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 voltPlot(tree, workDir=None, neatoLayout=False):
''' Draw a color-coded map of the voltage drop on a feeder.
Returns a matplotlib object. '''
# Get rid of schedules and climate:
for key in tree.keys():
if tree[key].get("argument","") == "\"schedules.glm\"" or tree[key].get("tmyfile","") != "":
del tree[key]
# Make sure we have a voltDump:
def safeInt(x):
try: return int(x)
except: return 0
biggestKey = max([safeInt(x) for x in tree.keys()])
tree[str(biggestKey*10)] = {"object":"voltdump","filename":"voltDump.csv"}
# Run Gridlab.
if not workDir:
workDir = tempfile.mkdtemp()
print "gridlabD runInFilesystem with no specified workDir. Working in", workDir
gridlabOut = gridlabd.runInFilesystem(tree, attachments=[], workDir=workDir)
with open(pJoin(workDir,'voltDump.csv'),'r') as dumpFile:
reader = csv.reader(dumpFile)
reader.next() # Burn the header.
keys = reader.next()
voltTable = []
for row in reader:
rowDict = {}
for pos,key in enumerate(keys):
rowDict[key] = row[pos]
voltTable.append(rowDict)
# Calculate average node voltage deviation. First, helper functions.
def pythag(x,y):
''' For right triangle with sides a and b, return the hypotenuse. '''
return math.sqrt(x**2+y**2)
def digits(x):
''' Returns number of digits before the decimal in the float x. '''
return math.ceil(math.log10(x+1))
def avg(l):
''' Average of a list of ints or floats. '''
return sum(l)/len(l)
# Detect the feeder nominal voltage:
for key in tree:
ob = tree[key]
if type(ob)==dict and ob.get('bustype','')=='SWING':
feedVoltage = float(ob.get('nominal_voltage',1))
# Tot it all up.
nodeVolts = {}
for row in voltTable:
allVolts = []
for phase in ['A','B','C']:
phaseVolt = pythag(float(row['volt'+phase+'_real']),
float(row['volt'+phase+'_imag']))
if phaseVolt != 0.0:
if digits(phaseVolt)>3:
# Normalize to 120 V standard
phaseVolt = phaseVolt*(120/feedVoltage)
allVolts.append(phaseVolt)
nodeVolts[row.get('node_name','')] = avg(allVolts)
# Color nodes by VOLTAGE.
fGraph = feeder.treeToNxGraph(tree)
voltChart = plt.figure(figsize=(10,10))
plt.axes(frameon = 0)
plt.axis('off')
if neatoLayout:
# HACK: work on a new graph without attributes because graphViz tries to read attrs.
cleanG = nx.Graph(fGraph.edges())
cleanG.add_nodes_from(fGraph)
positions = nx.graphviz_layout(cleanG, prog='neato')
else:
positions = {n:fGraph.node[n].get('pos',(0,0)) for n in fGraph}
edgeIm = nx.draw_networkx_edges(fGraph, positions)
nodeIm = nx.draw_networkx_nodes(fGraph,
pos = positions,
node_color = [nodeVolts.get(n,0) for n in fGraph.nodes()],
linewidths = 0,
node_size = 30,
cmap = plt.cm.jet)
plt.sci(nodeIm)
plt.clim(110,130)
plt.colorbar()
return voltChart
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 generateVoltChart(tree, rawOut, modelDir, neatoLayout=True):
''' Map the voltages on a feeder over time using a movie.'''
# We need to timestamp frames with the system clock to make sure the browser caches them appropriately.
genTime = str(datetime.datetime.now()).replace(':', '.')
# Detect the feeder nominal voltage:
for key in tree:
ob = tree[key]
if type(ob) == dict and ob.get('bustype', '') == 'SWING':
feedVoltage = float(ob.get('nominal_voltage', 1))
# Make a graph object.
fGraph = feeder.treeToNxGraph(tree)
if neatoLayout:
# HACK: work on a new graph without attributes because graphViz tries to read attrs.
cleanG = nx.Graph(fGraph.edges())
cleanG.add_nodes_from(fGraph)
positions = nx.graphviz_layout(cleanG, prog='neato')
else:
rawPositions = {n: fGraph.node[n].get('pos', (0, 0)) for n in fGraph}
#HACK: the import code reverses the y coords.
def yFlip(pair):
try:
return (pair[0], -1.0 * pair[1])
except:
return (0, 0)
positions = {k: yFlip(rawPositions[k]) for k in rawPositions}
# Plot all time steps.
nodeVolts = {}
for step, stamp in enumerate(rawOut['aVoltDump.csv']['# timestamp']):
# Build voltage map.
nodeVolts[step] = {}
for nodeName in [
x for x in rawOut['aVoltDump.csv'].keys() if x != '# timestamp'
]:
allVolts = []
for phase in ['a', 'b', 'c']:
voltStep = rawOut[phase + 'VoltDump.csv'][nodeName][step]
# HACK: Gridlab complex number format sometimes uses i, sometimes j, sometimes d. WTF?
if type(voltStep) is str: voltStep = voltStep.replace('i', 'j')
v = complex(voltStep)
phaseVolt = abs(v)
if phaseVolt != 0.0:
if _digits(phaseVolt) > 3:
# Normalize to 120 V standard
phaseVolt = phaseVolt * (120 / feedVoltage)
allVolts.append(phaseVolt)
# HACK: Take average of all phases to collapse dimensionality.
nodeVolts[step][nodeName] = avg(allVolts)
# Draw animation.
voltChart = plt.figure(figsize=(10, 10))
plt.axes(frameon=0)
plt.axis('off')
voltChart.subplots_adjust(left=0.03,
bottom=0.03,
right=0.97,
top=0.97,
wspace=None,
hspace=None)
custom_cm = matplotlib.colors.LinearSegmentedColormap.from_list(
'custColMap', [(0.0, 'blue'), (0.25, 'darkgray'), (0.75, 'darkgray'),
(1.0, 'yellow')])
edgeIm = nx.draw_networkx_edges(fGraph, positions)
nodeIm = nx.draw_networkx_nodes(
fGraph,
pos=positions,
node_color=[nodeVolts[0].get(n, 0) for n in fGraph.nodes()],
linewidths=0,
node_size=30,
cmap=custom_cm)
plt.sci(nodeIm)
plt.clim(110, 130)
plt.colorbar()
plt.title(rawOut['aVoltDump.csv']['# timestamp'][0])
def update(step):
nodeColors = np.array(
[nodeVolts[step].get(n, 0) for n in fGraph.nodes()])
plt.title(rawOut['aVoltDump.csv']['# timestamp'][step])
nodeIm.set_array(nodeColors)
return nodeColors,
anim = FuncAnimation(voltChart,
update,
frames=len(rawOut['aVoltDump.csv']['# timestamp']),
interval=200,
blit=False)
anim.save(pJoin(modelDir, 'voltageChart.mp4'),
codec='h264',
extra_args=['-pix_fmt', 'yuv420p'])
# Reclaim memory by closing, deleting and garbage collecting the last chart.
voltChart.clf()
plt.close()
del voltChart
gc.collect()
return genTime
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 voltPlot(tree, workDir=None, neatoLayout=False):
''' Draw a color-coded map of the voltage drop on a feeder.
Returns a matplotlib object. '''
# Get rid of schedules and climate:
for key in tree.keys():
if tree[key].get("argument",
"") == "\"schedules.glm\"" or tree[key].get(
"tmyfile", "") != "":
del tree[key]
# Make sure we have a voltDump:
def safeInt(x):
try:
return int(x)
except:
return 0
biggestKey = max([safeInt(x) for x in tree.keys()])
tree[str(biggestKey * 10)] = {
"object": "voltdump",
"filename": "voltDump.csv"
}
# Run Gridlab.
if not workDir:
workDir = tempfile.mkdtemp()
print "gridlabD runInFilesystem with no specified workDir. Working in", workDir
gridlabOut = gridlabd.runInFilesystem(tree,
attachments=[],
workDir=workDir)
with open(pJoin(workDir, 'voltDump.csv'), 'r') as dumpFile:
reader = csv.reader(dumpFile)
reader.next() # Burn the header.
keys = reader.next()
voltTable = []
for row in reader:
rowDict = {}
for pos, key in enumerate(keys):
rowDict[key] = row[pos]
voltTable.append(rowDict)
# Calculate average node voltage deviation. First, helper functions.
def pythag(x, y):
''' For right triangle with sides a and b, return the hypotenuse. '''
return math.sqrt(x**2 + y**2)
def digits(x):
''' Returns number of digits before the decimal in the float x. '''
return math.ceil(math.log10(x + 1))
def avg(l):
''' Average of a list of ints or floats. '''
return sum(l) / len(l)
# Detect the feeder nominal voltage:
for key in tree:
ob = tree[key]
if type(ob) == dict and ob.get('bustype', '') == 'SWING':
feedVoltage = float(ob.get('nominal_voltage', 1))
# Tot it all up.
nodeVolts = {}
for row in voltTable:
allVolts = []
for phase in ['A', 'B', 'C']:
phaseVolt = pythag(float(row['volt' + phase + '_real']),
float(row['volt' + phase + '_imag']))
if phaseVolt != 0.0:
if digits(phaseVolt) > 3:
# Normalize to 120 V standard
phaseVolt = phaseVolt * (120 / feedVoltage)
allVolts.append(phaseVolt)
nodeVolts[row.get('node_name', '')] = avg(allVolts)
# Color nodes by VOLTAGE.
fGraph = feeder.treeToNxGraph(tree)
voltChart = plt.figure(figsize=(10, 10))
plt.axes(frameon=0)
plt.axis('off')
if neatoLayout:
# HACK: work on a new graph without attributes because graphViz tries to read attrs.
cleanG = nx.Graph(fGraph.edges())
cleanG.add_nodes_from(fGraph)
positions = nx.graphviz_layout(cleanG, prog='neato')
else:
positions = {n: fGraph.node[n].get('pos', (0, 0)) for n in fGraph}
edgeIm = nx.draw_networkx_edges(fGraph, positions)
nodeIm = nx.draw_networkx_nodes(
fGraph,
pos=positions,
node_color=[nodeVolts.get(n, 0) for n in fGraph.nodes()],
linewidths=0,
node_size=30,
cmap=plt.cm.jet)
plt.sci(nodeIm)
plt.clim(110, 130)
plt.colorbar()
return voltChart
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 generateVoltChart(tree, rawOut, modelDir, neatoLayout=True):
''' Map the voltages on a feeder over time using a movie.'''
# We need to timestamp frames with the system clock to make sure the browser caches them appropriately.
genTime = str(datetime.datetime.now()).replace(':','.')
# Detect the feeder nominal voltage:
for key in tree:
ob = tree[key]
if type(ob)==dict and ob.get('bustype','')=='SWING':
feedVoltage = float(ob.get('nominal_voltage',1))
# Make a graph object.
fGraph = feeder.treeToNxGraph(tree)
if neatoLayout:
# HACK: work on a new graph without attributes because graphViz tries to read attrs.
cleanG = nx.Graph(fGraph.edges())
cleanG.add_nodes_from(fGraph)
positions = nx.graphviz_layout(cleanG, prog='neato')
else:
rawPositions = {n:fGraph.node[n].get('pos',(0,0)) for n in fGraph}
#HACK: the import code reverses the y coords.
def yFlip(pair):
try: return (pair[0], -1.0*pair[1])
except: return (0,0)
positions = {k:yFlip(rawPositions[k]) for k in rawPositions}
# Plot all time steps.
nodeVolts = {}
for step, stamp in enumerate(rawOut['aVoltDump.csv']['# timestamp']):
# Build voltage map.
nodeVolts[step] = {}
for nodeName in [x for x in rawOut['aVoltDump.csv'].keys() if x != '# timestamp']:
allVolts = []
for phase in ['a','b','c']:
voltStep = rawOut[phase + 'VoltDump.csv'][nodeName][step]
# HACK: Gridlab complex number format sometimes uses i, sometimes j, sometimes d. WTF?
if type(voltStep) is str: voltStep = voltStep.replace('i','j')
v = complex(voltStep)
phaseVolt = abs(v)
if phaseVolt != 0.0:
if _digits(phaseVolt)>3:
# Normalize to 120 V standard
phaseVolt = phaseVolt*(120/feedVoltage)
allVolts.append(phaseVolt)
# HACK: Take average of all phases to collapse dimensionality.
nodeVolts[step][nodeName] = avg(allVolts)
# Draw animation.
voltChart = plt.figure(figsize=(10,10))
plt.axes(frameon = 0)
plt.axis('off')
voltChart.subplots_adjust(left=0.03, bottom=0.03, right=0.97, top=0.97, wspace=None, hspace=None)
custom_cm = matplotlib.colors.LinearSegmentedColormap.from_list('custColMap',[(0.0,'blue'),(0.25,'darkgray'),(0.75,'darkgray'),(1.0,'yellow')])
edgeIm = nx.draw_networkx_edges(fGraph, positions)
nodeIm = nx.draw_networkx_nodes(fGraph,
pos = positions,
node_color = [nodeVolts[0].get(n,0) for n in fGraph.nodes()],
linewidths = 0,
node_size = 30,
cmap = custom_cm)
plt.sci(nodeIm)
plt.clim(110,130)
plt.colorbar()
plt.title(rawOut['aVoltDump.csv']['# timestamp'][0])
def update(step):
nodeColors = np.array([nodeVolts[step].get(n,0) for n in fGraph.nodes()])
plt.title(rawOut['aVoltDump.csv']['# timestamp'][step])
nodeIm.set_array(nodeColors)
return nodeColors,
anim = FuncAnimation(voltChart, update, frames=len(rawOut['aVoltDump.csv']['# timestamp']), interval=200, blit=False)
anim.save(pJoin(modelDir,'voltageChart.mp4'), codec='h264', extra_args=['-pix_fmt', 'yuv420p'])
# Reclaim memory by closing, deleting and garbage collecting the last chart.
voltChart.clf()
plt.close()
del voltChart
gc.collect()
return genTime
def generateVoltChart(tree, rawOut, modelDir, neatoLayout=True):
''' Map the voltages on a feeder over time using a set of PNGs.'''
# Make the subfolder we need.
try: shutil.rmtree(pJoin(modelDir, 'pngs'))
except: pass
try: os.mkdir(pJoin(modelDir, 'pngs'))
except: pass
# We need to timestamp images with the system clock to make sure the browser caches them appropriately.
genTime = str(datetime.datetime.now()).replace(':','.')
# Detect the feeder nominal voltage:
for key in tree:
ob = tree[key]
if type(ob)==dict and ob.get('bustype','')=='SWING':
feedVoltage = float(ob.get('nominal_voltage',1))
# Make a graph object.
fGraph = feeder.treeToNxGraph(tree)
if neatoLayout:
# HACK: work on a new graph without attributes because graphViz tries to read attrs.
cleanG = nx.Graph(fGraph.edges())
cleanG.add_nodes_from(fGraph)
positions = nx.graphviz_layout(cleanG, prog='neato')
else:
rawPositions = {n:fGraph.node[n].get('pos',(0,0)) for n in fGraph}
#HACK: the import code reverses the y coords.
def yFlip(pair):
try: return (pair[0], -1.0*pair[1])
except: return (0,0)
positions = {k:yFlip(rawPositions[k]) for k in rawPositions}
# Plot all time steps.
for step, stamp in enumerate(rawOut['aVoltDump.csv']['# timestamp']):
# Build voltage map.
nodeVolts = {}
for nodeName in [x for x in rawOut['aVoltDump.csv'].keys() if x != '# timestamp']:
allVolts = []
for phase in ['a','b','c']:
voltStep = rawOut[phase + 'VoltDump.csv'][nodeName][step]
# HACK: Gridlab complex number format sometimes uses i, sometimes j, sometimes d. WTF?
if type(voltStep) is str: voltStep = voltStep.replace('i','j')
v = complex(voltStep)
phaseVolt = abs(v)
if phaseVolt != 0.0:
if _digits(phaseVolt)>3:
# Normalize to 120 V standard
phaseVolt = phaseVolt*(120/feedVoltage)
allVolts.append(phaseVolt)
# HACK: Take average of all phases to collapse dimensionality.
nodeVolts[nodeName] = avg(allVolts)
# Apply voltage map and chart it.
voltChart = plt.figure(figsize=(10,10))
plt.axes(frameon = 0)
plt.axis('off')
custom_cm = matplotlib.colors.LinearSegmentedColormap.from_list('custColMap',[(0.0,'blue'),(0.25,'darkgray'),(0.75,'darkgray'),(1.0,'yellow')])
edgeIm = nx.draw_networkx_edges(fGraph, positions)
nodeIm = nx.draw_networkx_nodes(fGraph,
pos = positions,
node_color = [nodeVolts.get(n,0) for n in fGraph.nodes()],
linewidths = 0,
node_size = 30,
cmap = custom_cm)
plt.sci(nodeIm)
plt.clim(110,130)
plt.colorbar()
plt.title(stamp)
voltChart.savefig(pJoin(modelDir,'pngs','volts' + str(step).zfill(3) + "-" + genTime + '.png'))
# Reclaim memory by closing, deleting and garbage collecting the last chart.
voltChart.clf()
plt.close()
del voltChart
gc.collect()
return genTime