def test_drop_measurement(self):
"""Test drop measurement for TestInfluxDBClient object."""
cli = DataFrameClient(database='db')
with requests_mock.Mocker() as m:
m.register_uri(requests_mock.POST,
"http://localhost:8086/query",
text='{"results":[{}]}')
cli.drop_measurement('new_measurement')
self.assertEqual(m.last_request.qs['q'][0],
'drop measurement "new_measurement"')
def test_drop_measurement(self):
"""Test drop measurement for TestInfluxDBClient object."""
cli = DataFrameClient(database='db')
with requests_mock.Mocker() as m:
m.register_uri(
requests_mock.POST,
"http://localhost:8086/query",
text='{"results":[{}]}'
)
cli.drop_measurement('new_measurement')
self.assertEqual(
m.last_request.qs['q'][0],
'drop measurement "new_measurement"'
)
def write_data_to_influxdb(data):
influxClient = DataFrameClient("localhost", 8087, "admin", "admin",
"corona_data")
influxClient.drop_measurement("dus")
influxClient.write_points(data, "dus")
influxClient.close()
def optimization():
# Optimization timestep
TIMESTEP = 12 # 5min interval (60/5)
# Query uncontrolled demand
# Note: uncontrolled demand is already on a 5min timestep
client = DataFrameClient(host, port, user, password, dbname)
start = datetime.now()
query = ("select * from uncontr " + "WHERE time >= '" +
(start + timedelta(minutes=5)).strftime("%Y-%m-%dT%H:%M:%SZ") +
"' AND time <= '" +
(start + timedelta(hours=24)).strftime("%Y-%m-%dT%H:%M:%SZ") +
"'")
uncontr = client.query(query)['uncontr']
# Get the reference t=0
first_t = uncontr.iloc[0].name
uncontr_t = uncontr.index
uncontr_index = list(range(0, len(uncontr_t)))
# Change index to integers and rename column
opt_uncontr = uncontr.copy()
opt_uncontr['index'] = uncontr_index
opt_uncontr.set_index('index', drop=True, inplace=True)
opt_uncontr.rename(columns={'uncontr': 'p'}, inplace=True)
# Query order books
try:
query = ("select * from bbook " + "WHERE startby >= " +
str(int((start + timedelta(minutes=5)).timestamp() * 1000)) +
" AND endby <= " +
str(int((start + timedelta(hours=24)).timestamp() * 1000)))
bbook = client.query(query)['bbook']
# Set startby and endby as integers
opt_bbook = bbook.copy()
opt_bbook['startby'] -= first_t.timestamp() * 1000
opt_bbook['startby'] /= 60 * 1000 * 60 / TIMESTEP
opt_bbook['endby'] -= first_t.timestamp() * 1000
opt_bbook['endby'] /= 60 * 1000 * 60 / TIMESTEP
opt_bbook['id'] = list(range(0, len(opt_bbook)))
opt_bbook.set_index('id', drop=True, inplace=True)
except:
# No orders at the moment
opt_bbook = pandas.DataFrame()
try:
query = ("select * from sbook " + "WHERE startby >= " +
str(int((start + timedelta(minutes=5)).timestamp() * 1000)) +
" AND endby <= " +
str(int((start + timedelta(hours=24)).timestamp() * 1000)))
sbook = client.query(query)['sbook']
# Set startby and endby as integers
opt_sbook = sbook.copy()
opt_sbook['startby'] -= first_t.timestamp() * 1000
opt_sbook['startby'] /= 60 * 1000 * 60 / TIMESTEP
opt_sbook['endby'] -= first_t.timestamp() * 1000
opt_sbook['endby'] /= 60 * 1000 * 60 / TIMESTEP
opt_sbook['id'] = list(range(0, len(opt_sbook)))
opt_sbook.set_index('id', drop=True, inplace=True)
except:
# No orders at the moment
opt_sbook = pandas.DataFrame()
try:
query = ("select * from dbook " + "WHERE startby >= " +
str(int((start + timedelta(minutes=5)).timestamp() * 1000)) +
" AND endby <= " +
str(int((start + timedelta(hours=24)).timestamp() * 1000)))
dbook = client.query(query)['dbook']
opt_dbook = dbook.copy()
opt_dbook['startby'] -= first_t.timestamp() * 1000
opt_dbook['startby'] /= 60 * 1000 * 60 / TIMESTEP
opt_dbook['endby'] -= first_t.timestamp() * 1000
opt_dbook['endby'] /= 60 * 1000 * 60 / TIMESTEP
opt_dbook['id'] = list(range(0, len(opt_dbook)))
opt_dbook.set_index('id', drop=True, inplace=True)
# Turn profile_kw from str to floats
opt_dbook['profile_kw'] = opt_dbook['profile_kw'].apply(
lambda x:
[float(v) for v in x[1:][:-1].replace(" ", "").split(',')])
except:
# No orders at the moment
opt_dbook = pandas.DataFrame()
# Run the optimization
tic = datetime.now()
result = maximize_self_consumption(opt_uncontr,
opt_bbook,
opt_sbook,
opt_dbook,
timestep=1 / TIMESTEP,
solver='glpk',
verbose=False,
timelimit=60)
logger.info('GLPK time elapsed (hh:mm:ss.ms) {}'.format(datetime.now() -
tic))
# Save results back to influxDB (and remove previous schedule)
total = uncontr.copy()
total.rename(columns={'uncontr': 'contr'}, inplace=True)
total['contr'] += result['demand_controllable']
client.write_points(total, 'contr')
client.drop_measurement('bschedule')
if result['batteryin'] is not None:
bschedule = (result['batteryin'] - result['batteryout']).copy()
bschedule['index'] = uncontr_t
bschedule.set_index('index', drop=True, inplace=True)
bschedule.rename_axis(None, inplace=True)
client.write_points(bschedule, 'bschedule')
client.drop_measurement('sschedule')
if result['demandshape'] is not None:
sschedule = result['demandshape'].copy()
sschedule['index'] = uncontr_t
sschedule.set_index('index', drop=True, inplace=True)
sschedule.rename_axis(None, inplace=True)
client.write_points(sschedule, 'sschedule')
client.drop_measurement('dschedule')
if result['demanddeferr'] is not None:
dschedule = result['demanddeferr'].copy()
dschedule['index'] = uncontr_t
dschedule.set_index('index', drop=True, inplace=True)
dschedule.rename_axis(None, inplace=True)
client.write_points(dschedule, 'dschedule')
# Close DB connection
client.close()
