def setUpClass(self):
# This method instantiates the fleet and executes a simple request to get the device response for evaluation
# Create test fleet
kwargs = {}
kwargs['start_time'] = start_time
kwargs['service_weight'] = 0.75
kwargs['sim_step'] = delt
grid_type = 1
fleet = create_fleet(fleet_name, grid_type, **kwargs)
if fleet is None:
raise 'Could not create fleet with name ' + fleet_name
cycle = 5 #24
dt_day = [(start_time + delt * i) for i in range(cycle)]
p_needed = [(100 * i) for i in range(cycle)]
requests = [
FleetRequest(ts=dt_day[i],
sim_step=delt,
start_time=start_time,
p=p_needed[i]) for i in range(cycle)
]
fleet_responses = fleet.forecast(requests)
self.responses = fleet_responses
def integration_test(service_name, fleet_name, **kwargs):
# Create test service
service = create_service(service_name)
if service is None:
raise 'Could not create service with name ' + service_name
grid_type = 2
# Create test fleet
fleet = create_fleet(fleet_name, grid_type, **kwargs)
if fleet is None:
raise 'Could not create fleet with name ' + fleet_name
# Assign test fleet to test service to use
service.fleet = fleet
assigned_fleet_name = service.fleet.__class__.__name__
start_time = kwargs['start_time']
# Run test
if service_name == 'DistributionVoltageService':
fleet_responses, fleet_requests = service.request_loop()
else:
pass
def test_assigned_service_kW(self):
kwargs = {}
kwargs['start_time'] = start_time
kwargs['service_weight'] = 0.75
kwargs['sim_step'] = delt
grid_type = 1
fleet = create_fleet(fleet_name, grid_type, **kwargs)
if fleet is None:
raise 'Could not create fleet with name ' + fleet_name
service_kW = fleet.assigned_service_kW()
self.assertGreater(
service_kW, 0,
'Fleet Service weight is not returned by assign_service_kW')
def setUpClass(self):
#This method instantiates the fleet and executes a simple request to get the device response for evaluation
# Create test fleet
kwargs = {}
kwargs['start_time'] = start_time
kwargs['service_weight'] = 0.75
kwargs['sim_step'] = delt
grid_type = 1
fleet = create_fleet(fleet_name, grid_type, **kwargs)
if fleet is None:
raise 'Could not create fleet with name ' + fleet_name
fleet_request = FleetRequest(cur_time, delt, start_time, Prequest,
Qrequest)
fleet_response = fleet.process_request(fleet_request)
self.response = fleet_response
def setUpClass(self):
# This method instantiates the fleet and executes a simple request to get the device response for evaluation
# Create test fleet
from grid_info_artificial_inertia import GridInfo
grid = GridInfo('Grid_Info_data_artificial_inertia.csv')
grid_type = 2
kwargs = {}
kwargs['start_time'] = start_time
kwargs['service_weight'] = 0.75
kwargs['sim_step'] = delt
kwargs['autonomous'] = 'autonomous'
cur_time = start_time + timedelta(seconds=75)
fleet = create_fleet(fleet_name, grid_type, **kwargs)
if fleet is None:
raise 'Could not create fleet with name ' + fleet_name
fleet_request = FleetRequest(cur_time, delt, start_time, Prequest,
Qrequest)
fleet_response = fleet.process_request(fleet_request)
self.response = fleet_response
self.f = grid.get_frequency(cur_time, 0, start_time)
def integration_test(service_name, fleet_name, service_type='Traditional', **kwargs):
start_time = kwargs['start_time']
sim_step = dynamic_time_step(service_name, fleet_name)
kwargs['sim_step'] = sim_step
# Create test service
service = create_service(service_name, **kwargs)
if service is None:
raise 'Could not create service with name ' + service_name
grid_type = 1
if service_name == 'ArtificialInertia':
grid_type = 2
# Create test fleet
fleet = create_fleet(fleet_name, grid_type, **kwargs)
if fleet is None:
raise 'Could not create fleet with name ' + fleet_name
# Assign test fleet to test service to use
service.fleet = fleet
assigned_fleet_name = service.fleet.__class__.__name__
# Run test
if service_name == 'Regulation':
monthtimes = dict({
# 'January': ["2017-01-01 00:00:00", "2017-01-31 23:59:59"],
# 'February': ["2017-02-01 00:00:00", "2017-02-28 23:59:59"],
# 'March': ["2017-03-01 00:00:00", "2017-03-31 23:59:59"],
# 'April': ["2017-04-01 00:00:00", "2017-04-30 23:59:59"],
# 'May': ["2017-05-01 00:00:00", "2017-05-31 23:59:59"],
# 'June': ["2017-06-01 00:00:00", "2017-06-30 23:59:59"],
# 'July': ["2017-07-01 00:00:00", "2017-07-31 23:59:59"],
'August': ["2017-08-01 16:00:00", "2017-08-01 18:59:59"],
# 'September': ["2017-09-01 00:00:00", "2017-09-30 23:59:59"],
# 'October': ["2017-10-01 00:00:00", "2017-10-31 23:59:59"],
# 'November': ["2017-11-01 00:00:00", "2017-11-30 23:59:59"],
# 'December': ["2017-12-01 00:00:00", "2017-12-31 23:59:00"]
})
all_results = pd.DataFrame(columns=['performance_score', 'hourly_integrated_MW',
'mileage_ratio', 'Regulation_Market_Clearing_Price(RMCP)',
'Reg_Clearing_Price_Credit'])
for month in monthtimes.keys():
print('Starting ' + str(month) + ' ' + service_type + ' at ' + datetime.now().strftime('%H:%M:%S'))
fleet_response = service.request_loop(service_type=service_type,
start_time=parser.parse(monthtimes[month][0]),
end_time=parser.parse(monthtimes[month][1]),
sim_step=sim_step,
clearing_price_filename='historical-ancillary-service-data-2017.xls',
fleet_name=assigned_fleet_name)
month_results = pd.DataFrame.from_dict(fleet_response, orient='index')
all_results = pd.concat([all_results, month_results])
print(' Finished ' + str(month) + ' ' + service_type)
# Fix formatting of all_results dataframe to remove tuples
all_results[['Perf_score', 'Delay_score', 'Corr_score', 'Prec_score']] = all_results['performance_score'].apply(
pd.Series)
all_results[['MCP', 'REG_CCP', 'REG_PCP']] = all_results['Regulation_Market_Clearing_Price(RMCP)'].apply(
pd.Series)
all_results[['Reg_Clr_Pr_Credit', 'Reg_RMCCP_Credit', 'Reg_RMPCP_Credit']] = all_results[
'Reg_Clearing_Price_Credit'].apply(pd.Series)
all_results.drop(
columns=['performance_score', 'Regulation_Market_Clearing_Price(RMCP)', 'Reg_Clearing_Price_Credit'],
inplace=True)
print('Writing result .csv')
file_dir = join(dirname(abspath(__file__)), 'services', 'reg_service', 'results', '')
all_results.to_csv(file_dir + datetime.now().strftime(
'%Y%m%d') + '_annual_hourlyresults_' + service_type + '_' + fleet_name + '.csv')
elif service_name == 'Reserve':
monthtimes = dict({
# 'January': ["2017-01-08 00:00:00", "2017-01-08 23:59:59"],
# 'February': ["2017-02-01 00:00:00", "2017-02-28 23:59:59"],
# 'March': ["2017-03-01 00:00:00", "2017-03-31 23:59:59"],
# 'April': ["2017-04-01 00:00:00", "2017-04-30 23:59:59"],
# 'May': ["2017-05-01 00:00:00", "2017-05-31 23:59:59"],
# 'June': ["2017-06-01 00:00:00", "2017-06-30 23:59:59"],
'June': ["2017-06-07 00:00:00", "2017-06-07 23:59:59"],
# 'June': ["2017-06-07 00:00:00", "2017-06-08 23:59:59"],
# 'July': ["2017-07-01 00:00:00", "2017-07-31 23:59:59"],
# 'August': ["2017-08-01 00:00:00", "2017-08-31 23:59:59"],
# 'September': ["2017-09-01 00:00:00", "2017-09-30 23:59:59"],
# 'October': ["2017-10-01 00:00:00", "2017-10-31 23:59:59"],
# 'November': ["2017-11-01 00:00:00", "2017-11-30 23:59:59"],
# 'December': ["2017-12-01 00:00:00", "2017-12-31 23:59:00"]
})
all_results = pd.DataFrame(columns=['Event_Start_Time', 'Event_End_Time',
'Response_to_Request_Ratio', 'Response_MeetReqOrMax_Index_number',
'Event_Duration_mins', 'Response_After10minToEndOr30min_To_First10min_Ratio',
'Requested_MW', 'Responded_MW_at_10minOrEnd',
'Responded_MW_After10minToEndOr30min', 'Shortfall_Ratio',
'Response_0min_Min_MW', 'Response_10minOrEnd_Max_MW',
'Response_After10minToEnd_MW', 'Avg_Ramp_Rate', 'Best_Ramp_Rate',
'SRMCP_DollarsperMWh_DuringEvent',
'SRMCP_DollarsperMWh_SinceLastEvent',
'Service_Value_NotInclShortfall_dollars',
'Service_Value_InclShortfall_dollars',
'Period_from_Last_Event_Hours',
'Period_from_Last_Event_Days'])
if 'battery' in assigned_fleet_name.lower():
annual_signals = pd.DataFrame(columns=['Date_Time', 'Request', 'Response', 'SoC'])
else:
annual_signals = pd.DataFrame(columns=['Date_Time', 'Request', 'Response'])
previous_event_end = pd.Timestamp('05/01/2017 00:00:00')
for month in monthtimes.keys():
print('Starting ' + str(month) + ' at ' + datetime.now().strftime('%H:%M:%S'))
start_time = parser.parse(monthtimes[month][0])
fleet_response = service.request_loop(start_time=start_time,
end_time=parser.parse(monthtimes[month][1]),
sim_step=sim_step,
clearing_price_filename=start_time.strftime('%Y%m') + '.csv',
previous_event_end=previous_event_end,
four_scenario_testing=False,
fleet_name=assigned_fleet_name)
try:
previous_event_end = fleet_response[0].Event_End_Time[-1]
except:
# If the dataframe in fleet_response[0] has no entries, then attempting
# to index the Event_End_Time column will throw an error. This allows
# for skipping past that error.
pass
all_results = pd.concat([all_results, fleet_response[0]], sort=True)
annual_signals = pd.concat([annual_signals, fleet_response[1]], sort=True)
print('Writing event results .csv')
file_dir = join(dirname(abspath(__file__)), 'integration_test', 'reserve_service')
all_results.to_csv(join(file_dir,
datetime.now().strftime('%Y%m%d') + '_event_results_reserve_' + assigned_fleet_name + '.csv'))
print('Saving .csv of annual signals and SOC (if necessary)')
annual_signals.to_csv(
join(file_dir,
datetime.now().strftime('%Y%m%d') + '_annual_signals_reserve_' + assigned_fleet_name + '.csv'))
elif service_name == 'ArtificialInertia':
fleet_responses = service.request_loop(start_time=start_time, sim_step=sim_step)
metrics_calc_start_time = kwargs['metrics_calc_start_time']
metrics_calc_end_time = kwargs['metrics_calc_end_time']
# service_efficacy, p_service, p_togrid, t, f = service.calculation(fleet_name, fleet_responses, start_time,
# metrics_calc_start_time=metrics_calc_start_time,
# metrics_calc_end_time=metrics_calc_end_time)
service_efficacy, p_service, p_togrid, t, f = service.calculation(fleet_name, fleet_responses, start_time)
print(service_efficacy)
elif service_name == 'DistributionVoltageService':
service.sim_step = kwargs['sim_step']
fleet_responses, fleet_requests = service.request_loop(start_time=start_time)
elif service_name == 'EnergyMarketService':
fleet_requests, fleet_responses = service.request_loop(sim_step=sim_step)
elif service_name == 'PeakManagementService':
start_time = service.drive_cycle["dt"][0]
service.request_loop(start_time, service_name, fleet_name=assigned_fleet_name)
service.fleet.output_impact_metrics(service_name)
else:
raise 'Could not recognize service with name ' + service_name
