def sensAnalysis(pathX, extraCosts, extraCO2, extraPrim, solarFeat, ntwFeat, gen):
gV = glob.globalVariables()
step = gV.sensibilityStep
bandwidth = sensBandwidth()
os.chdir(pathX.pathMasterRes)
pop, eps, testedPop = sFn.readCheckPoint(pathX, gen, 0)
toolbox = base.Toolbox()
os.chdir(pathX.pathRaw)
buildList = sFn.extractList("Total.csv")
ParetoResults = np.zeros( len(pop) )
FactorResults = np.zeros((step + 1, bandwidth.nFactors * 2))
def sensOneFactor(obj, factor_name, mini, maxi, colNumber):
iniValue = getattr(obj, factor_name)
index = 0
for delta in np.arange(mini, maxi + 1E-5, (maxi-mini)/step):
FactorResults[index][colNumber + 0] = delta
if abs(delta) > 1E-10:
setattr(obj, factor_name, iniValue * (1+delta))
newpop = []
for ind in pop:
newInd = toolbox.clone(ind)
newpop.append(newInd)
(costs, CO2, prim) = eI.evalInd(newInd, buildList, pathX, extraCosts, extraCO2, extraPrim, solarFeat, ntwFeat, obj)
newInd.fitness.values = (costs, CO2, prim)
selection = sel.selectPareto(newpop)
for i in range(len(newpop)):
if newpop[i] in selection:
ParetoResults[i] += 1
FactorResults[index][colNumber + 1] = len(newpop) - len(selection)
index += 1
setattr(obj, factor_name, iniValue)
sensOneFactor(gV, 'ELEC_PRICE', bandwidth.minElec, bandwidth.maxElec, 0)
sensOneFactor(gV, 'NG_PRICE', bandwidth.minNG, bandwidth.maxNG, 2)
sensOneFactor(gV, 'BG_PRICE', bandwidth.minBG, bandwidth.maxBG, 4)
indexSensible = FactorResults.argmax()%(2*bandwidth.nFactors)
if indexSensible == 1:
mostSensitive = 'Electricity price'
elif indexSensible == 3:
mostSensitive = 'NG price'
else:
mostSensitive = 'BG price'
return ParetoResults, FactorResults, mostSensitive
def sensAnalysis(pathX, extraCosts, extraCO2, extraPrim, solarFeat, ntwFeat,
gen):
gV = glob.globalVariables()
step = gV.sensibilityStep
bandwidth = sensBandwidth()
os.chdir(pathX.pathMasterRes)
pop, eps, testedPop = sFn.readCheckPoint(pathX, gen, 0)
toolbox = base.Toolbox()
os.chdir(pathX.pathRaw)
buildList = sFn.extractList("Total.csv")
ParetoResults = np.zeros(len(pop))
FactorResults = np.zeros((step + 1, bandwidth.nFactors * 2))
def sensOneFactor(obj, factor_name, mini, maxi, colNumber):
iniValue = getattr(obj, factor_name)
index = 0
for delta in np.arange(mini, maxi + 1E-5, (maxi - mini) / step):
FactorResults[index][colNumber + 0] = delta
if abs(delta) > 1E-10:
setattr(obj, factor_name, iniValue * (1 + delta))
newpop = []
for ind in pop:
newInd = toolbox.clone(ind)
newpop.append(newInd)
(costs, CO2, prim) = eI.evalInd(newInd, buildList, pathX,
extraCosts, extraCO2,
extraPrim, solarFeat,
ntwFeat, obj)
newInd.fitness.values = (costs, CO2, prim)
selection = sel.selectPareto(newpop)
for i in range(len(newpop)):
if newpop[i] in selection:
ParetoResults[i] += 1
FactorResults[index][colNumber +
1] = len(newpop) - len(selection)
index += 1
setattr(obj, factor_name, iniValue)
sensOneFactor(gV, 'ELEC_PRICE', bandwidth.minElec, bandwidth.maxElec, 0)
sensOneFactor(gV, 'NG_PRICE', bandwidth.minNG, bandwidth.maxNG, 2)
sensOneFactor(gV, 'BG_PRICE', bandwidth.minBG, bandwidth.maxBG, 4)
indexSensible = FactorResults.argmax() % (2 * bandwidth.nFactors)
if indexSensible == 1:
mostSensitive = 'Electricity price'
elif indexSensible == 3:
mostSensitive = 'NG price'
else:
mostSensitive = 'BG price'
return ParetoResults, FactorResults, mostSensitive
def mcda_indicators(individual, pathX, plot = 0):
"""
Computes the specific operational costs and the share of local resources
"""
configKey = "".join(str(e)[0:4] for e in individual)
print configKey
buildList = sFn.extractList(pathX.pathRaw + "/Total.csv")
gV = glob.globalVariables()
# Recover data from the PP activation file
resourcesFile = pathX.pathSlaveRes + "/" + configKey + "PPActivationPattern.csv"
resourcesdf = pd.read_csv(resourcesFile, usecols=["ESolarProducedPVandPVT", "Q_AddBoiler", "Q_BoilerBase", "Q_BoilerPeak", "Q_CC", "Q_GHP", \
"Q_HPLake", "Q_HPSew", "Q_primaryAddBackupSum", "Q_uncontrollable"])
Electricity_Solar = resourcesdf.ESolarProducedPVandPVT.sum()
Q_AddBoiler = resourcesdf.Q_AddBoiler.sum()
Q_BoilerBase = resourcesdf.Q_BoilerBase.sum()
Q_BoilerPeak = resourcesdf.Q_BoilerPeak.sum()
Q_CC = resourcesdf.Q_CC.sum()
Q_GHP = resourcesdf.Q_GHP.sum()
Q_HPLake = resourcesdf.Q_HPLake.sum()
Q_HPSew = resourcesdf.Q_HPSew.sum()
Q_storage_missmatch = resourcesdf.Q_primaryAddBackupSum.sum()
Q_uncontrollable = resourcesdf.Q_uncontrollable.sum()
# Recover data from the Storage Operation file
resourcesFile = pathX.pathSlaveRes + "/" + configKey + "StorageOperationData.csv"
resourcesdf = pd.read_csv(resourcesFile, usecols=["Q_SCandPVT_coldstream"])
Q_fromSolar = resourcesdf.Q_SCandPVT_coldstream.sum()
# Recover heating needs for decentralized buildings
indCombi = sFn.readCombi(individual, gV)
QfromNG = 0
QfromBG = 0
QfromGHP = 0
for i in range(len(indCombi)):
if indCombi[i] == "0": # Decentralized building
buildName = buildList[i]
DescentFile = pathX.pathDiscRes + "/DiscOp_" + buildName + "_result.csv"
df = pd.read_csv(DescentFile)
dfBest = df[ df["Best configuration"] == 1 ]
QfromNG += dfBest["QfromNG"].iloc[0]
QfromBG += dfBest["QfromBG"].iloc[0]
QfromGHP += dfBest["QfromGHP"].iloc[0]
# Recover electricity and cooling needs
totalFile = pathX.pathRaw + "/Total.csv"
df = pd.read_csv(totalFile, usecols=["Ealf", "Eauxf", "Ecaf", "Edataf", "Epf"])
arrayTotal = np.array(df)
totalElec = np.sum(arrayTotal) * 1E6 # [Wh]
df = pd.read_csv(totalFile, usecols=["Qcdataf", "Qcicef", "Qcpf", "Qcsf"])
arrayTotal = np.array(df)
if individual[12] == 0:
totalCool = ( np.sum((arrayTotal)[:,:-1]) + np.sum((arrayTotal)[:,-1:]) * (1+gV.DCNetworkLoss) ) * 1E6 # [Wh]
else:
totalCool = ( np.sum((arrayTotal)[:,1:-1]) + np.sum((arrayTotal)[:,-1:]) * (1+gV.DCNetworkLoss) ) * 1E6 # [Wh]
# Computes the specific operational costs
totalEnergy = Q_AddBoiler + Q_BoilerBase + Q_BoilerPeak + Q_CC + Q_GHP + Q_HPLake + \
Q_HPSew + Q_uncontrollable + totalElec + totalCool + \
QfromNG + QfromBG + QfromGHP# + Q_storage_missmatch
shareCC_NG = individual[0] % 2 * Q_CC / totalEnergy
shareCC_BG = ( individual[0] + 1 ) % 2 * Q_CC / totalEnergy
shareBB_NG = individual[2] % 2 * Q_BoilerBase / totalEnergy
shareBB_BG = ( individual[2] + 1 ) % 2 * Q_BoilerBase / totalEnergy
shareBP_NG = individual[4] % 2 * Q_BoilerPeak / totalEnergy
shareBP_BG = ( individual[4] + 1 ) % 2 * Q_BoilerPeak / totalEnergy
shareHPLake = Q_HPLake / totalEnergy
shareHPSew = Q_HPSew / totalEnergy
shareGHP = Q_GHP / totalEnergy
shareExtraNG = (Q_AddBoiler + Q_storage_missmatch) / totalEnergy
shareHR = (Q_uncontrollable - Q_fromSolar) / totalEnergy
shareHeatSolar = Q_fromSolar / totalEnergy
shareDecentralizedNG = QfromNG / totalEnergy
shareDecentralizedBG = QfromBG / totalEnergy
shareDecentralizedGHP = QfromGHP / totalEnergy
shareElecGrid = (totalElec - Electricity_Solar) / totalEnergy
shareElecSolar = Electricity_Solar / totalEnergy
shareCoolLake = totalCool / totalEnergy
printout = 1
if printout:
print 'CC_NG', individual[0] % 2 * Q_CC
print 'CC_BG', ( individual[0] + 1 ) % 2 * Q_CC
print 'BB_NG', individual[2] % 2 * Q_BoilerBase
print 'BB_BG', ( individual[2] + 1 ) % 2 * Q_BoilerBase
print 'BP_NG', individual[4] % 2 * Q_BoilerPeak
print 'BP_BG', ( individual[4] + 1 ) % 2 * Q_BoilerPeak
print 'HPLake', Q_HPLake
print 'HPSew', Q_HPSew
print 'GHP', Q_GHP
print 'ExtraNG', (Q_AddBoiler + Q_storage_missmatch)
print 'HR', (Q_uncontrollable - Q_fromSolar)
print 'HeatSolar', Q_fromSolar
print 'DecentralizedNG', QfromNG
print 'DecentralizedBG', QfromBG
print 'DecentralizedGHP', QfromGHP
print 'ElecGrid', (totalElec - Electricity_Solar)
print 'ElecSolar', Electricity_Solar
print 'CoolLake', totalCool
print 'shareCC_NG', shareCC_NG*100
print 'shareCC_BG', shareCC_BG*100
print 'shareBB_NG', shareBB_NG*100
print 'shareBB_BG', shareBB_BG*100
print 'shareBP_NG', shareBP_NG*100
print 'shareBP_BG', shareBP_BG*100
print 'shareHPLake', shareHPLake*100
print 'shareHPSew', shareHPSew*100
print 'shareGHP', shareGHP*100
print 'shareExtraNG', shareExtraNG*100
print 'shareHR', shareHR*100
print 'shareHeatSolar', shareHeatSolar*100
print 'shareDecentralizedNG', shareDecentralizedNG*100
print 'shareDecentralizedBG', shareDecentralizedBG*100
print 'shareDecentralizedGHP', shareDecentralizedGHP*100
print 'shareElecGrid' ,shareElecGrid*100
print 'shareElecSolar', shareElecSolar*100
print 'shareCoolLake', shareCoolLake*100
specificCosts = gV.ELEC_PRICE * shareElecGrid + \
gV.NG_PRICE * (shareCC_NG + shareBB_NG + shareBP_NG + shareExtraNG + shareDecentralizedNG) + \
gV.BG_PRICE * (shareCC_BG + shareBB_BG + shareBP_BG + shareDecentralizedBG)
# Computes the share of local resources
shareLocal = shareHPLake + shareHPSew + shareGHP + shareHR + shareHeatSolar + shareDecentralizedGHP + shareElecSolar + shareCoolLake
# Plots the pie chart for energy resources use
if plot == 1:
fig = plt.figure()
subplot = fig.add_subplot(111, adjustable = 'box', aspect = 1, title = 'Energy mix')
labels = ['Lake', 'Solar', 'HR', 'Ground', 'Gas', 'Grid']
shareLake = shareHPLake + shareCoolLake
shareSolar = shareHeatSolar + shareElecSolar
shareHRAll = shareHPSew + shareHR
shareGround = shareGHP + shareDecentralizedGHP
shareGas = 1 - shareLake - shareSolar - shareHRAll - shareGround - shareElecGrid
fracs = [ shareLake, shareSolar, shareHRAll, shareGround, shareGas, shareElecGrid ]
colors = ['RoyalBlue', 'Gold', 'LightGreen', 'SandyBrown', 'OrangeRed', 'Gray']
zipper = [ (l,f,c) for (l,f,c) in zip(labels,fracs,colors) if f > 0.001 ]
labelsPlot, fracsPlot, colorsPlot = map( list, zip(*zipper) )
subplot.pie(fracsPlot, labels = labelsPlot, colors = colorsPlot, startangle = 90, autopct='%1.1f%%', pctdistance = 0.65)
plt.show()
return specificCosts, shareLocal
