def convertTests():
''' Test convert every windmil feeder we have (in uploads). Return number of exceptions we hit. '''
exceptionCount = 0
testFiles = [('OrvilleTreePond.std', 'OrvilleTreePond.seq')]
# ,('OlinBarre.std','OlinBarre.seq'),('OlinBeckenham.std','OlinBeckenham.seq'), ('AutocliAlberich.std','AutocliAlberich.seq')
for stdString, seqString in testFiles:
try:
# Convert the std+seq.
with open(stdString, 'r') as stdFile, open(seqString,
'r') as seqFile:
outGlm, x, y = milToGridlab.convert(stdFile.read(),
seqFile.read())
with open(stdString.replace('.std', '.glm'), 'w') as outFile:
outFile.write(feeder.sortedWrite(outGlm))
print 'WROTE GLM FOR', stdString
try:
# Draw the GLM.
myGraph = feeder.treeToNxGraph(outGlm)
feeder.latLonNxGraph(myGraph, neatoLayout=False)
plt.savefig(stdString.replace('.std', '.png'))
print 'DREW GLM OF', stdString
except:
exceptionCount += 1
print 'FAILED DRAWING', stdString
try:
# Run powerflow on the GLM.
output = gridlabd.runInFilesystem(outGlm, keepFiles=False)
with open(stdString.replace('.std', '.json'), 'w') as outFile:
json.dump(output, outFile, indent=4)
print 'RAN GRIDLAB ON', stdString
except:
exceptionCount += 1
print 'POWERFLOW FAILED', stdString
except:
print 'FAILED CONVERTING', stdString
exceptionCount += 1
traceback.print_exc()
print exceptionCount
def _tests():
pathPrefix = '../../uploads/'
testFiles = [
('INEC-RENOIR.std', 'INEC.seq'), ('INEC-GRAHAM.std', 'INEC.seq'),
('Olin-Barre.std', 'Olin.seq'), ('Olin-Brown.std', 'Olin.seq'),
('ABEC-Frank.std', 'ABEC.seq'), ('ABEC-COLUMBIA.std', 'ABEC.seq')
]
for stdPath, seqPath in testFiles:
try:
# Conver the std+seq.
with open(pathPrefix + stdPath,
'r') as stdFile, open(pathPrefix + seqPath,
'r') as seqFile:
outGlm, x, y = m2g.convert(stdFile.read(), seqFile.read())
with open(stdPath.replace('.std', '.glm'), 'w') as outFile:
outFile.write(feeder.sortedWrite(outGlm))
print 'WROTE GLM FOR', stdPath
try:
# Draw the GLM.
myGraph = feeder.treeToNxGraph(outGlm)
feeder.latLonNxGraph(myGraph, neatoLayout=False)
plt.savefig(stdPath.replace('.std', '.png'))
except:
print 'FAILED DRAWING', stdPath
try:
# Run powerflow on the GLM.
output = gridlabd.runInFilesystem(outGlm, keepFiles=False)
with open(stdPath.replace('.std', '.json'), 'w') as outFile:
json.dump(output, outFile, indent=4)
except:
print 'POWERFLOW FAILED', stdPath
except:
print 'FAILED CONVERTING', stdPath
traceback.print_exc()
print os.listdir('.')
def convertTests():
''' Test convert every windmil feeder we have (in uploads). Return number of exceptions we hit. '''
exceptionCount = 0
testFiles = [('OrvilleTreePond.std','OrvilleTreePond.seq')]
# ,('OlinBarre.std','OlinBarre.seq'),('OlinBeckenham.std','OlinBeckenham.seq'), ('AutocliAlberich.std','AutocliAlberich.seq')
for stdString, seqString in testFiles:
try:
# Convert the std+seq.
with open(stdString,'r') as stdFile, open(seqString,'r') as seqFile:
outGlm,x,y = milToGridlab.convert(stdFile.read(),seqFile.read())
with open(stdString.replace('.std','.glm'),'w') as outFile:
outFile.write(feeder.sortedWrite(outGlm))
print 'WROTE GLM FOR', stdString
try:
# Draw the GLM.
myGraph = feeder.treeToNxGraph(outGlm)
feeder.latLonNxGraph(myGraph, neatoLayout=False)
plt.savefig(stdString.replace('.std','.png'))
print 'DREW GLM OF', stdString
except:
exceptionCount += 1
print 'FAILED DRAWING', stdString
try:
# Run powerflow on the GLM.
output = gridlabd.runInFilesystem(outGlm, keepFiles=False)
with open(stdString.replace('.std','.json'),'w') as outFile:
json.dump(output, outFile, indent=4)
print 'RAN GRIDLAB ON', stdString
except:
exceptionCount += 1
print 'POWERFLOW FAILED', stdString
except:
print 'FAILED CONVERTING', stdString
exceptionCount += 1
traceback.print_exc()
print exceptionCount
def runForeground(modelDir, inputDict):
''' Run the model in the foreground. WARNING: can take about a minute. '''
# Global vars, and load data from the model directory.
print "STARTING TO RUN", modelDir
try:
startTime = datetime.datetime.now()
if not os.path.isdir(modelDir):
os.makedirs(modelDir)
inputDict["created"] = str(startTime)
feederName = inputDict.get('feederName1','feeder1')
feederPath = pJoin(modelDir,feederName+'.omd')
feederJson = json.load(open(feederPath))
tree = feederJson.get("tree",{})
attachments = feederJson.get("attachments",{})
allOutput = {}
''' Run CVR analysis. '''
# Reformate monthData and rates.
rates = {k:float(inputDict[k]) for k in ["capitalCost", "omCost", "wholesaleEnergyCostPerKwh",
"retailEnergyCostPerKwh", "peakDemandCostSpringPerKw", "peakDemandCostSummerPerKw",
"peakDemandCostFallPerKw", "peakDemandCostWinterPerKw"]}
# print "RATES", rates
monthNames = ["January", "February", "March", "April", "May", "June", "July", "August",
"September", "October", "November", "December"]
monthToSeason = {'January':'Winter','February':'Winter','March':'Spring','April':'Spring',
'May':'Spring','June':'Summer','July':'Summer','August':'Summer',
'September':'Fall','October':'Fall','November':'Fall','December':'Winter'}
monthData = []
for i, x in enumerate(monthNames):
monShort = x[0:3].lower()
season = monthToSeason[x]
histAvg = float(inputDict.get(monShort + "Avg", 0))
histPeak = float(inputDict.get(monShort + "Peak", 0))
monthData.append({"monthId":i, "monthName":x, "histAverage":histAvg,
"histPeak":histPeak, "season":season})
# for row in monthData:
# print row
# Graph the SCADA data.
fig = plt.figure(figsize=(10,6))
indices = [r['monthName'] for r in monthData]
d1 = [r['histPeak']/(10**3) for r in monthData]
d2 = [r['histAverage']/(10**3) for r in monthData]
ticks = range(len(d1))
bar_peak = plt.bar(ticks,d1,color='gray')
bar_avg = plt.bar(ticks,d2,color='dimgray')
plt.legend([bar_peak[0],bar_avg[0]],['histPeak','histAverage'],bbox_to_anchor=(0., 1.015, 1., .102), loc=3,
ncol=2, mode="expand", borderaxespad=0.1)
plt.xticks([t+0.5 for t in ticks],indices)
plt.ylabel('Mean and peak historical power consumptions (kW)')
fig.autofmt_xdate()
plt.savefig(pJoin(modelDir,"scadaChart.png"))
allOutput["histPeak"] = d1
allOutput["histAverage"] = d2
allOutput["monthName"] = [name[0:3] for name in monthNames]
# Graph feeder.
fig = plt.figure(figsize=(10,10))
myGraph = feeder.treeToNxGraph(tree)
feeder.latLonNxGraph(myGraph, neatoLayout=False)
plt.savefig(pJoin(modelDir,"feederChart.png"))
with open(pJoin(modelDir,"feederChart.png"),"rb") as inFile:
allOutput["feederChart"] = inFile.read().encode("base64")
# Get the load levels we need to test.
allLoadLevels = [x.get('histPeak',0) for x in monthData] + [y.get('histAverage',0) for y in monthData]
maxLev = _roundOne(max(allLoadLevels),'up')
minLev = _roundOne(min(allLoadLevels),'down')
tenLoadLevels = range(int(minLev),int(maxLev),int((maxLev-minLev)/10))
# Gather variables from the feeder.
for key in tree.keys():
# Set clock to single timestep.
if tree[key].get('clock','') == 'clock':
tree[key] = {"timezone":"PST+8PDT",
"stoptime":"'2013-01-01 00:00:00'",
"starttime":"'2013-01-01 00:00:00'",
"clock":"clock"}
# Save swing node index.
if tree[key].get('bustype','').lower() == 'swing':
swingIndex = key
swingName = tree[key].get('name')
# Remove all includes.
if tree[key].get('omftype','') == '#include':
del key
# Find the substation regulator and config.
for key in tree:
if tree[key].get('object','') == 'regulator' and tree[key].get('from','') == swingName:
regIndex = key
regConfName = tree[key]['configuration']
if not regConfName: regConfName = False
for key in tree:
if tree[key].get('name','') == regConfName:
regConfIndex = key
# Set substation regulator to manual operation.
baselineTap = int(inputDict.get("baselineTap")) # GLOBAL VARIABLE FOR DEFAULT TAP POSITION
tree[regConfIndex] = {
'name':tree[regConfIndex]['name'],
'object':'regulator_configuration',
'connect_type':'1',
'raise_taps':'10',
'lower_taps':'10',
'CT_phase':'ABC',
'PT_phase':'ABC',
'regulation':'0.10', #Yo, 0.10 means at tap_pos 10 we're 10% above 120V.
'Control':'MANUAL',
'control_level':'INDIVIDUAL',
'Type':'A',
'tap_pos_A':str(baselineTap),
'tap_pos_B':str(baselineTap),
'tap_pos_C':str(baselineTap) }
# Attach recorders relevant to CVR.
recorders = [
{'object': 'collector',
'file': 'ZlossesTransformer.csv',
'group': 'class=transformer',
'limit': '0',
'property': 'sum(power_losses_A.real),sum(power_losses_A.imag),sum(power_losses_B.real),sum(power_losses_B.imag),sum(power_losses_C.real),sum(power_losses_C.imag)'},
{'object': 'collector',
'file': 'ZlossesUnderground.csv',
'group': 'class=underground_line',
'limit': '0',
'property': 'sum(power_losses_A.real),sum(power_losses_A.imag),sum(power_losses_B.real),sum(power_losses_B.imag),sum(power_losses_C.real),sum(power_losses_C.imag)'},
{'object': 'collector',
'file': 'ZlossesOverhead.csv',
'group': 'class=overhead_line',
'limit': '0',
'property': 'sum(power_losses_A.real),sum(power_losses_A.imag),sum(power_losses_B.real),sum(power_losses_B.imag),sum(power_losses_C.real),sum(power_losses_C.imag)'},
{'object': 'recorder',
'file': 'Zregulator.csv',
'limit': '0',
'parent': tree[regIndex]['name'],
'property': 'tap_A,tap_B,tap_C,power_in.real,power_in.imag'},
{'object': 'collector',
'file': 'ZvoltageJiggle.csv',
'group': 'class=triplex_meter',
'limit': '0',
'property': 'min(voltage_12.mag),mean(voltage_12.mag),max(voltage_12.mag),std(voltage_12.mag)'},
{'object': 'recorder',
'file': 'ZsubstationTop.csv',
'limit': '0',
'parent': tree[swingIndex]['name'],
'property': 'voltage_A,voltage_B,voltage_C'},
{'object': 'recorder',
'file': 'ZsubstationBottom.csv',
'limit': '0',
'parent': tree[regIndex]['to'],
'property': 'voltage_A,voltage_B,voltage_C'} ]
biggest = 1 + max([int(k) for k in tree.keys()])
for index, rec in enumerate(recorders):
tree[biggest + index] = rec
# Change constant PF loads to ZIP loads. (See evernote for rationale about 50/50 power/impedance mix.)
blankZipModel = {'object':'triplex_load',
'name':'NAMEVARIABLE',
'base_power_12':'POWERVARIABLE',
'power_fraction_12': str(inputDict.get("p_percent")),
'impedance_fraction_12': str(inputDict.get("z_percent")),
'current_fraction_12': str(inputDict.get("i_percent")),
'power_pf_12': str(inputDict.get("power_factor")), #MAYBEFIX: we can probably get this PF data from the Milsoft loads.
'impedance_pf_12':str(inputDict.get("power_factor")),
'current_pf_12':str(inputDict.get("power_factor")),
'nominal_voltage':'120',
'phases':'PHASESVARIABLE',
'parent':'PARENTVARIABLE' }
def powerClean(powerStr):
''' take 3339.39+1052.29j to 3339.39 '''
return powerStr[0:powerStr.find('+')]
for key in tree:
if tree[key].get('object','') == 'triplex_node':
# Get existing variables.
name = tree[key].get('name','')
power = tree[key].get('power_12','')
parent = tree[key].get('parent','')
phases = tree[key].get('phases','')
# Replace object and reintroduce variables.
tree[key] = copy(blankZipModel)
tree[key]['name'] = name
tree[key]['base_power_12'] = powerClean(power)
tree[key]['parent'] = parent
tree[key]['phases'] = phases
# Function to determine how low we can tap down in the CVR case:
def loweringPotential(baseLine):
''' Given a baseline end of line voltage, how many more percent can we shave off the substation voltage? '''
''' testsWePass = [122.0,118.0,200.0,110.0] '''
lower = int(math.floor((baseLine/114.0-1)*100)) - 1
# If lower is negative, we can't return it because we'd be undervolting beyond what baseline already was!
if lower < 0:
return baselineTap
else:
return baselineTap - lower
# Run all the powerflows.
powerflows = []
for doingCvr in [False, True]:
# For each load level in the tenLoadLevels, run a powerflow with the load objects scaled to the level.
for desiredLoad in tenLoadLevels:
# Find the total load that was defined in Milsoft:
loadList = []
for key in tree:
if tree[key].get('object','') == 'triplex_load':
loadList.append(tree[key].get('base_power_12',''))
totalLoad = sum([float(x) for x in loadList])
# Rescale each triplex load:
for key in tree:
if tree[key].get('object','') == 'triplex_load':
currentPow = float(tree[key]['base_power_12'])
ratio = desiredLoad/totalLoad
tree[key]['base_power_12'] = str(currentPow*ratio)
# If we're doing CVR then lower the voltage.
if doingCvr:
# Find the minimum voltage we can tap down to:
newTapPos = baselineTap
for row in powerflows:
if row.get('loadLevel','') == desiredLoad:
newTapPos = loweringPotential(row.get('lowVoltage',114))
# Tap it down to there.
# MAYBEFIX: do each phase separately because that's how it's done in the field... Oof.
tree[regConfIndex]['tap_pos_A'] = str(newTapPos)
tree[regConfIndex]['tap_pos_B'] = str(newTapPos)
tree[regConfIndex]['tap_pos_C'] = str(newTapPos)
# Run the model through gridlab and put outputs in the table.
output = gridlabd.runInFilesystem(tree, attachments=attachments,
keepFiles=True, workDir=modelDir)
os.remove(pJoin(modelDir,"PID.txt"))
p = output['Zregulator.csv']['power_in.real'][0]
q = output['Zregulator.csv']['power_in.imag'][0]
s = math.sqrt(p**2+q**2)
lossTotal = 0.0
for device in ['ZlossesOverhead.csv','ZlossesTransformer.csv','ZlossesUnderground.csv']:
for letter in ['A','B','C']:
r = output[device]['sum(power_losses_' + letter + '.real)'][0]
i = output[device]['sum(power_losses_' + letter + '.imag)'][0]
lossTotal += math.sqrt(r**2 + i**2)
## Entire output:
powerflows.append({
'doingCvr':doingCvr,
'loadLevel':desiredLoad,
'realPower':p,
'powerFactor':p/s,
'losses':lossTotal,
'subVoltage': (
output['ZsubstationBottom.csv']['voltage_A'][0] +
output['ZsubstationBottom.csv']['voltage_B'][0] +
output['ZsubstationBottom.csv']['voltage_C'][0] )/3/60,
'lowVoltage':output['ZvoltageJiggle.csv']['min(voltage_12.mag)'][0]/2,
'highVoltage':output['ZvoltageJiggle.csv']['max(voltage_12.mag)'][0]/2 })
# For a given load level, find two points to interpolate on.
def getInterpPoints(t):
''' Find the two points we can interpolate from. '''
''' tests pass on [tenLoadLevels[0],tenLoadLevels[5]+499,tenLoadLevels[-1]-988] '''
loc = sorted(tenLoadLevels + [t]).index(t)
if loc==0:
return (tenLoadLevels[0],tenLoadLevels[1])
elif loc>len(tenLoadLevels)-2:
return (tenLoadLevels[-2],tenLoadLevels[-1])
else:
return (tenLoadLevels[loc-1],tenLoadLevels[loc+1])
# Calculate peak reduction.
for row in monthData:
peak = row['histPeak']
peakPoints = getInterpPoints(peak)
peakTopBase = [x for x in powerflows if x.get('loadLevel','') == peakPoints[-1] and x.get('doingCvr','') == False][0]
peakTopCvr = [x for x in powerflows if x.get('loadLevel','') == peakPoints[-1] and x.get('doingCvr','') == True][0]
peakBottomBase = [x for x in powerflows if x.get('loadLevel','') == peakPoints[0] and x.get('doingCvr','') == False][0]
peakBottomCvr = [x for x in powerflows if x.get('loadLevel','') == peakPoints[0] and x.get('doingCvr','') == True][0]
# Linear interpolation so we aren't running umpteen million loadflows.
x = (peakPoints[0],peakPoints[1])
y = (peakTopBase['realPower'] - peakTopCvr['realPower'],
peakBottomBase['realPower'] - peakBottomCvr['realPower'])
peakRed = y[0] + (y[1] - y[0]) * (peak - x[0]) / (x[1] - x[0])
row['peakReduction'] = peakRed
# Calculate energy reduction and loss reduction based on average load.
for row in monthData:
avgEnergy = row['histAverage']
energyPoints = getInterpPoints(avgEnergy)
avgTopBase = [x for x in powerflows if x.get('loadLevel','') == energyPoints[-1] and x.get('doingCvr','') == False][0]
avgTopCvr = [x for x in powerflows if x.get('loadLevel','') == energyPoints[-1] and x.get('doingCvr','') == True][0]
avgBottomBase = [x for x in powerflows if x.get('loadLevel','') == energyPoints[0] and x.get('doingCvr','') == False][0]
avgBottomCvr = [x for x in powerflows if x.get('loadLevel','') == energyPoints[0] and x.get('doingCvr','') == True][0]
# Linear interpolation so we aren't running umpteen million loadflows.
x = (energyPoints[0], energyPoints[1])
y = (avgTopBase['realPower'] - avgTopCvr['realPower'],
avgBottomBase['realPower'] - avgBottomCvr['realPower'])
energyRed = y[0] + (y[1] - y[0]) * (avgEnergy - x[0]) / (x[1] - x[0])
row['energyReduction'] = energyRed
lossY = (avgTopBase['losses'] - avgTopCvr['losses'],
avgBottomBase['losses'] - avgBottomCvr['losses'])
lossRed = lossY[0] + (lossY[1] - lossY[0]) * (avgEnergy - x[0]) / (x[1] - x[0])
row['lossReduction'] = lossRed
# Multiply by dollars.
for row in monthData:
row['energyReductionDollars'] = row['energyReduction']/1000 * (rates['wholesaleEnergyCostPerKwh'] - rates['retailEnergyCostPerKwh'])
row['peakReductionDollars'] = row['peakReduction']/1000 * rates['peakDemandCost' + row['season'] + 'PerKw']
row['lossReductionDollars'] = row['lossReduction']/1000 * rates['wholesaleEnergyCostPerKwh']
# Pretty output
def plotTable(inData):
fig = plt.figure(figsize=(10,5))
plt.axis('off')
plt.tight_layout()
plt.table(cellText=[row for row in inData[1:]],
loc = 'center',
rowLabels = range(len(inData)-1),
colLabels = inData[0])
def dictalToMatrix(dictList):
''' Take our dictal format to a matrix. '''
matrix = [dictList[0].keys()]
for row in dictList:
matrix.append(row.values())
return matrix
# Powerflow results.
plotTable(dictalToMatrix(powerflows))
plt.savefig(pJoin(modelDir,"powerflowTable.png"))
# Monetary results.
## To print partial money table
monthDataMat = dictalToMatrix(monthData)
dimX = len(monthDataMat)
dimY = len(monthDataMat[0])
monthDataPart = []
for k in range (0,dimX):
monthDatatemp = []
for m in range (4,dimY):
monthDatatemp.append(monthDataMat[k][m])
monthDataPart.append(monthDatatemp)
plotTable(monthDataPart)
plt.savefig(pJoin(modelDir,"moneyTable.png"))
allOutput["monthDataMat"] = dictalToMatrix(monthData)
allOutput["monthDataPart"] = monthDataPart
# Graph the money data.
fig = plt.figure(figsize=(10,8))
indices = [r['monthName'] for r in monthData]
d1 = [r['energyReductionDollars'] for r in monthData]
d2 = [r['lossReductionDollars'] for r in monthData]
d3 = [r['peakReductionDollars'] for r in monthData]
ticks = range(len(d1))
bar_erd = plt.bar(ticks,d1,color='red')
bar_lrd = plt.bar(ticks,d2,color='green')
bar_prd = plt.bar(ticks,d3,color='blue',yerr=d2)
plt.legend([bar_prd[0], bar_lrd[0], bar_erd[0]], ['peakReductionDollars','lossReductionDollars','energyReductionDollars'],bbox_to_anchor=(0., 1.015, 1., .102), loc=3,
ncol=2, mode="expand", borderaxespad=0.1)
plt.xticks([t+0.5 for t in ticks],indices)
plt.ylabel('Utility Savings ($)')
plt.tight_layout(5.5,1.3,1.2)
fig.autofmt_xdate()
plt.savefig(pJoin(modelDir,"spendChart.png"))
allOutput["energyReductionDollars"] = d1
allOutput["lossReductionDollars"] = d2
allOutput["peakReductionDollars"] = d3
# Graph the cumulative savings.
fig = plt.figure(figsize=(10,5))
annualSavings = sum(d1) + sum(d2) + sum(d3)
annualSave = lambda x:(annualSavings - rates['omCost']) * x - rates['capitalCost']
simplePayback = rates['capitalCost']/(annualSavings - rates['omCost'])
plt.xlabel('Year After Installation')
plt.xlim(0,30)
plt.ylabel('Cumulative Savings ($)')
plt.plot([0 for x in range(31)],c='gray')
plt.axvline(x=simplePayback, ymin=0, ymax=1, c='gray', linestyle='--')
plt.plot([annualSave(x) for x in range(31)], c='green')
plt.savefig(pJoin(modelDir,"savingsChart.png"))
allOutput["annualSave"] = [annualSave(x) for x in range(31)]
# Update the runTime in the input file.
endTime = datetime.datetime.now()
inputDict["runTime"] = str(datetime.timedelta(seconds=int((endTime - startTime).total_seconds())))
with open(pJoin(modelDir,"allInputData.json"),"w") as inFile:
json.dump(inputDict, inFile, indent=4)
# Write output file.
with open(pJoin(modelDir,"allOutputData.json"),"w") as outFile:
json.dump(allOutput, outFile, indent=4)
# For autotest, there won't be such file.
try:
os.remove(pJoin(modelDir, "PPID.txt"))
except:
pass
print "DONE RUNNING", modelDir
except Exception as e:
# If input range wasn't valid delete output, write error to disk.
cancel(modelDir)
thisErr = traceback.format_exc()
print 'ERROR IN MODEL', modelDir, thisErr
inputDict['stderr'] = thisErr
with open(os.path.join(modelDir,'stderr.txt'),'w') as errorFile:
errorFile.write(thisErr)
with open(pJoin(modelDir,"allInputData.json"),"w") as inFile:
json.dump(inputDict, inFile, indent=4)
def runForeground(modelDir, inputDict):
''' Run the model in the foreground. WARNING: can take about a minute. '''
# Global vars, and load data from the model directory.
print "STARTING TO RUN", modelDir
try:
startTime = datetime.datetime.now()
if not os.path.isdir(modelDir):
os.makedirs(modelDir)
inputDict["created"] = str(startTime)
feederPath = pJoin(__metaModel__._omfDir,"data", "Feeder", inputDict["feederName"].split("___")[0], inputDict["feederName"].split("___")[1]+'.json')
feederJson = json.load(open(feederPath))
tree = feederJson.get("tree",{})
attachments = feederJson.get("attachments",{})
allOutput = {}
''' Run CVR analysis. '''
# Reformate monthData and rates.
rates = {k:float(inputDict[k]) for k in ["capitalCost", "omCost", "wholesaleEnergyCostPerKwh",
"retailEnergyCostPerKwh", "peakDemandCostSpringPerKw", "peakDemandCostSummerPerKw",
"peakDemandCostFallPerKw", "peakDemandCostWinterPerKw"]}
# print "RATES", rates
monthNames = ["January", "February", "March", "April", "May", "June", "July", "August",
"September", "October", "November", "December"]
monthToSeason = {'January':'Winter','February':'Winter','March':'Spring','April':'Spring',
'May':'Spring','June':'Summer','July':'Summer','August':'Summer',
'September':'Fall','October':'Fall','November':'Fall','December':'Winter'}
monthData = []
for i, x in enumerate(monthNames):
monShort = x[0:3].lower()
season = monthToSeason[x]
histAvg = float(inputDict.get(monShort + "Avg", 0))
histPeak = float(inputDict.get(monShort + "Peak", 0))
monthData.append({"monthId":i, "monthName":x, "histAverage":histAvg,
"histPeak":histPeak, "season":season})
# for row in monthData:
# print row
# Graph the SCADA data.
fig = plt.figure(figsize=(10,6))
indices = [r['monthName'] for r in monthData]
d1 = [r['histPeak']/(10**3) for r in monthData]
d2 = [r['histAverage']/(10**3) for r in monthData]
ticks = range(len(d1))
bar_peak = plt.bar(ticks,d1,color='gray')
bar_avg = plt.bar(ticks,d2,color='dimgray')
plt.legend([bar_peak[0],bar_avg[0]],['histPeak','histAverage'],bbox_to_anchor=(0., 1.015, 1., .102), loc=3,
ncol=2, mode="expand", borderaxespad=0.1)
plt.xticks([t+0.5 for t in ticks],indices)
plt.ylabel('Mean and peak historical power consumptions (kW)')
fig.autofmt_xdate()
plt.savefig(pJoin(modelDir,"scadaChart.png"))
allOutput["histPeak"] = d1
allOutput["histAverage"] = d2
allOutput["monthName"] = [name[0:3] for name in monthNames]
# Graph feeder.
fig = plt.figure(figsize=(10,10))
myGraph = feeder.treeToNxGraph(tree)
feeder.latLonNxGraph(myGraph, neatoLayout=False)
plt.savefig(pJoin(modelDir,"feederChart.png"))
with open(pJoin(modelDir,"feederChart.png"),"rb") as inFile:
allOutput["feederChart"] = inFile.read().encode("base64")
# Get the load levels we need to test.
allLoadLevels = [x.get('histPeak',0) for x in monthData] + [y.get('histAverage',0) for y in monthData]
maxLev = _roundOne(max(allLoadLevels),'up')
minLev = _roundOne(min(allLoadLevels),'down')
tenLoadLevels = range(int(minLev),int(maxLev),int((maxLev-minLev)/10))
# Gather variables from the feeder.
for key in tree.keys():
# Set clock to single timestep.
if tree[key].get('clock','') == 'clock':
tree[key] = {"timezone":"PST+8PDT",
"stoptime":"'2013-01-01 00:00:00'",
"starttime":"'2013-01-01 00:00:00'",
"clock":"clock"}
# Save swing node index.
if tree[key].get('bustype','').lower() == 'swing':
swingIndex = key
swingName = tree[key].get('name')
# Remove all includes.
if tree[key].get('omftype','') == '#include':
del key
# Find the substation regulator and config.
for key in tree:
if tree[key].get('object','') == 'regulator' and tree[key].get('from','') == swingName:
regIndex = key
regConfName = tree[key]['configuration']
if not regConfName: regConfName = False
for key in tree:
if tree[key].get('name','') == regConfName:
regConfIndex = key
# Set substation regulator to manual operation.
baselineTap = int(inputDict.get("baselineTap")) # GLOBAL VARIABLE FOR DEFAULT TAP POSITION
tree[regConfIndex] = {
'name':tree[regConfIndex]['name'],
'object':'regulator_configuration',
'connect_type':'1',
'raise_taps':'10',
'lower_taps':'10',
'CT_phase':'ABC',
'PT_phase':'ABC',
'regulation':'0.10', #Yo, 0.10 means at tap_pos 10 we're 10% above 120V.
'Control':'MANUAL',
'control_level':'INDIVIDUAL',
'Type':'A',
'tap_pos_A':str(baselineTap),
'tap_pos_B':str(baselineTap),
'tap_pos_C':str(baselineTap) }
# Attach recorders relevant to CVR.
recorders = [
{'object': 'collector',
'file': 'ZlossesTransformer.csv',
'group': 'class=transformer',
'limit': '0',
'property': 'sum(power_losses_A.real),sum(power_losses_A.imag),sum(power_losses_B.real),sum(power_losses_B.imag),sum(power_losses_C.real),sum(power_losses_C.imag)'},
{'object': 'collector',
'file': 'ZlossesUnderground.csv',
'group': 'class=underground_line',
'limit': '0',
'property': 'sum(power_losses_A.real),sum(power_losses_A.imag),sum(power_losses_B.real),sum(power_losses_B.imag),sum(power_losses_C.real),sum(power_losses_C.imag)'},
{'object': 'collector',
'file': 'ZlossesOverhead.csv',
'group': 'class=overhead_line',
'limit': '0',
'property': 'sum(power_losses_A.real),sum(power_losses_A.imag),sum(power_losses_B.real),sum(power_losses_B.imag),sum(power_losses_C.real),sum(power_losses_C.imag)'},
{'object': 'recorder',
'file': 'Zregulator.csv',
'limit': '0',
'parent': tree[regIndex]['name'],
'property': 'tap_A,tap_B,tap_C,power_in.real,power_in.imag'},
{'object': 'collector',
'file': 'ZvoltageJiggle.csv',
'group': 'class=triplex_meter',
'limit': '0',
'property': 'min(voltage_12.mag),mean(voltage_12.mag),max(voltage_12.mag),std(voltage_12.mag)'},
{'object': 'recorder',
'file': 'ZsubstationTop.csv',
'limit': '0',
'parent': tree[swingIndex]['name'],
'property': 'voltage_A,voltage_B,voltage_C'},
{'object': 'recorder',
'file': 'ZsubstationBottom.csv',
'limit': '0',
'parent': tree[regIndex]['to'],
'property': 'voltage_A,voltage_B,voltage_C'} ]
biggest = 1 + max([int(k) for k in tree.keys()])
for index, rec in enumerate(recorders):
tree[biggest + index] = rec
# Change constant PF loads to ZIP loads. (See evernote for rationale about 50/50 power/impedance mix.)
blankZipModel = {'object':'triplex_load',
'name':'NAMEVARIABLE',
'base_power_12':'POWERVARIABLE',
'power_fraction_12': str(inputDict.get("p_percent")),
'impedance_fraction_12': str(inputDict.get("z_percent")),
'current_fraction_12': str(inputDict.get("i_percent")),
'power_pf_12': str(inputDict.get("power_factor")), #MAYBEFIX: we can probably get this PF data from the Milsoft loads.
'impedance_pf_12':str(inputDict.get("power_factor")),
'current_pf_12':str(inputDict.get("power_factor")),
'nominal_voltage':'120',
'phases':'PHASESVARIABLE',
'parent':'PARENTVARIABLE' }
def powerClean(powerStr):
''' take 3339.39+1052.29j to 3339.39 '''
return powerStr[0:powerStr.find('+')]
for key in tree:
if tree[key].get('object','') == 'triplex_node':
# Get existing variables.
name = tree[key].get('name','')
power = tree[key].get('power_12','')
parent = tree[key].get('parent','')
phases = tree[key].get('phases','')
# Replace object and reintroduce variables.
tree[key] = copy(blankZipModel)
tree[key]['name'] = name
tree[key]['base_power_12'] = powerClean(power)
tree[key]['parent'] = parent
tree[key]['phases'] = phases
# Function to determine how low we can tap down in the CVR case:
def loweringPotential(baseLine):
''' Given a baseline end of line voltage, how many more percent can we shave off the substation voltage? '''
''' testsWePass = [122.0,118.0,200.0,110.0] '''
lower = int(math.floor((baseLine/114.0-1)*100)) - 1
# If lower is negative, we can't return it because we'd be undervolting beyond what baseline already was!
if lower < 0:
return baselineTap
else:
return baselineTap - lower
# Run all the powerflows.
powerflows = []
for doingCvr in [False, True]:
# For each load level in the tenLoadLevels, run a powerflow with the load objects scaled to the level.
for desiredLoad in tenLoadLevels:
# Find the total load that was defined in Milsoft:
loadList = []
for key in tree:
if tree[key].get('object','') == 'triplex_load':
loadList.append(tree[key].get('base_power_12',''))
totalLoad = sum([float(x) for x in loadList])
# Rescale each triplex load:
for key in tree:
if tree[key].get('object','') == 'triplex_load':
currentPow = float(tree[key]['base_power_12'])
ratio = desiredLoad/totalLoad
tree[key]['base_power_12'] = str(currentPow*ratio)
# If we're doing CVR then lower the voltage.
if doingCvr:
# Find the minimum voltage we can tap down to:
newTapPos = baselineTap
for row in powerflows:
if row.get('loadLevel','') == desiredLoad:
newTapPos = loweringPotential(row.get('lowVoltage',114))
# Tap it down to there.
# MAYBEFIX: do each phase separately because that's how it's done in the field... Oof.
tree[regConfIndex]['tap_pos_A'] = str(newTapPos)
tree[regConfIndex]['tap_pos_B'] = str(newTapPos)
tree[regConfIndex]['tap_pos_C'] = str(newTapPos)
# Run the model through gridlab and put outputs in the table.
output = gridlabd.runInFilesystem(tree, attachments=attachments,
keepFiles=True, workDir=modelDir)
os.remove(pJoin(modelDir,"PID.txt"))
p = output['Zregulator.csv']['power_in.real'][0]
q = output['Zregulator.csv']['power_in.imag'][0]
s = math.sqrt(p**2+q**2)
lossTotal = 0.0
for device in ['ZlossesOverhead.csv','ZlossesTransformer.csv','ZlossesUnderground.csv']:
for letter in ['A','B','C']:
r = output[device]['sum(power_losses_' + letter + '.real)'][0]
i = output[device]['sum(power_losses_' + letter + '.imag)'][0]
lossTotal += math.sqrt(r**2 + i**2)
## Entire output:
powerflows.append({
'doingCvr':doingCvr,
'loadLevel':desiredLoad,
'realPower':p,
'powerFactor':p/s,
'losses':lossTotal,
'subVoltage': (
output['ZsubstationBottom.csv']['voltage_A'][0] +
output['ZsubstationBottom.csv']['voltage_B'][0] +
output['ZsubstationBottom.csv']['voltage_C'][0] )/3/60,
'lowVoltage':output['ZvoltageJiggle.csv']['min(voltage_12.mag)'][0]/2,
'highVoltage':output['ZvoltageJiggle.csv']['max(voltage_12.mag)'][0]/2 })
# For a given load level, find two points to interpolate on.
def getInterpPoints(t):
''' Find the two points we can interpolate from. '''
''' tests pass on [tenLoadLevels[0],tenLoadLevels[5]+499,tenLoadLevels[-1]-988] '''
loc = sorted(tenLoadLevels + [t]).index(t)
if loc==0:
return (tenLoadLevels[0],tenLoadLevels[1])
elif loc>len(tenLoadLevels)-2:
return (tenLoadLevels[-2],tenLoadLevels[-1])
else:
return (tenLoadLevels[loc-1],tenLoadLevels[loc+1])
# Calculate peak reduction.
for row in monthData:
peak = row['histPeak']
peakPoints = getInterpPoints(peak)
peakTopBase = [x for x in powerflows if x.get('loadLevel','') == peakPoints[-1] and x.get('doingCvr','') == False][0]
peakTopCvr = [x for x in powerflows if x.get('loadLevel','') == peakPoints[-1] and x.get('doingCvr','') == True][0]
peakBottomBase = [x for x in powerflows if x.get('loadLevel','') == peakPoints[0] and x.get('doingCvr','') == False][0]
peakBottomCvr = [x for x in powerflows if x.get('loadLevel','') == peakPoints[0] and x.get('doingCvr','') == True][0]
# Linear interpolation so we aren't running umpteen million loadflows.
x = (peakPoints[0],peakPoints[1])
y = (peakTopBase['realPower'] - peakTopCvr['realPower'],
peakBottomBase['realPower'] - peakBottomCvr['realPower'])
peakRed = y[0] + (y[1] - y[0]) * (peak - x[0]) / (x[1] - x[0])
row['peakReduction'] = peakRed
# Calculate energy reduction and loss reduction based on average load.
for row in monthData:
avgEnergy = row['histAverage']
energyPoints = getInterpPoints(avgEnergy)
avgTopBase = [x for x in powerflows if x.get('loadLevel','') == energyPoints[-1] and x.get('doingCvr','') == False][0]
avgTopCvr = [x for x in powerflows if x.get('loadLevel','') == energyPoints[-1] and x.get('doingCvr','') == True][0]
avgBottomBase = [x for x in powerflows if x.get('loadLevel','') == energyPoints[0] and x.get('doingCvr','') == False][0]
avgBottomCvr = [x for x in powerflows if x.get('loadLevel','') == energyPoints[0] and x.get('doingCvr','') == True][0]
# Linear interpolation so we aren't running umpteen million loadflows.
x = (energyPoints[0], energyPoints[1])
y = (avgTopBase['realPower'] - avgTopCvr['realPower'],
avgBottomBase['realPower'] - avgBottomCvr['realPower'])
energyRed = y[0] + (y[1] - y[0]) * (avgEnergy - x[0]) / (x[1] - x[0])
row['energyReduction'] = energyRed
lossY = (avgTopBase['losses'] - avgTopCvr['losses'],
avgBottomBase['losses'] - avgBottomCvr['losses'])
lossRed = lossY[0] + (lossY[1] - lossY[0]) * (avgEnergy - x[0]) / (x[1] - x[0])
row['lossReduction'] = lossRed
# Multiply by dollars.
for row in monthData:
row['energyReductionDollars'] = row['energyReduction']/1000 * (rates['wholesaleEnergyCostPerKwh'] - rates['retailEnergyCostPerKwh'])
row['peakReductionDollars'] = row['peakReduction']/1000 * rates['peakDemandCost' + row['season'] + 'PerKw']
row['lossReductionDollars'] = row['lossReduction']/1000 * rates['wholesaleEnergyCostPerKwh']
# Pretty output
def plotTable(inData):
fig = plt.figure(figsize=(10,5))
plt.axis('off')
plt.tight_layout()
plt.table(cellText=[row for row in inData[1:]],
loc = 'center',
rowLabels = range(len(inData)-1),
colLabels = inData[0])
def dictalToMatrix(dictList):
''' Take our dictal format to a matrix. '''
matrix = [dictList[0].keys()]
for row in dictList:
matrix.append(row.values())
return matrix
# Powerflow results.
plotTable(dictalToMatrix(powerflows))
plt.savefig(pJoin(modelDir,"powerflowTable.png"))
# Monetary results.
## To print partial money table
monthDataMat = dictalToMatrix(monthData)
dimX = len(monthDataMat)
dimY = len(monthDataMat[0])
monthDataPart = []
for k in range (0,dimX):
monthDatatemp = []
for m in range (4,dimY):
monthDatatemp.append(monthDataMat[k][m])
monthDataPart.append(monthDatatemp)
plotTable(monthDataPart)
plt.savefig(pJoin(modelDir,"moneyTable.png"))
allOutput["monthDataMat"] = dictalToMatrix(monthData)
allOutput["monthDataPart"] = monthDataPart
# Graph the money data.
fig = plt.figure(figsize=(10,8))
indices = [r['monthName'] for r in monthData]
d1 = [r['energyReductionDollars'] for r in monthData]
d2 = [r['lossReductionDollars'] for r in monthData]
d3 = [r['peakReductionDollars'] for r in monthData]
ticks = range(len(d1))
bar_erd = plt.bar(ticks,d1,color='red')
bar_lrd = plt.bar(ticks,d2,color='green')
bar_prd = plt.bar(ticks,d3,color='blue',yerr=d2)
plt.legend([bar_prd[0], bar_lrd[0], bar_erd[0]], ['peakReductionDollars','lossReductionDollars','energyReductionDollars'],bbox_to_anchor=(0., 1.015, 1., .102), loc=3,
ncol=2, mode="expand", borderaxespad=0.1)
plt.xticks([t+0.5 for t in ticks],indices)
plt.ylabel('Utility Savings ($)')
plt.tight_layout(5.5,1.3,1.2)
fig.autofmt_xdate()
plt.savefig(pJoin(modelDir,"spendChart.png"))
allOutput["energyReductionDollars"] = d1
allOutput["lossReductionDollars"] = d2
allOutput["peakReductionDollars"] = d3
# Graph the cumulative savings.
fig = plt.figure(figsize=(10,5))
annualSavings = sum(d1) + sum(d2) + sum(d3)
annualSave = lambda x:(annualSavings - rates['omCost']) * x - rates['capitalCost']
simplePayback = rates['capitalCost']/(annualSavings - rates['omCost'])
plt.xlabel('Year After Installation')
plt.xlim(0,30)
plt.ylabel('Cumulative Savings ($)')
plt.plot([0 for x in range(31)],c='gray')
plt.axvline(x=simplePayback, ymin=0, ymax=1, c='gray', linestyle='--')
plt.plot([annualSave(x) for x in range(31)], c='green')
plt.savefig(pJoin(modelDir,"savingsChart.png"))
allOutput["annualSave"] = [annualSave(x) for x in range(31)]
# Update the runTime in the input file.
endTime = datetime.datetime.now()
inputDict["runTime"] = str(datetime.timedelta(seconds=int((endTime - startTime).total_seconds())))
with open(pJoin(modelDir,"allInputData.json"),"w") as inFile:
json.dump(inputDict, inFile, indent=4)
# Write output file.
with open(pJoin(modelDir,"allOutputData.json"),"w") as outFile:
json.dump(allOutput, outFile, indent=4)
# For autotest, there won't be such file.
try:
os.remove(pJoin(modelDir, "PPID.txt"))
except:
pass
print "DONE RUNNING", modelDir
except Exception as e:
# If input range wasn't valid delete output, write error to disk.
cancel(modelDir)
thisErr = traceback.format_exc()
print 'ERROR IN MODEL', modelDir, thisErr
inputDict['stderr'] = thisErr
with open(os.path.join(modelDir,'stderr.txt'),'w') as errorFile:
errorFile.write(thisErr)
with open(pJoin(modelDir,"allInputData.json"),"w") as inFile:
json.dump(inputDict, inFile, indent=4)
def runAnalysis(tree, monthData, rates):
''' Run CVR analysis. '''
# Graph the SCADA data.
fig = plt.figure(figsize=(17,5))
indices = [r['monthName'] for r in monthData]
d1 = [r['histPeak']/(10**3) for r in monthData]
d2 = [r['histAverage']/(10**3) for r in monthData]
ticks = range(len(d1))
plt.bar(ticks,d1,color='gray')
plt.bar(ticks,d2,color='dimgray')
plt.xticks([t+0.5 for t in ticks],indices)
plt.ylabel('Mean and peak historical power consumptions (kW)')
# Graph feeder.
myGraph = feeder.treeToNxGraph(tree)
feeder.latLonNxGraph(myGraph, neatoLayout=False)
# Get the load levels we need to test.
allLoadLevels = [x.get('histPeak',0) for x in monthData] + [y.get('histAverage',0) for y in monthData]
maxLev = _roundOne(max(allLoadLevels),'up')
minLev = _roundOne(min(allLoadLevels),'down')
tenLoadLevels = range(int(minLev),int(maxLev),int((maxLev-minLev)/10))
# Gather variables from the feeder.
for key in tree.keys():
# Set clock to single timestep.
if tree[key].get('clock','') == 'clock':
tree[key] = {"timezone":"PST+8PDT",
"stoptime":"'2013-01-01 00:00:00'",
"starttime":"'2013-01-01 00:00:00'",
"clock":"clock"}
# Save swing node index.
if tree[key].get('bustype','').lower() == 'swing':
swingIndex = key
swingName = tree[key].get('name')
# Remove all includes.
if tree[key].get('omftype','') == '#include':
del key
# Find the substation regulator and config.
for key in tree:
if tree[key].get('object','') == 'regulator' and tree[key].get('from','') == swingName:
regIndex = key
regConfName = tree[key]['configuration']
for key in tree:
if tree[key].get('name','') == regConfName:
regConfIndex = key
# Set substation regulator to manual operation.
baselineTap = 3 # GLOBAL VARIABLE FOR DEFAULT TAP POSITION
tree[regConfIndex] = {
'name':tree[regConfIndex]['name'],
'object':'regulator_configuration',
'connect_type':'1',
'raise_taps':'10',
'lower_taps':'10',
'CT_phase':'ABC',
'PT_phase':'ABC',
'regulation':'0.10', #Yo, 0.10 means at tap_pos 10 we're 10% above 120V.
'Control':'MANUAL',
'control_level':'INDIVIDUAL',
'Type':'A',
'tap_pos_A':str(baselineTap),
'tap_pos_B':str(baselineTap),
'tap_pos_C':str(baselineTap) }
# Attach recorders relevant to CVR.
recorders = [
{'object': 'collector',
'file': 'ZlossesTransformer.csv',
'group': 'class=transformer',
'limit': '0',
'property': 'sum(power_losses_A.real),sum(power_losses_A.imag),sum(power_losses_B.real),sum(power_losses_B.imag),sum(power_losses_C.real),sum(power_losses_C.imag)'},
{'object': 'collector',
'file': 'ZlossesUnderground.csv',
'group': 'class=underground_line',
'limit': '0',
'property': 'sum(power_losses_A.real),sum(power_losses_A.imag),sum(power_losses_B.real),sum(power_losses_B.imag),sum(power_losses_C.real),sum(power_losses_C.imag)'},
{'object': 'collector',
'file': 'ZlossesOverhead.csv',
'group': 'class=overhead_line',
'limit': '0',
'property': 'sum(power_losses_A.real),sum(power_losses_A.imag),sum(power_losses_B.real),sum(power_losses_B.imag),sum(power_losses_C.real),sum(power_losses_C.imag)'},
{'object': 'recorder',
'file': 'Zregulator.csv',
'limit': '0',
'parent': tree[regIndex]['name'],
'property': 'tap_A,tap_B,tap_C,power_in.real,power_in.imag'},
{'object': 'collector',
'file': 'ZvoltageJiggle.csv',
'group': 'class=triplex_meter',
'limit': '0',
'property': 'min(voltage_12.mag),mean(voltage_12.mag),max(voltage_12.mag),std(voltage_12.mag)'},
{'object': 'recorder',
'file': 'ZsubstationTop.csv',
'limit': '0',
'parent': tree[swingIndex]['name'],
'property': 'voltage_A,voltage_B,voltage_C'},
{'object': 'recorder',
'file': 'ZsubstationBottom.csv',
'limit': '0',
'parent': tree[regIndex]['to'],
'property': 'voltage_A,voltage_B,voltage_C'} ]
biggest = 1 + max([int(k) for k in tree.keys()])
for index, rec in enumerate(recorders):
tree[biggest + index] = rec
# Change constant PF loads to ZIP loads. (See evernote for rationale about 50/50 power/impedance mix.)
blankZipModel = {'object':'triplex_load',
'name':'NAMEVARIABLE',
'base_power_12':'POWERVARIABLE',
'power_fraction_12':'0.5',
'impedance_fraction_12':'0.5',
'power_pf_12':'0.9', #TODO: we can probably get this PF data from the Milsoft loads.
'impedance_pf_12':'0.97',
'nominal_voltage':'120',
'phases':'PHASESVARIABLE',
'parent':'PARENTVARIABLE'}
def powerClean(powerStr):
''' take 3339.39+1052.29j to 3339.39 '''
return powerStr[0:powerStr.find('+')]
for key in tree:
if tree[key].get('object','') == 'triplex_node':
# Get existing variables.
name = tree[key].get('name','')
power = tree[key].get('power_12','')
parent = tree[key].get('parent','')
phases = tree[key].get('phases','')
# Replace object and reintroduce variables.
tree[key] = copy(blankZipModel)
tree[key]['name'] = name
tree[key]['base_power_12'] = powerClean(power)
tree[key]['parent'] = parent
tree[key]['phases'] = phases
# Function to determine how low we can tap down in the CVR case:
def loweringPotential(baseLine):
''' Given a baseline end of line voltage, how many more percent can we shave off the substation voltage? '''
''' testsWePass = [122.0,118.0,200.0,110.0] '''
lower = int(math.floor((baseLine/114.0-1)*100)) - 1
# If lower is negative, we can't return it because we'd be undervolting beyond what baseline already was!
if lower < 0:
return baselineTap
else:
return baselineTap - lower
# Run all the powerflows.
powerflows = []
for doingCvr in [False, True]:
# For each load level in the tenLoadLevels, run a powerflow with the load objects scaled to the level.
for desiredLoad in tenLoadLevels:
# Find the total load that was defined in Milsoft:
loadList = []
for key in tree:
if tree[key].get('object','') == 'triplex_load':
loadList.append(tree[key].get('base_power_12',''))
totalLoad = sum([float(x) for x in loadList])
# Rescale each triplex load:
for key in tree:
if tree[key].get('object','') == 'triplex_load':
currentPow = float(tree[key]['base_power_12'])
ratio = desiredLoad/totalLoad
tree[key]['base_power_12'] = str(currentPow*ratio)
# If we're doing CVR then lower the voltage.
if doingCvr:
# Find the minimum voltage we can tap down to:
newTapPos = baselineTap
for row in powerflows:
if row.get('loadLevel','') == desiredLoad:
newTapPos = loweringPotential(row.get('lowVoltage',114))
# Tap it down to there.
# TODO: do each phase separately because that's how it's done in the field... Oof.
tree[regConfIndex]['tap_pos_A'] = str(newTapPos)
tree[regConfIndex]['tap_pos_B'] = str(newTapPos)
tree[regConfIndex]['tap_pos_C'] = str(newTapPos)
# Run the model through gridlab and put outputs in the table.
output = gridlabd.runInFilesystem(tree, keepFiles=False)
p = output['Zregulator.csv']['power_in.real'][0]
q = output['Zregulator.csv']['power_in.imag'][0]
s = math.sqrt(p**2+q**2)
lossTotal = 0.0
for device in ['ZlossesOverhead.csv','ZlossesTransformer.csv','ZlossesUnderground.csv']:
for letter in ['A','B','C']:
r = output[device]['sum(power_losses_' + letter + '.real)'][0]
i = output[device]['sum(power_losses_' + letter + '.imag)'][0]
lossTotal += math.sqrt(r**2 + i**2)
## Entire output:
powerflows.append({
'doingCvr':doingCvr,
'loadLevel':desiredLoad,
'realPower':p,
'powerFactor':p/s,
'losses':lossTotal,
'subVoltage': (
output['ZsubstationBottom.csv']['voltage_A'][0] +
output['ZsubstationBottom.csv']['voltage_B'][0] +
output['ZsubstationBottom.csv']['voltage_C'][0] )/3/60,
'lowVoltage':output['ZvoltageJiggle.csv']['min(voltage_12.mag)'][0]/2,
'highVoltage':output['ZvoltageJiggle.csv']['max(voltage_12.mag)'][0]/2 })
# For a given load level, find two points to interpolate on.
def getInterpPoints(t):
''' Find the two points we can interpolate from. '''
''' tests pass on [tenLoadLevels[0],tenLoadLevels[5]+499,tenLoadLevels[-1]-988] '''
loc = sorted(tenLoadLevels + [t]).index(t)
if loc==0:
return (tenLoadLevels[0],tenLoadLevels[1])
elif loc>len(tenLoadLevels)-2:
return (tenLoadLevels[-2],tenLoadLevels[-1])
else:
return (tenLoadLevels[loc-1],tenLoadLevels[loc+1])
# Calculate peak reduction.
for row in monthData:
peak = row['histPeak']
peakPoints = getInterpPoints(peak)
peakTopBase = [x for x in powerflows if x.get('loadLevel','') == peakPoints[-1] and x.get('doingCvr','') == False][0]
peakTopCvr = [x for x in powerflows if x.get('loadLevel','') == peakPoints[-1] and x.get('doingCvr','') == True][0]
peakBottomBase = [x for x in powerflows if x.get('loadLevel','') == peakPoints[0] and x.get('doingCvr','') == False][0]
peakBottomCvr = [x for x in powerflows if x.get('loadLevel','') == peakPoints[0] and x.get('doingCvr','') == True][0]
# Linear interpolation so we aren't running umpteen million loadflows.
x = (peakPoints[0],peakPoints[1])
y = (peakTopBase['realPower'] - peakTopCvr['realPower'],
peakBottomBase['realPower'] - peakBottomCvr['realPower'])
peakRed = y[0] + (y[1] - y[0]) * (peak - x[0]) / (x[1] - x[0])
row['peakReduction'] = peakRed
# Calculate energy reduction and loss reduction based on average load.
for row in monthData:
avgEnergy = row['histAverage']
energyPoints = getInterpPoints(avgEnergy)
avgTopBase = [x for x in powerflows if x.get('loadLevel','') == energyPoints[-1] and x.get('doingCvr','') == False][0]
avgTopCvr = [x for x in powerflows if x.get('loadLevel','') == energyPoints[-1] and x.get('doingCvr','') == True][0]
avgBottomBase = [x for x in powerflows if x.get('loadLevel','') == energyPoints[0] and x.get('doingCvr','') == False][0]
avgBottomCvr = [x for x in powerflows if x.get('loadLevel','') == energyPoints[0] and x.get('doingCvr','') == True][0]
# Linear interpolation so we aren't running umpteen million loadflows.
x = (energyPoints[0], energyPoints[1])
y = (avgTopBase['realPower'] - avgTopCvr['realPower'],
avgBottomBase['realPower'] - avgBottomCvr['realPower'])
energyRed = y[0] + (y[1] - y[0]) * (avgEnergy - x[0]) / (x[1] - x[0])
row['energyReduction'] = energyRed
lossY = (avgTopBase['losses'] - avgTopCvr['losses'],
avgBottomBase['losses'] - avgBottomCvr['losses'])
lossRed = lossY[0] + (lossY[1] - lossY[0]) * (avgEnergy - x[0]) / (x[1] - x[0])
row['lossReduction'] = lossRed
# Multiply by dollars.
for row in monthData:
row['energyReductionDollars'] = row['energyReduction'] * (rates['wholesaleEnergyCostPerKwh'] - rates['retailEnergyCostPerKwh'])
row['peakReductionDollars'] = row['peakReduction'] * rates['peakDemandCost' + row['season'] + 'PerKw']
row['lossReductionDollars'] = row['lossReduction'] * rates['wholesaleEnergyCostPerKwh']
# Pretty output
def plotTable(inData):
fig = plt.figure(figsize=(20,10))
plt.axis('off')
plt.tight_layout()
plt.table(cellText=[row[1:] for row in inData[1:]],
loc = 'center',
rowLabels = [row[0] for row in inData[1:]],
colLabels = inData[0])
def dictalToMatrix(dictList):
''' Take our dictal format to a matrix. '''
matrix = [dictList[0].keys()]
for row in dictList:
matrix.append(row.values())
return matrix
# Powerflow results.
plotTable(dictalToMatrix(powerflows))
# Monetary results.
plotTable(dictalToMatrix(monthData))
# Graph the money data.
fig = plt.figure(figsize=(17,10))
indices = [r['monthName'] for r in monthData]
d1 = [r['energyReductionDollars'] for r in monthData]
d2 = [r['lossReductionDollars'] for r in monthData]
d3 = [r['peakReductionDollars'] for r in monthData]
ticks = range(len(d1))
plt.bar(ticks,d1,color='red')
plt.bar(ticks,d2,color='green')
plt.bar(ticks,d3,color='blue',yerr=d2)
plt.xticks([t+0.5 for t in ticks],indices)
plt.ylabel('Utility Savings ($)')
# Graph the cumulative savings.
fig = plt.figure(figsize=(17,8))
annualSavings = sum(d1) + sum(d2) + sum(d3)
annualSave = lambda x:(annualSavings - rates['omCost']) * x - rates['capitalCost']
simplePayback = rates['capitalCost']/(annualSavings - rates['omCost'])
plt.xlabel('Year After Installation')
plt.xlim(0,30)
plt.ylabel('Cumulative Savings ($)')
plt.plot([0 for x in range(31)],c='gray')
plt.axvline(x=simplePayback, ymin=0, ymax=1, c='gray', linestyle='--')
plt.plot([annualSave(x) for x in range(31)], c='green')
def _tests(Network, Equipment, keepFiles=True):
import os, json, traceback, shutil
from solvers import gridlabd
from matplotlib import pyplot as plt
import feeder
exceptionCount = 0
try:
#db_network = os.path.abspath('./uploads/IEEE13.mdb')
#db_equipment = os.path.abspath('./uploads/IEEE13.mdb')
prefix = str(Path("testPEC.py").resolve()).strip('scratch\cymeToGridlabTests\testPEC.py') + "\uploads\\"
db_network = "C" + prefix + Network
db_equipment = "C" + prefix + Equipment
id_feeder = '650'
conductors = prefix + "conductor_data.csv"
#print "dbnet", db_network
#print "eqnet", db_equipment
#print "conductors", conductors
#cyme_base, x, y = convertCymeModel(db_network, db_equipment, id_feeder, conductors)
cyme_base, x, y = convertCymeModel(str(db_network), str(db_equipment), test=True, type=2, feeder_id='CV160')
feeder.attachRecorders(cyme_base, "TriplexLosses", None, None)
feeder.attachRecorders(cyme_base, "TransformerLosses", None, None)
glmString = feeder.sortedWrite(cyme_base)
feederglm = "C:\Users\Asus\Documents\GitHub\omf\omf\uploads\PEC.glm"
#print "feeederglm", feederglm
gfile = open(feederglm, 'w')
gfile.write(glmString)
gfile.close()
#print 'WROTE GLM FOR'
outPrefix = "C:\Users\Asus\Documents\GitHub\omf\omf\scratch\cymeToGridlabTests\\"
try:
os.mkdir(outPrefix)
except:
pass # Directory already there.
'''Attempt to graph'''
try:
# Draw the GLM.
print "trying to graph"
myGraph = feeder.treeToNxGraph(cyme_base)
feeder.latLonNxGraph(myGraph, neatoLayout=False)
plt.savefig(outPrefix + "PEC.png")
print "outprefix", outPrefix + "PEC.png"
print 'DREW GLM OF'
except:
exceptionCount += 1
print 'FAILED DRAWING'
try:
# Run powerflow on the GLM.
output = gridlabd.runInFilesystem(glmString, keepFiles=False)
with open(outPrefix + "PEC.JSON",'w') as outFile:
json.dump(output, outFile, indent=4)
print 'RAN GRIDLAB ON\n'
except:
exceptionCount += 1
print 'POWERFLOW FAILED'
except:
print 'FAILED CONVERTING'
exceptionCount += 1
traceback.print_exc()
if not keepFiles:
shutil.rmtree(outPrefix)
return exceptionCount
'''db_network = os.path.abspath('./uploads/PasoRobles11cymsectiondevice[device]['phases']08.mdb')
def _tests(Network, Equipment, keepFiles=True):
import os, json, traceback, shutil
from solvers import gridlabd
from matplotlib import pyplot as plt
import feeder
exceptionCount = 0
try:
#db_network = os.path.abspath('./uploads/IEEE13.mdb')
#db_equipment = os.path.abspath('./uploads/IEEE13.mdb')
prefix = str(Path("testPEC.py").resolve()).strip(
'scratch\cymeToGridlabTests\testPEC.py') + "\uploads\\"
db_network = "C" + prefix + Network
db_equipment = "C" + prefix + Equipment
id_feeder = '650'
conductors = prefix + "conductor_data.csv"
#print "dbnet", db_network
#print "eqnet", db_equipment
#print "conductors", conductors
#cyme_base, x, y = convertCymeModel(db_network, db_equipment, id_feeder, conductors)
cyme_base, x, y = convertCymeModel(str(db_network),
str(db_equipment),
test=True,
type=2,
feeder_id='CV160')
feeder.attachRecorders(cyme_base, "TriplexLosses", None, None)
feeder.attachRecorders(cyme_base, "TransformerLosses", None, None)
glmString = feeder.sortedWrite(cyme_base)
feederglm = "C:\Users\Asus\Documents\GitHub\omf\omf\uploads\PEC.glm"
#print "feeederglm", feederglm
gfile = open(feederglm, 'w')
gfile.write(glmString)
gfile.close()
#print 'WROTE GLM FOR'
outPrefix = "C:\Users\Asus\Documents\GitHub\omf\omf\scratch\cymeToGridlabTests\\"
try:
os.mkdir(outPrefix)
except:
pass # Directory already there.
'''Attempt to graph'''
try:
# Draw the GLM.
print "trying to graph"
myGraph = feeder.treeToNxGraph(cyme_base)
feeder.latLonNxGraph(myGraph, neatoLayout=False)
plt.savefig(outPrefix + "PEC.png")
print "outprefix", outPrefix + "PEC.png"
print 'DREW GLM OF'
except:
exceptionCount += 1
print 'FAILED DRAWING'
try:
# Run powerflow on the GLM.
output = gridlabd.runInFilesystem(glmString, keepFiles=False)
with open(outPrefix + "PEC.JSON", 'w') as outFile:
json.dump(output, outFile, indent=4)
print 'RAN GRIDLAB ON\n'
except:
exceptionCount += 1
print 'POWERFLOW FAILED'
except:
print 'FAILED CONVERTING'
exceptionCount += 1
traceback.print_exc()
if not keepFiles:
shutil.rmtree(outPrefix)
return exceptionCount
'''db_network = os.path.abspath('./uploads/PasoRobles11cymsectiondevice[device]['phases']08.mdb')
