING_COST'] = plant_stats['Volume'] * plant_stats['TRADING_VWAP']
plant_stats['DISPATCH_COST'] = plant_stats['Volume'] * plant_stats['DISPATCH_VWAP']
plant_stats.to_csv('C:/Users/user/Documents/plant_stats_tp.csv')
print(time.time() - t0)
def test_registration_list(self):
start_time = ''
end_time = ''
table = 'Generators and Scheduled Loads'
cols = ['DUID', 'Technology Type - Primary']
filter_cols = ('DUID',)
data = data_fetch_methods.static_table_xl( start_time, end_time, table, defaults.raw_data_cache,
select_columns=cols, filter_cols=filter_cols, filter_values=(['AGLHAL'],))
expected_length = 1
expected_number_of_columns = 2
self.assertEqual(expected_length, data.shape[0])
self.assertEqual(expected_number_of_columns, data.shape[1])
print('Passed')
def get_residual_demand(start_time, end_time, raw_data_cache):
cols = ['DUID', 'Region', 'Fuel Source - Descriptor']
tech_data = data_fetch_methods.static_table_xl(
'Generators and Scheduled Loads', raw_data_cache, select_columns=cols)
zero_srmc_techs = ['Wind', 'Solar', 'Solar ']
tech_data = tech_data[tech_data['Fuel Source - Descriptor'].isin(
zero_srmc_techs)]
scada_data = data_fetch_methods.dynamic_data_compiler(
start_time, end_time, 'DISPATCH_UNIT_SCADA', raw_data_cache)
scada_data = pd.merge(scada_data, tech_data, on='DUID')
scada_data['SCADAVALUE'] = pd.to_numeric(scada_data['SCADAVALUE'])
scada_data = scada_data.groupby(['SETTLEMENTDATE', 'Region'],
as_index=False).agg({'SCADAVALUE': 'sum'})
regional_demand = data_fetch_methods.dynamic_data_compiler(
start_time, end_time, 'DISPATCHREGIONSUM', raw_data_cache)
regional_demand = regional_demand[regional_demand['INTERVENTION'] == 0]
regional_demand = pd.merge(regional_demand,
scada_data,
left_on=['SETTLEMENTDATE', 'REGIONID'],
right_on=['SETTLEMENTDATE', 'Region'])
regional_demand['TOTALDEMAND'] = pd.to_numeric(
regional_demand['TOTALDEMAND'])
regional_demand['RESIDUALDEMAND'] = regional_demand[
'TOTALDEMAND'] - regional_demand['SCADAVALUE']
regional_demand = regional_demand.pivot_table(values='RESIDUALDEMAND',
index='SETTLEMENTDATE',
columns='REGIONID')
regional_demand = regional_demand.reset_index().fillna('0.0')
regional_demand = regional_demand.rename(columns={
'QLD1': 'qld',
'NSW1': 'nsw',
'VIC1': 'vic',
'SA1': 'sa',
'TAS1': 'tas'
})
regional_demand.columns = [
col + '-demand' if col != 'SETTLEMENTDATE' else col
for col in regional_demand.columns
]
return regional_demand
def get_duid_techs(raw_data_cache):
cols = [
'DUID', 'Region', 'Fuel Source - Descriptor',
'Technology Type - Descriptor'
]
tech_data = data_fetch_methods.static_table_xl(
'Generators and Scheduled Loads', raw_data_cache, select_columns=cols)
def tech_classifier(fuel_source, technology_type):
category = fuel_source
if technology_type == 'Hydro - Gravity':
category = 'Hydro'
elif technology_type == 'Open Cycle Gas turbines (OCGT)':
category = 'OCGT'
elif technology_type == 'Combined Cycle Gas Turbine (CCGT)':
category = 'CCGT'
elif technology_type == 'Run of River' or fuel_source == 'Solar' or fuel_source == 'Wind' or fuel_source == 'Solar ':
category = 'ZEROSRMC'
elif technology_type == 'Spark Ignition Reciprocating Engine':
category = 'Engine'
elif technology_type == 'Compression Reciprocating Engine':
category = 'Engine'
elif technology_type == 'Steam Sub-Critical' and (
fuel_source == 'Natural Gas / Fuel Oil'
or fuel_source == 'Natural Gas'):
category = 'Gas Thermal'
elif technology_type == 'Pump Storage' or technology_type == 'Battery':
category = 'Storage'
return category
tech_data['TECH'] = tech_data.apply(lambda x: tech_classifier(
x['Fuel Source - Descriptor'], x['Technology Type - Descriptor']),
axis=1)
return tech_data.loc[:, ['DUID', 'Region', 'TECH']]