time.sleep(15) ###
# get L and rho
[L, rho,
curtailPV] = contractADMM.functions.getOptimizationParameters().call()
print('Optimization parameters received')
L = np.array(L)
L = L / 10**6
rho = rho / 10**6
if curtailPV == 1: # sum(q_i)<0 <=> PVproduction > PVimported ==> curtailment of PV, no diminution of PV import
L[length:2 * length] = np.minimum(L[length:2 * length], 0)
# optimization of profile
EV1.optimization(startTime, endTime, timeStep, price_imp, price_PV,
price_B, rho, L, yPVforecast)
profileToSend = tl.toListInt(EV1.optimizedProfile)
tx_hash = contractADMM.functions.sendOptimizedProfile(
profileToSend).transact()
tx_receipt = web3.eth.waitForTransactionReceipt(tx_hash)
gasSpend += tx_receipt.gasUsed
print('Optimized profile send')
# wait for global results
l = 0
while l != 1:
#while l!=iterations:
event_filter = contractADMM.events.EndIteration.createFilter(
fromBlock='latest')
#event_filter = contractADMM.events.EndIteration.createFilter(fromBlock=0)
eventlist = event_filter.get_all_entries()
l = len(eventlist)
if startTime.month == 6:
month = 'juin'
elif startTime.month == 12:
month = 'decembre'
# get price function
fname = '../Data_' + month + '19/price_' + month + '19.csv'
price_imp = pd.read_csv(fname,
sep=';',
names=['timeVector', 'price'],
index_col=0,
squeeze=True,
parse_dates=True)
price_imp = price_imp.asfreq(freq=str(timeStep) + 'T', method='ffill')
price_imp = 1 / 1000 * price_imp[startTime:endTime] # from €/Mwh to €/kWh
price_imp = tl.toListInt(price_imp)
price_PV = LCOE_PV * np.ones(length)
price_B = LCOS_B * np.ones(length)
# call setTimeInterval
startTime = int(startTime.timestamp())
endTime = int(endTime.timestamp())
timeStep = timeStep * 60
tx_hash = contractADMM.functions.setTimeInterval(startTime, endTime,
timeStep).transact()
tx_receipt = web3.eth.waitForTransactionReceipt(tx_hash)
gasSpend += tx_receipt.gasUsed
print('Set time')
# call setPriceImp and setPriceLocal
beta2,
parametersB,
timestepM,
startTimeM,
endTimeM,
price_PV_M,
price_b_M,
price_imp_M,
rho_M,
L_M,
X_k_M,
p_forecast_PV_total_M,
nargout=2)
X_storage = np.asarray(X_storage)
Storage.optimizedProfile = X_storage[:, 0]
profileToSend = tl.toListInt(Storage.optimizedProfile)
tx_hash = contractADMM.functions.sendOptimizedProfile(
profileToSend).transact()
tx_receipt = web3.eth.waitForTransactionReceipt(tx_hash)
gasSpend += tx_receipt.gasUsed
print('Optimized profile send')
# # optimization of profile
# Storage.optimization(startTime, endTime, timeStep, price_imp, price_PV, price_B, rho, L, yPVforecast)
# profileToSend = tl.toListInt(Storage.optimizedProfile)
# tx_hash = contractADMM.functions.sendOptimizedProfile(profileToSend).transact()
# tx_receipt = web3.eth.waitForTransactionReceipt(tx_hash)
# gasSpend += tx_receipt.gasUsed
# print('Optimized profile send')
# wait for global results
price_B = pd.Series(data=coeff_priceB * np.ones(length), index=timeVector)
# get forecast_consumer_HEI profile
fnamePV = '../Data_' + month + '19/conso_HEI_' + month + '19.csv'
forecast_consumer_HEI = pd.read_csv(fnamePV,
sep=';',
names=['timeVector', 'power'],
index_col=0,
squeeze=True,
parse_dates=True)
forecast_consumer_HEI = forecast_consumer_HEI.asfreq(freq=str(timeStep) +
'T') # retime
forecast_consumer_HEI = forecast_consumer_HEI.interpolate()
forecast_consumer_HEI = forecast_consumer_HEI[startTime:endTime]
Building.setForecast(forecast_consumer_HEI)
forecast_consumer_HEI = tl.toListInt(forecast_consumer_HEI)
# listen to event to know when total PV forecast is available
l = 0
while l != 1:
event_filter = contractADMM.events.SendForecast.createFilter(fromBlock=0)
eventlist = event_filter.get_all_entries()
l = len(eventlist)
print('Get total PV forecast')
# get PV forecast
yPVforecast = contractADMM.functions.getPVforecast().call()
yPVforecast = np.array(yPVforecast)
yPVforecast = pd.Series(yPVforecast / 10**6, timeVector)
# initialisation
price_B = pd.Series(data=coeff_priceB * np.ones(length), index=timeVector)
# send forecast_PV_5RNS profile
fnamePV = '../Data_' + month + '19/PV_production5RNS_' + month + '19.csv'
forecast_PV_5RNS = pd.read_csv(fnamePV,
sep=';',
names=['timeVector', 'power'],
index_col=0,
squeeze=True,
parse_dates=True)
forecast_PV_5RNS = forecast_PV_5RNS.asfreq(freq=str(timeStep) + 'T') # retime
forecast_PV_5RNS = forecast_PV_5RNS.interpolate()
forecast_PV_5RNS = forecast_PV_5RNS[startTime:endTime]
PV1.setForecast(forecast_PV_5RNS)
forecast_PV_5RNS = tl.toListInt(forecast_PV_5RNS)
tx_hash = contractADMM.functions.sendPVforecast(forecast_PV_5RNS).transact()
web3.eth.waitForTransactionReceipt(tx_hash)
print('Forecast send')
result = 0
iterations = 0
X_historic = []
L_historic = []
gasSpend_initialization = gasSpend
while result == 0: # while stopping criteria of global optimization not reached
time.sleep(15) ###
# send forecast_PV_RIZOMME profile
fnamePV = '../Data_' + month + '19/PV_productionRIZOMME_' + month + '19.csv'
forecast_PV_RIZOMME = pd.read_csv(fnamePV,
sep=';',
names=['timeVector', 'power'],
index_col=0,
squeeze=True,
parse_dates=True)
forecast_PV_RIZOMME = forecast_PV_RIZOMME.asfreq(freq=str(timeStep) +
'T') # retime
forecast_PV_RIZOMME = forecast_PV_RIZOMME.interpolate()
forecast_PV_RIZOMME = forecast_PV_RIZOMME[startTime:
endTime] # negative values!
PV1.setForecast(forecast_PV_RIZOMME)
forecast_PV_RIZOMME = tl.toListInt(forecast_PV_RIZOMME)
tx_hash = contractADMM.functions.sendPVforecast(forecast_PV_RIZOMME).transact()
web3.eth.waitForTransactionReceipt(tx_hash)
print('Forecast send')
result = 0
iterations = 0
X_historic = []
L_historic = []
gasSpend_initialization = gasSpend
while result == 0: # while stopping criteria of global optimization not reached
time.sleep(15) ###
price_B = pd.Series(data=coeff_priceB * np.ones(length), index=timeVector)
# get forecast_consumer_RIZOMME profile
fnamePV = '../Data_' + month + '19/conso_RIZOMME_' + month + '19.csv'
forecast_consumer_RIZOMME = pd.read_csv(fnamePV,
sep=';',
names=['timeVector', 'power'],
index_col=0,
squeeze=True,
parse_dates=True)
forecast_consumer_RIZOMME = forecast_consumer_RIZOMME.asfreq(
freq=str(timeStep) + 'T') # retime
forecast_consumer_RIZOMME = forecast_consumer_RIZOMME.interpolate()
forecast_consumer_RIZOMME = forecast_consumer_RIZOMME[startTime:endTime]
Building.setForecast(forecast_consumer_RIZOMME)
forecast_consumer_RIZOMME = tl.toListInt(forecast_consumer_RIZOMME)
# initialisation
result = 0
iterations = 0
X_historic = []
L_historic = []
# listen to event to know when total PV forecast is available
l = 0
while l != 1:
event_filter = contractADMM.events.SendForecast.createFilter(fromBlock=0)
eventlist = event_filter.get_all_entries()
l = len(eventlist)
print('Get total PV forecast')