def runPowerflowIter(tree,scadaSubPower):
'''Runs powerflow once, then iterates.'''
# Run initial powerflow to get power.
print "Starting calibration."
print "Goal of calibration: Error: %s, Iterations: <%s, trim: %s"%(calibrateError[0], calibrateError[1], trim)
output = gridlabd.runInFilesystem(tree, keepFiles=True, workDir=pJoin(workDir,"gridlabD"))
outRealPow = output["caliSub.csv"]["measured_real_power"][trim:simLength]
outImagPower = output["caliSub.csv"]["measured_reactive_power"][trim:simLength]
outAppPowerKw = [(x[0]**2 + x[1]**2)**0.5/1000 for x in zip(outRealPow, outImagPower)]
lastFile = "subScada.player"
nextFile = "subScadaCalibrated.player"
nextPower = outAppPowerKw
error = (sum(outRealPow)/1000-sum(scadaSubPower))/sum(scadaSubPower)
iteration = 1
print "First error:", error
while abs(error)>calibrateError[0] and iteration<calibrateError[1]:
# Run calibration and iterate up to 5 times.
SCAL_CONST = sum(scadaSubPower)/sum(nextPower)
print "Calibrating & running again... Error: %s, Iteration: %s, SCAL_CONST: %s"%(str(round(abs(error*100),6)), str(iteration), round(SCAL_CONST,6))
newPlayData = []
with open(pJoin(pJoin(workDir,"gridlabD"), lastFile), "r") as playerFile:
for line in playerFile:
(key,val) = line.split(',')
newPlayData.append(str(key) + ',' + str(float(val)*SCAL_CONST) + "\n")
with open(pJoin(pJoin(workDir,"gridlabD"), nextFile), "w") as playerFile:
for row in newPlayData:
playerFile.write(row)
tree[playerKey]["file"] = nextFile
tree[outputRecorderKey]["file"] = "caliSubCheck.csv"
nextOutput = gridlabd.runInFilesystem(tree, keepFiles=True, workDir=pJoin(workDir,"gridlabD"))
outRealPowIter = nextOutput["caliSubCheck.csv"]["measured_real_power"][trim:simLength]
outImagPowerIter = nextOutput["caliSubCheck.csv"]["measured_reactive_power"][trim:simLength]
nextAppKw = [(x[0]**2 + x[1]**2)**0.5/1000
for x in zip(outRealPowIter, outImagPowerIter)]
lastFile = nextFile
nextFile = "subScadaCalibrated"+str(iteration)+".player"
nextPower = nextAppKw
# Compute error and iterate.
error = (sum(outRealPowIter)/1000-sum(scadaSubPower))/sum(scadaSubPower)
iteration+=1
else:
if iteration==1: outRealPowIter = outRealPow
SCAL_CONST = 1.0
print "Calibration done: Error: %s, Iteration: %s, SCAL_CONST: %s"%(str(round(abs(error*100),2)), str(iteration), round(SCAL_CONST,2))
return outRealPow, outRealPowIter, lastFile, iteration
def milsoftToGridlabTests(keepFiles=False):
openPrefix = '../uploads/'
outPrefix = './milToGridlabTests/'
import os, json, traceback, shutil
from omf.solvers import gridlabd
from matplotlib import pyplot as plt
from milToGridlab import convert
import omf.feeder as feeder
try:
os.mkdir(outPrefix)
except:
pass # Directory already there.
exceptionCount = 0
# 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'),('OMF_Norfork1.std', 'OMF_Norfork1.seq')]
testFiles = [('Olin-Brown.std', 'Olin.seq')]
testAttachments = {'schedules.glm':''}
# testAttachments = {'schedules.glm':'', 'climate.tmy2':open('./data/Climate/KY-LEXINGTON.tmy2','r').read()}
for stdString, seqString in testFiles:
try:
# Convert the std+seq.
with open(openPrefix + stdString,'r') as stdFile, open(openPrefix + seqString,'r') as seqFile:
outGlm,x,y = convert(stdFile.read(),seqFile.read())
with open(outPrefix + 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(outPrefix + stdString.replace('.std','.png'))
print 'DREW GLM OF', stdString
except:
exceptionCount += 1
print 'FAILED DRAWING', stdString
try:
# Run powerflow on the GLM. HACK:blank attachments for now.
output = gridlabd.runInFilesystem(outGlm, attachments=testAttachments, keepFiles=False)
with open(outPrefix + 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()
if not keepFiles:
shutil.rmtree(outPrefix)
return exceptionCount
def gridlabRun(temp_dir):
'''
Run a .glm through GridLAB-D and return the results as JSON.
Form parameters:
:param glm: a GLM file.
Details:
:OMF fuction: omf.solvers.gridlabd.runInFileSystem().
:run-time: up to a few hours.
TODO: think about attachment support.
'''
fName = 'in.glm'
f = request.files['glm']
glmOnDisk = os.path.join(temp_dir, fName)
f.save(glmOnDisk)
feed = feeder.parse(glmOnDisk)
outDict = gridlabd.runInFilesystem(feed,
attachments=[],
keepFiles=True,
workDir=temp_dir,
glmName='out.glm')
with open(os.path.join(temp_dir, filenames["glrun"]), 'w') as f:
json.dump(outDict, f)
def attachVolts(workDir, feederPath, voltVectorA, voltVectorB, voltVectorC,
simStartDate, simLength, simLengthUnits):
'''read voltage vectors of 3 different phases, run gridlabd, and attach output to the feeder.'''
try:
timeStamp = [simStartDate['Date']]
for x in range(1, 8760):
timeStamp.append(timeStamp[x - 1] + dt.timedelta(hours=1))
firstDateTime = timeStamp[1]
with open(pJoin(pJoin(workDir, "gridlabD"), "phaseAVoltage.player"),
"w") as voltFile:
for x in range(0, 8760):
timestamp = timeStamp[x]
voltage = str("%0.2f" % float(voltVectorA[x])) + "+0j"
line = timestamp.strftime(
"%Y-%m-%d %H:%M:%S"
) + " " + simStartDate['timeZone'] + "," + str(voltage) + "\n"
voltFile.write(line)
with open(pJoin(pJoin(workDir, "gridlabD"), "phaseBVoltage.player"),
"w") as voltFile:
for x in range(0, 8760):
timestamp = timeStamp[x]
voltage = str("%0.2f" % float(voltVectorB[x])) + "-" + str(
"%0.4f" % float(random.uniform(6449, 6460))) + "j"
line = timestamp.strftime(
"%Y-%m-%d %H:%M:%S"
) + " " + simStartDate['timeZone'] + "," + str(voltage) + "\n"
voltFile.write(line)
with open(pJoin(pJoin(workDir, "gridlabD"), "phaseCVoltage.player"),
"w") as voltFile:
for x in range(0, 8760):
timestamp = timeStamp[x]
voltage = str("%0.2f" % float(voltVectorC[x])) + "+" + str(
"%0.4f" % float(random.uniform(6449, 6460))) + "j"
line = timestamp.strftime(
"%Y-%m-%d %H:%M:%S"
) + " " + simStartDate['timeZone'] + "," + str(voltage) + "\n"
voltFile.write(line)
with open(feederPath, "r") as jsonIn:
feederJson = json.load(jsonIn)
tree = feederJson.get("tree", {})
# Find swingNode name.
for key in tree:
if tree[key].get('bustype', '').lower() == 'swing':
swingName = tree[key].get('name')
# Attach player.
classOb = {'omftype': 'class player', 'argument': '{double value;}'}
voltageObA = {
"object": "player",
"property": "voltage_A",
"file": "phaseAVoltage.player",
"loop": "0",
"parent": swingName
}
voltageObB = {
"object": "player",
"property": "voltage_B",
"file": "phaseBVoltage.player",
"loop": "0",
"parent": swingName
}
voltageObC = {
"object": "player",
"property": "voltage_C",
"file": "phaseCVoltage.player",
"loop": "0",
"parent": swingName
}
maxKey = omf.feeder.getMaxKey(tree)
voltplayerKeyA = maxKey + 2
voltplayerKeyB = maxKey + 3
voltplayerKeyC = maxKey + 4
tree[maxKey + 1] = classOb
tree[voltplayerKeyA] = voltageObA
tree[voltplayerKeyB] = voltageObB
tree[voltplayerKeyC] = voltageObC
# Adjust time and run output.
omf.feeder.adjustTime(tree, simLength, simLengthUnits,
firstDateTime.strftime("%Y-%m-%d %H:%M:%S"))
output = gridlabd.runInFilesystem(tree,
attachments=feederJson.get(
'attachments', {}),
keepFiles=True,
workDir=pJoin(workDir, "gridlabD"))
# Write the output.
with open(pJoin(pJoin(workDir, "gridlabD"),
"phaseAVoltage.player")) as f:
playerStringA = f.read()
with open(pJoin(pJoin(workDir, "gridlabD"),
"phaseBVoltage.player")) as f:
playerStringB = f.read()
with open(pJoin(pJoin(workDir, "gridlabD"),
"phaseCVoltage.player")) as f:
playerStringC = f.read()
with open(pJoin(workDir, "calibratedFeeder.omd"), "w") as outJson:
feederJson["attachments"]["phaseAVoltage.player"] = playerStringA
feederJson["attachments"]["phaseBVoltage.player"] = playerStringB
feederJson["attachments"]["phaseCVoltage.player"] = playerStringC
feederJson["tree"] = tree
json.dump(feederJson, outJson, indent=4)
return pJoin(workDir, "calibratedFeeder.omd"), True
except:
print("Failed to run gridlabD with voltage players.")
return "", False
def runForeground(modelDir):
''' Run the model in its directory. WARNING: GRIDLAB CAN TAKE HOURS TO COMPLETE. '''
inputDict = json.load(open(pJoin(modelDir, 'allInputData.json')))
print "STARTING TO RUN", modelDir
beginTime = datetime.datetime.now()
# Get prepare of data and clean workspace if re-run, If re-run remove all the data in the subfolders
for dirs in os.listdir(modelDir):
if os.path.isdir(pJoin(modelDir, dirs)):
shutil.rmtree(pJoin(modelDir, dirs))
# Get the names of the feeders from the .omd files:
feederNames = [x[0:-4] for x in os.listdir(modelDir) if x.endswith(".omd")]
for i, key in enumerate(feederNames):
inputDict['feederName' + str(i + 1)] = feederNames[i]
# Run GridLAB-D once for each feeder:
for feederName in feederNames:
try:
os.remove(pJoin(modelDir, feederName, "allOutputData.json"))
except Exception, e:
pass
if not os.path.isdir(pJoin(modelDir, feederName)):
os.makedirs(pJoin(modelDir, feederName)) # create subfolders for feeders
shutil.copy(pJoin(modelDir, feederName + ".omd"),
pJoin(modelDir, feederName, "feeder.omd"))
inputDict["climateName"] = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(_omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, feederName, "climate.tmy2"))
try:
startTime = datetime.datetime.now()
feederJson = json.load(open(pJoin(modelDir, feederName, "feeder.omd")))
tree = feederJson["tree"]
# Set up GLM with correct time and recorders:
feeder.attachRecorders(tree, "Regulator", "object", "regulator")
feeder.attachRecorders(tree, "Capacitor", "object", "capacitor")
feeder.attachRecorders(tree, "Inverter", "object", "inverter")
feeder.attachRecorders(tree, "Windmill", "object", "windturb_dg")
feeder.attachRecorders(tree, "CollectorVoltage", None, None)
feeder.attachRecorders(tree, "Climate", "object", "climate")
feeder.attachRecorders(tree, "OverheadLosses", None, None)
feeder.attachRecorders(tree, "UndergroundLosses", None, None)
feeder.attachRecorders(tree, "TriplexLosses", None, None)
feeder.attachRecorders(tree, "TransformerLosses", None, None)
feeder.groupSwingKids(tree)
feeder.adjustTime(tree=tree, simLength=float(inputDict["simLength"]),
simLengthUnits=inputDict["simLengthUnits"], simStartDate=inputDict["simStartDate"])
# RUN GRIDLABD IN FILESYSTEM (EXPENSIVE!)
rawOut = gridlabd.runInFilesystem(tree, attachments=feederJson["attachments"],
keepFiles=True, workDir=pJoin(modelDir, feederName))
cleanOut = {}
# Std Err and Std Out
cleanOut['stderr'] = rawOut['stderr']
cleanOut['stdout'] = rawOut['stdout']
# Time Stamps
for key in rawOut:
if '# timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key]['# timestamp']
break
elif '# property.. timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key]['# property.. timestamp']
else:
cleanOut['timeStamps'] = []
# Day/Month Aggregation Setup:
stamps = cleanOut.get('timeStamps',[])
level = inputDict.get('simLengthUnits','hours')
# Climate
for key in rawOut:
if key.startswith('Climate_') and key.endswith('.csv'):
cleanOut['climate'] = {}
cleanOut['climate']['Rain Fall (in/h)'] = hdmAgg(rawOut[key].get('rainfall'), sum, level)
cleanOut['climate']['Wind Speed (m/s)'] = hdmAgg(rawOut[key].get('wind_speed'), avg, level)
cleanOut['climate']['Temperature (F)'] = hdmAgg(rawOut[key].get('temperature'), max, level)
cleanOut['climate']['Snow Depth (in)'] = hdmAgg(rawOut[key].get('snowdepth'), max, level)
cleanOut['climate']['Direct Insolation (W/m^2)'] = hdmAgg(rawOut[key].get('solar_direct'), sum, level)
# Voltage Band
if 'VoltageJiggle.csv' in rawOut:
cleanOut['allMeterVoltages'] = {}
cleanOut['allMeterVoltages']['Min'] = hdmAgg([(i / 2) for i in rawOut['VoltageJiggle.csv']['min(voltage_12.mag)']], min, level)
cleanOut['allMeterVoltages']['Mean'] = hdmAgg([(i / 2) for i in rawOut['VoltageJiggle.csv']['mean(voltage_12.mag)']], avg, level)
cleanOut['allMeterVoltages']['StdDev'] = hdmAgg([(i / 2) for i in rawOut['VoltageJiggle.csv']['std(voltage_12.mag)']], avg, level)
cleanOut['allMeterVoltages']['Max'] = hdmAgg([(i / 2) for i in rawOut['VoltageJiggle.csv']['max(voltage_12.mag)']], max, level)
cleanOut['allMeterVoltages']['stdDevPos'] = [(x+y/2) for x,y in zip(cleanOut['allMeterVoltages']['Mean'], cleanOut['allMeterVoltages']['StdDev'])]
cleanOut['allMeterVoltages']['stdDevNeg'] = [(x-y/2) for x,y in zip(cleanOut['allMeterVoltages']['Mean'], cleanOut['allMeterVoltages']['StdDev'])]
# Total # of meters
count = 0
with open(pJoin(modelDir, feederName, "feeder.omd")) as f:
for line in f:
if "\"objectType\": \"triplex_meter\"" in line:
count+=1
# print "count=", count
cleanOut['allMeterVoltages']['triplexMeterCount'] = float(count)
# Power Consumption
cleanOut['Consumption'] = {}
# Set default value to be 0, avoiding missing value when computing Loads
cleanOut['Consumption']['Power'] = [0] * int(inputDict["simLength"])
cleanOut['Consumption']['Losses'] = [0] * int(inputDict["simLength"])
cleanOut['Consumption']['DG'] = [0] * int(inputDict["simLength"])
for key in rawOut:
if key.startswith('SwingKids_') and key.endswith('.csv'):
oneSwingPower = hdmAgg(vecPyth(rawOut[key]['sum(power_in.real)'],rawOut[key]['sum(power_in.imag)']), avg, level)
if 'Power' not in cleanOut['Consumption']:
cleanOut['Consumption']['Power'] = oneSwingPower
else:
cleanOut['Consumption']['Power'] = vecSum(oneSwingPower,cleanOut['Consumption']['Power'])
elif key.startswith('Inverter_') and key.endswith('.csv'):
realA = rawOut[key]['power_A.real']
realB = rawOut[key]['power_B.real']
realC = rawOut[key]['power_C.real']
imagA = rawOut[key]['power_A.imag']
imagB = rawOut[key]['power_B.imag']
imagC = rawOut[key]['power_C.imag']
oneDgPower = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'DG' not in cleanOut['Consumption']:
cleanOut['Consumption']['DG'] = oneDgPower
else:
cleanOut['Consumption']['DG'] = vecSum(oneDgPower,cleanOut['Consumption']['DG'])
elif key.startswith('Windmill_') and key.endswith('.csv'):
vrA = rawOut[key]['voltage_A.real']
vrB = rawOut[key]['voltage_B.real']
vrC = rawOut[key]['voltage_C.real']
viA = rawOut[key]['voltage_A.imag']
viB = rawOut[key]['voltage_B.imag']
viC = rawOut[key]['voltage_C.imag']
crB = rawOut[key]['current_B.real']
crA = rawOut[key]['current_A.real']
crC = rawOut[key]['current_C.real']
ciA = rawOut[key]['current_A.imag']
ciB = rawOut[key]['current_B.imag']
ciC = rawOut[key]['current_C.imag']
powerA = vecProd(vecPyth(vrA,viA),vecPyth(crA,ciA))
powerB = vecProd(vecPyth(vrB,viB),vecPyth(crB,ciB))
powerC = vecProd(vecPyth(vrC,viC),vecPyth(crC,ciC))
# HACK: multiply by negative one because turbine power sign is opposite all other DG:
oneDgPower = [-1.0 * x for x in hdmAgg(vecSum(powerA,powerB,powerC), avg, level)]
if 'DG' not in cleanOut['Consumption']:
cleanOut['Consumption']['DG'] = oneDgPower
else:
cleanOut['Consumption']['DG'] = vecSum(oneDgPower,cleanOut['Consumption']['DG'])
elif key in ['OverheadLosses.csv', 'UndergroundLosses.csv', 'TriplexLosses.csv', 'TransformerLosses.csv']:
realA = rawOut[key]['sum(power_losses_A.real)']
imagA = rawOut[key]['sum(power_losses_A.imag)']
realB = rawOut[key]['sum(power_losses_B.real)']
imagB = rawOut[key]['sum(power_losses_B.imag)']
realC = rawOut[key]['sum(power_losses_C.real)']
imagC = rawOut[key]['sum(power_losses_C.imag)']
oneLoss = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'Losses' not in cleanOut['Consumption']:
cleanOut['Consumption']['Losses'] = oneLoss
else:
cleanOut['Consumption']['Losses'] = vecSum(oneLoss,cleanOut['Consumption']['Losses'])
# Aggregate up the timestamps:
if level=='days':
cleanOut['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:10], 'days')
elif level=='months':
cleanOut['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:7], 'months')
# Write the output.
with open(pJoin(modelDir, feederName, "allOutputData.json"),"w") as outFile:
json.dump(cleanOut, outFile, indent=4)
# 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, feederName, "allInputData.json"),"w") as inFile:
json.dump(inputDict, inFile, indent=4)
# Clean up the PID file.
os.remove(pJoin(modelDir, feederName,"PID.txt"))
print "DONE RUNNING GRIDLABMULTI", modelDir, feederName
except Exception as e:
print "MODEL CRASHED GRIDLABMULTI", e, modelDir, feederName
cancel(pJoin(modelDir, feederName))
with open(pJoin(modelDir, feederName, "stderr.txt"), "a+") as stderrFile:
traceback.print_exc(file = stderrFile)
def omfCalibrate(workDir, feederPath, scadaPath):
'''calibrates a feeder and saves the calibrated tree at a location'''
logger.info('Calibrating feeder... work dir: %s; feeder path: %s; scada path: %s', workDir, feederPath, scadaPath)
with open(feederPath, "r") as jsonIn:
feederJson = json.load(jsonIn)
tree = feederJson.get("tree", {})
scadaSubPower, firstDateTime = _processScadaData(workDir, scadaPath)
# Force FBS powerflow, because NR fails a lot.
for key in tree:
if tree[key].get("module", "").lower() == "powerflow":
tree[key] = {"module": "powerflow", "solver_method": "FBS"}
# Attach player.
classOb = {"class": "player", "variable_names": [
"value"], "variable_types": ["double"]}
playerOb = {"object": "player", "property": "value",
"name": "scadaLoads", "file": "subScada.player", "loop": "0"}
maxKey = omf.feeder.getMaxKey(tree)
tree[maxKey + 1] = classOb
tree[maxKey + 2] = playerOb
# Make loads reference player.
loadTemplate = {"object": "triplex_load",
"power_pf_12": "0.95",
"impedance_pf_12": "0.98",
"power_pf_12": "0.90",
"impedance_fraction_12": "0.7",
"power_fraction_12": "0.3"}
for key in tree:
ob = tree[key]
if ob.get("object", "") == "triplex_node" and ob.get("power_12", "") != "":
newOb = dict(loadTemplate)
newOb["name"] = ob.get("name", "")
newOb["parent"] = ob.get("parent", "")
newOb["phases"] = ob.get("phases", "")
newOb["nominal_voltage"] = ob.get("nominal_voltage", "")
newOb["latitude"] = ob.get("latitude", "0")
newOb["longitude"] = ob.get("longitude", "0")
oldPow = ob.get("power_12", "").replace("j", "d")
pythagPower = gridlabd._strClean(oldPow)
newOb["base_power_12"] = "scadaLoads.value*" + str(pythagPower)
tree[key] = newOb
# Search for the substation regulator and attach a recorder there.
for key in tree:
if tree[key].get('bustype', '').lower() == 'swing':
swingName = tree[key].get('name')
for key in tree:
if tree[key].get('object', '') in ['regulator', 'overhead_line', 'underground_line', 'transformer', 'fuse'] and tree[key].get('from', '') == swingName:
SUB_REG_NAME = tree[key]['name']
recOb = {"object": "recorder",
"parent": SUB_REG_NAME,
"property": "power_in.real,power_in.imag",
"file": "caliSub.csv",
"interval": "900"}
tree[maxKey + 3] = recOb
HOURS = 100
omf.feeder.adjustTime(tree, HOURS, "hours", firstDateTime.strftime("%Y-%m-%d"))
# Run Gridlabd.
output = gridlabd.runInFilesystem(tree, keepFiles=True, workDir=workDir)
# Calculate scaling constant.
outRealPow = output["caliSub.csv"]["power_in.real"]
outImagPower = output["caliSub.csv"]["power_in.imag"]
outAppPowerKw = [
(x[0]**2 + x[1]**2)**0.5 / 1000 for x in zip(outRealPow, outImagPower)]
# HACK: ignore first time step in output and input because GLD sometimes
# breaks the first step.
SCAL_CONST = sum(scadaSubPower[1:HOURS]) / sum(outAppPowerKw[1:HOURS])
# Rewrite the subScada.player file so all the power values are multiplied
# by the SCAL_CONSTANT.
newPlayData = []
with open(pJoin(workDir, "subScada.player"), "r") as playerFile:
for line in playerFile:
(key, val) = line.split(',')
newPlayData.append(
str(key) + ',' + str(float(val) * SCAL_CONST) + "\n")
with open(pJoin(workDir, "subScadaCalibrated.player"), "w") as playerFile:
for row in newPlayData:
playerFile.write(row)
# Test by running a glm with subScadaCalibrated.player and caliSub.csv2.
tree[maxKey + 2]["file"] = "subScadaCalibrated.player"
tree[maxKey + 3]["file"] = "caliSubCheck.csv"
secondOutput = gridlabd.runInFilesystem(
tree, keepFiles=True, workDir=workDir)
plt.figure()
plt.plot(outAppPowerKw[1:HOURS], label="initialGuess")
plt.plot(scadaSubPower[1:HOURS], label="scadaSubPower")
secondAppKw = [(x[0]**2 + x[1]**2)**0.5 / 1000
for x in zip(secondOutput["caliSubCheck.csv"]["power_in.real"], secondOutput["caliSubCheck.csv"]["power_in.imag"])]
plt.plot(secondAppKw[1:HOURS], label="finalGuess")
plt.legend(loc=3)
Plot.save_fig(plt, pJoin(workDir, "caliCheckPlot.png"))
# Write the final output.
with open(pJoin(workDir, "calibratedFeeder.json"), "w") as outJson:
playerString = open(pJoin(workDir, "subScadaCalibrated.player")).read()
feederJson["attachments"]["subScadaCalibrated.player"] = playerString
feederJson["tree"] = tree
json.dump(feederJson, outJson, indent=4)
return
def generateData(pathToOmd, workDir, inputData, outputData):
omd = json.load(open(pathToOmd))
tree = omd.get('tree', {})
attachments = omd.get('attachments', [])
# check to see if work directory is specified
if not workDir:
workDir = tempfile.mkdtemp()
print '@@@@@@', workDir
def safeInt(x):
try:
return int(x)
except:
return 0
biggestKey = max([safeInt(x) for x in tree.keys()])
CLOCK_START = '2000-01-01 0:00:00'
dt_start = parser.parse(CLOCK_START)
dt_end = dt_start + relativedelta(day=0, hour=0, minute=5, second=0)
CLOCK_END = str(dt_end)
CLOCK_RANGE = CLOCK_START + ',' + CLOCK_END
index = 1
for key in tree:
if 'clock' in tree[key]:
tree[key]['starttime'] = "'" + CLOCK_START + "'"
tree[key]['stoptime'] = "'" + CLOCK_END + "'"
# create volt and current line dumps
tree[str(biggestKey * 10 + index)] = {
"object": "voltdump",
"filename": "voltDump.csv",
'runtime': 'INIT'
}
attachments = []
# Run Gridlab.
if not workDir:
workDir = tempfile.mkdtemp()
print '@@@@@@', workDir
gridlabOut = gridlabd.runInFilesystem(tree,
attachments=attachments,
workDir=workDir)
outageMap = geo.omdGeoJson(pathToOmd, conversion=False)
with open(workDir + '/nodes.csv', mode='wb') as nodes:
fieldnames = ['node_name', 'coord1', 'coord2']
writer = csv.DictWriter(nodes, fieldnames)
writer.writeheader()
for key in tree.keys():
obtype = tree[key].get("object", "")
if obtype == 'node' or obtype == 'load' or obtype == 'capacitor' or obtype == 'meter':
coord1, coord2 = nodeToCoords(outageMap, tree[key]['name'])
writer.writerow({
'node_name': tree[key]['name'],
'coord1': coord1,
'coord2': coord2
})
nodes.close()
with open(workDir + '/connectivity.csv', mode='wb') as connectivity:
fieldnames = ['first_node', 'second_node']
writer = csv.DictWriter(connectivity, fieldnames)
writer.writeheader()
for key in tree.keys():
obtype = tree[key].get("object", "")
if obtype == 'underground_line' or obtype == 'overhead_line' or obtype == 'triplex_line':
writer.writerow({
'first_node': tree[key]['from'],
'second_node': tree[key]['to']
})
connectivity.close()
connectivity = pd.read_csv(workDir + '/connectivity.csv')
print(connectivity)
nodes = pd.read_csv(workDir + '/nodes.csv')
row = 0
number_of_nodes = nodes.shape[0]
bad_nodes = []
while row < number_of_nodes:
if (not connectivity['first_node'].str.contains(
nodes.loc[row, 'node_name']).any()) and (
not connectivity['second_node'].str.contains(
nodes.loc[row, 'node_name']).any()):
bad_nodes.append(nodes.loc[row, 'node_name'])
row += 1
row = 0
number_of_bad = len(bad_nodes)
while row < number_of_bad:
delete_row = nodes[nodes['node_name'] == bad_nodes[row]].index
nodes = nodes.drop(delete_row)
row += 1
nodes.dropna()
nodes = nodes.sort_values('node_name')
nodes.to_csv(workDir + '/nodes1.csv')
nodes = pd.read_csv(workDir + '/nodes1.csv')
print(nodes)
volt = pd.read_csv(workDir + '/voltDump.csv', skiprows=1)
row = 0
number_of_volt = volt.shape[0]
bad_nodes = []
while row < number_of_volt:
if (not connectivity['first_node'].str.contains(
volt.loc[row, 'node_name']).any()) and (
not connectivity['second_node'].str.contains(
volt.loc[row, 'node_name']).any()):
bad_nodes.append(volt.loc[row, 'node_name'])
row += 1
number_of_bad = len(bad_nodes)
row = 0
while row < number_of_bad:
delete_row = volt[volt['node_name'] == bad_nodes[row]].index
volt = volt.drop(delete_row)
row += 1
volt = volt.sort_values('node_name')
volt.to_csv(workDir + '/volt1.csv')
volt = pd.read_csv(workDir + '/volt1.csv')
print(volt)
row_count = nodes.shape[0]
inputDataDist = [[0 for x in range(row_count)] for y in range(row_count)]
inputGraphDist = nx.Graph()
row = 0
while row < row_count:
column = 0
while column < row_count:
data = []
if ((nodes.loc[row, 'coord1'] - nodes.loc[column, 'coord1'])**2 +
(nodes.loc[row, 'coord2'] - nodes.loc[column, 'coord2'])**
2) > 10e-10:
distance = math.sqrt((nodes.loc[row, 'coord1'] -
nodes.loc[column, 'coord1'])**2 +
(nodes.loc[row, 'coord2'] -
nodes.loc[column, 'coord2'])**2)
else:
distance = 0.0
if distance > 10e-15:
data.append(distance)
#data.append(distance*10e5*1.09359893099)
else:
data.append(10e15)
inputDataDist[row][column] = sum(data)
if not inputGraphDist.has_edge(row, column):
inputGraphDist.add_edge(row,
column,
weight=inputDataDist[row][column])
column += 1
row += 1
row_count = volt.shape[0]
inputDataVolt = [[0 for x in range(row_count)] for y in range(row_count)]
inputGraphVolt = nx.Graph()
row = 0
while row < row_count:
column = 0
while column < row_count:
data = []
if (float(volt.loc[row, 'voltA_real']) > 10e-10
or float(volt.loc[row, 'voltA_imag']) > 10e-10
or float(volt.loc[row, 'voltA_real']) < -10e-10
or float(volt.loc[row, 'voltA_imag']) < -10e-10):
if (math.sqrt((float(volt.loc[row, 'voltA_real']) -
float(volt.loc[column, 'voltA_real']))**2 +
(float(volt.loc[row, 'voltA_imag']) -
float(volt.loc[column, 'voltA_imag']))**2)) > 0:
data.append(
math.sqrt((float(volt.loc[row, 'voltA_real']) -
float(volt.loc[column, 'voltA_real']))**2 +
(float(volt.loc[row, 'voltA_imag']) -
float(volt.loc[column, 'voltA_imag']))**2))
else:
data.append(100.0)
else:
data.append(100.0)
if (float(volt.loc[row, 'voltB_real']) > 10e-10
or float(volt.loc[row, 'voltB_imag']) > 10e-10
or float(volt.loc[row, 'voltB_real']) < -10e-10
or float(volt.loc[row, 'voltB_imag']) < -10e-10):
if (math.sqrt((float(volt.loc[row, 'voltB_real']) -
float(volt.loc[column, 'voltB_real']))**2 +
(float(volt.loc[row, 'voltB_imag']) -
float(volt.loc[column, 'voltB_imag']))**2)) > 0:
data.append(
math.sqrt((float(volt.loc[row, 'voltB_real']) -
float(volt.loc[column, 'voltB_real']))**2 +
(float(volt.loc[row, 'voltB_imag']) -
float(volt.loc[column, 'voltB_imag']))**2))
else:
data.append(100.0)
else:
data.append(100.0)
if (float(volt.loc[row, 'voltC_real']) > 10e-10
or float(volt.loc[row, 'voltC_imag']) > 10e-10
or float(volt.loc[row, 'voltC_real']) < -10e-10
or float(volt.loc[row, 'voltC_imag']) < -10e-10):
if (math.sqrt((float(volt.loc[row, 'voltC_real']) -
float(volt.loc[column, 'voltC_real']))**2 +
(float(volt.loc[row, 'voltC_imag']) -
float(volt.loc[column, 'voltC_imag']))**2)) > 0:
data.append(
math.sqrt((float(volt.loc[row, 'voltC_real']) -
float(volt.loc[column, 'voltC_real']))**2 +
(float(volt.loc[row, 'voltC_imag']) -
float(volt.loc[column, 'voltC_imag']))**2))
else:
data.append(100.0)
else:
data.append(100.0)
inputDataVolt[row][column] = sum(data)
if not inputGraphVolt.has_edge(row, column):
inputGraphVolt.add_edge(row,
column,
weight=inputDataVolt[row][column])
column += 1
row += 1
nodes_count = nodes.shape[0]
connectivity_count = connectivity.shape[0]
row = 0
outputData = [[0 for x in range(nodes_count)] for y in range(nodes_count)]
outputGraph = nx.Graph()
while row < nodes_count:
column = 0
while column < nodes_count:
rowName = nodes.loc[row, 'node_name']
colName = nodes.loc[column, 'node_name']
conEntry = 0
found = False
while conEntry < connectivity_count:
if ((rowName == connectivity.loc[conEntry, 'first_node']
and colName == connectivity.loc[conEntry, 'second_node'])
or
(rowName == connectivity.loc[conEntry, 'second_node']
and colName == connectivity.loc[conEntry, 'first_node'])):
outputData[row][column] = -1
if not outputGraph.has_edge(row, column):
outputGraph.add_edge(row, column, weight=-1)
found = True
break
conEntry += 1
if found == False:
if not outputGraph.has_edge(row, column):
outputData[row][column] = 0
outputGraph.add_edge(row, column, weight=0)
column += 1
row += 1
print(len(outputData))
print(len(inputDataDist))
return inputDataDist, inputGraphDist, inputDataVolt, inputGraphVolt, outputData, outputGraph
'power_factor': '1.0',
'generator_status': 'ONLINE',
'generator_mode': 'CONSTANT_PF'
}
feed[maxKey + 5] = {
'object': 'solar',
'name': 'test_solar',
'parent': 'test_solar_inverter',
'area': '1000000 sf',
'generator_status': 'ONLINE',
'efficiency': '0.2',
'generator_mode': 'SUPPLY_DRIVEN',
'panel_type': 'SINGLE_CRYSTAL_SILICON'
}
feed[maxKey + 6] = {
'object': 'recorder',
'parent': 'test_solar_meter',
'property':
'voltage_A.real,voltage_A.imag,voltage_B.real,voltage_B.imag,voltage_C.real,voltage_C.imag',
'file': 'GC-addSolar-voltages.csv',
'interval': '60',
'limit': '1440'
}
runInFilesystem(feed, keepFiles=True, workDir='.', glmName='GC-solarAdd.glm')
'''
output = open('GC-solarAdd.glm', 'w')
output.write(feeder.write(feed))
output.close()
'''
def heavyProcessing(modelDir, inputDict):
''' Run the model in its directory. WARNING: GRIDLAB CAN TAKE HOURS TO COMPLETE. '''
print "STARTING TO RUN", modelDir
beginTime = datetime.datetime.now()
# Get feeder name and data in.
try: os.mkdir(pJoin(modelDir,'gldContainer'))
except: pass
try:
feederName = inputDict["feederName1"]
weather = inputDict["weather"]
if weather == "typical":
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__metaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "gldContainer", "climate.tmy2"))
startTime = datetime.datetime.now()
else:
#hack for testing
makeClimateCsv('2010-07-01', '2010-08-01', 'DFW', 'Output/Automated dsoSimSuite Test/gldContainer/weather.csv')
startTime = datetime.datetime.now()
startTime = datetime.datetime.now()
feederJson = json.load(open(pJoin(modelDir, feederName+'.omd')))
tree = feederJson["tree"]
#add a check to see if there is already a climate object in the omd file
#if there is delete the climate from attachments and the climate object
attachKeys = feederJson["attachments"].keys()
for key in attachKeys:
if key.endswith('.tmy2'):
del feederJson['attachments'][key]
treeKeys = feederJson["tree"].keys()
for key in treeKeys:
if 'object' in feederJson['tree'][key]:
if feederJson['tree'][key]['object'] == 'climate':
del feederJson['tree'][key]
#add weather objects and modules to .glm if there is no climate file in the omd file
if weather == "historical":
oldMax = feeder.getMaxKey(tree)
tree[oldMax + 1] = {'omftype':'module', 'argument':'tape'}
tree[oldMax + 2] = {'omftype':'module', 'argument':'climate'}
tree[oldMax + 3] = {'object':'csv_reader', 'name':'weatherReader', 'filename':'weather.csv'}
tree[oldMax + 4] = {'object':'climate', 'name':'exampleClimate', 'tmyfile':'weather.csv', 'reader':'weatherReader'}
else:
oldMax = feeder.getMaxKey(tree)
tree[oldMax + 1] ={'object':'climate','name':'Climate','interpolate':'QUADRATIC', 'tmyfile':'climate.tmy2'}
# Set up GLM with correct time and recorders:
feeder.attachRecorders(tree, "Regulator", "object", "regulator")
feeder.attachRecorders(tree, "Capacitor", "object", "capacitor")
feeder.attachRecorders(tree, "Inverter", "object", "inverter")
feeder.attachRecorders(tree, "Windmill", "object", "windturb_dg")
feeder.attachRecorders(tree, "CollectorVoltage", None, None)
feeder.attachRecorders(tree, "Climate", "object", "climate")
feeder.attachRecorders(tree, "OverheadLosses", None, None)
feeder.attachRecorders(tree, "UndergroundLosses", None, None)
feeder.attachRecorders(tree, "TriplexLosses", None, None)
feeder.attachRecorders(tree, "TransformerLosses", None, None)
feeder.groupSwingKids(tree)
# Attach recorders for system voltage map:
stub = {'object':'group_recorder', 'group':'"class=node"', 'property':'voltage_A', 'interval':3600, 'file':'aVoltDump.csv'}
for phase in ['A','B','C']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'VoltDump.csv'
tree[feeder.getMaxKey(tree) + 1] = copyStub
feeder.adjustTime(tree=tree, simLength=float(inputDict["simLength"]),
simLengthUnits=inputDict["simLengthUnits"], simStartDate=inputDict["simStartDate"])
# RUN GRIDLABD IN FILESYSTEM (EXPENSIVE!)
rawOut = gridlabd.runInFilesystem(tree, attachments=feederJson["attachments"],
keepFiles=True, workDir=pJoin(modelDir,'gldContainer'))
cleanOut = {}
# Std Err and Std Out
cleanOut['stderr'] = rawOut['stderr']
cleanOut['stdout'] = rawOut['stdout']
# Time Stamps
for key in rawOut:
if '# timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key]['# timestamp']
break
elif '# property.. timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key]['# property.. timestamp']
else:
cleanOut['timeStamps'] = []
# Day/Month Aggregation Setup:
stamps = cleanOut.get('timeStamps',[])
level = inputDict.get('simLengthUnits','hours')
# Climate
for key in rawOut:
if key.startswith('Climate_') and key.endswith('.csv'):
cleanOut['climate'] = {}
cleanOut['climate']['Rain Fall (in/h)'] = hdmAgg(rawOut[key].get('rainfall'), sum, level)
cleanOut['climate']['Wind Speed (m/s)'] = hdmAgg(rawOut[key].get('wind_speed'), avg, level)
cleanOut['climate']['Temperature (F)'] = hdmAgg(rawOut[key].get('temperature'), max, level)
cleanOut['climate']['Snow Depth (in)'] = hdmAgg(rawOut[key].get('snowdepth'), max, level)
cleanOut['climate']['Direct Normal (W/sf)'] = hdmAgg(rawOut[key].get('solar_direct'), sum, level)
#cleanOut['climate']['Global Horizontal (W/sf)'] = hdmAgg(rawOut[key].get('solar_global'), sum, level)
climateWbySFList= hdmAgg(rawOut[key].get('solar_global'), sum, level)
#converting W/sf to W/sm
climateWbySMList= [x*10.76392 for x in climateWbySFList]
cleanOut['climate']['Global Horizontal (W/sm)']=climateWbySMList
# Voltage Band
if 'VoltageJiggle.csv' in rawOut:
cleanOut['allMeterVoltages'] = {}
cleanOut['allMeterVoltages']['Min'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['min(voltage_12.mag)']], min, level)
cleanOut['allMeterVoltages']['Mean'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['mean(voltage_12.mag)']], avg, level)
cleanOut['allMeterVoltages']['StdDev'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['std(voltage_12.mag)']], avg, level)
cleanOut['allMeterVoltages']['Max'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['max(voltage_12.mag)']], max, level)
# Power Consumption
cleanOut['Consumption'] = {}
# Set default value to be 0, avoiding missing value when computing Loads
cleanOut['Consumption']['Power'] = [0] * int(inputDict["simLength"])
cleanOut['Consumption']['Losses'] = [0] * int(inputDict["simLength"])
cleanOut['Consumption']['DG'] = [0] * int(inputDict["simLength"])
for key in rawOut:
if key.startswith('SwingKids_') and key.endswith('.csv'):
oneSwingPower = hdmAgg(vecPyth(rawOut[key]['sum(power_in.real)'],rawOut[key]['sum(power_in.imag)']), avg, level)
if 'Power' not in cleanOut['Consumption']:
cleanOut['Consumption']['Power'] = oneSwingPower
else:
cleanOut['Consumption']['Power'] = vecSum(oneSwingPower,cleanOut['Consumption']['Power'])
elif key.startswith('Inverter_') and key.endswith('.csv'):
realA = rawOut[key]['power_A.real']
realB = rawOut[key]['power_B.real']
realC = rawOut[key]['power_C.real']
imagA = rawOut[key]['power_A.imag']
imagB = rawOut[key]['power_B.imag']
imagC = rawOut[key]['power_C.imag']
oneDgPower = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'DG' not in cleanOut['Consumption']:
cleanOut['Consumption']['DG'] = oneDgPower
else:
cleanOut['Consumption']['DG'] = vecSum(oneDgPower,cleanOut['Consumption']['DG'])
elif key.startswith('Windmill_') and key.endswith('.csv'):
vrA = rawOut[key]['voltage_A.real']
vrB = rawOut[key]['voltage_B.real']
vrC = rawOut[key]['voltage_C.real']
viA = rawOut[key]['voltage_A.imag']
viB = rawOut[key]['voltage_B.imag']
viC = rawOut[key]['voltage_C.imag']
crB = rawOut[key]['current_B.real']
crA = rawOut[key]['current_A.real']
crC = rawOut[key]['current_C.real']
ciA = rawOut[key]['current_A.imag']
ciB = rawOut[key]['current_B.imag']
ciC = rawOut[key]['current_C.imag']
powerA = vecProd(vecPyth(vrA,viA),vecPyth(crA,ciA))
powerB = vecProd(vecPyth(vrB,viB),vecPyth(crB,ciB))
powerC = vecProd(vecPyth(vrC,viC),vecPyth(crC,ciC))
oneDgPower = hdmAgg(vecSum(powerA,powerB,powerC), avg, level)
if 'DG' not in cleanOut['Consumption']:
cleanOut['Consumption']['DG'] = oneDgPower
else:
cleanOut['Consumption']['DG'] = vecSum(oneDgPower,cleanOut['Consumption']['DG'])
elif key in ['OverheadLosses.csv', 'UndergroundLosses.csv', 'TriplexLosses.csv', 'TransformerLosses.csv']:
realA = rawOut[key]['sum(power_losses_A.real)']
imagA = rawOut[key]['sum(power_losses_A.imag)']
realB = rawOut[key]['sum(power_losses_B.real)']
imagB = rawOut[key]['sum(power_losses_B.imag)']
realC = rawOut[key]['sum(power_losses_C.real)']
imagC = rawOut[key]['sum(power_losses_C.imag)']
oneLoss = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'Losses' not in cleanOut['Consumption']:
cleanOut['Consumption']['Losses'] = oneLoss
else:
cleanOut['Consumption']['Losses'] = vecSum(oneLoss,cleanOut['Consumption']['Losses'])
elif key.startswith('Regulator_') and key.endswith('.csv'):
#split function to strip off .csv from filename and user rest of the file name as key. for example- Regulator_VR10.csv -> key would be Regulator_VR10
regName=""
regName = key
newkey=regName.split(".")[0]
cleanOut[newkey] ={}
cleanOut[newkey]['RegTapA'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['RegTapB'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['RegTapC'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['RegTapA'] = rawOut[key]['tap_A']
cleanOut[newkey]['RegTapB'] = rawOut[key]['tap_B']
cleanOut[newkey]['RegTapC'] = rawOut[key]['tap_C']
cleanOut[newkey]['RegPhases'] = rawOut[key]['phases'][0]
elif key.startswith('Capacitor_') and key.endswith('.csv'):
capName=""
capName = key
newkey=capName.split(".")[0]
cleanOut[newkey] ={}
cleanOut[newkey]['Cap1A'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['Cap1B'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['Cap1C'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['Cap1A'] = rawOut[key]['switchA']
cleanOut[newkey]['Cap1B'] = rawOut[key]['switchB']
cleanOut[newkey]['Cap1C'] = rawOut[key]['switchC']
cleanOut[newkey]['CapPhases'] = rawOut[key]['phases'][0]
# What percentage of our keys have lat lon data?
latKeys = [tree[key]['latitude'] for key in tree if 'latitude' in tree[key]]
latPerc = 1.0*len(latKeys)/len(tree)
if latPerc < 0.25: doNeato = True
else: doNeato = False
# Generate the frames for the system voltage map time traveling chart.
genTime = generateVoltChart(tree, rawOut, modelDir, neatoLayout=doNeato)
cleanOut['genTime'] = genTime
# Aggregate up the timestamps:
if level=='days':
cleanOut['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:10], 'days')
elif level=='months':
cleanOut['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:7], 'months')
# Write the output.
with open(pJoin(modelDir, "allOutputData.json"),"w") as outFile:
json.dump(cleanOut, outFile, indent=4)
# 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)
# Clean up the PID file.
os.remove(pJoin(modelDir, "gldContainer", "PID.txt"))
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)
finishTime = datetime.datetime.now()
inputDict["runTime"] = str(datetime.timedelta(seconds = int((finishTime - beginTime).total_seconds())))
with open(pJoin(modelDir, "allInputData.json"),"w") as inFile:
json.dump(inputDict, inFile, indent = 4)
try:
os.remove(pJoin(modelDir,"PPID.txt"))
except:
pass
def work(modelDir, inputDict):
''' Run the model in its directory. '''
outData = {}
feederName = inputDict["feederName1"]
with open(pJoin(modelDir, inputDict['weatherImpactsFileName']),
'w') as hazardFile:
hazardFile.write(inputDict['weatherImpacts'])
with open(pJoin(modelDir, feederName + '.omd'), "r") as jsonIn:
feederModel = json.load(jsonIn)
# Create GFM input file.
print "Running GFM ************************************"
gfmInputTemplate = {
'phase_variation': float(inputDict['phaseVariation']),
'chance_constraint': float(inputDict['chanceConstraint']),
'critical_load_met': float(inputDict['criticalLoadMet']),
'total_load_met':
1.0, #(float(inputDict['criticalLoadMet']) + float(inputDict['nonCriticalLoadMet'])),
'xrMatrices': inputDict["xrMatrices"],
'maxDGPerGenerator': float(inputDict["maxDGPerGenerator"]),
'newLineCandidates': inputDict['newLineCandidates'],
'hardeningCandidates': inputDict['hardeningCandidates'],
'switchCandidates': inputDict['switchCandidates'],
'hardeningUnitCost': inputDict['hardeningUnitCost'],
'switchCost': inputDict['switchCost'],
'generatorCandidates': inputDict['generatorCandidates'],
'lineUnitCost': inputDict['lineUnitCost']
}
gfmJson = convertToGFM(gfmInputTemplate, feederModel)
gfmInputFilename = 'gfmInput.json'
with open(pJoin(modelDir, gfmInputFilename), "w") as outFile:
json.dump(gfmJson, outFile, indent=4)
# Run GFM
gfmBinaryPath = pJoin(__neoMetaModel__._omfDir, 'solvers', 'gfm',
'Fragility.jar')
proc = subprocess.Popen([
'java', '-jar', gfmBinaryPath, '-r', gfmInputFilename, '-wf',
inputDict['weatherImpactsFileName'], '-num', '3'
],
cwd=modelDir)
proc.wait()
# HACK: rename the hardcoded gfm output
rdtInputFilePath = pJoin(modelDir, 'rdtInput.json')
print 'Before weird RENAMING STUFF!!!!'
os.rename(pJoin(modelDir, 'rdt_OUTPUT.json'), rdtInputFilePath)
# print 'RENAME FROM', pJoin(modelDir,'rdt_OUTPUT.json')
# print 'RENAME TO', rdtInputFilePath
# print 'After weird RENAMING STUFF!!!!'
#raise Exception('Go no further')
# Pull GFM input data on lines and generators for HTML presentation.
with open(rdtInputFilePath, 'r') as rdtInputFile:
# HACK: we use rdtInput as a string in the frontend.
rdtJsonAsString = rdtInputFile.read()
rdtJson = json.loads(rdtJsonAsString)
# Calculate line costs.
lineData = []
for line in rdtJson["lines"]:
lineData.append((line["id"], '{:,.2f}'.format(
float(line["length"]) * float(inputDict["lineUnitCost"]))))
outData["lineData"] = lineData
outData["generatorData"] = '{:,.2f}'.format(
float(inputDict["dgUnitCost"]) * float(inputDict["maxDGPerGenerator"]))
outData['gfmRawOut'] = rdtJsonAsString
if inputDict['scenarios'] != "":
rdtJson['scenarios'] = json.loads(inputDict['scenarios'])
with open(pJoin(rdtInputFilePath), "w") as rdtInputFile:
json.dump(rdtJson, rdtInputFile, indent=4)
# Run GridLAB-D first time to generate xrMatrices.
if platform.system() == "Windows":
omdPath = pJoin(modelDir, feederName + ".omd")
with open(omdPath, "r") as omd:
omd = json.load(omd)
#REMOVE NEWLINECANDIDATES
deleteList = []
newLines = inputDict["newLineCandidates"].strip().replace(
' ', '').split(',')
for newLine in newLines:
for omdObj in omd["tree"]:
if ("name" in omd["tree"][omdObj]):
if (newLine == omd["tree"][omdObj]["name"]):
deleteList.append(omdObj)
for delItem in deleteList:
del omd["tree"][delItem]
#Load a blank glm file and use it to write to it
feederPath = pJoin(modelDir, 'feeder.glm')
with open(feederPath, 'w') as glmFile:
#toWrite = omf.feeder.sortedWrite(omd['tree']) + "object jsondump {\n\tfilename_dump_reliability test_JSON_dump1.json;\n\twrite_reliability true;\n\tfilename_dump_line test_JSON_dump2.json;\n\twrite_line true;\n};\n"# + "object jsonreader {\n\tfilename " + insertRealRdtOutputNameHere + ";\n};"
toWrite = omf.feeder.sortedWrite(
omd['tree']
) + "object jsondump {\n\tfilename_dump_reliability test_JSON_dump.json;\n\twrite_system_info true;\n\twrite_per_unit true;\n\tsystem_base 100.0 MVA;\n};\n" # + "object jsonreader {\n\tfilename " + insertRealRdtOutputNameHere + ";\n};"
glmFile.write(toWrite)
#Write attachments from omd, if no file, one will be created
for fileName in omd['attachments']:
with open(os.path.join(modelDir, fileName), 'w') as file:
file.write(omd['attachments'][fileName])
#Wire in the file the user specifies via zipcode.
climateFileName, latforpvwatts = zipCodeToClimateName(
inputDict["simulationZipCode"])
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
climateFileName + ".tmy2"), pJoin(modelDir, 'climate.tmy2'))
proc = subprocess.Popen(['gridlabd', 'feeder.glm'],
stdout=subprocess.PIPE,
shell=True,
cwd=modelDir)
(out, err) = proc.communicate()
accumulator = ""
with open(pJoin(modelDir, "JSON_dump_line.json"), "r") as gldOut:
accumulator = json.load(gldOut)
outData['gridlabdRawOut'] = accumulator
#THIS IS THE CODE THAT ONCE FRANK GETS DONE WITH GRIDLAB-D NEEDS TO BE UNCOMMENTED
'''rdtJson["line_codes"] = accumulator["properties"]["line_codes"]
rdtJson["lines"] = accumulator["properties"]["lines"]
with open(pJoin(modelDir, rdtInputFilePath), "w") as outFile:
json.dump(rdtJson, outFile, indent=4)'''
else:
tree = feederModel.get("tree", {})
attachments = feederModel.get("attachments", {})
climateFileName, latforpvwatts = zipCodeToClimateName(
inputDict["simulationZipCode"])
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
climateFileName + ".tmy2"), pJoin(modelDir, 'climate.tmy2'))
gridlabdRawOut = gridlabd.runInFilesystem(tree,
attachments=attachments,
workDir=modelDir)
outData['gridlabdRawOut'] = gridlabdRawOut
# Run RDT.
print "Running RDT ************************************"
rdtOutFile = modelDir + '/rdtOutput.json'
rdtSolverFolder = pJoin(__neoMetaModel__._omfDir, 'solvers', 'rdt')
rdtJarPath = pJoin(rdtSolverFolder, 'micot-rdt.jar')
proc = subprocess.Popen([
'java', "-Djna.library.path=" + rdtSolverFolder, '-jar', rdtJarPath,
'-c', rdtInputFilePath, '-e', rdtOutFile
])
proc.wait()
rdtRawOut = open(rdtOutFile).read()
outData['rdtRawOut'] = rdtRawOut
# Indent the RDT output nicely.
with open(pJoin(rdtOutFile), "w") as outFile:
rdtOut = json.loads(rdtRawOut)
json.dump(rdtOut, outFile, indent=4)
# TODO: run GridLAB-D second time to validate RDT results with new control schemes.
# Draw the feeder.
genDiagram(modelDir, feederModel)
with open(pJoin(modelDir, "feederChart.png"), "rb") as inFile:
outData["oneLineDiagram"] = inFile.read().encode("base64")
return outData
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=(15, 15))
plt.axes(frameon=0)
plt.axis("off")
# set axes step equal
voltChart.gca().set_aspect("equal")
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 attachVolts(workDir, feederPath, voltVectorA, voltVectorB, voltVectorC, simStartDate, simLength, simLengthUnits):
'''read voltage vectors of 3 different phases, run gridlabd, and attach output to the feeder.'''
try:
timeStamp = [simStartDate['Date']]
for x in range (1, 8760):
timeStamp.append(timeStamp[x-1] + dt.timedelta(hours=1))
firstDateTime = timeStamp[1]
with open(pJoin(pJoin(workDir,"gridlabD"),"phaseAVoltage.player"),"w") as voltFile:
for x in range(0, 8760):
timestamp = timeStamp[x]
voltage = str("%0.2f"%float(voltVectorA[x]))+"+0j"
line = timestamp.strftime("%Y-%m-%d %H:%M:%S") + " " + simStartDate['timeZone'] + "," + str(voltage) + "\n"
voltFile.write(line)
with open(pJoin(pJoin(workDir,"gridlabD"),"phaseBVoltage.player"),"w") as voltFile:
for x in range(0, 8760):
timestamp = timeStamp[x]
voltage = str("%0.2f"%float(voltVectorB[x]))+"-"+str("%0.4f"%float(random.uniform(6449,6460)))+"j"
line = timestamp.strftime("%Y-%m-%d %H:%M:%S") + " " + simStartDate['timeZone'] + "," + str(voltage) + "\n"
voltFile.write(line)
with open(pJoin(pJoin(workDir,"gridlabD"),"phaseCVoltage.player"),"w") as voltFile:
for x in range(0, 8760):
timestamp = timeStamp[x]
voltage = str("%0.2f"%float(voltVectorC[x]))+"+"+str("%0.4f"%float(random.uniform(6449,6460)))+"j"
line = timestamp.strftime("%Y-%m-%d %H:%M:%S") + " " + simStartDate['timeZone'] + "," + str(voltage) + "\n"
voltFile.write(line)
with open(feederPath, "r") as jsonIn:
feederJson = json.load(jsonIn)
tree = feederJson.get("tree", {})
# Find swingNode name.
for key in tree:
if tree[key].get('bustype','').lower() == 'swing':
swingName = tree[key].get('name')
# Attach player.
classOb = {'omftype':'class player','argument':'{double value;}'}
voltageObA = {"object":"player", "property":"voltage_A", "file":"phaseAVoltage.player", "loop":"0", "parent":swingName}
voltageObB = {"object":"player", "property":"voltage_B", "file":"phaseBVoltage.player", "loop":"0", "parent":swingName}
voltageObC = {"object":"player", "property":"voltage_C", "file":"phaseCVoltage.player", "loop":"0", "parent":swingName}
maxKey = feeder.getMaxKey(tree)
voltplayerKeyA = maxKey + 2
voltplayerKeyB = maxKey + 3
voltplayerKeyC = maxKey + 4
tree[maxKey+1] = classOb
tree[voltplayerKeyA] = voltageObA
tree[voltplayerKeyB] = voltageObB
tree[voltplayerKeyC] = voltageObC
# Adjust time and run output.
feeder.adjustTime(tree, simLength, simLengthUnits, firstDateTime.strftime("%Y-%m-%d %H:%M:%S"))
output = gridlabd.runInFilesystem(tree, keepFiles=True, workDir=pJoin(workDir,"gridlabD"))
# Write the output.
with open(pJoin(workDir,"calibratedFeeder.omd"),"w") as outJson:
playerStringA = open(pJoin(pJoin(workDir,"gridlabD"),"phaseAVoltage.player")).read()
playerStringB = open(pJoin(pJoin(workDir,"gridlabD"),"phaseBVoltage.player")).read()
playerStringC = open(pJoin(pJoin(workDir,"gridlabD"),"phaseCVoltage.player")).read()
feederJson["attachments"]["phaseAVoltage.player"] = playerStringA
feederJson["attachments"]["phaseBVoltage.player"] = playerStringB
feederJson["attachments"]["phaseCVoltage.player"] = playerStringC
feederJson["tree"] = tree
json.dump(feederJson, outJson, indent=4)
return pJoin(workDir,"calibratedFeeder.omd"), True
except:
print "Failed to run gridlabD with voltage players."
return "", False
def drawPlot(path,
workDir=None,
neatoLayout=False,
edgeLabs=None,
nodeLabs=None,
edgeCol=None,
nodeCol=None,
faultLoc=None,
faultType=None,
customColormap=False,
scaleMin=None,
scaleMax=None,
rezSqIn=400,
simTime='2000-01-01 0:00:00',
loadLoc=None):
''' Draw a color-coded map of the voltage drop on a feeder.
path is the full path to the GridLAB-D .glm file or OMF .omd file.
workDir is where GridLAB-D will run, if it's None then a temp dir is used.
neatoLayout=True means the circuit is displayed using a force-layout approach.
edgeCol property must be either 'Current', 'Power', 'Rating', 'PercentOfRating', or None
nodeCol property must be either 'Voltage', 'VoltageImbalance', 'perUnitVoltage', 'perUnit120Voltage', or None
edgeLabs and nodeLabs properties must be either 'Name', 'Value', or None
edgeCol and nodeCol can be set to false to avoid coloring edges or nodes
customColormap=True means use a one that is nicely scaled to perunit values highlighting extremes.
faultType and faultLoc are the type of fault and the name of the line that it occurs on.
Returns a matplotlib object.'''
# Be quiet matplotlib:
# warnings.filterwarnings("ignore")
if path.endswith('.glm'):
tree = feeder.parse(path)
attachments = []
elif path.endswith('.omd'):
with open(path) as f:
omd = json.load(f)
tree = omd.get('tree', {})
attachments = omd.get('attachments', [])
else:
raise Exception('Invalid input file type. We require a .glm or .omd.')
#print path
# add fault object to tree
def safeInt(x):
try:
return int(x)
except:
return 0
biggestKey = max([safeInt(x) for x in tree.keys()])
# Add Reliability module
tree[str(biggestKey * 10)] = {
"module": "reliability",
"maximum_event_length": "18000",
"report_event_log": "true"
}
CLOCK_START = simTime
dt_start = parser.parse(CLOCK_START)
dt_end = dt_start + relativedelta(seconds=+20)
CLOCK_END = str(dt_end)
CLOCK_RANGE = CLOCK_START + ',' + CLOCK_END
if faultType != None:
# Add eventgen object (the fault)
tree[str(biggestKey * 10 + 1)] = {
"object": "eventgen",
"name": "ManualEventGen",
"parent": "RelMetrics",
"fault_type": faultType,
"manual_outages": faultLoc + ',' + CLOCK_RANGE
} # TODO: change CLOCK_RANGE to read the actual start and stop time, not just hard-coded
# Add fault_check object
tree[str(biggestKey * 10 + 2)] = {
"object": "fault_check",
"name": "test_fault",
"check_mode": "ONCHANGE",
"eventgen_object": "ManualEventGen",
"output_filename": "Fault_check_out.txt"
}
# Add reliabilty metrics object
tree[str(biggestKey * 10 + 3)] = {
"object": "metrics",
"name": "RelMetrics",
"report_file": "Metrics_Output.csv",
"module_metrics_object": "PwrMetrics",
"metrics_of_interest": '"SAIFI,SAIDI,CAIDI,ASAI,MAIFI"',
"customer_group": '"groupid=METERTEST"',
"metric_interval": "5 h",
"report_interval": "5 h"
}
# Add power_metrics object
tree[str(biggestKey * 10 + 4)] = {
"object": "power_metrics",
"name": "PwrMetrics",
"base_time_value": "1 h"
}
# HACK: set groupid for all meters so outage stats are collected.
noMeters = True
for key in tree:
if tree[key].get('object', '') in ['meter', 'triplex_meter']:
tree[key]['groupid'] = "METERTEST"
noMeters = False
if noMeters:
raise Exception(
"No meters detected on the circuit. Please add at least one meter to allow for collection of outage statistics."
)
for key in tree:
if 'clock' in tree[key]:
tree[key]['starttime'] = "'" + CLOCK_START + "'"
tree[key]['stoptime'] = "'" + CLOCK_END + "'"
# dictionary to hold info on lines present in glm
edge_bools = dict.fromkeys([
'underground_line', 'overhead_line', 'triplex_line', 'transformer',
'regulator', 'fuse', 'switch'
], False)
# Map to speed up name lookups.
nameToIndex = {tree[key].get('name', ''): key for key in tree.keys()}
# Get rid of schedules and climate and check for all edge types:
for key in list(tree.keys()):
obtype = tree[key].get("object", "")
if obtype == 'underground_line':
edge_bools['underground_line'] = True
elif obtype == 'overhead_line':
edge_bools['overhead_line'] = True
elif obtype == 'triplex_line':
edge_bools['triplex_line'] = True
elif obtype == 'transformer':
edge_bools['transformer'] = True
elif obtype == 'regulator':
edge_bools['regulator'] = True
elif obtype == 'fuse':
edge_bools['fuse'] = True
elif obtype == 'switch':
edge_bools['switch'] = True
if tree[key].get("argument",
"") == "\"schedules.glm\"" or tree[key].get(
"tmyfile", "") != "":
del tree[key]
# Make sure we have a voltage dump and current dump:
tree[str(biggestKey * 10 + 5)] = {
"object": "voltdump",
"filename": "voltDump.csv"
}
tree[str(biggestKey * 10 + 6)] = {
"object": "currdump",
"filename": "currDump.csv"
}
# Line rating dumps
tree[feeder.getMaxKey(tree) + 1] = {'module': 'tape'}
for key in edge_bools.keys():
if edge_bools[key]:
tree[feeder.getMaxKey(tree) + 1] = {
'object': 'group_recorder',
'group': '"class=' + key + '"',
'property': 'continuous_rating',
'file': key + '_cont_rating.csv'
}
#Record initial status readout of each fuse/recloser/switch/sectionalizer before running
# Reminder: fuse objects have 'phase_X_status' instead of 'phase_X_state'
protDevices = dict.fromkeys(
['fuse', 'recloser', 'switch', 'sectionalizer'], False)
#dictionary of protective device initial states for each phase
protDevInitStatus = {}
#dictionary of protective devices final states for each phase after running Gridlab-D
protDevFinalStatus = {}
#dictionary of protective device types to help the testing and debugging process
protDevTypes = {}
protDevOpModes = {}
for key in tree:
obj = tree[key]
obType = obj.get('object')
if obType in protDevices.keys():
obName = obj.get('name', '')
protDevTypes[obName] = obType
if obType != 'fuse':
protDevOpModes[obName] = obj.get('operating_mode',
'INDIVIDUAL')
protDevices[obType] = True
protDevInitStatus[obName] = {}
protDevFinalStatus[obName] = {}
for phase in ['A', 'B', 'C']:
if obType != 'fuse':
phaseState = obj.get('phase_' + phase + '_state', 'CLOSED')
else:
phaseState = obj.get('phase_' + phase + '_status', 'GOOD')
if phase in obj.get('phases', ''):
protDevInitStatus[obName][phase] = phaseState
#print protDevInitStatus
#Create a recorder for protective device states
for key in protDevices.keys():
if protDevices[key]:
for phase in ['A', 'B', 'C']:
if key != 'fuse':
tree[feeder.getMaxKey(tree) + 1] = {
'object': 'group_recorder',
'group': '"class=' + key + '"',
'property': 'phase_' + phase + '_state',
'file': key + '_phase_' + phase + '_state.csv'
}
else:
tree[feeder.getMaxKey(tree) + 1] = {
'object': 'group_recorder',
'group': '"class=' + key + '"',
'property': 'phase_' + phase + '_status',
'file': key + '_phase_' + phase + '_state.csv'
}
# Run Gridlab.
if not workDir:
workDir = tempfile.mkdtemp()
print('@@@@@@', workDir)
# for i in range(6):
# gridlabOut = gridlabd.runInFilesystem(tree, attachments=attachments, workDir=workDir)
# #HACK: workaround for shoddy macOS gridlabd build.
# if 'error when setting parent' not in gridlabOut.get('stderr','OOPS'):
# break
gridlabOut = gridlabd.runInFilesystem(tree,
attachments=attachments,
workDir=workDir)
#Record final status readout of each fuse/recloser/switch/sectionalizer after running
try:
for key in protDevices.keys():
if protDevices[key]:
for phase in ['A', 'B', 'C']:
with open(pJoin(workDir,
key + '_phase_' + phase + '_state.csv'),
newline='') as statusFile:
reader = csv.reader(statusFile)
# loop past the header,
keys = []
vals = []
for row in reader:
if '# timestamp' in row:
keys = row
i = keys.index('# timestamp')
keys.pop(i)
vals = next(reader)
vals.pop(i)
for pos, key2 in enumerate(keys):
protDevFinalStatus[key2][phase] = vals[pos]
except:
pass
#print protDevFinalStatus
#compare initial and final states of protective devices
#quick compare to see if they are equal
#print cmp(protDevInitStatus, protDevFinalStatus)
#find which values changed
changedStates = {}
#read voltDump values into a dictionary.
try:
with open(pJoin(workDir, 'voltDump.csv'), newline='') as dumpFile:
reader = csv.reader(dumpFile)
next(reader) # Burn the header.
keys = next(reader)
voltTable = []
for row in reader:
rowDict = {}
for pos, key in enumerate(keys):
rowDict[key] = row[pos]
voltTable.append(rowDict)
except:
raise Exception(
'GridLAB-D failed to run with the following errors:\n' +
gridlabOut['stderr'])
# read currDump values into a dictionary
with open(pJoin(workDir, 'currDump.csv'), newline='') as currDumpFile:
reader = csv.reader(currDumpFile)
next(reader) # Burn the header.
keys = next(reader)
currTable = []
for row in reader:
rowDict = {}
for pos, key in enumerate(keys):
rowDict[key] = row[pos]
currTable.append(rowDict)
# read line rating values into a single dictionary
lineRatings = {}
rating_in_VA = []
for key1 in edge_bools.keys():
if edge_bools[key1]:
with open(pJoin(workDir, key1 + '_cont_rating.csv'),
newline='') as ratingFile:
reader = csv.reader(ratingFile)
# loop past the header,
keys = []
vals = []
for row in reader:
if '# timestamp' in row:
keys = row
i = keys.index('# timestamp')
keys.pop(i)
vals = next(reader)
vals.pop(i)
for pos, key2 in enumerate(keys):
lineRatings[key2] = abs(float(vals[pos]))
#edgeTupleRatings = lineRatings copy with to-from tuple as keys for labeling
edgeTupleRatings = {}
for edge in lineRatings:
for obj in tree.values():
if obj.get('name', '').replace('"', '') == edge:
nodeFrom = obj.get('from')
nodeTo = obj.get('to')
coord = (nodeFrom, nodeTo)
ratingVal = lineRatings.get(edge)
edgeTupleRatings[coord] = ratingVal
# Calculate average node voltage deviation. First, helper functions.
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. '''
# HACK: add a small value to the denominator to avoid divide by zero for out of service locations (i.e. zero voltage).
return sum(l) / (len(l) + 0.00000000000000001)
# 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 = {}
nodeVoltsPU = {}
nodeVoltsPU120 = {}
voltImbalances = {}
for row in voltTable:
allVolts = []
allVoltsPU = []
allDiffs = []
nodeName = row.get('node_name', '')
for phase in ['A', 'B', 'C']:
realVolt = abs(float(row['volt' + phase + '_real']))
imagVolt = abs(float(row['volt' + phase + '_imag']))
phaseVolt = math.sqrt((realVolt**2) + (imagVolt**2))
if phaseVolt != 0.0:
treeKey = nameToIndex.get(nodeName, 0)
nodeObj = tree.get(treeKey, {})
try:
nominal_voltage = float(nodeObj['nominal_voltage'])
except:
nominal_voltage = feedVoltage
allVolts.append(phaseVolt)
normVolt = (phaseVolt / nominal_voltage)
allVoltsPU.append(normVolt)
avgVolts = avg(allVolts)
avgVoltsPU = avg(allVoltsPU)
avgVoltsPU120 = 120 * avgVoltsPU
nodeVolts[nodeName] = float("{0:.2f}".format(avgVolts))
nodeVoltsPU[nodeName] = float("{0:.2f}".format(avgVoltsPU))
nodeVoltsPU120[nodeName] = float("{0:.2f}".format(avgVoltsPU120))
if len(allVolts) == 3:
voltA = allVolts.pop()
voltB = allVolts.pop()
voltC = allVolts.pop()
allDiffs.append(abs(float(voltA - voltB)))
allDiffs.append(abs(float(voltA - voltC)))
allDiffs.append(abs(float(voltB - voltC)))
maxDiff = max(allDiffs)
voltImbal = maxDiff / avgVolts
voltImbalances[nodeName] = float("{0:.2f}".format(voltImbal))
# Use float("{0:.2f}".format(avg(allVolts))) if displaying the node labels
nodeLoadNames = {}
nodeNames = {}
for key in nodeVolts.keys():
nodeNames[key] = key
if key == loadLoc:
nodeLoadNames[key] = "LOAD: " + key
# find edge currents by parsing currdump
edgeCurrentSum = {}
edgeCurrentMax = {}
for row in currTable:
allCurr = []
for phase in ['A', 'B', 'C']:
realCurr = abs(float(row['curr' + phase + '_real']))
imagCurr = abs(float(row['curr' + phase + '_imag']))
phaseCurr = math.sqrt((realCurr**2) + (imagCurr**2))
allCurr.append(phaseCurr)
edgeCurrentSum[row.get('link_name', '')] = sum(allCurr)
edgeCurrentMax[row.get('link_name', '')] = max(allCurr)
# When just showing current as labels, use sum of the three lines' current values, when showing the per unit values (current/rating), use the max of the three
#edgeTupleCurrents = edgeCurrents copy with to-from tuple as keys for labeling
edgeTupleCurrents = {}
#edgeValsPU = values normalized per unit by line ratings
edgeValsPU = {}
#edgeTupleValsPU = edgeValsPU copy with to-from tuple as keys for labeling
edgeTupleValsPU = {}
#edgeTuplePower = dict with to-from tuples as keys and sim power as values for debugging
edgeTuplePower = {}
#edgeTupleNames = dict with to-from tuples as keys and names as values for debugging
edgeTupleNames = {}
#edgeTupleFaultNames = dict with to-from tuples as keys and the name of the Fault as the only value
edgeTupleFaultNames = {}
#edgeTupleProtDevs = dict with to-from tuples as keys and the initial of the type of protective device as the value
edgeTupleProtDevs = {}
#linePhases = dictionary containing the number of phases on each line for line-width purposes
linePhases = {}
edgePower = {}
for edge in edgeCurrentSum:
for obj in tree.values():
obname = obj.get('name', '').replace('"', '')
if obname == edge:
objType = obj.get('object')
nodeFrom = obj.get('from')
nodeTo = obj.get('to')
coord = (nodeFrom, nodeTo)
currVal = edgeCurrentSum.get(edge)
voltVal = avg([nodeVolts.get(nodeFrom), nodeVolts.get(nodeTo)])
power = (currVal * voltVal) / 1000
lineRating = lineRatings.get(edge, 10.0**9)
edgePerUnitVal = (edgeCurrentMax.get(edge)) / lineRating
edgeTupleCurrents[coord] = "{0:.2f}".format(currVal)
edgeTuplePower[coord] = "{0:.2f}".format(power)
edgePower[edge] = power
edgeValsPU[edge] = edgePerUnitVal
edgeTupleValsPU[coord] = "{0:.2f}".format(edgePerUnitVal)
edgeTupleNames[coord] = edge
if faultLoc == edge:
edgeTupleFaultNames[coord] = "FAULT: " + edge
phaseStr = obj.get('phases', '').replace('"', '').replace(
'N', '').replace('S', '')
numPhases = len(phaseStr)
if (numPhases < 1) or (numPhases > 3):
numPhases = 1
linePhases[edge] = numPhases
protDevLabel = ""
protDevBlownStr = ""
if objType in protDevices.keys():
for phase in protDevFinalStatus[obname].keys():
if objType == 'fuse':
if protDevFinalStatus[obname][phase] == "BLOWN":
protDevBlownStr = "!"
else:
if protDevFinalStatus[obname][phase] == "OPEN":
protDevBlownStr = "!"
if objType == 'fuse':
protDevLabel = 'F'
elif objType == 'switch':
protDevLabel = 'S'
elif objType == 'recloser':
protDevLabel = 'R'
elif objType == 'sectionalizer':
protDevLabel = 'X'
edgeTupleProtDevs[coord] = protDevLabel + protDevBlownStr
#define which dict will be used for edge line color
edgeColors = edgeValsPU
#define which dict will be used for edge label
edgeLabels = edgeTupleValsPU
# Build the graph.
fGraph = feeder.treeToNxGraph(tree)
# TODO: consider whether we can set figsize dynamically.
wlVal = int(math.sqrt(float(rezSqIn)))
voltChart = plt.figure(figsize=(wlVal, wlVal))
plt.axes(frameon=0)
plt.axis('off')
voltChart.gca().set_aspect('equal')
plt.tight_layout()
#set axes step equal
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 = graphviz_layout(cleanG, prog='neato')
else:
remove_nodes = [
n for n in fGraph if fGraph.nodes[n].get('pos', (0, 0)) == (0, 0)
]
fGraph.remove_nodes_from(remove_nodes)
positions = {n: fGraph.nodes[n].get('pos', (0, 0)) for n in fGraph}
# Need to get edge names from pairs of connected node names.
edgeNames = []
for e in fGraph.edges():
edgeNames.append((fGraph.edges[e].get('name',
'BLANK')).replace('"', ''))
#create custom colormap
if customColormap:
if scaleMin != None and scaleMax != None:
scaleDif = scaleMax - scaleMin
custom_cm = matplotlib.colors.LinearSegmentedColormap.from_list(
'custColMap', [(scaleMin, 'blue'),
(scaleMin + (0.12 * scaleDif), 'darkgray'),
(scaleMin + (0.56 * scaleDif), 'darkgray'),
(scaleMin + (0.8 * scaleDif), 'red')])
vmin = scaleMin
vmax = scaleMax
else:
custom_cm = matplotlib.colors.LinearSegmentedColormap.from_list(
'custColMap', [(0.0, 'blue'), (0.15, 'darkgray'),
(0.7, 'darkgray'), (1.0, 'red')])
vmin = 0
vmax = 1.25
custom_cm.set_under(color='black')
else:
custom_cm = plt.cm.get_cmap('viridis')
if scaleMin != None and scaleMax != None:
vmin = scaleMin
vmax = scaleMax
else:
vmin = None
vmax = None
drawColorbar = False
emptyColors = {}
#draw edges with or without colors
if edgeCol != None:
drawColorbar = True
if edgeCol == "Current":
edgeList = [edgeCurrentSum.get(n, 1) for n in edgeNames]
drawColorbar = True
elif edgeCol == "Power":
edgeList = [edgePower.get(n, 1) for n in edgeNames]
drawColorbar = True
elif edgeCol == "Rating":
edgeList = [lineRatings.get(n, 10.0**9) for n in edgeNames]
drawColorbar = True
elif edgeCol == "PercentOfRating":
edgeList = [edgeValsPU.get(n, .5) for n in edgeNames]
drawColorbar = True
else:
edgeList = [emptyColors.get(n, .6) for n in edgeNames]
print(
"WARNING: edgeCol property must be 'Current', 'Power', 'Rating', 'PercentOfRating', or None"
)
else:
edgeList = [emptyColors.get(n, .6) for n in edgeNames]
edgeIm = nx.draw_networkx_edges(
fGraph,
pos=positions,
edge_color=edgeList,
width=[linePhases.get(n, 1) for n in edgeNames],
edge_cmap=custom_cm)
#draw edge labels
if edgeLabs != None:
if edgeLabs == "Name":
edgeLabels = edgeTupleNames
elif edgeLabs == "Fault":
edgeLabels = edgeTupleFaultNames
elif edgeLabs == "Value":
if edgeCol == "Current":
edgeLabels = edgeTupleCurrents
elif edgeCol == "Power":
edgeLabels = edgeTuplePower
elif edgeCol == "Rating":
edgeLabels = edgeTupleRatings
elif edgeCol == "PercentOfRating":
edgeLabels = edgeTupleValsPU
else:
edgeLabels = None
print(
"WARNING: edgeCol property cannot be set to None when edgeLabs property is set to 'Value'"
)
elif edgeLabs == "ProtDevs":
edgeLabels = edgeTupleProtDevs
else:
edgeLabs = None
print(
"WARNING: edgeLabs property must be either 'Name', 'Value', or None"
)
if edgeLabs != None:
edgeLabelsIm = nx.draw_networkx_edge_labels(fGraph,
pos=positions,
edge_labels=edgeLabels,
font_size=8)
# draw nodes with or without color
if nodeCol != None:
if nodeCol == "Voltage":
nodeList = [nodeVolts.get(n, 1) for n in fGraph.nodes()]
drawColorbar = True
elif nodeCol == "VoltageImbalance":
nodeList = [voltImbalances.get(n, 1) for n in fGraph.nodes()]
drawColorbar = True
elif nodeCol == "perUnitVoltage":
nodeList = [nodeVoltsPU.get(n, .5) for n in fGraph.nodes()]
drawColorbar = True
elif nodeCol == "perUnit120Voltage":
nodeList = [nodeVoltsPU120.get(n, 120) for n in fGraph.nodes()]
drawColorbar = True
else:
nodeList = [emptyColors.get(n, 1) for n in fGraph.nodes()]
print(
"WARNING: nodeCol property must be 'Voltage', 'VoltageImbalance', 'perUnitVoltage', 'perUnit120Voltage', or None"
)
else:
nodeList = [emptyColors.get(n, .6) for n in fGraph.nodes()]
nodeIm = nx.draw_networkx_nodes(fGraph,
pos=positions,
node_color=nodeList,
linewidths=0,
node_size=30,
vmin=vmin,
vmax=vmax,
cmap=custom_cm)
#draw node labels
nodeLabels = {}
if nodeLabs != None:
if nodeLabs == "Name":
nodeLabels = nodeNames
elif nodeLabs == "Value":
if nodeCol == "Voltage":
nodeLabels = nodeVolts
elif nodeCol == "VoltageImbalance":
nodeLabels = voltImbalances
elif nodeCol == "perUnitVoltage":
nodeLabels = nodeVoltsPU
elif nodeCol == "perUnit120Voltage":
nodeLabels = nodeVoltsPU120
else:
nodeLabels = None
print(
"WARNING: nodeCol property cannot be set to None when nodeLabs property is set to 'Value'"
)
#HACK: add hidden node label option for displaying specified load name
elif nodeLabs == "Load":
nodeLabels = nodeLoadNames
else:
nodeLabs = None
print(
"WARNING: nodeLabs property must be either 'Name', 'Value', or None"
)
if nodeLabs != None:
nodeLabelsIm = nx.draw_networkx_labels(fGraph,
pos=positions,
labels=nodeLabels,
font_size=8)
plt.sci(nodeIm)
# plt.clim(110,130)
if drawColorbar:
plt.colorbar()
return voltChart
# Delete all climate then reinsert.
reader_name = 'weatherReader'
climate_name = 'MyClimate'
for key in myTree.keys():
obName = myTree[key].get('name','')
obType = myTree[key].get('object','')
if obName in [reader_name, climate_name] or obType is 'climate':
del myTree[key]
oldMax = feeder.getMaxKey(myTree)
myTree[oldMax + 1] = {'omftype':'module', 'argument':'tape'}
myTree[oldMax + 2] = {'omftype':'module', 'argument':'climate'}
myTree[oldMax + 3] = {'object':'csv_reader', 'name':reader_name, 'filename':CSV_NAME}
myTree[oldMax + 4] = {'object':'climate', 'name':climate_name, 'reader': reader_name, 'tmyfile':CSV_NAME}
# Set the time correctly.
feeder.adjustTime(myTree, 240, 'hours', '{}-{}-{}'.format(INIT_TIME.year, INIT_TIME.month, INIT_TIME.day))
# Run here to test.
rawOut = runInFilesystem(myTree, attachments=[], keepFiles=True, workDir='.', glmName='./outFile.glm')
# Write back the full feeder.
# outJson = dict(myFeed)
# with open(CSV_NAME,'r') as weatherFile:
# weatherString = weatherFile.read()
# outJson['attachments']['weatheryearDCA.csv'] = weatherString
# outJson['tree'] = myTree
# try: os.remove('./Orville Tree Pond Calibrated With Weather.json')
# except: pass
# with open('./Orville Tree Pond Calibrated With Weather.json', 'w') as outFile:
# json.dump(outJson, outFile, indent=4)
def run(modelDir, inputDict):
''' Run the model in its directory. '''
try:
# Set up GLM with correct time and recorders:
omd = json.load(open(pJoin(modelDir, 'feeder.omd')))
tree = omd.get('tree', {})
feeder.attachRecorders(tree, "CollectorVoltage", None, None)
feeder.attachRecorders(tree, "Climate", "object", "climate")
feeder.attachRecorders(tree, "OverheadLosses", None, None)
feeder.attachRecorders(tree, "UndergroundLosses", None, None)
feeder.attachRecorders(tree, "TriplexLosses", None, None)
feeder.attachRecorders(tree, "TransformerLosses", None, None)
feeder.groupSwingKids(tree)
feeder.adjustTime(tree, 120, 'hours', '2011-01-01')
# Run GridLAB-D
startTime = dt.datetime.now()
rawOut = gridlabd.runInFilesystem(tree,
attachments=omd.get(
'attachments', {}),
workDir=modelDir)
# Clean the output.
cleanOut = {}
# Std Err and Std Out
cleanOut['stderr'] = rawOut['stderr']
cleanOut['stdout'] = rawOut['stdout']
# Time Stamps
for key in rawOut:
if '# timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key]['# timestamp']
break
elif '# property.. timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key]['# property.. timestamp']
else:
cleanOut['timeStamps'] = []
# Day/Month Aggregation Setup:
stamps = cleanOut.get('timeStamps', [])
level = inputDict.get('simLengthUnits', 'hours')
# Climate
for key in rawOut:
if key.startswith('Climate_') and key.endswith('.csv'):
cleanOut['climate'] = {}
cleanOut['climate']['Rain Fall (in/h)'] = rawOut[key].get(
'rainfall')
cleanOut['climate']['Wind Speed (m/s)'] = rawOut[key].get(
'wind_speed')
cleanOut['climate']['Temperature (F)'] = rawOut[key].get(
'temperature')
cleanOut['climate']['Snow Depth (in)'] = rawOut[key].get(
'snowdepth')
cleanOut['climate']['Direct Normal (W/sf)'] = rawOut[key].get(
'solar_direct')
climateWbySFList = rawOut[key].get('solar_global')
#converting W/sf to W/sm
climateWbySMList = [x * 10.76392 for x in climateWbySFList]
cleanOut['climate'][
'Global Horizontal (W/sm)'] = climateWbySMList
# Voltage Band
if 'VoltageJiggle.csv' in rawOut:
cleanOut['allMeterVoltages'] = {}
cleanOut['allMeterVoltages']['Min'] = [
float(i / 2)
for i in rawOut['VoltageJiggle.csv']['min(voltage_12.mag)']
]
cleanOut['allMeterVoltages']['Mean'] = [
float(i / 2)
for i in rawOut['VoltageJiggle.csv']['mean(voltage_12.mag)']
]
cleanOut['allMeterVoltages']['StdDev'] = [
float(i / 2)
for i in rawOut['VoltageJiggle.csv']['std(voltage_12.mag)']
]
cleanOut['allMeterVoltages']['Max'] = [
float(i / 2)
for i in rawOut['VoltageJiggle.csv']['max(voltage_12.mag)']
]
# Dump the results.
endTime = dt.datetime.now()
inputDict["runTime"] = str(
dt.timedelta(seconds=int((endTime - startTime).total_seconds())))
with open(pJoin(modelDir, "allInputData.json"), "w") as inFile:
json.dump(inputDict, inFile, indent=4)
with open(pJoin(modelDir, "allOutputData.json"), "w") as outFile:
json.dump(cleanOut, outFile, indent=4)
except:
# 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)
from flask import url_for, request
import omf
from omf import feeder
from omf.solvers import gridlabd
from omf.scratch.GRIP import grip
# Place to hack on stuff. Not intended to be run with real tests via pytest
if __name__ == '__main__':
temp_dir = tempfile.mkdtemp()
path = Path(omf.omfDir) / 'scratch/CIGAR/test_ieee123nodeBetter.glm'
#path = Path(__file__).parent / 'test-files/ieee123_pole_vulnerability.glm'
feed = feeder.parse(path)
#import pdb; pdb.set_trace()
outDict = gridlabd.runInFilesystem(feed,
attachments=[],
keepFiles=True,
workDir=temp_dir,
glmName='out.glm')
print(outDict)
@pytest.fixture(scope="module") # The client should only be created once
def client():
# testing must be set to true on the Flask application
grip.app.config['TESTING'] = True
# create a test client with built-in Flask code
client = grip.app.test_client()
# 'yield' instead of 'return' due to how fixtures work in pytest
yield client
# Could put teardown code here if needed
'object': 'underground_line', 'name': 'test_solar_line', 'phases': 'ABCN',
'from': 'test_solar_node', 'to': 'GC-12-47-1_node_26', 'length': '100',
'configuration': 'line_configuration:6'
}
feed[maxKey + 3] = {
'object': 'meter', 'name': 'test_solar_meter', 'parent': 'test_solar_node',
'phases': 'ABCN', 'nominal_voltage': '480'
}
feed[maxKey + 4] = {
'object': 'inverter', 'name': 'test_solar_inverter', 'parent': 'test_solar_meter',
'phases': 'AS', 'inverter_type': 'PWM', 'power_factor': '1.0',
'generator_status': 'ONLINE', 'generator_mode': 'CONSTANT_PF'
}
feed[maxKey + 5] = {
'object': 'solar', 'name': 'test_solar', 'parent': 'test_solar_inverter', 'area': '1000000 sf',
'generator_status': 'ONLINE', 'efficiency': '0.2', 'generator_mode': 'SUPPLY_DRIVEN',
'panel_type': 'SINGLE_CRYSTAL_SILICON'
}
feed[maxKey + 6] = {
'object': 'recorder', 'parent': 'test_solar_meter', 'property': 'voltage_A.real,voltage_A.imag,voltage_B.real,voltage_B.imag,voltage_C.real,voltage_C.imag',
'file': 'GC-addSolar-voltages.csv', 'interval': '60', 'limit': '1440'
}
runInFilesystem(feed, keepFiles = True, workDir = '.', glmName = 'GC-solarAdd.glm')
'''
output = open('GC-solarAdd.glm', 'w')
output.write(feeder.write(feed))
output.close()
'''
if simNum == 0:
rmtree(file)
# copy model and its schedule into the working directory
copy2(TEMPLATE_DIR + MODEL_FILE, WORKING_DIR)
copy2(TEMPLATE_DIR + SCHEDULE_FILE, WORKING_DIR)
# get gridlab model from template
gridlabModel = getRandomGridlabModelFromTemplate( \
WORKING_DIR+MODEL_FILE, deleteEVCharger, \
waterHeaterType, coolingType, heatingType)
# run gridlabd
start = time.time()
gridlabd.runInFilesystem(gridlabModel,
workDir=WORKING_DIR,
keepFiles=True)
end = time.time()
# move all csv files into a new folder,
# delete all files, retain directories
newFolder = WORKING_DIR + SIMULATION_DIR_PREFIX + str(
simNum)
os.mkdir(newFolder)
files = glob(WORKING_DIR + '*')
for file in files:
if file[-3:] == 'csv':
copy2(file, newFolder)
try:
os.remove(file)
except IsADirectoryError:
def runForeground(modelDir, inputDict, fs):
''' 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()
feederJson = json.load(open(pJoin(modelDir, "feeder.json")))
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()
Plot.save_fig(plt, 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 = omf.feeder.treeToNxGraph(tree)
omf.feeder.latLonNxGraph(myGraph, neatoLayout=False)
Plot.save_fig(plt, 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']
for key in tree:
if tree[key].get('name', '') == regConfName:
regConfIndex = key
# Set substation regulator to manual operation.
# GLOBAL VARIABLE FOR DEFAULT TAP POSITION
baselineTap = int(inputDict.get("baselineTap"))
tree[regConfIndex] = {
'name': tree[regConfIndex]['name'],
'object': 'regulator_configuration',
'connect_type': '1',
'raise_taps': '10',
'lower_taps': '10',
'CT_phase': 'ABC',
'PT_phase': 'ABC',
# Yo, 0.10 means at tap_pos 10 we're 10% above 120V.
'regulation': '0.10',
'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")),
# MAYBEFIX: we can probably get this PF data from the
# Milsoft loads.
'power_pf_12': str(inputDict.get("power_factor")),
'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))
Plot.save_fig(plt, 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)
Plot.save_fig(plt, 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 ($)')
plt.tight_layout(5.5, 1.3, 1.2)
fig.autofmt_xdate()
Plot.save_fig(plt, pJoin(modelDir, "spendChart.png"))
allOutput["energyReductionDollars"] = d1
allOutput["lossReductionDollars"] = d2
allOutput["peakReductionDollars"] = d3
# Graph the cumulative savings.
fig = plt.figure(figsize=(10, 5))
annualSavings = sum(d1) + sum(d2) + sum(d3)
annualSave = lambda x: (
annualSavings - rates['omCost']) * x - rates['capitalCost']
simplePayback = rates['capitalCost'] / \
(annualSavings - rates['omCost'])
plt.xlabel('Year After Installation')
plt.xlim(0, 30)
plt.ylabel('Cumulative Savings ($)')
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')
Plot.save_fig(plt, 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())))
fs.save(pJoin(modelDir, "allInputData.json"), json.dumps(inputDict, indent=4))
# Write output file.
fs.save(pJoin(modelDir, "allOutputData.json"), json.dumps(allOutput, 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:
print "Oops, Model Crashed!!!"
print e
cancel(modelDir)
def work(modelDir, inputDict):
''' Run the model in its directory. '''
outData = {}
feederName = [x for x in os.listdir(modelDir)
if x.endswith('.omd')][0][:-4]
inputDict["feederName1"] = feederName
with open(pJoin(modelDir, inputDict['weatherImpactsFileName']),
'w') as hazardFile:
hazardFile.write(inputDict['weatherImpacts'])
with open(pJoin(modelDir, feederName + '.omd'), "r") as jsonIn:
feederModel = json.load(jsonIn)
# Create GFM input file.
print "RUNNING GFM FOR", modelDir
gfmInputTemplate = {
'phase_variation': float(inputDict['phaseVariation']),
'chance_constraint': float(inputDict['chanceConstraint']),
'critical_load_met': float(inputDict['criticalLoadMet']),
'total_load_met':
1.0, #(float(inputDict['criticalLoadMet']) + float(inputDict['nonCriticalLoadMet'])),
'xrMatrices': inputDict["xrMatrices"],
'maxDGPerGenerator': float(inputDict["maxDGPerGenerator"]),
'dgUnitCost': float(inputDict["dgUnitCost"]),
'newLineCandidates': inputDict['newLineCandidates'],
'hardeningCandidates': inputDict['hardeningCandidates'],
'switchCandidates': inputDict['switchCandidates'],
'hardeningUnitCost': inputDict['hardeningUnitCost'],
'switchCost': inputDict['switchCost'],
'generatorCandidates': inputDict['generatorCandidates'],
'lineUnitCost': inputDict['lineUnitCost']
}
gfmJson = convertToGFM(gfmInputTemplate, feederModel)
gfmInputFilename = 'gfmInput.json'
with open(pJoin(modelDir, gfmInputFilename), "w") as outFile:
json.dump(gfmJson, outFile, indent=4)
# Run GFM
gfmBinaryPath = pJoin(__neoMetaModel__._omfDir, 'solvers', 'gfm',
'Fragility.jar')
proc = subprocess.Popen([
'java', '-jar', gfmBinaryPath, '-r', gfmInputFilename, '-wf',
inputDict['weatherImpactsFileName'], '-num', '3'
],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=modelDir)
(stdout, stderr) = proc.communicate()
with open(pJoin(modelDir, "gfmConsoleOut.txt"), "w") as gfmConsoleOut:
gfmConsoleOut.write(stdout)
# HACK: rename the hardcoded gfm output
rdtInputFilePath = pJoin(modelDir, 'rdtInput.json')
#fix for windows web server hangup
rdtInputFilePath = pJoin(modelDir, 'rdt_OUTPUT.json')
#os.rename(pJoin(modelDir,'rdt_OUTPUT.json'),rdtInputFilePath)
# Pull GFM input data on lines and generators for HTML presentation.
with open(rdtInputFilePath, 'r') as rdtInputFile:
# HACK: we use rdtInput as a string in the frontend.
rdtJsonAsString = rdtInputFile.read()
rdtJson = json.loads(rdtJsonAsString)
# Calculate line costs.
lineData = {}
for line in rdtJson["lines"]:
lineData[line["id"]] = '{:,.2f}'.format(
float(line["length"]) * float(inputDict["lineUnitCost"]))
outData["lineData"] = lineData
outData["generatorData"] = '{:,.2f}'.format(
float(inputDict["dgUnitCost"]) * float(inputDict["maxDGPerGenerator"]))
outData['gfmRawOut'] = rdtJsonAsString
if inputDict['scenarios'] != "":
rdtJson['scenarios'] = json.loads(inputDict['scenarios'])
with open(pJoin(rdtInputFilePath), "w") as rdtInputFile:
json.dump(rdtJson, rdtInputFile, indent=4)
# Run GridLAB-D first time to generate xrMatrices.
print "RUNNING GLD FOR", modelDir
if platform.system() == "Windows":
omdPath = pJoin(modelDir, feederName + ".omd")
with open(omdPath, "r") as omd:
omd = json.load(omd)
#REMOVE NEWLINECANDIDATES
deleteList = []
newLines = inputDict["newLineCandidates"].strip().replace(
' ', '').split(',')
for newLine in newLines:
for omdObj in omd["tree"]:
if ("name" in omd["tree"][omdObj]):
if (newLine == omd["tree"][omdObj]["name"]):
deleteList.append(omdObj)
for delItem in deleteList:
del omd["tree"][delItem]
#Load a blank glm file and use it to write to it
feederPath = pJoin(modelDir, 'feeder.glm')
with open(feederPath, 'w') as glmFile:
toWrite = omf.feeder.sortedWrite(
omd['tree']
) + "object jsondump {\n\tfilename_dump_reliability test_JSON_dump.json;\n\twrite_system_info true;\n\twrite_per_unit true;\n\tsystem_base 100.0 MVA;\n};\n" # + "object jsonreader {\n\tfilename " + insertRealRdtOutputNameHere + ";\n};"
glmFile.write(toWrite)
#Write attachments from omd, if no file, one will be created
for fileName in omd['attachments']:
with open(os.path.join(modelDir, fileName), 'w') as file:
file.write(omd['attachments'][fileName])
#Wire in the file the user specifies via zipcode.
climateFileName, latforpvwatts = zipCodeToClimateName(
inputDict["simulationZipCode"])
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
climateFileName + ".tmy2"), pJoin(modelDir, 'climate.tmy2'))
proc = subprocess.Popen(['gridlabd', 'feeder.glm'],
stdout=subprocess.PIPE,
shell=True,
cwd=modelDir)
(out, err) = proc.communicate()
with open(pJoin(modelDir, "gldConsoleOut.txt"), "w") as gldConsoleOut:
gldConsoleOut.write(out)
accumulator = ""
with open(pJoin(modelDir, "JSON_dump_line.json"), "r") as gldOut:
accumulator = json.load(gldOut)
outData['gridlabdRawOut'] = accumulator
#Data trabsformation for GLD
rdtJson["line_codes"] = accumulator["properties"]["line_codes"]
rdtJson["lines"] = accumulator["properties"]["lines"]
for item in rdtJson["lines"]:
item['node1_id'] = item['node1_id'] + "_bus"
item['node2_id'] = item['node2_id'] + "_bus"
with open(pJoin(modelDir, rdtInputFilePath), "w") as outFile:
json.dump(rdtJson, outFile, indent=4)
'''rdtJson["line_codes"] = accumulator["properties"]["line_codes"]
counter = 1
lineCodeTracker = {}
for item in rdtJson["line_codes"]:
lineCodeTracker[item['line_code']] = counter
item['line_code'] = counter
counter = counter + 1
rdtJson["lines"] = accumulator["properties"]["lines"]
print lineCodeTracker
for line in rdtJson["lines"]:
line["line_code"] = lineCodeTracker[line["line_code"]]
with open(pJoin(modelDir, rdtInputFilePath), "w") as outFile:
json.dump(rdtJson, outFile, indent=4)'''
else:
tree = feederModel.get("tree", {})
attachments = feederModel.get("attachments", {})
climateFileName, latforpvwatts = zipCodeToClimateName(
inputDict["simulationZipCode"])
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
climateFileName + ".tmy2"), pJoin(modelDir, 'climate.tmy2'))
gridlabdRawOut = gridlabd.runInFilesystem(tree,
attachments=attachments,
workDir=modelDir)
outData['gridlabdRawOut'] = gridlabdRawOut
# Run RDT.
print "RUNNING RDT FOR", modelDir
rdtOutFile = modelDir + '/rdtOutput.json'
rdtSolverFolder = pJoin(__neoMetaModel__._omfDir, 'solvers', 'rdt')
rdtJarPath = pJoin(rdtSolverFolder, 'micot-rdt.jar')
#TEST RUSSELL THING, DELETE WHEN DONE
#shutil.copy(pJoin(__neoMetaModel__._omfDir, "scratch", "rdt_OUTPUTTEST.json"), pJoin(modelDir, 'rdt_OUTPUT.json'))
#############
proc = subprocess.Popen([
'java', "-Djna.library.path=" + rdtSolverFolder, '-jar', rdtJarPath,
'-c', rdtInputFilePath, '-e', rdtOutFile
],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
(stdout, stderr) = proc.communicate()
with open(pJoin(modelDir, "rdtConsoleOut.txt"), "w") as rdtConsoleOut:
rdtConsoleOut.write(stdout)
rdtRawOut = open(rdtOutFile).read()
outData['rdtRawOut'] = rdtRawOut
# Indent the RDT output nicely.
with open(pJoin(rdtOutFile), "w") as outFile:
rdtOut = json.loads(rdtRawOut)
json.dump(rdtOut, outFile, indent=4)
# Generate and run 2nd copy of GridLAB-D model with changes specified by RDT.
print "RUNNING GLD FOR", modelDir
feederCopy = copy.deepcopy(feederModel)
lineSwitchList = []
for line in rdtOut['design_solution']['lines']:
if ('switch_built' in line):
lineSwitchList.append(line['id'])
# Remove nonessential lines in second model as indicated by RDT output.
for key in feederCopy['tree'].keys():
value = feederCopy['tree'][key]
if ('object' in value):
if (value['object'] == 'underground_line') or (value['object']
== 'overhead_line'):
if value['name'] not in lineSwitchList:
del feederCopy['tree'][key]
#Add generators to second model.
maxTreeKey = int(max(feederCopy['tree'], key=int)) + 1
'''for gen in rdtOut['design_solution']['generators']:
newGen = {}
newGen["object"] = "diesel_dg"
newGen["name"] = gen['id']
newGen["parent"] = gen['id'][:-4]
newGen["phases"] = "ABC"
newGen["Gen_type"] = "CONSTANT_PQ"
newGen["Rated_VA"] = "5.0 kVA"
newGen["power_out_A"] = "250.0+120.0j"
newGen["power_out_B"] = "230.0+130.0j"
newGen["power_out_C"] = "220.0+150.0j"
feederCopy['tree'][str(maxTreeKey)] = newGen
maxTreeKey = maxTreeKey + 1
'''
maxTreeKey = max(feederCopy['tree'], key=int)
# Load a blank glm file and use it to write to it
feederPath = pJoin(modelDir, 'feederSecond.glm')
with open(feederPath, 'w') as glmFile:
toWrite = "module generators;\n\n" + omf.feeder.sortedWrite(
feederCopy['tree']
) + "object voltdump {\n\tfilename voltDump2ndRun.csv;\n};\nobject jsondump {\n\tfilename_dump_reliability test_JSON_dump.json;\n\twrite_system_info true;\n\twrite_per_unit true;\n\tsystem_base 100.0 MVA;\n};\n" # + "object jsonreader {\n\tfilename " + insertRealRdtOutputNameHere + ";\n};"
glmFile.write(toWrite)
# Run GridLAB-D second time.
if platform.system() == "Windows":
proc = subprocess.Popen(['gridlabd', 'feederSecond.glm'],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True,
cwd=modelDir)
(out, err) = proc.communicate()
outData["secondGLD"] = str(
os.path.isfile(pJoin(modelDir, "voltDump2ndRun.csv")))
else:
# TODO: make 2nd run of GridLAB-D work on Unixes.
outData["secondGLD"] = str(False)
# Draw the feeder.
genDiagram(modelDir, feederModel)
with open(pJoin(modelDir, "feederChart.png"), "rb") as inFile:
outData["oneLineDiagram"] = inFile.read().encode("base64")
# And we're done.
return outData
def runForeground(modelDir, inputDict, fs):
""" Run the model in its directory. WARNING: GRIDLAB CAN TAKE HOURS TO COMPLETE. """
print "STARTING TO RUN", modelDir
beginTime = datetime.datetime.now()
feederList = []
# Get prepare of data and clean workspace if re-run, If re-run remove all
# the data in the subfolders
for dirs in os.listdir(modelDir):
if os.path.isdir(pJoin(modelDir, dirs)):
shutil.rmtree(pJoin(modelDir, dirs))
# Get each feeder, prepare data in separate folders, and run there.
for key in sorted(inputDict, key=inputDict.get):
if key.startswith("feederName"):
feederDir, feederName = inputDict[key].split("___")
feederList.append(feederName)
try:
os.remove(pJoin(modelDir, feederName, "allOutputData.json"))
fs.remove(pJoin(modelDir, feederName, "allOutputData.json"))
except Exception, e:
pass
if not os.path.isdir(pJoin(modelDir, feederName)):
# create subfolders for feeders
os.makedirs(pJoin(modelDir, feederName))
fs.export_from_fs_to_local(
pJoin("data", "Feeder", feederDir, feederName + ".json"), pJoin(modelDir, feederName, "feeder.json")
)
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(inputDict["zipCode"], fs)
fs.export_from_fs_to_local(
pJoin("data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, feederName, "climate.tmy2"),
)
try:
startTime = datetime.datetime.now()
feederJson = json.load(open(pJoin(modelDir, feederName, "feeder.json")))
tree = feederJson["tree"]
# Set up GLM with correct time and recorders:
feeder.attachRecorders(tree, "Regulator", "object", "regulator")
feeder.attachRecorders(tree, "Capacitor", "object", "capacitor")
feeder.attachRecorders(tree, "Inverter", "object", "inverter")
feeder.attachRecorders(tree, "Windmill", "object", "windturb_dg")
feeder.attachRecorders(tree, "CollectorVoltage", None, None)
feeder.attachRecorders(tree, "Climate", "object", "climate")
feeder.attachRecorders(tree, "OverheadLosses", None, None)
feeder.attachRecorders(tree, "UndergroundLosses", None, None)
feeder.attachRecorders(tree, "TriplexLosses", None, None)
feeder.attachRecorders(tree, "TransformerLosses", None, None)
feeder.groupSwingKids(tree)
feeder.adjustTime(
tree=tree,
simLength=float(inputDict["simLength"]),
simLengthUnits=inputDict["simLengthUnits"],
simStartDate=inputDict["simStartDate"],
)
if "attachments" in feederJson:
attachments = feederJson["attachments"]
else:
attachments = []
# RUN GRIDLABD IN FILESYSTEM (EXPENSIVE!)
rawOut = gridlabd.runInFilesystem(
tree, attachments=attachments, keepFiles=True, workDir=pJoin(modelDir, feederName)
)
cleanOut = {}
# Std Err and Std Out
cleanOut["stderr"] = rawOut["stderr"]
cleanOut["stdout"] = rawOut["stdout"]
# Time Stamps
for key in rawOut:
if "# timestamp" in rawOut[key]:
cleanOut["timeStamps"] = rawOut[key]["# timestamp"]
break
elif "# property.. timestamp" in rawOut[key]:
cleanOut["timeStamps"] = rawOut[key]["# property.. timestamp"]
else:
cleanOut["timeStamps"] = []
# Day/Month Aggregation Setup:
stamps = cleanOut.get("timeStamps", [])
level = inputDict.get("simLengthUnits", "hours")
# Climate
for key in rawOut:
if key.startswith("Climate_") and key.endswith(".csv"):
cleanOut["climate"] = {}
cleanOut["climate"]["Rain Fall (in/h)"] = hdmAgg(
rawOut[key].get("rainfall"), sum, level, stamps
)
cleanOut["climate"]["Wind Speed (m/s)"] = hdmAgg(
rawOut[key].get("wind_speed"), avg, level, stamps
)
cleanOut["climate"]["Temperature (F)"] = hdmAgg(
rawOut[key].get("temperature"), max, level, stamps
)
cleanOut["climate"]["Snow Depth (in)"] = hdmAgg(
rawOut[key].get("snowdepth"), max, level, stamps
)
cleanOut["climate"]["Direct Insolation (W/m^2)"] = hdmAgg(
rawOut[key].get("solar_direct"), sum, level, stamps
)
# Voltage Band
if "VoltageJiggle.csv" in rawOut:
cleanOut["allMeterVoltages"] = {}
cleanOut["allMeterVoltages"]["Min"] = hdmAgg(
[float(i / 2) for i in rawOut["VoltageJiggle.csv"]["min(voltage_12.mag)"]], min, level, stamps
)
cleanOut["allMeterVoltages"]["Mean"] = hdmAgg(
[float(i / 2) for i in rawOut["VoltageJiggle.csv"]["mean(voltage_12.mag)"]], avg, level, stamps
)
cleanOut["allMeterVoltages"]["StdDev"] = hdmAgg(
[float(i / 2) for i in rawOut["VoltageJiggle.csv"]["std(voltage_12.mag)"]], avg, level, stamps
)
cleanOut["allMeterVoltages"]["Max"] = hdmAgg(
[float(i / 2) for i in rawOut["VoltageJiggle.csv"]["max(voltage_12.mag)"]], max, level, stamps
)
# Power Consumption
cleanOut["Consumption"] = {}
# Set default value to be 0, avoiding missing value when
# computing Loads
cleanOut["Consumption"]["Power"] = [0] * int(inputDict["simLength"])
cleanOut["Consumption"]["Losses"] = [0] * int(inputDict["simLength"])
cleanOut["Consumption"]["DG"] = [0] * int(inputDict["simLength"])
for key in rawOut:
if key.startswith("SwingKids_") and key.endswith(".csv"):
oneSwingPower = hdmAgg(
vecPyth(rawOut[key]["sum(power_in.real)"], rawOut[key]["sum(power_in.imag)"]),
avg,
level,
stamps,
)
if "Power" not in cleanOut["Consumption"]:
cleanOut["Consumption"]["Power"] = oneSwingPower
else:
cleanOut["Consumption"]["Power"] = vecSum(oneSwingPower, cleanOut["Consumption"]["Power"])
elif key.startswith("Inverter_") and key.endswith(".csv"):
realA = rawOut[key]["power_A.real"]
realB = rawOut[key]["power_B.real"]
realC = rawOut[key]["power_C.real"]
imagA = rawOut[key]["power_A.imag"]
imagB = rawOut[key]["power_B.imag"]
imagC = rawOut[key]["power_C.imag"]
oneDgPower = hdmAgg(
vecSum(vecPyth(realA, imagA), vecPyth(realB, imagB), vecPyth(realC, imagC)),
avg,
level,
stamps,
)
if "DG" not in cleanOut["Consumption"]:
cleanOut["Consumption"]["DG"] = oneDgPower
else:
cleanOut["Consumption"]["DG"] = vecSum(oneDgPower, cleanOut["Consumption"]["DG"])
elif key.startswith("Windmill_") and key.endswith(".csv"):
vrA = rawOut[key]["voltage_A.real"]
vrB = rawOut[key]["voltage_B.real"]
vrC = rawOut[key]["voltage_C.real"]
viA = rawOut[key]["voltage_A.imag"]
viB = rawOut[key]["voltage_B.imag"]
viC = rawOut[key]["voltage_C.imag"]
crB = rawOut[key]["current_B.real"]
crA = rawOut[key]["current_A.real"]
crC = rawOut[key]["current_C.real"]
ciA = rawOut[key]["current_A.imag"]
ciB = rawOut[key]["current_B.imag"]
ciC = rawOut[key]["current_C.imag"]
powerA = vecProd(vecPyth(vrA, viA), vecPyth(crA, ciA))
powerB = vecProd(vecPyth(vrB, viB), vecPyth(crB, ciB))
powerC = vecProd(vecPyth(vrC, viC), vecPyth(crC, ciC))
# HACK: multiply by negative one because turbine power
# sign is opposite all other DG:
oneDgPower = [-1.0 * x for x in hdmAgg(vecSum(powerA, powerB, powerC), avg, level, stamps)]
if "DG" not in cleanOut["Consumption"]:
cleanOut["Consumption"]["DG"] = oneDgPower
else:
cleanOut["Consumption"]["DG"] = vecSum(oneDgPower, cleanOut["Consumption"]["DG"])
elif key in [
"OverheadLosses.csv",
"UndergroundLosses.csv",
"TriplexLosses.csv",
"TransformerLosses.csv",
]:
realA = rawOut[key]["sum(power_losses_A.real)"]
imagA = rawOut[key]["sum(power_losses_A.imag)"]
realB = rawOut[key]["sum(power_losses_B.real)"]
imagB = rawOut[key]["sum(power_losses_B.imag)"]
realC = rawOut[key]["sum(power_losses_C.real)"]
imagC = rawOut[key]["sum(power_losses_C.imag)"]
oneLoss = hdmAgg(
vecSum(vecPyth(realA, imagA), vecPyth(realB, imagB), vecPyth(realC, imagC)),
avg,
level,
stamps,
)
if "Losses" not in cleanOut["Consumption"]:
cleanOut["Consumption"]["Losses"] = oneLoss
else:
cleanOut["Consumption"]["Losses"] = vecSum(oneLoss, cleanOut["Consumption"]["Losses"])
# Aggregate up the timestamps:
if level == "days":
cleanOut["timeStamps"] = aggSeries(stamps, stamps, lambda x: x[0][0:10], "days")
elif level == "months":
cleanOut["timeStamps"] = aggSeries(stamps, stamps, lambda x: x[0][0:7], "months")
# Write the output.
with open(pJoin(modelDir, feederName, "allOutputData.json"), "w") as outFile:
json.dump(cleanOut, outFile, indent=4)
# 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, feederName, "allInputData.json"), "w") as inFile:
json.dump(inputDict, inFile, indent=4)
# Clean up the PID file.
os.remove(pJoin(modelDir, feederName, "PID.txt"))
print "DONE RUNNING GRIDLABMULTI", modelDir, feederName
except Exception as e:
print "MODEL CRASHED GRIDLABMULTI", e, modelDir, feederName
cancel(pJoin(modelDir, feederName))
with open(pJoin(modelDir, feederName, "stderr.txt"), "a+") as stderrFile:
traceback.print_exc(file=stderrFile)
def runForeground(modelDir, test_mode=False):
''' Run the model in its directory. WARNING: GRIDLAB CAN TAKE HOURS TO COMPLETE. '''
with open(pJoin(modelDir, 'allInputData.json')) as f:
inputDict = json.load(f)
print("STARTING TO RUN", modelDir)
beginTime = datetime.datetime.now()
# Get prepare of data and clean workspace if re-run, If re-run remove all the data in the subfolders
for dirs in os.listdir(modelDir):
if os.path.isdir(pJoin(modelDir, dirs)):
shutil.rmtree(pJoin(modelDir, dirs))
# Get the names of the feeders from the .omd files:
feederNames = [x[0:-4] for x in os.listdir(modelDir) if x.endswith(".omd")]
for i, key in enumerate(feederNames):
inputDict['feederName' + str(i + 1)] = feederNames[i]
# Run GridLAB-D once for each feeder:
for feederName in feederNames:
try:
os.remove(pJoin(modelDir, feederName, "allOutputData.json"))
except Exception as e:
pass
if not os.path.isdir(pJoin(modelDir, feederName)):
os.makedirs(pJoin(modelDir,
feederName)) # create subfolders for feeders
shutil.copy(pJoin(modelDir, feederName + ".omd"),
pJoin(modelDir, feederName, "feeder.omd"))
inputDict["climateName"] = weather.zipCodeToClimateName(
inputDict["zipCode"])
shutil.copy(
pJoin(_omfDir, "data", "Climate",
inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, feederName, "climate.tmy2"))
try:
startTime = datetime.datetime.now()
with open(pJoin(modelDir, feederName, "feeder.omd")) as f:
feederJson = json.load(f)
tree = feederJson["tree"]
# Set up GLM with correct time and recorders:
feeder.attachRecorders(tree, "Regulator", "object", "regulator")
feeder.attachRecorders(tree, "Capacitor", "object", "capacitor")
feeder.attachRecorders(tree, "Inverter", "object", "inverter")
feeder.attachRecorders(tree, "Windmill", "object", "windturb_dg")
feeder.attachRecorders(tree, "CollectorVoltage", None, None)
feeder.attachRecorders(tree, "Climate", "object", "climate")
feeder.attachRecorders(tree, "OverheadLosses", None, None)
feeder.attachRecorders(tree, "UndergroundLosses", None, None)
feeder.attachRecorders(tree, "TriplexLosses", None, None)
feeder.attachRecorders(tree, "TransformerLosses", None, None)
feeder.groupSwingKids(tree)
feeder.adjustTime(tree=tree,
simLength=float(inputDict["simLength"]),
simLengthUnits=inputDict["simLengthUnits"],
simStartDate=inputDict["simStartDate"])
# RUN GRIDLABD IN FILESYSTEM (EXPENSIVE!)
rawOut = gridlabd.runInFilesystem(
tree,
attachments=feederJson["attachments"],
keepFiles=True,
workDir=pJoin(modelDir, feederName))
cleanOut = {}
# Std Err and Std Out
cleanOut['stderr'] = rawOut['stderr']
cleanOut['stdout'] = rawOut['stdout']
# Time Stamps
for key in rawOut:
if '# timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key]['# timestamp']
break
elif '# property.. timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key][
'# property.. timestamp']
else:
cleanOut['timeStamps'] = []
# Day/Month Aggregation Setup:
stamps = cleanOut.get('timeStamps', [])
level = inputDict.get('simLengthUnits', 'hours')
# Climate
for key in rawOut:
if key.startswith('Climate_') and key.endswith('.csv'):
cleanOut['climate'] = {}
cleanOut['climate']['Rain Fall (in/h)'] = hdmAgg(
rawOut[key].get('rainfall'), sum, level)
cleanOut['climate']['Wind Speed (m/s)'] = hdmAgg(
rawOut[key].get('wind_speed'), avg, level)
cleanOut['climate']['Temperature (F)'] = hdmAgg(
rawOut[key].get('temperature'), max, level)
cleanOut['climate']['Snow Depth (in)'] = hdmAgg(
rawOut[key].get('snowdepth'), max, level)
cleanOut['climate']['Direct Insolation (W/m^2)'] = hdmAgg(
rawOut[key].get('solar_direct'), sum, level)
# Voltage Band
if 'VoltageJiggle.csv' in rawOut:
cleanOut['allMeterVoltages'] = {}
cleanOut['allMeterVoltages']['Min'] = hdmAgg([
(i / 2)
for i in rawOut['VoltageJiggle.csv']['min(voltage_12.mag)']
], min, level)
cleanOut['allMeterVoltages']['Mean'] = hdmAgg(
[(i / 2) for i in rawOut['VoltageJiggle.csv']
['mean(voltage_12.mag)']], avg, level)
cleanOut['allMeterVoltages']['StdDev'] = hdmAgg([
(i / 2)
for i in rawOut['VoltageJiggle.csv']['std(voltage_12.mag)']
], avg, level)
cleanOut['allMeterVoltages']['Max'] = hdmAgg([
(i / 2)
for i in rawOut['VoltageJiggle.csv']['max(voltage_12.mag)']
], max, level)
cleanOut['allMeterVoltages']['stdDevPos'] = [
(x + y / 2)
for x, y in zip(cleanOut['allMeterVoltages']['Mean'],
cleanOut['allMeterVoltages']['StdDev'])
]
cleanOut['allMeterVoltages']['stdDevNeg'] = [
(x - y / 2)
for x, y in zip(cleanOut['allMeterVoltages']['Mean'],
cleanOut['allMeterVoltages']['StdDev'])
]
# Total # of meters
count = 0
with open(pJoin(modelDir, feederName, "feeder.omd")) as f:
for line in f:
if "\"objectType\": \"triplex_meter\"" in line:
count += 1
# print "count=", count
cleanOut['allMeterVoltages']['triplexMeterCount'] = float(count)
# Power Consumption
cleanOut['Consumption'] = {}
# Set default value to be 0, avoiding missing value when computing Loads
cleanOut['Consumption']['Power'] = [0] * int(
inputDict["simLength"])
cleanOut['Consumption']['Losses'] = [0] * int(
inputDict["simLength"])
cleanOut['Consumption']['DG'] = [0] * int(inputDict["simLength"])
for key in rawOut:
if key.startswith('SwingKids_') and key.endswith('.csv'):
oneSwingPower = hdmAgg(
vecPyth(rawOut[key]['sum(power_in.real)'],
rawOut[key]['sum(power_in.imag)']), avg, level)
if 'Power' not in cleanOut['Consumption']:
cleanOut['Consumption']['Power'] = oneSwingPower
else:
cleanOut['Consumption']['Power'] = vecSum(
oneSwingPower, cleanOut['Consumption']['Power'])
elif key.startswith('Inverter_') and key.endswith('.csv'):
realA = rawOut[key]['power_A.real']
realB = rawOut[key]['power_B.real']
realC = rawOut[key]['power_C.real']
imagA = rawOut[key]['power_A.imag']
imagB = rawOut[key]['power_B.imag']
imagC = rawOut[key]['power_C.imag']
oneDgPower = hdmAgg(
vecSum(vecPyth(realA, imagA), vecPyth(realB, imagB),
vecPyth(realC, imagC)), avg, level)
if 'DG' not in cleanOut['Consumption']:
cleanOut['Consumption']['DG'] = oneDgPower
else:
cleanOut['Consumption']['DG'] = vecSum(
oneDgPower, cleanOut['Consumption']['DG'])
elif key.startswith('Windmill_') and key.endswith('.csv'):
vrA = rawOut[key]['voltage_A.real']
vrB = rawOut[key]['voltage_B.real']
vrC = rawOut[key]['voltage_C.real']
viA = rawOut[key]['voltage_A.imag']
viB = rawOut[key]['voltage_B.imag']
viC = rawOut[key]['voltage_C.imag']
crB = rawOut[key]['current_B.real']
crA = rawOut[key]['current_A.real']
crC = rawOut[key]['current_C.real']
ciA = rawOut[key]['current_A.imag']
ciB = rawOut[key]['current_B.imag']
ciC = rawOut[key]['current_C.imag']
powerA = vecProd(vecPyth(vrA, viA), vecPyth(crA, ciA))
powerB = vecProd(vecPyth(vrB, viB), vecPyth(crB, ciB))
powerC = vecProd(vecPyth(vrC, viC), vecPyth(crC, ciC))
# HACK: multiply by negative one because turbine power sign is opposite all other DG:
oneDgPower = [
-1.0 * x for x in hdmAgg(
vecSum(powerA, powerB, powerC), avg, level)
]
if 'DG' not in cleanOut['Consumption']:
cleanOut['Consumption']['DG'] = oneDgPower
else:
cleanOut['Consumption']['DG'] = vecSum(
oneDgPower, cleanOut['Consumption']['DG'])
elif key in [
'OverheadLosses.csv', 'UndergroundLosses.csv',
'TriplexLosses.csv', 'TransformerLosses.csv'
]:
realA = rawOut[key]['sum(power_losses_A.real)']
imagA = rawOut[key]['sum(power_losses_A.imag)']
realB = rawOut[key]['sum(power_losses_B.real)']
imagB = rawOut[key]['sum(power_losses_B.imag)']
realC = rawOut[key]['sum(power_losses_C.real)']
imagC = rawOut[key]['sum(power_losses_C.imag)']
oneLoss = hdmAgg(
vecSum(vecPyth(realA, imagA), vecPyth(realB, imagB),
vecPyth(realC, imagC)), avg, level)
if 'Losses' not in cleanOut['Consumption']:
cleanOut['Consumption']['Losses'] = oneLoss
else:
cleanOut['Consumption']['Losses'] = vecSum(
oneLoss, cleanOut['Consumption']['Losses'])
# Aggregate up the timestamps:
if level == 'days':
cleanOut['timeStamps'] = aggSeries(stamps, stamps,
lambda x: x[0][0:10],
'days')
elif level == 'months':
cleanOut['timeStamps'] = aggSeries(stamps, stamps,
lambda x: x[0][0:7],
'months')
# Write the output.
with open(pJoin(modelDir, feederName, "allOutputData.json"),
"w") as outFile:
json.dump(cleanOut, outFile, indent=4)
# 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, feederName, "allInputData.json"),
"w") as inFile:
json.dump(inputDict, inFile, indent=4)
# Clean up the PID file.
os.remove(pJoin(modelDir, feederName, "PID.txt"))
print("DONE RUNNING GRIDLABMULTI", modelDir, feederName)
except Exception as e:
if test_mode == True:
raise e
print("MODEL CRASHED GRIDLABMULTI", e, modelDir, feederName)
cancel(pJoin(modelDir, feederName))
with open(pJoin(modelDir, feederName, "stderr.txt"),
"a+") as stderrFile:
traceback.print_exc(file=stderrFile)
finishTime = datetime.datetime.now()
inputDict["runTime"] = str(
datetime.timedelta(seconds=int((finishTime -
beginTime).total_seconds())))
with open(pJoin(modelDir, "allInputData.json"), "w") as inFile:
json.dump(inputDict, inFile, indent=4)
# Integrate data into allOutputData.json, if error happens, cancel it
try:
output = {}
output["failures"] = {}
numOfFeeders = 0
for root, dirs, files in os.walk(modelDir):
# dump error info into dict
if "stderr.txt" in files:
with open(pJoin(root, "stderr.txt"), "r") as stderrFile:
tempString = stderrFile.read()
if "ERROR" in tempString or "FATAL" in tempString or "Traceback" in tempString:
output["failures"]["feeder_" +
str(os.path.split(root)[-1])] = {
"stderr": tempString
}
continue
# dump simulated data into dict
if "allOutputData.json" in files:
with open(pJoin(root, "allOutputData.json"),
"r") as feederOutputData:
numOfFeeders += 1
feederOutput = json.load(feederOutputData)
# TODO: a better feeder name
output["feeder_" + str(os.path.split(root)[-1])] = {}
output["feeder_" +
str(os.path.split(root)[-1]
)]["Consumption"] = feederOutput["Consumption"]
output["feeder_" + str(os.path.split(root)[-1])][
"allMeterVoltages"] = feederOutput["allMeterVoltages"]
output["feeder_" + str(os.path.split(
root)[-1])]["stderr"] = feederOutput["stderr"]
output["feeder_" + str(os.path.split(
root)[-1])]["stdout"] = feederOutput["stdout"]
# output[root] = {feederOutput["Consumption"], feederOutput["allMeterVoltages"], feederOutput["stdout"], feederOutput["stderr"]}
output["numOfFeeders"] = numOfFeeders
output["timeStamps"] = feederOutput.get("timeStamps", [])
output["climate"] = feederOutput.get("climate", [])
# Add feederNames to output so allInputData feederName changes don't cause output rendering to disappear.
for key, feederName in inputDict.items():
if 'feederName' in key:
output[key] = feederName
with open(pJoin(modelDir, "allOutputData.json"), "w") as outFile:
json.dump(output, outFile, indent=4)
try:
os.remove(pJoin(modelDir, "PPID.txt"))
except:
pass
# Send email to user on model success.
emailStatus = inputDict.get('emailStatus', 0)
if (emailStatus == "on"):
print("\n EMAIL ALERT ON")
email = session['user_id']
try:
with open("data/User/" + email + ".json") as f:
user = json.load(f)
modelPath, modelName = pSplit(modelDir)
message = "The model " + "<i>" + str(
modelName
) + "</i>" + " has successfully completed running. It ran for a total of " + str(
inputDict["runTime"]) + " seconds from " + str(
beginTime) + ", to " + str(finishTime) + "."
return web.send_link(email, message, user)
except Exception as e:
print("ERROR: Failed sending model status email to user: "******", with exception: \n", e)
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)
# Send email to user on model failure.
email = 'NoEmail'
try:
email = session['user_id']
with open("data/User/" + email + ".json") as f:
user = json.load(f)
modelPath, modelName = pSplit(modelDir)
message = "The model " + "<i>" + str(
modelName
) + "</i>" + " has failed to complete running. It ran for a total of " + str(
inputDict["runTime"]) + " seconds from " + str(
beginTime) + ", to " + str(finishTime) + "."
return web.send_link(email, message, user)
except Exception as e:
print("Failed sending model status email to user: "******", with exception: \n", e)
def work(modelDir,inputDict):
'''This reads a glm file, changes the method of powerflow and reruns'''
feederName = [x for x in os.listdir(modelDir) if x.endswith('.omd')][0][:-4]
inputDict['feederName1'] = feederName
feederPath = pJoin(modelDir,feederName+'.omd')
# Reads a pre-calibrated feeder.
outData = {}
with open(feederPath, 'r') as jsonIn:
feederJson = json.load(jsonIn)
localTree = feederJson.get('tree', {})
attachments = feederJson.get('attachments', {})
for key in localTree:
if 'solver_method' in localTree[key].keys():
localTree[key]['solver_method'] = 'FBS'
#find the swing bus and recorder attached to substation
try:
for key in localTree:
if localTree[key].get('bustype','').lower() == 'swing':
swingIndex = key
swingName = localTree[key].get('name')
if localTree[key].get('object','') == 'regulator' and localTree[key].get('from','') == swingName:
regIndex = key
regConfName = localTree[key]['configuration']
except: raise ValueError('Invalid feeder selected:', str(inputDict['feederName1']))
#find the regulator and capacitor names and combine to form a string for volt-var control object
regKeys = []
accum_reg = ''
for key in localTree:
if localTree[key].get('object','') == 'regulator':
accum_reg += localTree[key].get('name','ERROR') + ','
regKeys.append(key)
regstr = accum_reg[:-1]
capKeys = []
accum_cap = ''
for key in localTree:
if localTree[key].get('object','') == 'capacitor':
accum_cap += localTree[key].get('name','ERROR') + ','
capKeys.append(key)
if localTree[key].get('control','').lower() == 'manual':
localTree[key]['control'] = 'VOLT'
capstr = accum_cap[:-1]
# 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': localTree[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': localTree[swingIndex]['name'],
'property': 'voltage_A,voltage_B,voltage_C'},
{'object': 'recorder',
'file': 'ZsubstationBottom.csv',
'limit': '0',
'parent': localTree[regIndex]['to'],
'property': 'voltage_A,voltage_B,voltage_C'}]
#recorder object for capacitor switching - if capacitors exist
if capKeys != []:
for key in capKeys:
recorders.append({'object': 'recorder',
'file': 'ZcapSwitch' + str(key) + '.csv',
'limit': '0',
'parent': localTree[key]['name'],
'property': 'switchA,switchB,switchC'})
#attach recorder process
biggest = 1 + max([int(k) for k in localTree.keys()])
for index, rec in enumerate(recorders):
localTree[biggest + index] = rec
#run a reference load flow
HOURS = float(inputDict['simLengthHours'])
simStartDate = inputDict['simStart']
feeder.adjustTime(localTree,HOURS,'hours',simStartDate)
output = gridlabd.runInFilesystem(localTree, attachments, keepFiles=False,workDir=modelDir)
try: os.remove(pJoin(modelDir,'PID.txt'))
except: pass
p = output['Zregulator.csv']['power_in.real']
q = output['Zregulator.csv']['power_in.imag']
#calculating length of simulation because it migth be different from the simulation input HOURS
simRealLength = int(len(p))
#time delays from configuration files
time_delay_reg = '30.0'
time_delay_cap = '300.0'
for key in localTree:
if localTree[key].get('object','') == 'regulator_configuration':
time_delay_reg = localTree[key]['time_delay']
# if localTree[key].get('object','') == "capacitor":
# time_delay_cap = localTree[key]['time_delay']
#change the recorder names
for key in localTree:
if localTree[key].get('object','') == 'collector' or localTree[key].get('object','') == 'recorder':
if localTree[key].get('file','').startswith('Z'):
localTree[key]['file'] = localTree[key].get('file','').replace('Z','NewZ')
#create volt-var control object
max_key = max([int(key) for key in localTree.keys()])
localTree[max_key+1] = {'object' : 'volt_var_control',
'name' : 'IVVC1',
'control_method' : 'ACTIVE',
'capacitor_delay' : str(time_delay_cap),
'regulator_delay' : str(time_delay_reg),
'desired_pf' : '0.99',
'd_max' : '0.6',
'd_min' : '0.1',
'substation_link' : str(localTree[regIndex]['name']),
'regulator_list' : regstr,
'capacitor_list': capstr,
'voltage_measurements': str(inputDict.get('voltageNodes', 'IVVC1')),
}
#running powerflow analysis via gridalab after attaching a regulator
feeder.adjustTime(localTree,HOURS,'hours',simStartDate)
output1 = gridlabd.runInFilesystem(localTree,attachments,keepFiles=True,workDir=modelDir)
os.remove(pJoin(modelDir,'PID.txt'))
pnew = output1['NewZregulator.csv']['power_in.real']
qnew = output1['NewZregulator.csv']['power_in.imag']
#total real and imaginary losses as a function of time
def vecSum(u,v):
''' Add vectors u and v element-wise. Return has len <= len(u) and <=len(v). '''
return map(sum, zip(u,v))
def zeroVec(length):
''' Give a zero vector of input length. '''
return [0 for x in xrange(length)]
(realLoss, imagLoss, realLossnew, imagLossnew) = (zeroVec(int(HOURS)) for x in range(4))
for device in ['ZlossesOverhead.csv','ZlossesTransformer.csv','ZlossesUnderground.csv']:
for letter in ['A','B','C']:
realLoss = vecSum(realLoss, output[device]['sum(power_losses_' + letter + '.real)'])
imagLoss = vecSum(imagLoss, output[device]['sum(power_losses_' + letter + '.imag)'])
realLossnew = vecSum(realLossnew, output1['New'+device]['sum(power_losses_' + letter + '.real)'])
imagLossnew = vecSum(imagLossnew, output1['New'+device]['sum(power_losses_' + letter + '.imag)'])
#voltage calculations and tap calculations
def divby2(u):
'''divides by 2'''
return u/2
lowVoltage = []
meanVoltage = []
highVoltage = []
lowVoltagenew = []
meanVoltagenew = []
highVoltagenew = []
tap = {'A':[],'B':[],'C':[]}
tapnew = {'A':[],'B':[],'C':[]}
volt = {'A':[],'B':[],'C':[]}
voltnew = {'A':[],'B':[],'C':[]}
switch = {'A':[],'B':[],'C':[]}
switchnew = {'A':[],'B':[],'C':[]}
for letter in ['A','B','C']:
tap[letter] = output['Zregulator.csv']['tap_' + letter]
tapnew[letter] = output1['NewZregulator.csv']['tap_' + letter]
if capKeys != []:
switch[letter] = output['ZcapSwitch' + str(int(capKeys[0])) + '.csv']['switch'+ letter]
switchnew[letter] = output1['NewZcapSwitch' + str(int(capKeys[0])) + '.csv']['switch'+ letter]
volt[letter] = map(returnMag,output['ZsubstationBottom.csv']['voltage_'+letter])
voltnew[letter] = map(returnMag,output1['NewZsubstationBottom.csv']['voltage_'+letter])
lowVoltage = map(divby2,output['ZvoltageJiggle.csv']['min(voltage_12.mag)'])
lowVoltagenew = map(divby2,output1['NewZvoltageJiggle.csv']['min(voltage_12.mag)'])
meanVoltage = map(divby2,output['ZvoltageJiggle.csv']['mean(voltage_12.mag)'])
meanVoltagenew = map(divby2,output1['NewZvoltageJiggle.csv']['mean(voltage_12.mag)'])
highVoltage = map(divby2,output['ZvoltageJiggle.csv']['max(voltage_12.mag)'])
highVoltagenew = map(divby2,output1['NewZvoltageJiggle.csv']['max(voltage_12.mag)'])
#energy calculations
whEnergy = []
whLosses = []
whLoads = []
whEnergy.append(sum(p)/10**6)
whLosses.append(sum(realLoss)/10**6)
whLoads.append((sum(p)-sum(realLoss))/10**6)
whEnergy.append(sum(pnew)/10**6)
whLosses.append(sum(realLossnew)/10**6)
whLoads.append((sum(pnew)-sum(realLossnew))/10**6)
indices = ['No IVVC', 'With IVVC']
# energySalesRed = (whLoads[1]-whLoads[0])*(inputDict['wholesaleEnergyCostPerKwh'])*1000
# lossSav = (whLosses[0]-whLosses[1])*inputDict['wholesaleEnergyCostPerKwh']*1000
#plots
ticks = []
plt.clf()
plt.title('total energy')
plt.ylabel('total load and losses (MWh)')
for element in range(2):
ticks.append(element)
bar_loss = plt.bar(element, whLosses[element], 0.15, color= 'red')
bar_load = plt.bar(element+0.15, whLoads[element], 0.15, color= 'orange')
plt.legend([bar_load[0],bar_loss[0]],['total load', 'total losses'],bbox_to_anchor=(0., 0.915, 1., .102), loc=3,
ncol=2, mode='expand', borderaxespad=0.1)
plt.xticks([t+0.15 for t in ticks],indices)
plt.savefig(pJoin(modelDir,'totalEnergy.png'))
#real and imaginary power
plt.figure('real power')
plt.title('Real Power at substation')
plt.ylabel('substation real power (MW)')
pMW = [element/10**6 for element in p]
pMWn = [element/10**6 for element in pnew]
pw = plt.plot(pMW)
npw = plt.plot(pMWn)
plt.legend([pw[0], npw[0]], ['NO IVVC','WITH IVVC'],bbox_to_anchor=(0., 0.915, 1., .102), loc=3,
ncol=2, mode='expand', borderaxespad=0.1)
plt.savefig(pJoin(modelDir,'realPower.png'))
plt.figure('Reactive power')
plt.title('Reactive Power at substation')
plt.ylabel('substation reactive power (MVAR)')
qMVAR = [element/10**6 for element in q]
qMVARn = [element/10**6 for element in qnew]
iw = plt.plot(qMVAR)
niw = plt.plot(qMVARn)
plt.legend([iw[0], niw[0]], ['NO IVVC','WITH IVVC'],bbox_to_anchor=(0., 0.915, 1., .102), loc=3,
ncol=2, mode='expand', borderaxespad=0.1)
plt.savefig(pJoin(modelDir,'imaginaryPower.png'))
#voltage plots
plt.figure('voltages as a function of time')
f,ax = plt.subplots(2,sharex=True)
f.suptitle('Min and Max voltages on the feeder')
lv = ax[0].plot(lowVoltage,color = 'cadetblue')
mv = ax[0].plot(meanVoltage,color = 'blue')
hv = ax[0].plot(highVoltage, color = 'cadetblue')
ax[0].legend([lv[0], mv[0], hv[0]], ['low voltage','mean voltage','high voltage'],bbox_to_anchor=(0., 0.915, 1., .1), loc=3,
ncol=3, mode='expand', borderaxespad=0.1)
ax[0].set_ylabel('NO IVVC')
nlv = ax[1].plot(lowVoltagenew,color = 'cadetblue')
nmv = ax[1].plot(meanVoltagenew,color = 'blue')
nhv = ax[1].plot(highVoltagenew, color = 'cadetblue')
ax[1].set_ylabel('WITH IVVC')
plt.savefig(pJoin(modelDir,'Voltages.png'))
#tap positions
plt.figure('TAP positions NO IVVC')
f,ax = plt.subplots(6,sharex=True)
f.set_size_inches(10,12.0)
#f.suptitle("Regulator Tap positions")
ax[0].plot(tap['A'])
ax[0].set_title('Regulator Tap positions NO IVVC')
ax[0].set_ylabel('TAP A')
ax[1].plot(tap['B'])
ax[1].set_ylabel('TAP B')
ax[2].plot(tap['C'])
ax[2].set_ylabel('TAP C')
ax[3].plot(tapnew['A'])
ax[3].set_title('WITH IVVC')
ax[3].set_ylabel('TAP A')
ax[4].plot(tapnew['B'])
ax[4].set_ylabel('TAP B')
ax[5].plot(tapnew['C'])
ax[5].set_ylabel('TAP C')
for subplot in range(6):
ax[subplot].set_ylim(-20,20)
f.tight_layout()
plt.savefig(pJoin(modelDir,'RegulatorTAPpositions.png'))
#substation voltages
plt.figure('substation voltage as a function of time')
f,ax = plt.subplots(6,sharex=True)
f.set_size_inches(10,12.0)
#f.suptitle("voltages at substation NO IVVC")
ax[0].plot(volt['A'])
ax[0].set_title('Substation voltages NO IVVC')
ax[0].set_ylabel('voltage A')
ax[1].plot(volt['B'])
ax[1].set_ylabel('voltage B')
ax[2].plot(volt['C'])
ax[2].set_ylabel('voltage C')
ax[3].plot(voltnew['A'])
ax[3].set_title('WITH IVVC')
ax[3].set_ylabel('voltage A')
ax[4].plot(voltnew['B'])
ax[4].set_ylabel('voltage B')
ax[5].plot(voltnew['C'])
ax[5].set_ylabel('voltage C')
f.tight_layout()
plt.savefig(pJoin(modelDir,'substationVoltages.png'))
#cap switches
plt.figure('capacitor switch state as a function of time')
f,ax = plt.subplots(6,sharex=True)
f.set_size_inches(10,12.0)
#f.suptitle("Capacitor switch state NO IVVC")
ax[0].plot(switch['A'])
ax[0].set_title('Capacitor switch state NO IVVC')
ax[0].set_ylabel('switch A')
ax[1].plot(switch['B'])
ax[1].set_ylabel('switch B')
ax[2].plot(switch['C'])
ax[2].set_ylabel('switch C')
ax[3].plot(switchnew['A'])
ax[3].set_title('WITH IVVC')
ax[3].set_ylabel('switch A')
ax[4].plot(switchnew['B'])
ax[4].set_ylabel('switch B')
ax[5].plot(switchnew['C'])
ax[5].set_ylabel('switch C')
for subplot in range(6):
ax[subplot].set_ylim(-2,2)
f.tight_layout()
plt.savefig(pJoin(modelDir,'capacitorSwitch.png'))
#plt.show()
#monetization
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'}
#calculate the month and hour of simulation start and month and hour of simulation end
simStartTimestamp = simStartDate + ' 00:00:00'
simFormattedDate = dt.strptime(simStartTimestamp,'%Y-%m-%d %H:%M:%S')
simStartMonthNum = int(simFormattedDate.strftime('%m'))
simstartMonth = monthNames[simStartMonthNum-1]
simStartDay = int(simFormattedDate.strftime('%d'))
if calendar.isleap(int(simFormattedDate.strftime('%Y'))):
febDays = 29
else:
febDays = 28
monthHours = [int(31*24),int(febDays*24),int(31*24),int(30*24),int(31*24),int(30*24),int(31*24),int(31*24),int(30*24),int(31*24),int(30*24),int(31*24)]
simStartIndex = int(sum(monthHours[:(simStartMonthNum-1)])+(simStartDay-1)*24)
temp = 0
cumulHours = [0]
for x in range(12):
temp += monthHours[x]
cumulHours.append(temp)
for i in range((simStartMonthNum),13):
if int(simStartIndex+simRealLength)<=cumulHours[i] and int(simStartIndex+simRealLength)>cumulHours[i-1]:
simEndMonthNum = i-1
simEndMonth = monthNames[simEndMonthNum]
#calculate peaks for the number of months in simulation
previndex = 0
monthPeak = {}
monthPeakNew = {}
peakSaveDollars = {}
energyLostDollars = {}
lossRedDollars = {}
simMonthList = monthNames[monthNames.index(simstartMonth):(monthNames.index(simEndMonth)+1)]
for monthElement in simMonthList:
month = monthNames.index(monthElement)
index1 = int(previndex)
index2 = int(min((index1 + int(monthHours[month])), simRealLength))
monthPeak[monthElement] = max(p[index1:index2])/1000.0
monthPeakNew[monthElement] = max(pnew[index1:index2])/1000.0
peakSaveDollars[monthElement] = (monthPeak[monthElement]-monthPeakNew[monthElement])*float(inputDict['peakDemandCost'+str(monthToSeason[monthElement])+'PerKw'])
lossRedDollars[monthElement] = (sum(realLoss[index1:index2])/1000.0 - sum(realLossnew[index1:index2])/1000.0)*(float(inputDict['wholesaleEnergyCostPerKwh']))
energyLostDollars[monthElement] = (sum(p[index1:index2])/1000.0 - sum(pnew[index1:index2])/1000.0 - sum(realLoss[index1:index2])/1000.0
+ sum(realLossnew[index1:index2])/1000.0 )*(float(inputDict['wholesaleEnergyCostPerKwh']) - float(inputDict['retailEnergyCostPerKwh']))
previndex = index2
#money charts
fig = plt.figure('cost benefit barchart',figsize=(10,8))
ticks = range(len(simMonthList))
ticks1 = [element+0.15 for element in ticks]
ticks2 = [element+0.30 for element in ticks]
eld = [energyLostDollars[month] for month in simMonthList]
lrd = [lossRedDollars[month] for month in simMonthList]
psd = [peakSaveDollars[month] for month in simMonthList]
bar_eld = plt.bar(ticks,eld,0.15,color='red')
bar_psd = plt.bar(ticks1,psd,0.15,color='blue')
bar_lrd = plt.bar(ticks2,lrd,0.15,color='green')
plt.legend([bar_eld[0], bar_psd[0], bar_lrd[0]], ['energyLostDollars','peakReductionDollars','lossReductionDollars'],bbox_to_anchor=(0., 1.015, 1., .102), loc=3,
ncol=2, mode='expand', borderaxespad=0.1)
monShort = [element[0:3] for element in simMonthList]
plt.xticks([t+0.15 for t in ticks],monShort)
plt.ylabel('Utility Savings ($)')
plt.savefig(pJoin(modelDir,'spendChart.png'))
#cumulative savings graphs
fig = plt.figure('cost benefit barchart',figsize=(10,5))
annualSavings = sum(eld) + sum(lrd) + sum(psd)
annualSave = lambda x:(annualSavings - float(inputDict['omCost'])) * x - float(inputDict['capitalCost'])
simplePayback = float(inputDict['capitalCost'])/(annualSavings - float(inputDict['omCost']))
plt.xlabel('Year After Installation')
plt.xlim(0,30)
plt.ylabel('Cumulative Savings ($)')
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'))
#get exact time stamps from the CSV files generated by Gridlab-D
timeWithZone = output['Zregulator.csv']['# timestamp']
timestamps = [element[:19] for element in timeWithZone]
#data for highcharts
outData['timeStamps'] = timestamps
outData['noCVRPower'] = p
outData['withCVRPower'] = pnew
outData['noCVRLoad'] = whLoads[0]
outData['withCVRLoad'] = whLoads[1]
outData['noCVRLosses'] = whLosses[0]
outData['withCVRLosses'] = whLosses[1]
outData['noCVRTaps'] = tap
outData['withCVRTaps'] = tapnew
outData['noCVRSubVolts'] = volt
outData['withCVRSubVolts'] = voltnew
outData['noCVRCapSwitch'] = switch
outData['withCVRCapSwitch'] = switchnew
outData['noCVRHighVolt'] = highVoltage
outData['withCVRHighVolt'] = highVoltagenew
outData['noCVRLowVolt'] = lowVoltage
outData['withCVRLowVolt'] = lowVoltagenew
outData['noCVRMeanVolt'] = meanVoltage
outData['withCVRMeanVolt'] = meanVoltagenew
#monetization
outData['simMonthList'] = monShort
outData['energyLostDollars'] = energyLostDollars
outData['lossRedDollars'] = lossRedDollars
outData['peakSaveDollars'] = peakSaveDollars
outData['annualSave'] = [annualSave(x) for x in range(31)]
# Generate warnings
#TODO: Timezone adjustment
try:
# Check if times for simulation and scada match.
scadaDates = []
with open(pJoin(modelDir,'subScadaCalibrated1.player'),'r') as scadaFile:
for line in scadaFile:
(date,val) = line.split(',')
scadaDates.append(str(date))
simFormattedEndDate = simFormattedDate + timedelta(hours=HOURS)
scadaStartDate = dt.strptime(scadaDates[0].split(' PST')[0],"%Y-%m-%d %H:%M:%S")
scadaEndDate = dt.strptime(scadaDates[len(scadaDates)-1].split(' PST')[0],"%Y-%m-%d %H:%M:%S")
beginRange = (scadaStartDate - simFormattedDate).total_seconds()
endRange = (scadaEndDate - simFormattedEndDate).total_seconds()
# Check if houses exist.
housesExist, voltageNodeExists = False, False
for key in localTree:
if localTree[key].get('object','') == 'house': housesExist = True
if localTree[key].get('name','') == str(inputDict.get('voltageNodes', 0)): voltageNodeExists = True
if (beginRange > 0.0 or endRange < 0.0) and not housesExist:
outData['warnings'] = '<strong>WARNING:</strong> The simulation dates entered are not compatible with the scada curve in the feeder.'
# Check if voltage node exists.
if not voltageNodeExists:
if outData.get('warnings','') != '':
previousWarning = outData['warnings']
outData['warnings'] = previousWarning + ' The voltage node: ' + str(inputDict.get('voltageNodes', 0)) + ' does not exist in the feeder.'
else: outData['warnings'] = '<strong>WARNING:</strong> The voltage node <i>' + str(inputDict.get('voltageNodes', 0)) + '</i> does not exist in the feeder.'
except:
pass
# # Update the runTime in the input file.
# endTime = dt.now()
# with open(pJoin(modelDir,"allInputData.json"),"w") as inFile:
# json.dump(inputDict, inFile, indent=4)
# with open(pJoin(modelDir,"outDataData.json"),"w") as outFile:
# json.dump(outData, outFile, indent=4)
# # For autotest, there won't be such file.
# try:
# os.remove(pJoin(modelDir, "PPID.txt"))
# except Exception, e:
# pass
return outData
def runForeground(modelDir, inData):
'''This reads a glm file, changes the method of powerflow and reruns'''
print "STARTING TO RUN", modelDir
try:
startTime = dt.now()
if not os.path.isdir(modelDir):
os.makedirs(modelDir)
inData["created"] = str(startTime)
#read pre-calibrated feeder and run cvrdynamic
feederName = inData.get('feederName1', 'feeder1')
feederPath = pJoin(modelDir, feederName + '.omd')
# Reads a pre-calibrated feeder.
allOutput = {}
with open(feederPath, "r") as jsonIn:
feederJson = json.load(jsonIn)
localTree = feederJson.get("tree", {})
attachments = feederJson.get("attachments", {})
for key in localTree:
if "solver_method" in localTree[key].keys():
# print "current solver method", localTree[key]["solver_method"]
localTree[key]["solver_method"] = 'FBS'
#find the swing bus and recorder attached to substation
try:
for key in localTree:
if localTree[key].get('bustype', '').lower() == 'swing':
swingIndex = key
swingName = localTree[key].get('name')
if localTree[key].get(
'object', '') == 'regulator' and localTree[key].get(
'from', '') == swingName:
regIndex = key
regConfName = localTree[key]['configuration']
except:
raise ValueError('Invalid feeder selected:',
str(inData["feederName1"]))
#find the regulator and capacitor names and combine to form a string for volt-var control object
regKeys = []
accum_reg = ""
for key in localTree:
if localTree[key].get("object", "") == "regulator":
accum_reg += localTree[key].get("name", "ERROR") + ","
regKeys.append(key)
regstr = accum_reg[:-1]
# print regKeys
capKeys = []
accum_cap = ""
for key in localTree:
if localTree[key].get("object", "") == "capacitor":
accum_cap += localTree[key].get("name", "ERROR") + ","
capKeys.append(key)
if localTree[key].get("control", "").lower() == "manual":
localTree[key]['control'] = "VOLT"
# print "changing capacitor control from manual to volt"
capstr = accum_cap[:-1]
# print capKeys
# 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':
localTree[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': localTree[swingIndex]['name'],
'property': 'voltage_A,voltage_B,voltage_C'
}, {
'object': 'recorder',
'file': 'ZsubstationBottom.csv',
'limit': '0',
'parent': localTree[regIndex]['to'],
'property': 'voltage_A,voltage_B,voltage_C'
}]
#recorder object for capacitor switching - if capacitors exist
if capKeys != []:
for key in capKeys:
recorders.append({
'object': 'recorder',
'file': 'ZcapSwitch' + str(key) + '.csv',
'limit': '0',
'parent': localTree[key]['name'],
'property': 'switchA,switchB,switchC'
})
#attach recorder process
biggest = 1 + max([int(k) for k in localTree.keys()])
for index, rec in enumerate(recorders):
localTree[biggest + index] = rec
#run a reference load flow
HOURS = float(inData['simLengthHours'])
simStartDate = inData['simStart']
feeder.adjustTime(localTree, HOURS, "hours", simStartDate)
output = gridlabd.runInFilesystem(localTree,
attachments,
keepFiles=False,
workDir=modelDir)
try:
os.remove(pJoin(modelDir, "PID.txt"))
except:
pass
p = output['Zregulator.csv']['power_in.real']
q = output['Zregulator.csv']['power_in.imag']
#calculating length of simulation because it migth be different from the simulation input HOURS
simRealLength = int(len(p))
#time delays from configuration files
time_delay_reg = '30.0'
time_delay_cap = '300.0'
for key in localTree:
if localTree[key].get('object', '') == "regulator_configuration":
time_delay_reg = localTree[key]['time_delay']
# print "time_delay_reg",time_delay_reg
# if localTree[key].get('object','') == "capacitor":
# time_delay_cap = localTree[key]['time_delay']
# print "time_delay_cap",time_delay_cap
#change the recorder names
for key in localTree:
if localTree[key].get('object',
'') == "collector" or localTree[key].get(
'object', '') == "recorder":
if localTree[key].get('file', '').startswith('Z'):
localTree[key]['file'] = localTree[key].get(
'file', '').replace('Z', 'NewZ')
#create volt-var control object
max_key = max([int(key) for key in localTree.keys()])
# print max_key
localTree[max_key + 1] = {
'object': 'volt_var_control',
'name': 'IVVC1',
'control_method': 'ACTIVE',
'capacitor_delay': str(time_delay_cap),
'regulator_delay': str(time_delay_reg),
'desired_pf': '0.99',
'd_max': '0.6',
'd_min': '0.1',
'substation_link': str(localTree[regIndex]['name']),
'regulator_list': regstr,
'capacitor_list': capstr,
'voltage_measurements': str(inData.get("voltageNodes", "IVVC1")),
}
#running powerflow analysis via gridalab after attaching a regulator
feeder.adjustTime(localTree, HOURS, "hours", simStartDate)
output1 = gridlabd.runInFilesystem(localTree,
attachments,
keepFiles=True,
workDir=modelDir)
os.remove(pJoin(modelDir, "PID.txt"))
pnew = output1['NewZregulator.csv']['power_in.real']
qnew = output1['NewZregulator.csv']['power_in.imag']
#total real and imaginary losses as a function of time
def vecSum(u, v):
''' Add vectors u and v element-wise. Return has len <= len(u) and <=len(v). '''
return map(sum, zip(u, v))
def zeroVec(length):
''' Give a zero vector of input length. '''
return [0 for x in xrange(length)]
(realLoss, imagLoss, realLossnew, imagLossnew) = (zeroVec(int(HOURS))
for x in range(4))
for device in [
'ZlossesOverhead.csv', 'ZlossesTransformer.csv',
'ZlossesUnderground.csv'
]:
for letter in ['A', 'B', 'C']:
realLoss = vecSum(
realLoss,
output[device]['sum(power_losses_' + letter + '.real)'])
imagLoss = vecSum(
imagLoss,
output[device]['sum(power_losses_' + letter + '.imag)'])
realLossnew = vecSum(
realLossnew, output1['New' + device]['sum(power_losses_' +
letter + '.real)'])
imagLossnew = vecSum(
imagLossnew, output1['New' + device]['sum(power_losses_' +
letter + '.imag)'])
#voltage calculations and tap calculations
def divby2(u):
'''divides by 2'''
return u / 2
lowVoltage = []
meanVoltage = []
highVoltage = []
lowVoltagenew = []
meanVoltagenew = []
highVoltagenew = []
tap = {'A': [], 'B': [], 'C': []}
tapnew = {'A': [], 'B': [], 'C': []}
volt = {'A': [], 'B': [], 'C': []}
voltnew = {'A': [], 'B': [], 'C': []}
switch = {'A': [], 'B': [], 'C': []}
switchnew = {'A': [], 'B': [], 'C': []}
for letter in ['A', 'B', 'C']:
tap[letter] = output['Zregulator.csv']['tap_' + letter]
tapnew[letter] = output1['NewZregulator.csv']['tap_' + letter]
if capKeys != []:
switch[letter] = output['ZcapSwitch' + str(int(capKeys[0])) +
'.csv']['switch' + letter]
switchnew[letter] = output1['NewZcapSwitch' +
str(int(capKeys[0])) +
'.csv']['switch' + letter]
volt[letter] = map(
returnMag,
output['ZsubstationBottom.csv']['voltage_' + letter])
voltnew[letter] = map(
returnMag,
output1['NewZsubstationBottom.csv']['voltage_' + letter])
lowVoltage = map(divby2,
output['ZvoltageJiggle.csv']['min(voltage_12.mag)'])
lowVoltagenew = map(
divby2, output1['NewZvoltageJiggle.csv']['min(voltage_12.mag)'])
meanVoltage = map(divby2,
output['ZvoltageJiggle.csv']['mean(voltage_12.mag)'])
meanVoltagenew = map(
divby2, output1['NewZvoltageJiggle.csv']['mean(voltage_12.mag)'])
highVoltage = map(divby2,
output['ZvoltageJiggle.csv']['max(voltage_12.mag)'])
highVoltagenew = map(
divby2, output1['NewZvoltageJiggle.csv']['max(voltage_12.mag)'])
#energy calculations
whEnergy = []
whLosses = []
whLoads = []
whEnergy.append(sum(p) / 10**6)
whLosses.append(sum(realLoss) / 10**6)
whLoads.append((sum(p) - sum(realLoss)) / 10**6)
whEnergy.append(sum(pnew) / 10**6)
whLosses.append(sum(realLossnew) / 10**6)
whLoads.append((sum(pnew) - sum(realLossnew)) / 10**6)
indices = ['No IVVC', 'With IVVC']
# energySalesRed = (whLoads[1]-whLoads[0])*(inData['wholesaleEnergyCostPerKwh'])*1000
# lossSav = (whLosses[0]-whLosses[1])*inData['wholesaleEnergyCostPerKwh']*1000
# print energySalesRed, lossSav
#plots
ticks = []
plt.clf()
plt.title("total energy")
plt.ylabel("total load and losses (MWh)")
for element in range(2):
ticks.append(element)
bar_loss = plt.bar(element, whLosses[element], 0.15, color='red')
bar_load = plt.bar(element + 0.15,
whLoads[element],
0.15,
color='orange')
plt.legend([bar_load[0], bar_loss[0]], ['total load', 'total losses'],
bbox_to_anchor=(0., 0.915, 1., .102),
loc=3,
ncol=2,
mode="expand",
borderaxespad=0.1)
plt.xticks([t + 0.15 for t in ticks], indices)
plt.savefig(pJoin(modelDir, "totalEnergy.png"))
#real and imaginary power
plt.figure("real power")
plt.title("Real Power at substation")
plt.ylabel("substation real power (MW)")
pMW = [element / 10**6 for element in p]
pMWn = [element / 10**6 for element in pnew]
pw = plt.plot(pMW)
npw = plt.plot(pMWn)
plt.legend([pw[0], npw[0]], ['NO IVVC', 'WITH IVVC'],
bbox_to_anchor=(0., 0.915, 1., .102),
loc=3,
ncol=2,
mode="expand",
borderaxespad=0.1)
plt.savefig(pJoin(modelDir, "realPower.png"))
plt.figure("Reactive power")
plt.title("Reactive Power at substation")
plt.ylabel("substation reactive power (MVAR)")
qMVAR = [element / 10**6 for element in q]
qMVARn = [element / 10**6 for element in qnew]
iw = plt.plot(qMVAR)
niw = plt.plot(qMVARn)
plt.legend([iw[0], niw[0]], ['NO IVVC', 'WITH IVVC'],
bbox_to_anchor=(0., 0.915, 1., .102),
loc=3,
ncol=2,
mode="expand",
borderaxespad=0.1)
plt.savefig(pJoin(modelDir, "imaginaryPower.png"))
#voltage plots
plt.figure("voltages as a function of time")
f, ax = plt.subplots(2, sharex=True)
f.suptitle("Min and Max voltages on the feeder")
lv = ax[0].plot(lowVoltage, color='cadetblue')
mv = ax[0].plot(meanVoltage, color='blue')
hv = ax[0].plot(highVoltage, color='cadetblue')
ax[0].legend([lv[0], mv[0], hv[0]],
['low voltage', 'mean voltage', 'high voltage'],
bbox_to_anchor=(0., 0.915, 1., .1),
loc=3,
ncol=3,
mode="expand",
borderaxespad=0.1)
ax[0].set_ylabel('NO IVVC')
nlv = ax[1].plot(lowVoltagenew, color='cadetblue')
nmv = ax[1].plot(meanVoltagenew, color='blue')
nhv = ax[1].plot(highVoltagenew, color='cadetblue')
ax[1].set_ylabel('WITH IVVC')
plt.savefig(pJoin(modelDir, "Voltages.png"))
#tap positions
plt.figure("TAP positions NO IVVC")
f, ax = plt.subplots(6, sharex=True)
f.set_size_inches(10, 12.0)
#f.suptitle("Regulator Tap positions")
ax[0].plot(tap['A'])
ax[0].set_title("Regulator Tap positions NO IVVC")
ax[0].set_ylabel("TAP A")
ax[1].plot(tap['B'])
ax[1].set_ylabel("TAP B")
ax[2].plot(tap['C'])
ax[2].set_ylabel("TAP C")
ax[3].plot(tapnew['A'])
ax[3].set_title("WITH IVVC")
ax[3].set_ylabel("TAP A")
ax[4].plot(tapnew['B'])
ax[4].set_ylabel("TAP B")
ax[5].plot(tapnew['C'])
ax[5].set_ylabel("TAP C")
for subplot in range(6):
ax[subplot].set_ylim(-20, 20)
f.tight_layout()
plt.savefig(pJoin(modelDir, "RegulatorTAPpositions.png"))
#substation voltages
plt.figure("substation voltage as a function of time")
f, ax = plt.subplots(6, sharex=True)
f.set_size_inches(10, 12.0)
#f.suptitle("voltages at substation NO IVVC")
ax[0].plot(volt['A'])
ax[0].set_title('Substation voltages NO IVVC')
ax[0].set_ylabel('voltage A')
ax[1].plot(volt['B'])
ax[1].set_ylabel('voltage B')
ax[2].plot(volt['C'])
ax[2].set_ylabel('voltage C')
ax[3].plot(voltnew['A'])
ax[3].set_title("WITH IVVC")
ax[3].set_ylabel('voltage A')
ax[4].plot(voltnew['B'])
ax[4].set_ylabel('voltage B')
ax[5].plot(voltnew['C'])
ax[5].set_ylabel('voltage C')
f.tight_layout()
plt.savefig(pJoin(modelDir, "substationVoltages.png"))
#cap switches
plt.figure("capacitor switch state as a function of time")
f, ax = plt.subplots(6, sharex=True)
f.set_size_inches(10, 12.0)
#f.suptitle("Capacitor switch state NO IVVC")
ax[0].plot(switch['A'])
ax[0].set_title("Capacitor switch state NO IVVC")
ax[0].set_ylabel("switch A")
ax[1].plot(switch['B'])
ax[1].set_ylabel("switch B")
ax[2].plot(switch['C'])
ax[2].set_ylabel("switch C")
ax[3].plot(switchnew['A'])
ax[3].set_title("WITH IVVC")
ax[3].set_ylabel("switch A")
ax[4].plot(switchnew['B'])
ax[4].set_ylabel("switch B")
ax[5].plot(switchnew['C'])
ax[5].set_ylabel("switch C")
for subplot in range(6):
ax[subplot].set_ylim(-2, 2)
f.tight_layout()
plt.savefig(pJoin(modelDir, "capacitorSwitch.png"))
#plt.show()
#monetization
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'
}
#calculate the month and hour of simulation start and month and hour of simulation end
simStartTimestamp = simStartDate + " 00:00:00"
simFormattedDate = dt.strptime(simStartTimestamp, "%Y-%m-%d %H:%M:%S")
simStartMonthNum = int(simFormattedDate.strftime('%m'))
simstartMonth = monthNames[simStartMonthNum - 1]
simStartDay = int(simFormattedDate.strftime('%d'))
if calendar.isleap(int(simFormattedDate.strftime('%Y'))):
febDays = 29
else:
febDays = 28
monthHours = [
int(31 * 24),
int(febDays * 24),
int(31 * 24),
int(30 * 24),
int(31 * 24),
int(30 * 24),
int(31 * 24),
int(31 * 24),
int(30 * 24),
int(31 * 24),
int(30 * 24),
int(31 * 24)
]
simStartIndex = int(
sum(monthHours[:(simStartMonthNum - 1)]) + (simStartDay - 1) * 24)
temp = 0
cumulHours = [0]
for x in range(12):
temp += monthHours[x]
cumulHours.append(temp)
for i in range((simStartMonthNum), 13):
if int(simStartIndex + simRealLength) <= cumulHours[i] and int(
simStartIndex + simRealLength) > cumulHours[i - 1]:
simEndMonthNum = i - 1
simEndMonth = monthNames[simEndMonthNum]
# print simstartMonth,simEndMonth
#calculate peaks for the number of months in simulation
previndex = 0
monthPeak = {}
monthPeakNew = {}
peakSaveDollars = {}
energyLostDollars = {}
lossRedDollars = {}
simMonthList = monthNames[monthNames.index(simstartMonth):(
monthNames.index(simEndMonth) + 1)]
# print simMonthList
for monthElement in simMonthList:
# print monthElement
month = monthNames.index(monthElement)
index1 = int(previndex)
index2 = int(min((index1 + int(monthHours[month])), simRealLength))
monthPeak[monthElement] = max(p[index1:index2]) / 1000.0
monthPeakNew[monthElement] = max(pnew[index1:index2]) / 1000.0
peakSaveDollars[monthElement] = (
monthPeak[monthElement] - monthPeakNew[monthElement]) * float(
inData['peakDemandCost' +
str(monthToSeason[monthElement]) + 'PerKw'])
lossRedDollars[monthElement] = (
sum(realLoss[index1:index2]) / 1000.0 -
sum(realLossnew[index1:index2]) / 1000.0) * (float(
inData['wholesaleEnergyCostPerKwh']))
energyLostDollars[monthElement] = (
sum(p[index1:index2]) / 1000.0 - sum(pnew[index1:index2]) /
1000.0 - sum(realLoss[index1:index2]) / 1000.0 +
sum(realLossnew[index1:index2]) / 1000.0) * (
float(inData['wholesaleEnergyCostPerKwh']) -
float(inData['retailEnergyCostPerKwh']))
previndex = index2
#money charts
fig = plt.figure("cost benefit barchart", figsize=(10, 8))
ticks = range(len(simMonthList))
ticks1 = [element + 0.15 for element in ticks]
ticks2 = [element + 0.30 for element in ticks]
# print ticks
eld = [energyLostDollars[month] for month in simMonthList]
lrd = [lossRedDollars[month] for month in simMonthList]
psd = [peakSaveDollars[month] for month in simMonthList]
bar_eld = plt.bar(ticks, eld, 0.15, color='red')
bar_psd = plt.bar(ticks1, psd, 0.15, color='blue')
bar_lrd = plt.bar(ticks2, lrd, 0.15, color='green')
plt.legend([bar_eld[0], bar_psd[0], bar_lrd[0]], [
'energyLostDollars', 'peakReductionDollars', 'lossReductionDollars'
],
bbox_to_anchor=(0., 1.015, 1., .102),
loc=3,
ncol=2,
mode="expand",
borderaxespad=0.1)
monShort = [element[0:3] for element in simMonthList]
plt.xticks([t + 0.15 for t in ticks], monShort)
plt.ylabel('Utility Savings ($)')
plt.savefig(pJoin(modelDir, "spendChart.png"))
#cumulative savings graphs
fig = plt.figure("cost benefit barchart", figsize=(10, 5))
annualSavings = sum(eld) + sum(lrd) + sum(psd)
annualSave = lambda x: (annualSavings - float(inData['omCost'])
) * x - float(inData['capitalCost'])
simplePayback = float(
inData['capitalCost']) / (annualSavings - float(inData['omCost']))
plt.xlabel('Year After Installation')
plt.xlim(0, 30)
plt.ylabel('Cumulative Savings ($)')
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"))
#get exact time stamps from the CSV files generated by Gridlab-D
timeWithZone = output['Zregulator.csv']['# timestamp']
timestamps = [element[:19] for element in timeWithZone]
#data for highcharts
allOutput["timeStamps"] = timestamps
allOutput["noCVRPower"] = p
allOutput["withCVRPower"] = pnew
allOutput["noCVRLoad"] = whLoads[0]
allOutput["withCVRLoad"] = whLoads[1]
allOutput["noCVRLosses"] = whLosses[0]
allOutput["withCVRLosses"] = whLosses[1]
allOutput["noCVRTaps"] = tap
allOutput["withCVRTaps"] = tapnew
allOutput["noCVRSubVolts"] = volt
allOutput["withCVRSubVolts"] = voltnew
allOutput["noCVRCapSwitch"] = switch
allOutput["withCVRCapSwitch"] = switchnew
allOutput["noCVRHighVolt"] = highVoltage
allOutput["withCVRHighVolt"] = highVoltagenew
allOutput["noCVRLowVolt"] = lowVoltage
allOutput["withCVRLowVolt"] = lowVoltagenew
allOutput["noCVRMeanVolt"] = meanVoltage
allOutput["withCVRMeanVolt"] = meanVoltagenew
#monetization
allOutput["simMonthList"] = monShort
allOutput["energyLostDollars"] = energyLostDollars
allOutput["lossRedDollars"] = lossRedDollars
allOutput["peakSaveDollars"] = peakSaveDollars
allOutput["annualSave"] = [annualSave(x) for x in range(31)]
# Generate warnings
#TODO: Timezone adjustment
try:
# Check if times for simulation and scada match.
scadaDates = []
with open(pJoin(modelDir, "subScadaCalibrated1.player"),
"r") as scadaFile:
for line in scadaFile:
(date, val) = line.split(',')
scadaDates.append(str(date))
simFormattedEndDate = simFormattedDate + timedelta(hours=HOURS)
scadaStartDate = dt.strptime(scadaDates[0].split(' PST')[0],
"%Y-%m-%d %H:%M:%S")
scadaEndDate = dt.strptime(
scadaDates[len(scadaDates) - 1].split(' PST')[0],
"%Y-%m-%d %H:%M:%S")
beginRange = (scadaStartDate - simFormattedDate).total_seconds()
endRange = (scadaEndDate - simFormattedEndDate).total_seconds()
# Check if houses exist.
housesExist, voltageNodeExists = False, False
for key in localTree:
if localTree[key].get('object', '') == 'house':
housesExist = True
if localTree[key].get('name',
'') == str(inData.get("voltageNodes",
0)):
voltageNodeExists = True
if (beginRange > 0.0 or endRange < 0.0) and not housesExist:
allOutput[
"warnings"] = "<strong>WARNING:</strong> The simulation dates entered are not compatible with the scada curve in the feeder."
# Check if voltage node exists.
if not voltageNodeExists:
if allOutput.get('warnings', '') != "":
previousWarning = allOutput["warnings"]
allOutput[
"warnings"] = previousWarning + " The voltage node: " + str(
inData.get("voltageNodes",
0)) + " does not exist in the feeder."
else:
allOutput[
"warnings"] = "<strong>WARNING:</strong> The voltage node <i>" + str(
inData.get(
"voltageNodes",
0)) + "</i> does not exist in the feeder."
except:
pass
# Update the runTime in the input file.
endTime = dt.now()
inData["runTime"] = str(
timedelta(seconds=int((endTime - startTime).total_seconds())))
with open(pJoin(modelDir, "allInputData.json"), "w") as inFile:
json.dump(inData, inFile, indent=4)
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 Exception, e:
pass
print "DONE RUNNING", modelDir
def runGridlabAndProcessData(tree, attachments, edge_bools, workDir=False):
# Run Gridlab.
if not workDir:
workDir = tempfile.mkdtemp()
# print '@@@@@@', workDir
gridlabOut = gridlabd.runInFilesystem(tree,
attachments=attachments,
workDir=workDir)
# read voltDump values into a dictionary.
try:
with open(pJoin(workDir, 'voltDump.csv'), newline='') as dumpFile:
reader = csv.reader(dumpFile)
next(reader) # Burn the header.
keys = next(reader)
voltTable = []
for row in reader:
rowDict = {}
for pos, key in enumerate(keys):
rowDict[key] = row[pos]
voltTable.append(rowDict)
except:
raise Exception(
'GridLAB-D failed to run with the following errors:\n' +
gridlabOut['stderr'])
# read currDump values into a dictionary
with open(pJoin(workDir, 'currDump.csv'), newline='') as currDumpFile:
reader = csv.reader(currDumpFile)
next(reader) # Burn the header.
keys = next(reader)
currTable = []
for row in reader:
rowDict = {}
for pos, key in enumerate(keys):
rowDict[key] = row[pos]
currTable.append(rowDict)
# read line rating values into a single dictionary
lineRatings = {}
for key1 in edge_bools.keys():
if edge_bools[key1]:
with open(pJoin(workDir, key1 + '_cont_rating.csv'),
newline='') as ratingFile:
reader = csv.reader(ratingFile)
keys = []
vals = []
for row in reader:
if '# timestamp' in row:
keys = row
i = keys.index('# timestamp')
keys.pop(i)
vals = next(reader)
vals.pop(i)
for pos, key2 in enumerate(keys):
lineRatings[key2] = abs(float(vals[pos]))
# Calculate average node voltage deviation. First, helper functions.
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)
# Tot it all up.
nodeVolts = {}
for row in voltTable:
allVolts = []
for phase in ['A', 'B', 'C']:
realVolt = abs(float(row['volt' + phase + '_real']))
imagVolt = abs(float(row['volt' + phase + '_imag']))
phaseVolt = math.sqrt((realVolt**2) + (imagVolt**2))
if phaseVolt != 0.0:
allVolts.append(phaseVolt)
avgVolts = avg(allVolts)
nodeVolts[row.get('node_name', '')] = float("{0:.2f}".format(avgVolts))
nominalVolts = {}
percentChangeVolts = {}
for key in nodeVolts.keys():
for treeKey in tree:
ob = tree[treeKey]
obName = ob.get('name', '')
if obName == key:
nominalVolts[key] = float(ob.get('nominal_voltage', 1))
percentChangeVolts[key] = (nodeVolts[key] /
nominalVolts[key]) * 120
# find edge currents by parsing currdump
edgeCurrentSum = {}
edgeCurrentMax = {}
for row in currTable:
allCurr = []
for phase in ['A', 'B', 'C']:
realCurr = abs(float(row['curr' + phase + '_real']))
imagCurr = abs(float(row['curr' + phase + '_imag']))
phaseCurr = math.sqrt((realCurr**2) + (imagCurr**2))
allCurr.append(phaseCurr)
edgeCurrentSum[row.get('link_name', '')] = sum(allCurr)
edgeCurrentMax[row.get('link_name', '')] = max(allCurr)
# When just showing current as labels, use sum of the three lines' current values, when showing the per unit values (current/rating), use the max of the three
#edgeValsPU = current values normalized per unit by line ratings
edgeValsPU = {}
edgePower = {}
for edge in edgeCurrentSum:
for obj in tree.values():
obname = obj.get('name', '').replace('"', '')
if obname == edge:
nodeFrom = obj.get('from')
nodeTo = obj.get('to')
currVal = edgeCurrentSum.get(edge)
voltVal = avg([nodeVolts.get(nodeFrom), nodeVolts.get(nodeTo)])
lineRatings[edge] = lineRatings.get(edge, 10.0**9)
edgePerUnitVal = (edgeCurrentMax.get(edge)) / lineRatings[edge]
edgeValsPU[edge] = edgePerUnitVal
edgePower[edge] = ((currVal * voltVal) / 1000)
# read regulator tap position values values into a single dictionary
tapPositions = {}
if edge_bools['regulator']:
tapPositions['tapA'] = readGroupRecorderCSV(pJoin(
workDir, 'tap_A.csv'))
tapPositions['tapB'] = readGroupRecorderCSV(pJoin(
workDir, 'tap_B.csv'))
tapPositions['tapC'] = readGroupRecorderCSV(pJoin(
workDir, 'tap_C.csv'))
return {
'nominalVolts': nominalVolts,
'nodeVolts': nodeVolts,
'percentChangeVolts': percentChangeVolts,
'edgeCurrentSum': edgeCurrentSum,
'edgePower': edgePower,
'edgeValsPU': edgeValsPU,
'tapPositions': tapPositions
}
def work(modelDir, inputDict):
''' Run the model in its directory. WARNING: GRIDLAB CAN TAKE HOURS TO COMPLETE. '''
# feederName = inputDict["feederName1"]
feederName = [x for x in os.listdir(modelDir) if x.endswith('.omd')][0][:-4]
inputDict["feederName1"] = feederName
inputDict["climateName"] = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
feederJson = json.load(open(pJoin(modelDir, feederName + '.omd')))
tree = feederJson["tree"]
# Set up GLM with correct time and recorders:
feeder.attachRecorders(tree, "Regulator", "object", "regulator")
feeder.attachRecorders(tree, "Capacitor", "object", "capacitor")
feeder.attachRecorders(tree, "Inverter", "object", "inverter")
feeder.attachRecorders(tree, "Windmill", "object", "windturb_dg")
feeder.attachRecorders(tree, "CollectorVoltage", None, None)
feeder.attachRecorders(tree, "Climate", "object", "climate")
feeder.attachRecorders(tree, "OverheadLosses", None, None)
feeder.attachRecorders(tree, "UndergroundLosses", None, None)
feeder.attachRecorders(tree, "TriplexLosses", None, None)
feeder.attachRecorders(tree, "TransformerLosses", None, None)
feeder.groupSwingKids(tree)
# Attach recorders for system voltage map:
stub = {'object':'group_recorder', 'group':'"class=node"', 'interval':3600}
for phase in ['A','B','C']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'VoltDump.csv'
tree[feeder.getMaxKey(tree) + 1] = copyStub
# Attach recorders for system voltage map, triplex:
stub = {'object':'group_recorder', 'group':'"class=triplex_node"', 'interval':3600}
for phase in ['1','2']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'nVoltDump.csv'
tree[feeder.getMaxKey(tree) + 1] = copyStub
# Attach current recorder for overhead_lines
currentStub = {'object':'group_recorder', 'group':'"class=overhead_line"', 'interval':3600}
for phase in ['A','B','C']:
copyCurrentStub = dict(currentStub)
copyCurrentStub['property'] = 'current_out_' + phase
copyCurrentStub['file'] = 'OH_line_current_phase' + phase + '.csv'
tree[feeder.getMaxKey(tree) + 1] = copyCurrentStub
rating_stub = {'object':'group_recorder', 'group':'"class=overhead_line"', 'interval':3600}
copyRatingStub = dict(rating_stub)
copyRatingStub['property'] = 'continuous_rating'
copyRatingStub['file'] = 'OH_line_cont_rating.csv'
tree[feeder.getMaxKey(tree) + 1] = copyRatingStub
flow_stub = {'object':'group_recorder', 'group':'"class=overhead_line"', 'interval':3600}
copyFlowStub = dict(flow_stub)
copyFlowStub['property'] = 'flow_direction'
copyFlowStub['file'] = 'OH_line_flow_direc.csv'
tree[feeder.getMaxKey(tree) + 1] = copyFlowStub
# Attach current recorder for underground_lines
currentStubOH = {'object':'group_recorder', 'group':'"class=underground_line"', 'interval':3600}
for phase in ['A','B','C']:
copyCurrentStubOH = dict(currentStubOH)
copyCurrentStubOH['property'] = 'current_out_' + phase
copyCurrentStubOH['file'] = 'UG_line_current_phase' + phase + '.csv'
tree[feeder.getMaxKey(tree) + 1] = copyCurrentStubOH
ug_rating_stub = {'object':'group_recorder', 'group':'"class=underground_line"', 'interval':3600}
copyUGRatingStub = dict(ug_rating_stub)
copyUGRatingStub['property'] = 'continuous_rating'
copyUGRatingStub['file'] = 'UG_line_cont_rating.csv'
tree[feeder.getMaxKey(tree) + 1] = copyUGRatingStub
ug_flow_stub = {'object':'group_recorder', 'group':'"class=underground_line"', 'interval':3600}
ugCopyFlowStub = dict(ug_flow_stub)
ugCopyFlowStub['property'] = 'flow_direction'
ugCopyFlowStub['file'] = 'UG_line_flow_direc.csv'
tree[feeder.getMaxKey(tree) + 1] = ugCopyFlowStub
# And get meters for system voltage map:
stub = {'object':'group_recorder', 'group':'"class=triplex_meter"', 'interval':3600}
for phase in ['1','2']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'mVoltDump.csv'
tree[feeder.getMaxKey(tree) + 1] = copyStub
for key in tree:
if 'bustype' in tree[key].keys():
if tree[key]['bustype'] == 'SWING':
tree[key]['object'] = 'meter'
swingN = tree[key]['name']
swingRecord = {'object':'recorder', 'property':'voltage_A,measured_real_power,measured_power','file':'subVoltsA.csv','parent':swingN, 'interval':60}
tree[feeder.getMaxKey(tree) + 1] = swingRecord
for key in tree:
if 'omftype' in tree[key].keys() and tree[key]['argument']=='minimum_timestep=3600':
tree[key]['argument'] = 'minimum_timestep=60'
# If there is a varvolt object in the tree, add recorder to swingbus and node from voltage_measurements property
# Find var_volt object
downLineNode = 'None'
for key in tree:
if 'object' in tree[key].keys() and tree[key]['object']=='volt_var_control':
downLineNode = tree[key]['voltage_measurements']
if downLineNode != 'None':
downNodeRecord = {'object':'recorder', 'property':'voltage_A','file':'firstDownlineVoltsA.csv','parent':downLineNode, 'interval':60}
tree[feeder.getMaxKey(tree) + 1] = downNodeRecord
# Violation recorder to display to users
# violationRecorder = {'object':'violation_recorder','node_continuous_voltage_limit_lower':0.95,'file':'Violation_Log.csv',
# 'secondary_dist_voltage_rise_lower_limit':-0.042,'substation_pf_lower_limit':0.85,'substation_breaker_C_limit':300,
# 'secondary_dist_voltage_rise_upper_limit':0.025,'substation_breaker_B_limit':300,'violation_flag':'ALLVIOLATIONS',
# 'node_instantaneous_voltage_limit_upper':1.1, 'inverter_v_chng_per_interval_lower_bound':-0.05, 'virtual_substation':swingN,
# 'substation_breaker_A_limit':300, 'xfrmr_thermal_limit_lower':0,'node_continuous_voltage_interval':300,'strict':'false',
# 'node_instantaneous_voltage_limit_lower':0,'line_thermal_limit_upper':1,'echo':'false','node_continuous_voltage_limit_upper':1.05,
# 'interval':30,'line_thermal_limit_lower':0,'summary':'Violation_Summary.csv','inverter_v_chng_interval':60,
# 'xfrmr_thermal_limit_upper':2,'inverter_v_chng_per_interval_upper_bound':0.050}
# tree[feeder.getMaxKey(tree) + 1] = violationRecorder
feeder.adjustTime(tree=tree, simLength=float(inputDict["simLength"]),
simLengthUnits=inputDict["simLengthUnits"], simStartDate=inputDict["simStartDate"])
# RUN GRIDLABD IN FILESYSTEM (EXPENSIVE!)
rawOut = gridlabd.runInFilesystem(tree, attachments=feederJson["attachments"],
keepFiles=True, workDir=pJoin(modelDir))
# voltDumps have no values when gridlabD fails or the files dont exist
if not os.path.isfile(pJoin(modelDir,'aVoltDump.csv')):
with open (pJoin(modelDir,'stderr.txt')) as inFile:
stdErrText = inFile.read()
message = 'GridLAB-D crashed. Error log:\n' + stdErrText
raise Exception(message)
elif len(rawOut['aVoltDump.csv']['# timestamp']) == 0:
with open (pJoin(modelDir,'stderr.txt')) as inFile:
stdErrText = inFile.read()
message = 'GridLAB-D crashed. Error log:\n' + stdErrText
raise Exception(message)
outData = {}
# Std Err and Std Out
outData['stderr'] = rawOut['stderr']
outData['stdout'] = rawOut['stdout']
# Time Stamps
for key in rawOut:
if '# timestamp' in rawOut[key]:
outData['timeStamps'] = rawOut[key]['# timestamp']
break
elif '# property.. timestamp' in rawOut[key]:
outData['timeStamps'] = rawOut[key]['# property.. timestamp']
else:
outData['timeStamps'] = []
# Day/Month Aggregation Setup:
stamps = outData.get('timeStamps',[])
level = inputDict.get('simLengthUnits','hours')
# Climate
for key in rawOut:
if key.startswith('Climate_') and key.endswith('.csv'):
outData['climate'] = {}
outData['climate']['Rain Fall (in/h)'] = hdmAgg(rawOut[key].get('rainfall'), sum, level)
outData['climate']['Wind Speed (m/s)'] = hdmAgg(rawOut[key].get('wind_speed'), avg, level)
outData['climate']['Temperature (F)'] = hdmAgg(rawOut[key].get('temperature'), max, level)
outData['climate']['Snow Depth (in)'] = hdmAgg(rawOut[key].get('snowdepth'), max, level)
outData['climate']['Direct Normal (W/sf)'] = hdmAgg(rawOut[key].get('solar_direct'), sum, level)
#outData['climate']['Global Horizontal (W/sf)'] = hdmAgg(rawOut[key].get('solar_global'), sum, level)
climateWbySFList= hdmAgg(rawOut[key].get('solar_global'), sum, level)
#converting W/sf to W/sm
climateWbySMList= [x*10.76392 for x in climateWbySFList]
outData['climate']['Global Horizontal (W/sm)']=climateWbySMList
# Voltage Band
if 'VoltageJiggle.csv' in rawOut:
outData['allMeterVoltages'] = {}
outData['allMeterVoltages']['Min'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['min(voltage_12.mag)']], min, level)
outData['allMeterVoltages']['Mean'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['mean(voltage_12.mag)']], avg, level)
outData['allMeterVoltages']['StdDev'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['std(voltage_12.mag)']], avg, level)
outData['allMeterVoltages']['Max'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['max(voltage_12.mag)']], max, level)
# Power Consumption
outData['Consumption'] = {}
# Set default value to be 0, avoiding missing value when computing Loads
outData['Consumption']['Power'] = [0] * int(inputDict["simLength"])
outData['Consumption']['Losses'] = [0] * int(inputDict["simLength"])
outData['Consumption']['DG'] = [0] * int(inputDict["simLength"])
for key in rawOut:
if key.startswith('SwingKids_') and key.endswith('.csv'):
oneSwingPower = hdmAgg(vecPyth(rawOut[key]['sum(power_in.real)'],rawOut[key]['sum(power_in.imag)']), avg, level)
if 'Power' not in outData['Consumption']:
outData['Consumption']['Power'] = oneSwingPower
else:
outData['Consumption']['Power'] = vecSum(oneSwingPower,outData['Consumption']['Power'])
elif key.startswith('Inverter_') and key.endswith('.csv'):
realA = rawOut[key]['power_A.real']
realB = rawOut[key]['power_B.real']
realC = rawOut[key]['power_C.real']
imagA = rawOut[key]['power_A.imag']
imagB = rawOut[key]['power_B.imag']
imagC = rawOut[key]['power_C.imag']
oneDgPower = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'DG' not in outData['Consumption']:
outData['Consumption']['DG'] = oneDgPower
else:
outData['Consumption']['DG'] = vecSum(oneDgPower,outData['Consumption']['DG'])
elif key.startswith('Windmill_') and key.endswith('.csv'):
vrA = rawOut[key]['voltage_A.real']
vrB = rawOut[key]['voltage_B.real']
vrC = rawOut[key]['voltage_C.real']
viA = rawOut[key]['voltage_A.imag']
viB = rawOut[key]['voltage_B.imag']
viC = rawOut[key]['voltage_C.imag']
crB = rawOut[key]['current_B.real']
crA = rawOut[key]['current_A.real']
crC = rawOut[key]['current_C.real']
ciA = rawOut[key]['current_A.imag']
ciB = rawOut[key]['current_B.imag']
ciC = rawOut[key]['current_C.imag']
powerA = vecProd(vecPyth(vrA,viA),vecPyth(crA,ciA))
powerB = vecProd(vecPyth(vrB,viB),vecPyth(crB,ciB))
powerC = vecProd(vecPyth(vrC,viC),vecPyth(crC,ciC))
oneDgPower = hdmAgg(vecSum(powerA,powerB,powerC), avg, level)
if 'DG' not in outData['Consumption']:
outData['Consumption']['DG'] = oneDgPower
else:
outData['Consumption']['DG'] = vecSum(oneDgPower,outData['Consumption']['DG'])
elif key in ['OverheadLosses.csv', 'UndergroundLosses.csv', 'TriplexLosses.csv', 'TransformerLosses.csv']:
realA = rawOut[key]['sum(power_losses_A.real)']
imagA = rawOut[key]['sum(power_losses_A.imag)']
realB = rawOut[key]['sum(power_losses_B.real)']
imagB = rawOut[key]['sum(power_losses_B.imag)']
realC = rawOut[key]['sum(power_losses_C.real)']
imagC = rawOut[key]['sum(power_losses_C.imag)']
oneLoss = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'Losses' not in outData['Consumption']:
outData['Consumption']['Losses'] = oneLoss
else:
outData['Consumption']['Losses'] = vecSum(oneLoss,outData['Consumption']['Losses'])
elif key.startswith('Regulator_') and key.endswith('.csv'):
#split function to strip off .csv from filename and user rest of the file name as key. for example- Regulator_VR10.csv -> key would be Regulator_VR10
regName=""
regName = key
newkey=regName.split(".")[0]
outData[newkey] ={}
outData[newkey]['RegTapA'] = [0] * int(inputDict["simLength"])
outData[newkey]['RegTapB'] = [0] * int(inputDict["simLength"])
outData[newkey]['RegTapC'] = [0] * int(inputDict["simLength"])
outData[newkey]['RegTapA'] = rawOut[key]['tap_A']
outData[newkey]['RegTapB'] = rawOut[key]['tap_B']
outData[newkey]['RegTapC'] = rawOut[key]['tap_C']
outData[newkey]['RegPhases'] = rawOut[key]['phases'][0]
elif key.startswith('Capacitor_') and key.endswith('.csv'):
capName=""
capName = key
newkey=capName.split(".")[0]
outData[newkey] ={}
outData[newkey]['Cap1A'] = [0] * int(inputDict["simLength"])
outData[newkey]['Cap1B'] = [0] * int(inputDict["simLength"])
outData[newkey]['Cap1C'] = [0] * int(inputDict["simLength"])
outData[newkey]['Cap1A'] = rawOut[key]['switchA']
outData[newkey]['Cap1B'] = rawOut[key]['switchB']
outData[newkey]['Cap1C'] = rawOut[key]['switchC']
outData[newkey]['CapPhases'] = rawOut[key]['phases'][0]
# Capture voltages at the swingbus
# Loop through voltDump for swingbus voltages
subData = []
downData = []
with open(pJoin(modelDir,"subVoltsA.csv")) as subFile:
reader = csv.reader(subFile)
subData = [x for x in reader]
if downLineNode != 'None':
with open(pJoin(modelDir,"firstDownlineVoltsA.csv")) as downFile:
reader = csv.reader(downFile)
downData = [x for x in reader]
FIRST_DATA_ROW = 9
cleanDown = [stringToMag(x[1]) for x in downData[FIRST_DATA_ROW:-1]]
swingTimestamps = [x[0] for x in subData[FIRST_DATA_ROW:-1]]
cleanSub = [stringToMag(x[1]) for x in subData[FIRST_DATA_ROW:-1]]
# real_power / power
powerFactors = []
for row in subData[FIRST_DATA_ROW:-1]:
powerFactors.append(abs(float(row[2])/stringToMag(row[3])))
outData['powerFactors'] = powerFactors
outData['swingVoltage'] = cleanSub
outData['downlineNodeVolts'] = cleanDown
outData['swingTimestamps'] = swingTimestamps
# If there is a var volt system, find the min and max voltage for a band
minVoltBand = []
maxVoltBand = []
if downLineNode != 'None':
for key in tree:
objKeys = tree[key].keys()
if 'object' in objKeys:
if tree[key]['object']=='volt_var_control':
minVoltBand.append(float(tree[key]['minimum_voltages']))
maxVoltBand.append(float(tree[key]['maximum_voltages']))
outData['minVoltBand'] = minVoltBand
outData['maxVoltBand'] = maxVoltBand
# Violation Summary and Log
# violationData = ''
# violationArray = []
# with open(pJoin(modelDir,"Violation_Summary.csv")) as vioSum:
# reader = csv.reader(vioSum)
# for row in reader:
# violationArray.append(row)
# for row in violationArray[4:]:
# violationData += str(' '.join(row)) + "\n"
# outData["violationSummary"] = violationData
# violationLogArray = []
# violationLog = ''
# with open(pJoin(modelDir,"Violation_Log.csv")) as vioLog:
# logger = csv.reader(vioLog)
# for row in logger:
# violationLogArray.append(row)
# for row in violationLogArray[6:]:
# violationLog += str(' '.join(row)) + "\n"
# outData['violationLog'] = violationLog
# What percentage of our keys have lat lon data?
latKeys = [tree[key]['latitude'] for key in tree if 'latitude' in tree[key]]
latPerc = 1.0*len(latKeys)/len(tree)
if latPerc < 0.25: doNeato = True
else: doNeato = False
# Generate the frames for the system voltage map time traveling chart.
genTime, mapTimestamp = generateVoltChart(tree, rawOut, modelDir, neatoLayout=doNeato)
outData['genTime'] = genTime
outData['mapTimestamp'] = mapTimestamp
# Aggregate up the timestamps:
if level=='days':
outData['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:10], 'days')
elif level=='months':
outData['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:7], 'months')
return outData
def work(modelDir, inputDict):
feederName = inputDict["feederName1"]
inputDict["climateName"] = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
feederJson = json.load(open(pJoin(modelDir, feederName+'.omd')))
tree = feederJson["tree"]
# tree[feeder.getMaxKey(tree)+1] = {'object':'capacitor','control':'VOLT','phases':'ABCN','name':'CAPTEST','parent':'tm_1','capacitor_A':'0.10 MVAr','capacitor_B':'0.10 MVAr','capacitor_C':'0.10 MVAr','time_delay':'300.0','nominal_voltage':'2401.7771','voltage_set_high':'2350.0','voltage_set_low':'2340.0','switchA':'CLOSED','switchB':'CLOSED','switchC':'CLOSED','control_level':'INDIVIDUAL','phases_connected':'ABCN','dwell_time':'0.0','pt_phases':'ABCN'}
# Set up GLM with correct time and recorders:
feeder.attachRecorders(tree, "Regulator", "object", "regulator")
feeder.attachRecorders(tree, "Capacitor", "object", "capacitor")
feeder.attachRecorders(tree, "Inverter", "object", "inverter")
feeder.attachRecorders(tree, "Windmill", "object", "windturb_dg")
feeder.attachRecorders(tree, "CollectorVoltage", None, None)
feeder.attachRecorders(tree, "Climate", "object", "climate")
feeder.attachRecorders(tree, "OverheadLosses", None, None)
feeder.attachRecorders(tree, "UndergroundLosses", None, None)
feeder.attachRecorders(tree, "TriplexLosses", None, None)
feeder.attachRecorders(tree, "TransformerLosses", None, None)
feeder.groupSwingKids(tree)
# System check - linux doesn't support newer GridLAB-D versions
if sys.platform == 'linux2':
pass
else:
# print feeder.getMaxKey(tree)
# tree[14,20,27,28,47] empty for UCS Egan, add climate object to tree[14]
# HACK: tree[10:19] is empty
tree[11] = {'omftype':'#include', 'argument':'\"hot_water_demand.glm\"'}
tree[12] = {'omftype':'#include', 'argument':'\"lock_mode_schedule.glm\"'}
tree[13] = {'omftype':'#include', 'argument':'\"control_priority_schedule.glm\"'}
# Attach frequency player
tree[14] = {'omftype':'class player', 'argument':'{double value;}'}
tree[feeder.getMaxKey(tree)+1] = {'object':'player', 'file':'frequency.PLAYER', 'property':'value', 'name':'frequency', 'loop':0}
# Set up GridBallast Controls
totalWH = 0
totalZIP = 0
gbWH = 0
gbZIP = 0
for key in tree.keys():
# Waterheater Controller properties
if ('name' in tree[key]) and (tree[key].get('object') == 'waterheater'):
totalWH += 1
gbWH += 1
# Frequency control parameters
tree[key]['enable_freq_control'] = 'true'
tree[key]['measured_frequency'] = 'frequency.value'
tree[key]['freq_lowlimit'] = 59
tree[key]['freq_uplimit'] = 61
tree[key]['heat_mode'] = 'ELECTRIC'
# tree[key]['average_delay_time'] = 60
# Voltage control parameters
# tree[key]['enable_volt_control'] = 'true'
# tree[key]['volt_lowlimit'] = 240.4
# tree[key]['volt_uplimit'] = 241.4
# Lock Mode parameters
# tree[key]['enable_lock'] = 'temp_lock_enable'
# tree[key]['lock_STATUS'] = 'temp_lock_status'
# Controller Priority: a.lock, b.freq, c.volt, d.therm
tree[key]['controller_priority'] = 3214 #default:therm>lock>freq>volt
# tree[key]['controller_priority'] = 1423 #freq>therm>volt>lock
# tree[key]['controller_priority'] = 'control_priority'
# fix waterheater property demand to water_demand for newer GridLAB-D versions
if 'demand' in tree[key]:
# tree[key]['water_demand'] = tree[key]['demand']
tree[key]['water_demand'] = 'weekday_hotwater*1'
del tree[key]['demand']
# ZIPload Controller properties
if ('name' in tree[key]) and (tree[key].get('object') == 'ZIPload'):
totalZIP += 1
if tree[key]['name'].startswith('responsive'):
gbZIP += 1
# Frequency control parameters
tree[key]['enable_freq_control'] = 'true'
tree[key]['measured_frequency'] = 'frequency.value'
tree[key]['freq_lowlimit'] = 59
tree[key]['freq_uplimit'] = 61
# tree[key]['average_delay_time'] = 60
# Voltage control parameters
# tree[key]['enable_volt_control'] = 'true'
# tree[key]['volt_lowlimit'] = 240.4
# tree[key]['volt_uplimit'] = 241.4
# Lock Mode parameters
# tree[key]['enable_lock'] = 'temp_lock_enable'
# tree[key]['lock_STATUS'] = 'temp_lock_status'
tree[key]['controller_priority'] = 4321 #default:lock>freq>volt>therm
# tree[key]['controller_priority'] = 2431 #freq>volt>lock>therm
# tree[key]['groupid'] = 'fan'
# Attach collector for total network load
tree[feeder.getMaxKey(tree)+1] = {'object':'collector', 'group':'"class=triplex_meter"', 'property':'sum(measured_real_power)', 'interval':60, 'file':'allMeterPower.csv'}
# Attach collector for total waterheater load
tree[feeder.getMaxKey(tree)+1] = {'object':'collector', 'group':'"class=waterheater"', 'property':'sum(actual_load)', 'interval':60, 'file':'allWaterheaterLoad.csv'}
# Attach collector for total ZIPload power/load
tree[feeder.getMaxKey(tree)+1] = {'object':'collector', 'group':'"class=ZIPload"', 'property':'sum(base_power)', 'interval':60, 'file':'allZIPloadPower.csv'}
# Attach recorder for each ZIPload power/load
tree[feeder.getMaxKey(tree)+1] = {'object':'group_recorder', 'group':'"class=ZIPload"', 'property':'base_power', 'interval':60, 'file':'eachZIPloadPower.csv'}
# Attach recorder for all ZIPloads demand_rate
tree[feeder.getMaxKey(tree)+1] = {'object':'group_recorder', 'group':'"class=ZIPload"', 'property':'demand_rate', 'interval':60, 'file':'allZIPloadDemand.csv'}
# Attach recorder for waterheaters on/off
tree[feeder.getMaxKey(tree)+1] = {'object':'group_recorder', 'group':'"class=waterheater"', 'property':'is_waterheater_on', 'interval':60, 'file':'allWaterheaterOn.csv'}
# Attach recorder for waterheater tank temperatures
tree[feeder.getMaxKey(tree)+1] = {'object':'group_recorder', 'group':'"class=waterheater"', 'property':'temperature', 'interval':60, 'file':'allWaterheaterTemp.csv'}
# Attach recorders for system voltage map:
stub = {'object':'group_recorder', 'group':'"class=node"', 'interval':60}
for phase in ['A','B','C']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'VoltDump.csv'
tree[feeder.getMaxKey(tree) + 1] = copyStub
# Attach recorders for system voltage map, triplex:
stub = {'object':'group_recorder', 'group':'"class=triplex_node"', 'interval':60}
for phase in ['1','2']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'nVoltDump.csv'
tree[feeder.getMaxKey(tree) + 1] = copyStub
# And get meters for system voltage map:
stub = {'object':'group_recorder', 'group':'"class=triplex_meter"', 'interval':60}
for phase in ['1','2']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'mVoltDump.csv'
tree[feeder.getMaxKey(tree) + 1] = copyStub
feeder.adjustTime(tree=tree, simLength=float(inputDict["simLength"]),
simLengthUnits=inputDict["simLengthUnits"], simStartDate=inputDict["simStartDate"])
# RUN GRIDLABD IN FILESYSTEM (EXPENSIVE!)
rawOut = gridlabd.runInFilesystem(tree, attachments=feederJson["attachments"],
keepFiles=True, workDir=pJoin(modelDir))
outData = {}
# Std Err and Std Out
outData['stderr'] = rawOut['stderr']
outData['stdout'] = rawOut['stdout']
# Time Stamps
for key in rawOut:
if '# timestamp' in rawOut[key]:
outData['timeStamps'] = rawOut[key]['# timestamp']
break
elif '# property.. timestamp' in rawOut[key]:
outData['timeStamps'] = rawOut[key]['# property.. timestamp']
else:
outData['timeStamps'] = []
# Day/Month Aggregation Setup:
stamps = outData.get('timeStamps',[])
level = inputDict.get('simLengthUnits','hours')
# Climate
for key in rawOut:
if key.startswith('Climate_') and key.endswith('.csv'):
outData['climate'] = {}
outData['climate']['Rain Fall (in/h)'] = hdmAgg(rawOut[key].get('rainfall'), sum, level)
outData['climate']['Wind Speed (m/s)'] = hdmAgg(rawOut[key].get('wind_speed'), avg, level)
outData['climate']['Temperature (F)'] = hdmAgg(rawOut[key].get('temperature'), max, level)
outData['climate']['Snow Depth (in)'] = hdmAgg(rawOut[key].get('snowdepth'), max, level)
outData['climate']['Direct Normal (W/sf)'] = hdmAgg(rawOut[key].get('solar_direct'), sum, level)
#outData['climate']['Global Horizontal (W/sf)'] = hdmAgg(rawOut[key].get('solar_global'), sum, level)
climateWbySFList= hdmAgg(rawOut[key].get('solar_global'), sum, level)
#converting W/sf to W/sm
climateWbySMList= [x*10.76392 for x in climateWbySFList]
outData['climate']['Global Horizontal (W/sm)']=climateWbySMList
# Voltage Band
if 'VoltageJiggle.csv' in rawOut:
outData['allMeterVoltages'] = {}
outData['allMeterVoltages']['Min'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['min(voltage_12.mag)']], min, level)
outData['allMeterVoltages']['Mean'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['mean(voltage_12.mag)']], avg, level)
outData['allMeterVoltages']['StdDev'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['std(voltage_12.mag)']], avg, level)
outData['allMeterVoltages']['Max'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['max(voltage_12.mag)']], max, level)
# Power Consumption
outData['Consumption'] = {}
# Set default value to be 0, avoiding missing value when computing Loads
outData['Consumption']['Power'] = [0] * int(inputDict["simLength"])
outData['Consumption']['Losses'] = [0] * int(inputDict["simLength"])
outData['Consumption']['DG'] = [0] * int(inputDict["simLength"])
for key in rawOut:
if key.startswith('SwingKids_') and key.endswith('.csv'):
oneSwingPower = hdmAgg(vecPyth(rawOut[key]['sum(power_in.real)'],rawOut[key]['sum(power_in.imag)']), avg, level)
if 'Power' not in outData['Consumption']:
outData['Consumption']['Power'] = oneSwingPower
else:
outData['Consumption']['Power'] = vecSum(oneSwingPower,outData['Consumption']['Power'])
elif key.startswith('Inverter_') and key.endswith('.csv'):
realA = rawOut[key]['power_A.real']
realB = rawOut[key]['power_B.real']
realC = rawOut[key]['power_C.real']
imagA = rawOut[key]['power_A.imag']
imagB = rawOut[key]['power_B.imag']
imagC = rawOut[key]['power_C.imag']
oneDgPower = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'DG' not in outData['Consumption']:
outData['Consumption']['DG'] = oneDgPower
else:
outData['Consumption']['DG'] = vecSum(oneDgPower,outData['Consumption']['DG'])
elif key.startswith('Windmill_') and key.endswith('.csv'):
vrA = rawOut[key]['voltage_A.real']
vrB = rawOut[key]['voltage_B.real']
vrC = rawOut[key]['voltage_C.real']
viA = rawOut[key]['voltage_A.imag']
viB = rawOut[key]['voltage_B.imag']
viC = rawOut[key]['voltage_C.imag']
crB = rawOut[key]['current_B.real']
crA = rawOut[key]['current_A.real']
crC = rawOut[key]['current_C.real']
ciA = rawOut[key]['current_A.imag']
ciB = rawOut[key]['current_B.imag']
ciC = rawOut[key]['current_C.imag']
powerA = vecProd(vecPyth(vrA,viA),vecPyth(crA,ciA))
powerB = vecProd(vecPyth(vrB,viB),vecPyth(crB,ciB))
powerC = vecProd(vecPyth(vrC,viC),vecPyth(crC,ciC))
oneDgPower = hdmAgg(vecSum(powerA,powerB,powerC), avg, level)
if 'DG' not in outData['Consumption']:
outData['Consumption']['DG'] = oneDgPower
else:
outData['Consumption']['DG'] = vecSum(oneDgPower,outData['Consumption']['DG'])
elif key in ['OverheadLosses.csv', 'UndergroundLosses.csv', 'TriplexLosses.csv', 'TransformerLosses.csv']:
realA = rawOut[key]['sum(power_losses_A.real)']
imagA = rawOut[key]['sum(power_losses_A.imag)']
realB = rawOut[key]['sum(power_losses_B.real)']
imagB = rawOut[key]['sum(power_losses_B.imag)']
realC = rawOut[key]['sum(power_losses_C.real)']
imagC = rawOut[key]['sum(power_losses_C.imag)']
oneLoss = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'Losses' not in outData['Consumption']:
outData['Consumption']['Losses'] = oneLoss
else:
outData['Consumption']['Losses'] = vecSum(oneLoss,outData['Consumption']['Losses'])
elif key.startswith('Regulator_') and key.endswith('.csv'):
#split function to strip off .csv from filename and user rest of the file name as key. for example- Regulator_VR10.csv -> key would be Regulator_VR10
regName=""
regName = key
newkey=regName.split(".")[0]
outData[newkey] ={}
outData[newkey]['RegTapA'] = [0] * int(inputDict["simLength"])
outData[newkey]['RegTapB'] = [0] * int(inputDict["simLength"])
outData[newkey]['RegTapC'] = [0] * int(inputDict["simLength"])
outData[newkey]['RegTapA'] = rawOut[key]['tap_A']
outData[newkey]['RegTapB'] = rawOut[key]['tap_B']
outData[newkey]['RegTapC'] = rawOut[key]['tap_C']
outData[newkey]['RegPhases'] = rawOut[key]['phases'][0]
elif key.startswith('Capacitor_') and key.endswith('.csv'):
capName=""
capName = key
newkey=capName.split(".")[0]
outData[newkey] ={}
outData[newkey]['Cap1A'] = [0] * int(inputDict["simLength"])
outData[newkey]['Cap1B'] = [0] * int(inputDict["simLength"])
outData[newkey]['Cap1C'] = [0] * int(inputDict["simLength"])
outData[newkey]['Cap1A'] = rawOut[key]['switchA']
outData[newkey]['Cap1B'] = rawOut[key]['switchB']
outData[newkey]['Cap1C'] = rawOut[key]['switchC']
outData[newkey]['CapPhases'] = rawOut[key]['phases'][0]
# Print gridBallast Outputs to allOutputData.json
outData['gridBallast'] = {}
if 'allMeterPower.csv' in rawOut:
outData['gridBallast']['totalNetworkLoad'] = [x / 1000 for x in rawOut.get('allMeterPower.csv')['sum(measured_real_power)']] #Convert W to kW
if ('allZIPloadPower.csv' in rawOut) and ('allWaterheaterLoad.csv' in rawOut):
outData['gridBallast']['availabilityMagnitude'] = [x[0] + x[1] for x in zip(rawOut.get('allWaterheaterLoad.csv')['sum(actual_load)'], rawOut.get('allZIPloadPower.csv')['sum(base_power)'])]
if 'allZIPloadDemand.csv' in rawOut:
outData['gridBallast']['ZIPloadDemand'] = {}
for key in rawOut['allZIPloadDemand.csv']:
if (key.startswith('ZIPload')) or (key.startswith('responsive')) or (key.startswith('unresponsive')):
outData['gridBallast']['ZIPloadDemand'][key] = rawOut.get('allZIPloadDemand.csv')[key]
if 'eachZIPloadPower.csv' in rawOut:
outData['gridBallast']['ZIPloadPower'] = {}
for key in rawOut['eachZIPloadPower.csv']:
if (key.startswith('ZIPload')) or (key.startswith('responsive')) or (key.startswith('unresponsive')):
outData['gridBallast']['ZIPloadPower'][key] = rawOut.get('eachZIPloadPower.csv')[key]
if 'allWaterheaterOn.csv' in rawOut:
outData['gridBallast']['waterheaterOn'] = {}
for key in rawOut['allWaterheaterOn.csv']:
if (key.startswith('waterheater')) or (key.startswith('waterHeater')):
outData['gridBallast']['waterheaterOn'][key] = rawOut.get('allWaterheaterOn.csv')[key]
if 'allWaterheaterTemp.csv' in rawOut:
outData['gridBallast']['waterheaterTemp'] = {}
for key in rawOut['allWaterheaterTemp.csv']:
if (key.startswith('waterheater')) or (key.startswith('waterHeater')):
outData['gridBallast']['waterheaterTemp'][key] = rawOut.get('allWaterheaterTemp.csv')[key]
# System check - linux doesn't support newer GridLAB-D versions
if sys.platform == 'linux2':
pass
else:
outData['gridBallast']['penetrationLevel'] = 100*(gbWH+gbZIP)/(totalWH+totalZIP)
# Frequency Player
inArray = feederJson['attachments']['frequency.PLAYER'].split('\n')
tempArray = []
for each in inArray:
x = each.split(',')
y = float(x[1])
tempArray.append(y)
outData['frequencyPlayer'] = tempArray
# EventTime calculations
eventTime = inputDict['eventTime']
eventLength = inputDict['eventLength'].split(':')
eventDuration = datetime.timedelta(hours=int(eventLength[0]), minutes=int(eventLength[1]))
eventStart = datetime.datetime.strptime(eventTime, '%Y-%m-%d %H:%M')
eventEnd = eventStart + eventDuration
outData['gridBallast']['eventStart'] = str(eventStart)
outData['gridBallast']['eventEnd'] = str(eventEnd)
outData['gridBallast']['xMin'] = str(eventStart - datetime.timedelta(minutes=30))
outData['gridBallast']['xMax'] = str(eventEnd + datetime.timedelta(minutes=30))
# Convert string to date
# HACK: remove timezones, inconsistency in matching format
timeStampsDebug = [x[:19] for x in outData['timeStamps']]
dateTimeStamps = [datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S') for x in timeStampsDebug]
eventEndIdx = dateTimeStamps.index(eventEnd)
# Recovery Time
whOn = outData['gridBallast']['waterheaterOn']
whOnList = whOn.values()
whOnZip = zip(*whOnList)
whOnSum = [sum(x) for x in whOnZip]
anyOn = [x > 0 for x in whOnSum]
tRecIdx = anyOn.index(True, eventEndIdx)
tRec = dateTimeStamps[tRecIdx]
recoveryTime = tRec - eventEnd
outData['gridBallast']['recoveryTime'] = str(recoveryTime)
# Waterheaters Off-Duration
offDuration = tRec - eventStart
outData['gridBallast']['offDuration'] = str(offDuration)
# Reserve Magnitude (RM)
availMag = outData['gridBallast']['availabilityMagnitude']
totalNetLoad = outData['gridBallast']['totalNetworkLoad']
availPerc = [100 * x[0]/x[1] for x in zip(availMag,totalNetLoad)]
outData['gridBallast']['availabilityPercent'] = availPerc
outData['gridBallast']['rm'] = [100 - x for x in availPerc]
# Average RM during event
eventRM = [100 - x[1] for x in zip(dateTimeStamps, availPerc) if (x[0] == eventStart) or (x[0] == eventEnd)]
outData['gridBallast']['rmAvg'] = np.mean(eventRM)
# Reserve Magnitude Variability Tolerance (RMVT)
outData['gridBallast']['rmvt'] = np.std(eventRM)
# Availability
rmt = 7
available = [x[1] > rmt for x in zip(dateTimeStamps, availPerc) if (x[0] < eventStart) or (x[0] > eventEnd)]
outData['gridBallast']['availability'] = 100.0 * sum(available) / (int(inputDict['simLength']) - int(eventLength[1]) - 1)
# Waterheater Temperature Drop calculations
whTemp = outData['gridBallast']['waterheaterTemp']
whTempList = whTemp.values()
whTempZip = zip(*whTempList)
whTempDrops = []
LOWER_LIMIT_TEMP = 110 # Used for calculating quality of service. Typical hot shower temp = 105 F.
for time in whTempZip:
tempDrop = sum([t < LOWER_LIMIT_TEMP for t in time])
whTempDrops.append(tempDrop)
outData['gridBallast']['waterheaterTempDrops'] = whTempDrops
# ZIPload calculations for Availability and QoS
zPower = outData['gridBallast']['ZIPloadPower']
zPowerList = zPower.values()
zPowerZip = zip(*zPowerList)
zDemand = outData['gridBallast']['ZIPloadDemand']
zDemandList = zDemand.values()
zDemandZip = zip(*zDemandList)
zDrops = []
for x, y in zip(zPowerZip,zDemandZip):
zDrop = 0
for i in range(len(x)):
if (x[i] == 0) and (y[i] > 0):
zDrop += 1
zDrops.append(zDrop)
outData['gridBallast']['qualityDrops'] = [x + y for x, y in zip(zDrops, whTempDrops)]
# What percentage of our keys have lat lon data?
latKeys = [tree[key]['latitude'] for key in tree if 'latitude' in tree[key]]
latPerc = 1.0*len(latKeys)/len(tree)
if latPerc < 0.25:
doNeato = True
else:
doNeato = False
# Generate the frames for the system voltage map time traveling chart.
genTime = generateVoltChart(tree, rawOut, modelDir, neatoLayout=doNeato)
outData['genTime'] = genTime
# Aggregate up the timestamps:
if level=='days':
outData['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:10], 'days')
elif level=='months':
outData['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:7], 'months')
return outData
def voltPlot(omd, workDir=None, neatoLayout=False):
''' Draw a color-coded map of the voltage drop on a feeder.
Returns a matplotlib object. '''
tree = omd.get('tree', {})
# # 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=omd.get(
'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=(15, 15))
plt.axes(frameon=0)
plt.axis('off')
#set axes step equal
voltChart.gca().set_aspect('equal')
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 = 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 heavyProcessing(modelDir, inputDict):
''' Run the model in its directory. WARNING: GRIDLAB CAN TAKE HOURS TO COMPLETE. '''
print "STARTING TO RUN", modelDir
beginTime = datetime.datetime.now()
# Get feeder name and data in.
try: os.mkdir(pJoin(modelDir,'gldContainer'))
except: pass
try:
feederName = inputDict["feederName1"]
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__metaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "gldContainer", "climate.tmy2"))
startTime = datetime.datetime.now()
feederJson = json.load(open(pJoin(modelDir, feederName+'.omd')))
tree = feederJson["tree"]
#add a check to see if there is already a climate object in the omd file
#if there is delete the climate from attachments and the climate object
attachKeys = feederJson["attachments"].keys()
for key in attachKeys:
if key.endswith('.tmy2'):
del feederJson['attachments'][key]
treeKeys = feederJson["tree"].keys()
for key in treeKeys:
if 'object' in feederJson['tree'][key]:
if feederJson['tree'][key]['object'] == 'climate':
del feederJson['tree'][key]
oldMax = feeder.getMaxKey(tree)
tree[oldMax + 1] = {'omftype':'module','argument':'climate'}
tree[oldMax + 2] = {'object':'climate','name':'Climate','interpolate':'QUADRATIC','tmyfile':'climate.tmy2'}
# tree[oldMax + 3] = {'object':'capacitor','control':'VOLT','phases':'ABCN','name':'CAPTEST','parent':'tm_1','capacitor_A':'0.10 MVAr','capacitor_B':'0.10 MVAr','capacitor_C':'0.10 MVAr','time_delay':'300.0','nominal_voltage':'2401.7771','voltage_set_high':'2350.0','voltage_set_low':'2340.0','switchA':'CLOSED','switchB':'CLOSED','switchC':'CLOSED','control_level':'INDIVIDUAL','phases_connected':'ABCN','dwell_time':'0.0','pt_phases':'ABCN'}
# Set up GLM with correct time and recorders:
feeder.attachRecorders(tree, "Regulator", "object", "regulator")
feeder.attachRecorders(tree, "Capacitor", "object", "capacitor")
feeder.attachRecorders(tree, "Inverter", "object", "inverter")
feeder.attachRecorders(tree, "Windmill", "object", "windturb_dg")
feeder.attachRecorders(tree, "CollectorVoltage", None, None)
feeder.attachRecorders(tree, "Climate", "object", "climate")
feeder.attachRecorders(tree, "OverheadLosses", None, None)
feeder.attachRecorders(tree, "UndergroundLosses", None, None)
feeder.attachRecorders(tree, "TriplexLosses", None, None)
feeder.attachRecorders(tree, "TransformerLosses", None, None)
feeder.groupSwingKids(tree)
# Attach recorder for waterheaters on/off
stub = {'object':'group_recorder', 'group':'"class=waterheater"', 'property':'is_waterheater_on', 'interval':3600, 'file':'allWaterheaterOn.csv'}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
# Attach recorder for waterheater tank temperatures
stub = {'object':'group_recorder', 'group':'"class=waterheater"', 'property':'temperature', 'interval':3600, 'file':'allWaterheaterTemp.csv'}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
# Attach collector for total waterheater load
stub = {'object':'collector', 'group':'"class=waterheater"', 'property':'sum(actual_load)', 'interval':3600, 'file':'allWaterheaterLoad.csv'}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
# Attach collector for total network load
stub = {'object':'collector', 'group':'"class=triplex_meter"', 'property':'sum(measured_real_power)', 'interval':3600, 'file':'allMeterPower.csv'}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
# Attach collector for total overall ZIPload power/load
stub = {'object':'collector', 'group':'"class=ZIPload"', 'property':'sum(base_power)', 'interval':3600, 'file':'allZIPloadPower.csv'}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
# Attach recorder for each ZIPload power/load
stub = {'object':'group_recorder', 'group':'"class=ZIPload"', 'property':'base_power', 'interval':3600, 'file':'eachZIPloadPower.csv'}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
# Attach recorder for all ZIPloads demand_rate
stub = {'object':'group_recorder', 'group':'"class=ZIPload"', 'property':'demand_rate', 'interval':3600, 'file':'allZIPloadDemand.csv'}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
# Attach recorder for all ZIPloads on
stub = {'object':'group_recorder', 'group':'"class=ZIPload"', 'property':'number_of_devices_on', 'interval':3600, 'file':'allZIPloadOn.csv'}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
# Attach passive_controller
tree[feeder.getMaxKey(tree)+1] = {'omftype':'module','argument':'market'}
tree[feeder.getMaxKey(tree)+1] = {'omftype':'class auction','argument':'{\n\tdouble my_avg; double my_std;\n}'}
tree[feeder.getMaxKey(tree)+1] = {'omftype':'class player','argument':'{\n\tdouble value;\n}'}
stub = {
'object':'player',
'name':'cppDays',
'file':'superCpp.player'
}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
stub = {
'object':'player',
'name':'superClearing',
'file':'superClearingPrice.player',
'loop':10
}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
stub = {
'object':'auction',
'name':'MARKET_1',
'my_std':0.037953,
'period':900,
'my_avg':0.110000,
'current_market.clearing_price':'superClearing.value',
'special_mode':'BUYERS_ONLY',
'unit': 'kW'
}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
stub = {
'object':'passive_controller',
'name':'waterheater_controller_waterheater171923',
'parent':'waterheater171923',
'control_mode':'RAMP',
'range_high':5,
'range_low':-5,
'ramp_high':1,
'ramp_low':-1,
'period':900,
'setpoint':'is_waterheater_on',
'base_setpoint':1,
'expectation_object':'MARKET_1',
'expectation_property':'my_avg',
'observation_object':'MARKET_1',
'observation_property':'past_market.clearing_price',
'stdev_observation_property':'my_std',
'state_property':'override'
}
copyStub = dict(stub)
tree[feeder.getMaxKey(tree)+1] = copyStub
# stub = {
# 'object':'passive_controller',
# 'name':'ZIPload_controller_ZIPload171922',
# 'parent':'ZIPload171922',
# 'control_mode':'RAMP',
# 'range_high':5,
# 'range_low':-5,
# 'ramp_high':1,
# 'ramp_low':-1,
# 'period':900,
# 'setpoint':'base_power'
# 'base_setpoint':1,
# 'expectation_object':'MARKET_1',
# 'expectation_property':'my_avg',
# 'observation_object':'MARKET_1',
# 'observation_property':'past_market.clearing_price',
# 'stdev_observation_property':'my_std'
# 'state_property':'override'
# }
# copyStub = dict(stub)
# tree[feeder.getMaxKey(tree)+1] = copyStub
# Attach recorders for system voltage map:
stub = {'object':'group_recorder', 'group':'"class=node"', 'interval':3600}
for phase in ['A','B','C']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'VoltDump.csv'
tree[feeder.getMaxKey(tree) + 1] = copyStub
# Attach recorders for system voltage map, triplex:
stub = {'object':'group_recorder', 'group':'"class=triplex_node"', 'interval':3600}
for phase in ['1','2']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'nVoltDump.csv'
tree[feeder.getMaxKey(tree) + 1] = copyStub
# And get meters for system voltage map:
stub = {'object':'group_recorder', 'group':'"class=triplex_meter"', 'interval':3600}
for phase in ['1','2']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'mVoltDump.csv'
tree[feeder.getMaxKey(tree) + 1] = copyStub
feeder.adjustTime(tree=tree, simLength=float(inputDict["simLength"]),
simLengthUnits=inputDict["simLengthUnits"], simStartDate=inputDict["simStartDate"])
# RUN GRIDLABD IN FILESYSTEM (EXPENSIVE!)
rawOut = gridlabd.runInFilesystem(tree, attachments=feederJson["attachments"],
keepFiles=True, workDir=pJoin(modelDir,'gldContainer'))
cleanOut = {}
# Std Err and Std Out
cleanOut['stderr'] = rawOut['stderr']
cleanOut['stdout'] = rawOut['stdout']
# Time Stamps
for key in rawOut:
print key
if '# timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key]['# timestamp']
break
elif '# property.. timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key]['# property.. timestamp']
else:
cleanOut['timeStamps'] = []
# Day/Month Aggregation Setup:
stamps = cleanOut.get('timeStamps',[])
level = inputDict.get('simLengthUnits','hours')
# Climate
for key in rawOut:
if key.startswith('Climate_') and key.endswith('.csv'):
cleanOut['climate'] = {}
cleanOut['climate']['Rain Fall (in/h)'] = hdmAgg(rawOut[key].get('rainfall'), sum, level)
cleanOut['climate']['Wind Speed (m/s)'] = hdmAgg(rawOut[key].get('wind_speed'), avg, level)
cleanOut['climate']['Temperature (F)'] = hdmAgg(rawOut[key].get('temperature'), max, level)
cleanOut['climate']['Snow Depth (in)'] = hdmAgg(rawOut[key].get('snowdepth'), max, level)
cleanOut['climate']['Direct Normal (W/sf)'] = hdmAgg(rawOut[key].get('solar_direct'), sum, level)
#cleanOut['climate']['Global Horizontal (W/sf)'] = hdmAgg(rawOut[key].get('solar_global'), sum, level)
climateWbySFList= hdmAgg(rawOut[key].get('solar_global'), sum, level)
#converting W/sf to W/sm
climateWbySMList= [x*10.76392 for x in climateWbySFList]
cleanOut['climate']['Global Horizontal (W/sm)']=climateWbySMList
# Voltage Band
if 'VoltageJiggle.csv' in rawOut:
cleanOut['allMeterVoltages'] = {}
cleanOut['allMeterVoltages']['Min'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['min(voltage_12.mag)']], min, level)
cleanOut['allMeterVoltages']['Mean'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['mean(voltage_12.mag)']], avg, level)
cleanOut['allMeterVoltages']['StdDev'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['std(voltage_12.mag)']], avg, level)
cleanOut['allMeterVoltages']['Max'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['max(voltage_12.mag)']], max, level)
# Power Consumption
cleanOut['Consumption'] = {}
# Set default value to be 0, avoiding missing value when computing Loads
cleanOut['Consumption']['Power'] = [0] * int(inputDict["simLength"])
cleanOut['Consumption']['Losses'] = [0] * int(inputDict["simLength"])
cleanOut['Consumption']['DG'] = [0] * int(inputDict["simLength"])
for key in rawOut:
if key.startswith('SwingKids_') and key.endswith('.csv'):
oneSwingPower = hdmAgg(vecPyth(rawOut[key]['sum(power_in.real)'],rawOut[key]['sum(power_in.imag)']), avg, level)
if 'Power' not in cleanOut['Consumption']:
cleanOut['Consumption']['Power'] = oneSwingPower
else:
cleanOut['Consumption']['Power'] = vecSum(oneSwingPower,cleanOut['Consumption']['Power'])
elif key.startswith('Inverter_') and key.endswith('.csv'):
realA = rawOut[key]['power_A.real']
realB = rawOut[key]['power_B.real']
realC = rawOut[key]['power_C.real']
imagA = rawOut[key]['power_A.imag']
imagB = rawOut[key]['power_B.imag']
imagC = rawOut[key]['power_C.imag']
oneDgPower = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'DG' not in cleanOut['Consumption']:
cleanOut['Consumption']['DG'] = oneDgPower
else:
cleanOut['Consumption']['DG'] = vecSum(oneDgPower,cleanOut['Consumption']['DG'])
elif key.startswith('Windmill_') and key.endswith('.csv'):
vrA = rawOut[key]['voltage_A.real']
vrB = rawOut[key]['voltage_B.real']
vrC = rawOut[key]['voltage_C.real']
viA = rawOut[key]['voltage_A.imag']
viB = rawOut[key]['voltage_B.imag']
viC = rawOut[key]['voltage_C.imag']
crB = rawOut[key]['current_B.real']
crA = rawOut[key]['current_A.real']
crC = rawOut[key]['current_C.real']
ciA = rawOut[key]['current_A.imag']
ciB = rawOut[key]['current_B.imag']
ciC = rawOut[key]['current_C.imag']
powerA = vecProd(vecPyth(vrA,viA),vecPyth(crA,ciA))
powerB = vecProd(vecPyth(vrB,viB),vecPyth(crB,ciB))
powerC = vecProd(vecPyth(vrC,viC),vecPyth(crC,ciC))
oneDgPower = hdmAgg(vecSum(powerA,powerB,powerC), avg, level)
if 'DG' not in cleanOut['Consumption']:
cleanOut['Consumption']['DG'] = oneDgPower
else:
cleanOut['Consumption']['DG'] = vecSum(oneDgPower,cleanOut['Consumption']['DG'])
elif key in ['OverheadLosses.csv', 'UndergroundLosses.csv', 'TriplexLosses.csv', 'TransformerLosses.csv']:
realA = rawOut[key]['sum(power_losses_A.real)']
imagA = rawOut[key]['sum(power_losses_A.imag)']
realB = rawOut[key]['sum(power_losses_B.real)']
imagB = rawOut[key]['sum(power_losses_B.imag)']
realC = rawOut[key]['sum(power_losses_C.real)']
imagC = rawOut[key]['sum(power_losses_C.imag)']
oneLoss = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'Losses' not in cleanOut['Consumption']:
cleanOut['Consumption']['Losses'] = oneLoss
else:
cleanOut['Consumption']['Losses'] = vecSum(oneLoss,cleanOut['Consumption']['Losses'])
elif key.startswith('Regulator_') and key.endswith('.csv'):
#split function to strip off .csv from filename and user rest of the file name as key. for example- Regulator_VR10.csv -> key would be Regulator_VR10
regName=""
regName = key
newkey=regName.split(".")[0]
cleanOut[newkey] ={}
cleanOut[newkey]['RegTapA'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['RegTapB'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['RegTapC'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['RegTapA'] = rawOut[key]['tap_A']
cleanOut[newkey]['RegTapB'] = rawOut[key]['tap_B']
cleanOut[newkey]['RegTapC'] = rawOut[key]['tap_C']
cleanOut[newkey]['RegPhases'] = rawOut[key]['phases'][0]
elif key.startswith('Capacitor_') and key.endswith('.csv'):
capName=""
capName = key
newkey=capName.split(".")[0]
cleanOut[newkey] ={}
cleanOut[newkey]['Cap1A'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['Cap1B'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['Cap1C'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['Cap1A'] = rawOut[key]['switchA']
cleanOut[newkey]['Cap1B'] = rawOut[key]['switchB']
cleanOut[newkey]['Cap1C'] = rawOut[key]['switchC']
cleanOut[newkey]['CapPhases'] = rawOut[key]['phases'][0]
# Print gridBallast Outputs to allOutputData.json
cleanOut['gridBallast'] = {}
if 'allWaterheaterOn.csv' in rawOut:
cleanOut['gridBallast']['waterheaterOn'] = {}
for key in rawOut['allWaterheaterOn.csv']:
if key.startswith('waterheater'):
cleanOut['gridBallast']['waterheaterOn'][key] = rawOut.get('allWaterheaterOn.csv')[key]
if 'allWaterheaterTemp.csv' in rawOut:
cleanOut['gridBallast']['waterheaterTemp'] = {}
for key in rawOut['allWaterheaterTemp.csv']:
if key.startswith('waterheater'):
cleanOut['gridBallast']['waterheaterTemp'][key] = rawOut.get('allWaterheaterTemp.csv')[key]
if 'allMeterPower.csv' in rawOut:
cleanOut['gridBallast']['totalNetworkLoad'] = rawOut.get('allMeterPower.csv')['sum(measured_real_power)']
if ('allWaterheaterLoad.csv' in rawOut) and ('allZIPloadPower.csv' in rawOut):
cleanOut['gridBallast']['availabilityMagnitude'] = [x + y for x, y in zip(rawOut.get('allWaterheaterLoad.csv')['sum(actual_load)'], rawOut.get('allZIPloadPower.csv')['sum(base_power)'])]
if 'eachZIPloadPower.csv' in rawOut:
cleanOut['gridBallast']['ZIPloadPower'] = {}
for key in rawOut['eachZIPloadPower.csv']:
if key.startswith('ZIPload'):
cleanOut['gridBallast']['ZIPloadPower'][key] = rawOut.get('eachZIPloadPower.csv')[key]
if 'allZIPloadDemand.csv' in rawOut:
cleanOut['gridBallast']['ZIPloadDemand'] = {}
for key in rawOut['allZIPloadDemand.csv']:
if key.startswith('ZIPload'):
cleanOut['gridBallast']['ZIPloadDemand'][key] = rawOut.get('allZIPloadDemand.csv')[key]
if 'allZIPloadOn.csv' in rawOut:
cleanOut['gridBallast']['ZIPloadOn'] = {}
for key in rawOut['allZIPloadOn.csv']:
if key.startswith('ZIPload'):
cleanOut['gridBallast']['ZIPloadOn'][key] = rawOut.get('allZIPloadOn.csv')[key]
# EventTime calculations
eventTime = inputDict['eventTime']
eventLength = inputDict['eventLength']
eventLength = eventLength.split(':')
eventDuration = datetime.timedelta(hours=int(eventLength[0]), minutes=int(eventLength[1]))
eventStart = datetime.datetime.strptime(eventTime, '%Y-%m-%d %H:%M')
eventEnd = eventStart + eventDuration
cleanOut['gridBallast']['eventStart'] = str(eventStart)
cleanOut['gridBallast']['eventEnd'] = str(eventEnd)
# Drop timezone from timeStamp, Convert string to date
timeStamps = [x[:19] for x in cleanOut['timeStamps']]
dateTimeStamps = [datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S') for x in timeStamps]
eventEndIdx = dateTimeStamps.index(eventEnd)
# Recovery Time
whOn = cleanOut['gridBallast']['waterheaterOn']
whOnList = whOn.values()
whOnZip = zip(*whOnList)
whOnSum = [sum(x) for x in whOnZip]
anyOn = [x > 0 for x in whOnSum]
tRecIdx = anyOn.index(True, eventEndIdx)
tRec = dateTimeStamps[tRecIdx]
cleanOut['gridBallast']['recoveryTime'] = str(tRec)
# Waterheaters Off-Duration
offDuration = tRec - eventStart
cleanOut['gridBallast']['offDuration'] = str(offDuration)
# Reserve Magnitude Target (RMT)
availMag = cleanOut['gridBallast']['availabilityMagnitude']
totNetLoad = cleanOut['gridBallast']['totalNetworkLoad']
# loadZip = zip(availMag,totNetLoad)
# rmt = [x[0]/x[1] for x in loadZip]
rmt = (1000*sum(availMag))/sum(totNetLoad)
cleanOut['gridBallast']['rmt'] = rmt
# Reserve Magnitude Variability Tolerance (RMVT)
avgAvailMag = sum(availMag)/len(availMag)
rmvtMax = max(availMag)/avgAvailMag
rmvtMin = min(availMag)/avgAvailMag
rmvt = rmvtMax - rmvtMin
cleanOut['gridBallast']['rmvt'] = rmvt
# Availability
notAvail = availMag.count(0)/len(timeStamps)
avail = (1-notAvail)*100
cleanOut['gridBallast']['availability'] = avail
# Waterheater Temperature Drop calculations
whTemp = cleanOut['gridBallast']['waterheaterTemp']
whTempList = whTemp.values()
whTempZip = zip(*whTempList)
whTempDrops = []
LOWER_LIMIT_TEMP = 125 # Used for calculating quality of service.
for time in whTempZip:
tempDrop = sum([t < LOWER_LIMIT_TEMP for t in time])
whTempDrops.append(tempDrop)
cleanOut['gridBallast']['waterheaterTempDrops'] = whTempDrops
# ZIPload calculations for Availability and QoS
zPower = cleanOut['gridBallast']['ZIPloadPower']
zPowerList = zPower.values()
zPowerZip = zip(*zPowerList)
zPowerSum = [sum(x) for x in zPowerZip]
zDemand = cleanOut['gridBallast']['ZIPloadDemand']
zDemandList = zDemand.values()
zDemandZip = zip(*zDemandList)
zDrops = []
for time in zDemandZip:
for each in zPowerZip:
zIdx = 0
if each[zIdx] == 0:
zPowerIdx += 1
zDrop = sum([t > 0 for t in time])
zDrops.append(zDrop)
else:
zDrops.append(0)
cleanOut['gridBallast']['qualityDrops'] = [x + y for x, y in zip(whTempDrops, zDrops)]
# What percentage of our keys have lat lon data?
latKeys = [tree[key]['latitude'] for key in tree if 'latitude' in tree[key]]
latPerc = 1.0*len(latKeys)/len(tree)
if latPerc < 0.25: doNeato = True
else: doNeato = False
# Generate the frames for the system voltage map time traveling chart.
genTime = generateVoltChart(tree, rawOut, modelDir, neatoLayout=doNeato)
cleanOut['genTime'] = genTime
# Aggregate up the timestamps:
if level=='days':
cleanOut['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:10], 'days')
elif level=='months':
cleanOut['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:7], 'months')
# Write the output.
with open(pJoin(modelDir, "allOutputData.json"),"w") as outFile:
json.dump(cleanOut, outFile, indent=4)
# 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)
# Clean up the PID file.
os.remove(pJoin(modelDir, "gldContainer", "PID.txt"))
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)
finishTime = datetime.datetime.now()
inputDict["runTime"] = str(datetime.timedelta(seconds = int((finishTime - beginTime).total_seconds())))
with open(pJoin(modelDir, "allInputData.json"),"w") as inFile:
json.dump(inputDict, inFile, indent = 4)
try:
os.remove(pJoin(modelDir,"PPID.txt"))
except:
pass
tree[feeder.getMaxKey(tree) + 1] = {
'object': 'eventgen',
'name': 'ManualEventGen',
'parent': 'RelMetrics',
'fault_type': '\"' + FAULT_TYPE + '\"',
'manual_outages': '\"' +
FAULT_LOCATION + ',' + \
FAULT_START_TIME + ',' + \
FAULT_STOP_TIME + '\"'
}
# Run Gridlab ----------------------------------------------------------------------
start = time.time()
gridlabOut = gridlabd.runInFilesystem( tree,
attachments=attachments, workDir=WORKING_DIR )
end = time.time()
print((end - start)/60.0)
# Generate clean csv ---------------------------------------------------------------
with open( METER_FILENAME,'r' ) as meterFile:
reader = csv.reader(meterFile, delimiter=',')
with open( OUTPUT_FILENAME, 'w' ) as outputFile:
writer = csv.writer(outputFile, delimiter=',')
#loop past header
for row in reader:
if '# timestamp' in row:
row.append('fault')
row.append('meterID')
def runForeground(modelDir, inData, fs):
'''This reads a glm file, changes the method of powerflow and reruns'''
try:
startTime = datetime.now()
# calibrate and run cvrdynamic
feederPath = pJoin("data", "Feeder", inData[
"feederName"].split("___")[0], inData["feederName"].split("___")[1] + '.json')
fs.export_from_fs_to_local(feederPath, feederPath)
scadaPath = pJoin("uploads", (inData["scadaFile"] + '.tsv'))
fs.export_from_fs_to_local(scadaPath, scadaPath)
omf.calibrate.omfCalibrate(modelDir, feederPath, scadaPath)
allOutput = {}
print "here"
with open(pJoin(modelDir, "calibratedFeeder.json"), "r") as jsonIn:
feederJson = json.load(jsonIn)
localTree = feederJson.get("tree", {})
for key in localTree:
if "solver_method" in localTree[key].keys():
print "current solver method", localTree[key]["solver_method"]
localTree[key]["solver_method"] = 'FBS'
# find the swing bus and recorder attached to substation
for key in localTree:
if localTree[key].get('bustype', '').lower() == 'swing':
swingIndex = key
swingName = localTree[key].get('name')
if localTree[key].get('object', '') == 'regulator' and localTree[key].get('from', '') == swingName:
regIndex = key
regConfName = localTree[key]['configuration']
# find the regulator and capacitor names and combine to form a string
# for volt-var control object
regKeys = []
accum_reg = ""
for key in localTree:
if localTree[key].get("object", "") == "regulator":
accum_reg += localTree[key].get("name", "ERROR") + ","
regKeys.append(key)
regstr = accum_reg[:-1]
print regKeys
capKeys = []
accum_cap = ""
for key in localTree:
if localTree[key].get("object", "") == "capacitor":
accum_cap += localTree[key].get("name", "ERROR") + ","
capKeys.append(key)
if localTree[key].get("control", "").lower() == "manual":
localTree[key]['control'] = "VOLT"
print "changing capacitor control from manual to volt"
capstr = accum_cap[:-1]
print capKeys
# 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': localTree[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': localTree[swingIndex]['name'],
'property': 'voltage_A,voltage_B,voltage_C'},
{'object': 'recorder',
'file': 'ZsubstationBottom.csv',
'limit': '0',
'parent': localTree[regIndex]['to'],
'property': 'voltage_A,voltage_B,voltage_C'}]
# recorder object for capacitor switching - if capacitors exist
if capKeys != []:
for key in capKeys:
recorders.append({'object': 'recorder',
'file': 'ZcapSwitch' + str(key) + '.csv',
'limit': '0',
'parent': localTree[key]['name'],
'property': 'switchA,switchB,switchC'})
# attach recorder process
biggest = 1 + max([int(k) for k in localTree.keys()])
for index, rec in enumerate(recorders):
localTree[biggest + index] = rec
# run a reference load flow
HOURS = float(inData['simLengthHours'])
simStartDate = inData['simStart']
feeder.adjustTime(localTree, HOURS, "hours", simStartDate)
output = gridlabd.runInFilesystem(
localTree, keepFiles=False, workDir=modelDir)
os.remove(pJoin(modelDir, "PID.txt"))
p = output['Zregulator.csv']['power_in.real']
q = output['Zregulator.csv']['power_in.imag']
# calculating length of simulation because it migth be different from
# the simulation input HOURS
simRealLength = int(len(p))
# time delays from configuration files
time_delay_reg = '30.0'
time_delay_cap = '300.0'
for key in localTree:
if localTree[key].get('object', '') == "regulator_configuration":
time_delay_reg = localTree[key]['time_delay']
print "time_delay_reg", time_delay_reg
# if localTree[key].get('object','') == "capacitor":
# time_delay_cap = localTree[key]['time_delay']
# print "time_delay_cap",time_delay_cap
# change the recorder names
for key in localTree:
if localTree[key].get('object', '') == "collector" or localTree[key].get('object', '') == "recorder":
if localTree[key].get('file', '').startswith('Z'):
localTree[key]['file'] = localTree[key].get(
'file', '').replace('Z', 'NewZ')
# create volt-var control object
max_key = max([int(key) for key in localTree.keys()])
print max_key
localTree[max_key + 1] = {'object': 'volt_var_control',
'name': 'IVVC1',
'control_method': 'ACTIVE',
'capacitor_delay': str(time_delay_cap),
'regulator_delay': str(time_delay_reg),
'desired_pf': '0.99',
'd_max': '0.6',
'd_min': '0.1',
'substation_link': str(localTree[regIndex]['name']),
'regulator_list': regstr,
'capacitor_list': capstr}
# running powerflow analysis via gridalab after attaching a regulator
feeder.adjustTime(localTree, HOURS, "hours", simStartDate)
output1 = gridlabd.runInFilesystem(
localTree, keepFiles=True, workDir=modelDir)
os.remove(pJoin(modelDir, "PID.txt"))
pnew = output1['NewZregulator.csv']['power_in.real']
qnew = output1['NewZregulator.csv']['power_in.imag']
# total real and imaginary losses as a function of time
def vecSum(u, v):
''' Add vectors u and v element-wise. Return has len <= len(u) and <=len(v). '''
return map(sum, zip(u, v))
def zeroVec(length):
''' Give a zero vector of input length. '''
return [0 for x in xrange(length)]
(realLoss, imagLoss, realLossnew, imagLossnew) = (zeroVec(int(HOURS))
for x in range(4))
for device in ['ZlossesOverhead.csv', 'ZlossesTransformer.csv', 'ZlossesUnderground.csv']:
for letter in ['A', 'B', 'C']:
realLoss = vecSum(
realLoss, output[device]['sum(power_losses_' + letter + '.real)'])
imagLoss = vecSum(
imagLoss, output[device]['sum(power_losses_' + letter + '.imag)'])
realLossnew = vecSum(
realLossnew, output1['New' + device]['sum(power_losses_' + letter + '.real)'])
imagLossnew = vecSum(
imagLossnew, output1['New' + device]['sum(power_losses_' + letter + '.imag)'])
# voltage calculations and tap calculations
def divby2(u):
'''divides by 2'''
return u / 2
lowVoltage = []
meanVoltage = []
highVoltage = []
lowVoltagenew = []
meanVoltagenew = []
highVoltagenew = []
tap = {'A': [], 'B': [], 'C': []}
tapnew = {'A': [], 'B': [], 'C': []}
volt = {'A': [], 'B': [], 'C': []}
voltnew = {'A': [], 'B': [], 'C': []}
switch = {'A': [], 'B': [], 'C': []}
switchnew = {'A': [], 'B': [], 'C': []}
for letter in ['A', 'B', 'C']:
tap[letter] = output['Zregulator.csv']['tap_' + letter]
tapnew[letter] = output1['NewZregulator.csv']['tap_' + letter]
if capKeys != []:
switch[letter] = output[
'ZcapSwitch' + str(int(capKeys[0])) + '.csv']['switch' + letter]
switchnew[letter] = output1[
'NewZcapSwitch' + str(int(capKeys[0])) + '.csv']['switch' + letter]
volt[letter] = map(
returnMag, output['ZsubstationBottom.csv']['voltage_' + letter])
voltnew[letter] = map(
returnMag, output1['NewZsubstationBottom.csv']['voltage_' + letter])
lowVoltage = map(
divby2, output['ZvoltageJiggle.csv']['min(voltage_12.mag)'])
lowVoltagenew = map(
divby2, output1['NewZvoltageJiggle.csv']['min(voltage_12.mag)'])
meanVoltage = map(
divby2, output['ZvoltageJiggle.csv']['mean(voltage_12.mag)'])
meanVoltagenew = map(
divby2, output1['NewZvoltageJiggle.csv']['mean(voltage_12.mag)'])
highVoltage = map(
divby2, output['ZvoltageJiggle.csv']['max(voltage_12.mag)'])
highVoltagenew = map(
divby2, output1['NewZvoltageJiggle.csv']['max(voltage_12.mag)'])
# energy calculations
whEnergy = []
whLosses = []
whLoads = []
whEnergy.append(sum(p) / 10**6)
whLosses.append(sum(realLoss) / 10**6)
whLoads.append((sum(p) - sum(realLoss)) / 10**6)
whEnergy.append(sum(pnew) / 10**6)
whLosses.append(sum(realLossnew) / 10**6)
whLoads.append((sum(pnew) - sum(realLossnew)) / 10**6)
indices = ['No IVVC', 'With IVVC']
# energySalesRed = (whLoads[1]-whLoads[0])*(inData['wholesaleEnergyCostPerKwh'])*1000
# lossSav = (whLosses[0]-whLosses[1])*inData['wholesaleEnergyCostPerKwh']*1000
# print energySalesRed, lossSav
# plots
ticks = []
plt.clf()
plt.title("total energy")
plt.ylabel("total load and losses (MWh)")
for element in range(2):
ticks.append(element)
bar_loss = plt.bar(element, whLosses[element], 0.15, color='red')
bar_load = plt.bar(
element + 0.15, whLoads[element], 0.15, color='orange')
plt.legend([bar_load[0], bar_loss[0]], ['total load', 'total losses'], bbox_to_anchor=(0., 0.915, 1., .102), loc=3,
ncol=2, mode="expand", borderaxespad=0.1)
plt.xticks([t + 0.15 for t in ticks], indices)
Plot.save_fig(plt, pJoin(modelDir, "totalEnergy.png"))
# real and imaginary power
plt.figure("real power")
plt.title("Real Power at substation")
plt.ylabel("substation real power (MW)")
pMW = [element / 10**6 for element in p]
pMWn = [element / 10**6 for element in pnew]
pw = plt.plot(pMW)
npw = plt.plot(pMWn)
plt.legend([pw[0], npw[0]], ['NO IVVC', 'WITH IVVC'], bbox_to_anchor=(0., 0.915, 1., .102), loc=3,
ncol=2, mode="expand", borderaxespad=0.1)
Plot.save_fig(plt, pJoin(modelDir, "realPower.png"))
plt.figure("Reactive power")
plt.title("Reactive Power at substation")
plt.ylabel("substation reactive power (MVAR)")
qMVAR = [element / 10**6 for element in q]
qMVARn = [element / 10**6 for element in qnew]
iw = plt.plot(qMVAR)
niw = plt.plot(qMVARn)
plt.legend([iw[0], niw[0]], ['NO IVVC', 'WITH IVVC'], bbox_to_anchor=(0., 0.915, 1., .102), loc=3,
ncol=2, mode="expand", borderaxespad=0.1)
Plot.save_fig(plt, pJoin(modelDir, "imaginaryPower.png"))
# voltage plots
plt.figure("voltages as a function of time")
f, ax = plt.subplots(2, sharex=True)
f.suptitle("Min and Max voltages on the feeder")
lv = ax[0].plot(lowVoltage, color='cadetblue')
mv = ax[0].plot(meanVoltage, color='blue')
hv = ax[0].plot(highVoltage, color='cadetblue')
ax[0].legend([lv[0], mv[0], hv[0]], ['low voltage', 'mean voltage', 'high voltage'], bbox_to_anchor=(0., 0.915, 1., .1), loc=3,
ncol=3, mode="expand", borderaxespad=0.1)
ax[0].set_ylabel('NO IVVC')
nlv = ax[1].plot(lowVoltagenew, color='cadetblue')
nmv = ax[1].plot(meanVoltagenew, color='blue')
nhv = ax[1].plot(highVoltagenew, color='cadetblue')
ax[1].set_ylabel('WITH IVVC')
Plot.save_fig(plt, pJoin(modelDir, "Voltages.png"))
# tap positions
plt.figure("TAP positions NO IVVC")
f, ax = plt.subplots(6, sharex=True)
f.set_size_inches(10, 12.0)
#f.suptitle("Regulator Tap positions")
ax[0].plot(tap['A'])
ax[0].set_title("Regulator Tap positions NO IVVC")
ax[0].set_ylabel("TAP A")
ax[1].plot(tap['B'])
ax[1].set_ylabel("TAP B")
ax[2].plot(tap['C'])
ax[2].set_ylabel("TAP C")
ax[3].plot(tapnew['A'])
ax[3].set_title("WITH IVVC")
ax[3].set_ylabel("TAP A")
ax[4].plot(tapnew['B'])
ax[4].set_ylabel("TAP B")
ax[5].plot(tapnew['C'])
ax[5].set_ylabel("TAP C")
for subplot in range(6):
ax[subplot].set_ylim(-20, 20)
f.tight_layout()
Plot.save_fig(plt, pJoin(modelDir, "RegulatorTAPpositions.png"))
# substation voltages
plt.figure("substation voltage as a function of time")
f, ax = plt.subplots(6, sharex=True)
f.set_size_inches(10, 12.0)
#f.suptitle("voltages at substation NO IVVC")
ax[0].plot(volt['A'])
ax[0].set_title('Substation voltages NO IVVC')
ax[0].set_ylabel('voltage A')
ax[1].plot(volt['B'])
ax[1].set_ylabel('voltage B')
ax[2].plot(volt['C'])
ax[2].set_ylabel('voltage C')
ax[3].plot(voltnew['A'])
ax[3].set_title("WITH IVVC")
ax[3].set_ylabel('voltage A')
ax[4].plot(voltnew['B'])
ax[4].set_ylabel('voltage B')
ax[5].plot(voltnew['C'])
ax[5].set_ylabel('voltage C')
f.tight_layout()
Plot.save_fig(plt, pJoin(modelDir, "substationVoltages.png"))
# cap switches
plt.figure("capacitor switch state as a function of time")
f, ax = plt.subplots(6, sharex=True)
f.set_size_inches(10, 12.0)
#f.suptitle("Capacitor switch state NO IVVC")
ax[0].plot(switch['A'])
ax[0].set_title("Capacitor switch state NO IVVC")
ax[0].set_ylabel("switch A")
ax[1].plot(switch['B'])
ax[1].set_ylabel("switch B")
ax[2].plot(switch['C'])
ax[2].set_ylabel("switch C")
ax[3].plot(switchnew['A'])
ax[3].set_title("WITH IVVC")
ax[3].set_ylabel("switch A")
ax[4].plot(switchnew['B'])
ax[4].set_ylabel("switch B")
ax[5].plot(switchnew['C'])
ax[5].set_ylabel("switch C")
for subplot in range(6):
ax[subplot].set_ylim(-2, 2)
f.tight_layout()
Plot.save_fig(plt, pJoin(modelDir, "capacitorSwitch.png"))
# plt.show()
# monetization
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'}
# calculate the month and hour of simulation start and month and hour
# of simulation end
simStartTimestamp = simStartDate + " 00:00:00"
simFormattedDate = datetime.strptime(
simStartTimestamp, "%Y-%m-%d %H:%M:%S")
simStartMonthNum = int(simFormattedDate.strftime('%m'))
simstartMonth = monthNames[simStartMonthNum - 1]
simStartDay = int(simFormattedDate.strftime('%d'))
if calendar.isleap(int(simFormattedDate.strftime('%Y'))):
febDays = 29
else:
febDays = 28
monthHours = [int(31 * 24), int(febDays * 24), int(31 * 24), int(30 * 24), int(31 * 24), int(
30 * 24), int(31 * 24), int(31 * 24), int(30 * 24), int(31 * 24), int(30 * 24), int(31 * 24)]
simStartIndex = int(
sum(monthHours[:(simStartMonthNum - 1)]) + (simStartDay - 1) * 24)
temp = 0
cumulHours = [0]
for x in range(12):
temp += monthHours[x]
cumulHours.append(temp)
for i in range((simStartMonthNum), 13):
if int(simStartIndex + simRealLength) <= cumulHours[i] and int(simStartIndex + simRealLength) > cumulHours[i - 1]:
simEndMonthNum = i - 1
simEndMonth = monthNames[simEndMonthNum]
print simstartMonth, simEndMonth
# calculate peaks for the number of months in simulation
previndex = 0
monthPeak = {}
monthPeakNew = {}
peakSaveDollars = {}
energyLostDollars = {}
lossRedDollars = {}
simMonthList = monthNames[
monthNames.index(simstartMonth):(monthNames.index(simEndMonth) + 1)]
print simMonthList
for monthElement in simMonthList:
print monthElement
month = monthNames.index(monthElement)
index1 = int(previndex)
index2 = int(min((index1 + int(monthHours[month])), simRealLength))
monthPeak[monthElement] = max(p[index1:index2]) / 1000.0
monthPeakNew[monthElement] = max(pnew[index1:index2]) / 1000.0
peakSaveDollars[monthElement] = (monthPeak[monthElement] - monthPeakNew[monthElement]) * float(
inData['peakDemandCost' + str(monthToSeason[monthElement]) + 'PerKw'])
lossRedDollars[monthElement] = (sum(realLoss[index1:index2]) / 1000.0 - sum(
realLossnew[index1:index2]) / 1000.0) * (float(inData['wholesaleEnergyCostPerKwh']))
energyLostDollars[monthElement] = (sum(p[index1:index2]) / 1000.0 - sum(pnew[index1:index2]) / 1000.0 - sum(realLoss[index1:index2]) / 1000.0
+ sum(realLossnew[index1:index2]) / 1000.0) * (float(inData['wholesaleEnergyCostPerKwh']) - float(inData['retailEnergyCostPerKwh']))
previndex = index2
# money charts
fig = plt.figure("cost benefit barchart", figsize=(10, 8))
ticks = range(len(simMonthList))
ticks1 = [element + 0.15 for element in ticks]
ticks2 = [element + 0.30 for element in ticks]
print ticks
eld = [energyLostDollars[month] for month in simMonthList]
lrd = [lossRedDollars[month] for month in simMonthList]
psd = [peakSaveDollars[month] for month in simMonthList]
bar_eld = plt.bar(ticks, eld, 0.15, color='red')
bar_psd = plt.bar(ticks1, psd, 0.15, color='blue')
bar_lrd = plt.bar(ticks2, lrd, 0.15, color='green')
plt.legend([bar_eld[0], bar_psd[0], bar_lrd[0]], ['energyLostDollars', 'peakReductionDollars', 'lossReductionDollars'], bbox_to_anchor=(0., 1.015, 1., .102), loc=3,
ncol=2, mode="expand", borderaxespad=0.1)
monShort = [element[0:3] for element in simMonthList]
plt.xticks([t + 0.15 for t in ticks], monShort)
plt.ylabel('Utility Savings ($)')
Plot.save_fig(plt, pJoin(modelDir, "spendChart.png"))
# cumulative savings graphs
fig = plt.figure("cost benefit barchart", figsize=(10, 5))
annualSavings = sum(eld) + sum(lrd) + sum(psd)
annualSave = lambda x: (
annualSavings - float(inData['omCost'])) * x - float(inData['capitalCost'])
simplePayback = float(
inData['capitalCost']) / (annualSavings - float(inData['omCost']))
plt.xlabel('Year After Installation')
plt.xlim(0, 30)
plt.ylabel('Cumulative Savings ($)')
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')
Plot.save_fig(plt, pJoin(modelDir, "savingsChart.png"))
# get exact time stamps from the CSV files generated by Gridlab-D
timeWithZone = output['Zregulator.csv']['# timestamp']
timestamps = [element[:19] for element in timeWithZone]
# data for highcharts
allOutput["timeStamps"] = timestamps
allOutput["noCVRPower"] = p
allOutput["withCVRPower"] = pnew
allOutput["noCVRLoad"] = whLoads[0]
allOutput["withCVRLoad"] = whLoads[1]
allOutput["noCVRLosses"] = whLosses[0]
allOutput["withCVRLosses"] = whLosses[1]
allOutput["noCVRTaps"] = tap
allOutput["withCVRTaps"] = tapnew
allOutput["noCVRSubVolts"] = volt
allOutput["withCVRSubVolts"] = voltnew
allOutput["noCVRCapSwitch"] = switch
allOutput["withCVRCapSwitch"] = switchnew
allOutput["noCVRHighVolt"] = highVoltage
allOutput["withCVRHighVolt"] = highVoltagenew
allOutput["noCVRLowVolt"] = lowVoltage
allOutput["withCVRLowVolt"] = lowVoltagenew
allOutput["noCVRMeanVolt"] = meanVoltage
allOutput["withCVRMeanVolt"] = meanVoltagenew
# monetization
allOutput["simMonthList"] = monShort
allOutput["energyLostDollars"] = energyLostDollars
allOutput["lossRedDollars"] = lossRedDollars
allOutput["peakSaveDollars"] = peakSaveDollars
allOutput["annualSave"] = [annualSave(x) for x in range(31)]
# Update the runTime in the input file.
endTime = datetime.now()
inData["runTime"] = str(
timedelta(seconds=int((endTime - startTime).total_seconds())))
fs.save(pJoin(modelDir, "allInputData.json"), json.dumps(inData, indent=4))
fs.save(pJoin(modelDir, "allOutputData.json"), json.dumps(allOutput, 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:
print "Oops, Model Crashed!!!"
cancel(modelDir)
print e
def heavyProcessing(modelDir, inputDict):
''' Run the model in its directory. WARNING: GRIDLAB CAN TAKE HOURS TO COMPLETE. '''
print "STARTING TO RUN", modelDir
beginTime = datetime.datetime.now()
# Get feeder name and data in.
try: os.mkdir(pJoin(modelDir,'gldContainer'))
except: pass
try:
feederName = inputDict["feederName1"]
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__metaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "gldContainer", "climate.tmy2"))
startTime = datetime.datetime.now()
feederJson = json.load(open(pJoin(modelDir, feederName+'.omd')))
tree = feederJson["tree"]
#add a check to see if there is already a climate object in the omd file
#if there is delete the climate from attachments and the climate object
attachKeys = feederJson["attachments"].keys()
for key in attachKeys:
if key.endswith('.tmy2'):
del feederJson['attachments'][key]
treeKeys = feederJson["tree"].keys()
for key in treeKeys:
if 'object' in feederJson['tree'][key]:
if feederJson['tree'][key]['object'] == 'climate':
del feederJson['tree'][key]
oldMax = feeder.getMaxKey(tree)
tree[oldMax + 1] = {'omftype':'module', 'argument':'climate'}
tree[oldMax + 2] ={'object':'climate','name':'Climate','interpolate':'QUADRATIC', 'tmyfile':'climate.tmy2'}
# Set up GLM with correct time and recorders:
feeder.attachRecorders(tree, "Regulator", "object", "regulator")
feeder.attachRecorders(tree, "Capacitor", "object", "capacitor")
feeder.attachRecorders(tree, "Inverter", "object", "inverter")
feeder.attachRecorders(tree, "Windmill", "object", "windturb_dg")
feeder.attachRecorders(tree, "CollectorVoltage", None, None)
feeder.attachRecorders(tree, "Climate", "object", "climate")
feeder.attachRecorders(tree, "OverheadLosses", None, None)
feeder.attachRecorders(tree, "UndergroundLosses", None, None)
feeder.attachRecorders(tree, "TriplexLosses", None, None)
feeder.attachRecorders(tree, "TransformerLosses", None, None)
feeder.groupSwingKids(tree)
# Attach recorders for system voltage map:
stub = {'object':'group_recorder', 'group':'"class=node"', 'property':'voltage_A', 'interval':3600, 'file':'aVoltDump.csv'}
for phase in ['A','B','C']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'VoltDump.csv'
tree[feeder.getMaxKey(tree) + 1] = copyStub
feeder.adjustTime(tree=tree, simLength=float(inputDict["simLength"]),
simLengthUnits=inputDict["simLengthUnits"], simStartDate=inputDict["simStartDate"])
# RUN GRIDLABD IN FILESYSTEM (EXPENSIVE!)
rawOut = gridlabd.runInFilesystem(tree, attachments=feederJson["attachments"],
keepFiles=True, workDir=pJoin(modelDir,'gldContainer'))
cleanOut = {}
# Std Err and Std Out
cleanOut['stderr'] = rawOut['stderr']
cleanOut['stdout'] = rawOut['stdout']
# Time Stamps
for key in rawOut:
if '# timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key]['# timestamp']
break
elif '# property.. timestamp' in rawOut[key]:
cleanOut['timeStamps'] = rawOut[key]['# property.. timestamp']
else:
cleanOut['timeStamps'] = []
# Day/Month Aggregation Setup:
stamps = cleanOut.get('timeStamps',[])
level = inputDict.get('simLengthUnits','hours')
# Climate
for key in rawOut:
if key.startswith('Climate_') and key.endswith('.csv'):
cleanOut['climate'] = {}
cleanOut['climate']['Rain Fall (in/h)'] = hdmAgg(rawOut[key].get('rainfall'), sum, level)
cleanOut['climate']['Wind Speed (m/s)'] = hdmAgg(rawOut[key].get('wind_speed'), avg, level)
cleanOut['climate']['Temperature (F)'] = hdmAgg(rawOut[key].get('temperature'), max, level)
cleanOut['climate']['Snow Depth (in)'] = hdmAgg(rawOut[key].get('snowdepth'), max, level)
cleanOut['climate']['Direct Normal (W/sf)'] = hdmAgg(rawOut[key].get('solar_direct'), sum, level)
#cleanOut['climate']['Global Horizontal (W/sf)'] = hdmAgg(rawOut[key].get('solar_global'), sum, level)
climateWbySFList= hdmAgg(rawOut[key].get('solar_global'), sum, level)
#converting W/sf to W/sm
climateWbySMList= [x*10.76392 for x in climateWbySFList]
cleanOut['climate']['Global Horizontal (W/sm)']=climateWbySMList
# Voltage Band
if 'VoltageJiggle.csv' in rawOut:
cleanOut['allMeterVoltages'] = {}
cleanOut['allMeterVoltages']['Min'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['min(voltage_12.mag)']], min, level)
cleanOut['allMeterVoltages']['Mean'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['mean(voltage_12.mag)']], avg, level)
cleanOut['allMeterVoltages']['StdDev'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['std(voltage_12.mag)']], avg, level)
cleanOut['allMeterVoltages']['Max'] = hdmAgg([float(i / 2) for i in rawOut['VoltageJiggle.csv']['max(voltage_12.mag)']], max, level)
# Power Consumption
cleanOut['Consumption'] = {}
# Set default value to be 0, avoiding missing value when computing Loads
cleanOut['Consumption']['Power'] = [0] * int(inputDict["simLength"])
cleanOut['Consumption']['Losses'] = [0] * int(inputDict["simLength"])
cleanOut['Consumption']['DG'] = [0] * int(inputDict["simLength"])
for key in rawOut:
if key.startswith('SwingKids_') and key.endswith('.csv'):
oneSwingPower = hdmAgg(vecPyth(rawOut[key]['sum(power_in.real)'],rawOut[key]['sum(power_in.imag)']), avg, level)
if 'Power' not in cleanOut['Consumption']:
cleanOut['Consumption']['Power'] = oneSwingPower
else:
cleanOut['Consumption']['Power'] = vecSum(oneSwingPower,cleanOut['Consumption']['Power'])
elif key.startswith('Inverter_') and key.endswith('.csv'):
realA = rawOut[key]['power_A.real']
realB = rawOut[key]['power_B.real']
realC = rawOut[key]['power_C.real']
imagA = rawOut[key]['power_A.imag']
imagB = rawOut[key]['power_B.imag']
imagC = rawOut[key]['power_C.imag']
oneDgPower = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'DG' not in cleanOut['Consumption']:
cleanOut['Consumption']['DG'] = oneDgPower
else:
cleanOut['Consumption']['DG'] = vecSum(oneDgPower,cleanOut['Consumption']['DG'])
elif key.startswith('Windmill_') and key.endswith('.csv'):
vrA = rawOut[key]['voltage_A.real']
vrB = rawOut[key]['voltage_B.real']
vrC = rawOut[key]['voltage_C.real']
viA = rawOut[key]['voltage_A.imag']
viB = rawOut[key]['voltage_B.imag']
viC = rawOut[key]['voltage_C.imag']
crB = rawOut[key]['current_B.real']
crA = rawOut[key]['current_A.real']
crC = rawOut[key]['current_C.real']
ciA = rawOut[key]['current_A.imag']
ciB = rawOut[key]['current_B.imag']
ciC = rawOut[key]['current_C.imag']
powerA = vecProd(vecPyth(vrA,viA),vecPyth(crA,ciA))
powerB = vecProd(vecPyth(vrB,viB),vecPyth(crB,ciB))
powerC = vecProd(vecPyth(vrC,viC),vecPyth(crC,ciC))
oneDgPower = hdmAgg(vecSum(powerA,powerB,powerC), avg, level)
if 'DG' not in cleanOut['Consumption']:
cleanOut['Consumption']['DG'] = oneDgPower
else:
cleanOut['Consumption']['DG'] = vecSum(oneDgPower,cleanOut['Consumption']['DG'])
elif key in ['OverheadLosses.csv', 'UndergroundLosses.csv', 'TriplexLosses.csv', 'TransformerLosses.csv']:
realA = rawOut[key]['sum(power_losses_A.real)']
imagA = rawOut[key]['sum(power_losses_A.imag)']
realB = rawOut[key]['sum(power_losses_B.real)']
imagB = rawOut[key]['sum(power_losses_B.imag)']
realC = rawOut[key]['sum(power_losses_C.real)']
imagC = rawOut[key]['sum(power_losses_C.imag)']
oneLoss = hdmAgg(vecSum(vecPyth(realA,imagA),vecPyth(realB,imagB),vecPyth(realC,imagC)), avg, level)
if 'Losses' not in cleanOut['Consumption']:
cleanOut['Consumption']['Losses'] = oneLoss
else:
cleanOut['Consumption']['Losses'] = vecSum(oneLoss,cleanOut['Consumption']['Losses'])
elif key.startswith('Regulator_') and key.endswith('.csv'):
#split function to strip off .csv from filename and user rest of the file name as key. for example- Regulator_VR10.csv -> key would be Regulator_VR10
regName=""
regName = key
newkey=regName.split(".")[0]
cleanOut[newkey] ={}
cleanOut[newkey]['RegTapA'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['RegTapB'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['RegTapC'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['RegTapA'] = rawOut[key]['tap_A']
cleanOut[newkey]['RegTapB'] = rawOut[key]['tap_B']
cleanOut[newkey]['RegTapC'] = rawOut[key]['tap_C']
cleanOut[newkey]['RegPhases'] = rawOut[key]['phases'][0]
elif key.startswith('Capacitor_') and key.endswith('.csv'):
capName=""
capName = key
newkey=capName.split(".")[0]
cleanOut[newkey] ={}
cleanOut[newkey]['Cap1A'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['Cap1B'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['Cap1C'] = [0] * int(inputDict["simLength"])
cleanOut[newkey]['Cap1A'] = rawOut[key]['switchA']
cleanOut[newkey]['Cap1B'] = rawOut[key]['switchB']
cleanOut[newkey]['Cap1C'] = rawOut[key]['switchC']
cleanOut[newkey]['CapPhases'] = rawOut[key]['phases'][0]
# What percentage of our keys have lat lon data?
latKeys = [tree[key]['latitude'] for key in tree if 'latitude' in tree[key]]
latPerc = 1.0*len(latKeys)/len(tree)
if latPerc < 0.25: doNeato = True
else: doNeato = False
# Generate the frames for the system voltage map time traveling chart.
genTime = generateVoltChart(tree, rawOut, modelDir, neatoLayout=doNeato)
cleanOut['genTime'] = genTime
# Aggregate up the timestamps:
if level=='days':
cleanOut['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:10], 'days')
elif level=='months':
cleanOut['timeStamps'] = aggSeries(stamps, stamps, lambda x:x[0][0:7], 'months')
# Write the output.
with open(pJoin(modelDir, "allOutputData.json"),"w") as outFile:
json.dump(cleanOut, outFile, indent=4)
# 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)
# Clean up the PID file.
os.remove(pJoin(modelDir, "gldContainer", "PID.txt"))
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)
finishTime = datetime.datetime.now()
inputDict["runTime"] = str(datetime.timedelta(seconds = int((finishTime - beginTime).total_seconds())))
with open(pJoin(modelDir, "allInputData.json"),"w") as inFile:
json.dump(inputDict, inFile, indent = 4)
try:
os.remove(pJoin(modelDir,"PPID.txt"))
except:
pass
def runPowerflowIter(tree, scadaSubPower):
'''Runs powerflow once, then iterates.'''
# Run initial powerflow to get power.
print("Starting calibration.")
print("Goal of calibration: Error: %s, Iterations: <%s, trim: %s" %
(calibrateError[0], calibrateError[1], trim))
output = gridlabd.runInFilesystem(tree,
keepFiles=True,
workDir=pJoin(workDir, "gridlabD"))
outRealPow = output["caliSub.csv"]["measured_real_power"][
trim:simLength]
outImagPower = output["caliSub.csv"]["measured_reactive_power"][
trim:simLength]
outAppPowerKw = [(x[0]**2 + x[1]**2)**0.5 / 1000
for x in zip(outRealPow, outImagPower)]
lastFile = "subScada.player"
nextFile = "subScadaCalibrated.player"
nextPower = outAppPowerKw
error = (sum(outRealPow) / 1000 -
sum(scadaSubPower)) / sum(scadaSubPower)
iteration = 1
print("First error:", error)
while abs(error) > calibrateError[0] and iteration < calibrateError[1]:
# Run calibration and iterate up to 5 times.
SCAL_CONST = sum(scadaSubPower) / sum(nextPower)
print(
"Calibrating & running again... Error: %s, Iteration: %s, SCAL_CONST: %s"
% (str(round(abs(error * 100),
6)), str(iteration), round(SCAL_CONST, 6)))
newPlayData = []
with open(pJoin(pJoin(workDir, "gridlabD"), lastFile),
"r") as playerFile:
for line in playerFile:
(key, val) = line.split(',')
newPlayData.append(
str(key) + ',' + str(float(val) * SCAL_CONST) + "\n")
with open(pJoin(pJoin(workDir, "gridlabD"), nextFile),
"w") as playerFile:
for row in newPlayData:
playerFile.write(row)
tree[playerKey]["file"] = nextFile
tree[outputRecorderKey]["file"] = "caliSubCheck.csv"
nextOutput = gridlabd.runInFilesystem(tree,
keepFiles=True,
workDir=pJoin(
workDir, "gridlabD"))
outRealPowIter = nextOutput["caliSubCheck.csv"][
"measured_real_power"][trim:simLength]
outImagPowerIter = nextOutput["caliSubCheck.csv"][
"measured_reactive_power"][trim:simLength]
nextAppKw = [(x[0]**2 + x[1]**2)**0.5 / 1000
for x in zip(outRealPowIter, outImagPowerIter)]
lastFile = nextFile
nextFile = "subScadaCalibrated" + str(iteration) + ".player"
nextPower = nextAppKw
# Compute error and iterate.
error = (sum(outRealPowIter) / 1000 -
sum(scadaSubPower)) / sum(scadaSubPower)
iteration += 1
else:
if iteration == 1: outRealPowIter = outRealPow
SCAL_CONST = 1.0
print("Calibration done: Error: %s, Iteration: %s, SCAL_CONST: %s" %
(str(round(abs(error * 100),
2)), str(iteration), round(SCAL_CONST, 2)))
return outRealPow, outRealPowIter, lastFile, iteration
def work(modelDir, inputDict):
''' Run the model in the foreground. WARNING: can take about a minute. '''
# Global vars, and load data from the model directory.
feederName = [x for x in os.listdir(modelDir)
if x.endswith('.omd')][0][:-4]
inputDict["feederName1"] = feederName
feederPath = pJoin(modelDir, feederName + '.omd')
feederJson = json.load(open(feederPath))
tree = feederJson.get("tree", {})
attachments = feederJson.get("attachments", {})
outData = {}
''' Run CVR analysis. '''
# Reformate monthData and rates.
rates = {
k: float(inputDict[k])
for k in [
"capitalCost", "omCost", "wholesaleEnergyCostPerKwh",
"retailEnergyCostPerKwh", "peakDemandCostSpringPerKw",
"peakDemandCostSummerPerKw", "peakDemandCostFallPerKw",
"peakDemandCostWinterPerKw"
]
}
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
})
# 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"))
outData["histPeak"] = d1
outData["histAverage"] = d2
outData["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:
outData["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"))
outData["monthDataMat"] = dictalToMatrix(monthData)
outData["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 ($)')
plt.tight_layout(5.5, 1.3, 1.2)
fig.autofmt_xdate()
plt.savefig(pJoin(modelDir, "spendChart.png"))
outData["energyReductionDollars"] = d1
outData["lossReductionDollars"] = d2
outData["peakReductionDollars"] = d3
# Graph the cumulative savings.
fig = plt.figure(figsize=(10, 5))
annualSavings = sum(d1) + sum(d2) + sum(d3)
annualSave = lambda x: (annualSavings - rates['omCost']) * x - rates[
'capitalCost']
simplePayback = rates['capitalCost'] / (annualSavings - rates['omCost'])
plt.xlabel('Year After Installation')
plt.xlim(0, 30)
plt.ylabel('Cumulative Savings ($)')
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"))
outData["annualSave"] = [annualSave(x) for x in range(31)]
# For autotest, there won't be such file.
return outData
def main():
''' JSON manipulation, Gridlab running, etc. goes here. '''
# Import data.
feedJson = json.load(open('./ABEC Frank Calibrated.json'))
tree = feedJson['tree']
# Input data, model-style.
inputDict = {'simLength':24,'simStartDate':'2011-01-01', 'simLengthUnits':'hours'}
# Add recorders.
stub = {'object':'group_recorder', 'group':'"class=node"', 'property':'voltage_A', 'interval':3600, 'file':'aVoltDump.csv'}
for phase in ['A','B','C']:
copyStub = dict(stub)
copyStub['property'] = 'voltage_' + phase
copyStub['file'] = phase.lower() + 'VoltDump.csv'
tree[omf.feeder.getMaxKey(tree) + 1] = copyStub
feeder.adjustTime(tree, inputDict['simLength'], inputDict['simLengthUnits'], inputDict['simStartDate'])
# Run gridlab.
allOutputData = runInFilesystem(tree, attachments=feedJson['attachments'], keepFiles=True, workDir='.', glmName='ABEC Frank SolverGen.glm')
try: os.remove('PID.txt')
except: pass
print 'Gridlab ran correctly', allOutputData.keys()
# Make plots.
#TODO: figure out what to do about neato taking like 2 minutes to run.
neatoLayout = True
# 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 = omf.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:
positions = {n:fGraph.node[n].get('pos',(0,0)) for n in fGraph}
# Plot all time steps.
for step, stamp in enumerate(allOutputData['aVoltDump.csv']['# timestamp']):
# Build voltage map.
nodeVolts = {}
for nodeName in [x for x in allOutputData['aVoltDump.csv'].keys() if x != '# timestamp']:
allVolts = []
for phase in ['A','B','C']:
v = complex(allOutputData[phase.lower() + 'VoltDump.csv'][nodeName][step])
phaseVolt = _pythag(v.real, v.imag)
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')
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()
plt.title(stamp)
voltChart.savefig('./pngs/volts' + str(step).zfill(3) + '.png')
# Reclaim memory by closing, deleting and garbage collecting the last chart.
voltChart.clf()
plt.close()
del voltChart
gc.collect()
