def work(modelDir, inputDict):
''' Run the model in its directory. '''
# Copy spcific climate data into model directory
inputDict["climateName"] = weather.zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, b'file_name', bytes(modelDir + '/climate.tmy2', 'ascii'))
ssc.ssc_data_set_number(dat, b'system_size', float(inputDict['SystemSize']))
# SAM options where we take defaults.
ssc.ssc_data_set_number(dat, b'derate', 0.97)
ssc.ssc_data_set_number(dat, b'track_mode', 0)
ssc.ssc_data_set_number(dat, b'azimuth', 180)
ssc.ssc_data_set_number(dat, b'tilt_eq_lat', 1)
# Run PV system simulation.
mod = ssc.ssc_module_create(b'pvwattsv1')
ssc.ssc_module_exec(mod, dat)
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = "2013-01-01 00:00:00 UTC"
# Timestamp output.
outData = {}
outData['timeStamps'] = [
(datetime.datetime.strptime(startDateTime[0:19], '%Y-%m-%d %H:%M:%S') + datetime.timedelta(**{'hours':x})).strftime('%Y-%m-%d %H:%M:%S') + ' UTC'
for x in range(8760)
]
# HACK: makes it easier to calculate some things later.
outData["pythonTimeStamps"] = [datetime.datetime(2012,1,1,0) + x * datetime.timedelta(hours=1) for x in range(8760)]
# Geodata output.
outData['city'] = ssc.ssc_data_get_string(dat, b'city').decode()
outData['state'] = ssc.ssc_data_get_string(dat, b'state').decode()
outData['lat'] = ssc.ssc_data_get_number(dat, b'lat')
outData['lon'] = ssc.ssc_data_get_number(dat, b'lon')
outData['elev'] = ssc.ssc_data_get_number(dat, b'elev')
# Weather output.
outData["climate"] = {}
outData['climate']['Global Horizontal Radiation (W/m^2)'] = ssc.ssc_data_get_array(dat, b'gh')
outData['climate']['Plane of Array Irradiance (W/m^2)'] = ssc.ssc_data_get_array(dat, b'poa')
outData['climate']['Ambient Temperature (F)'] = ssc.ssc_data_get_array(dat, b'tamb')
outData['climate']['Cell Temperature (F)'] = ssc.ssc_data_get_array(dat, b'tcell')
outData['climate']['Wind Speed (m/s)'] = ssc.ssc_data_get_array(dat, b'wspd')
# Power generation.
outData['powerOutputAc'] = ssc.ssc_data_get_array(dat, b'ac')
# TODO: INSERT TJ CODE BELOW
tjCode(inputDict, outData)
del outData["pythonTimeStamps"]
# TODO: INSERT TJ CODE ABOVE
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
def work(modelDir, inputDict):
''' Run the model in its directory. '''
# Copy spcific climate data into model directory
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", float(inputDict["SystemSize"]))
# SAM options where we take defaults.
ssc.ssc_data_set_number(dat, "derate", 0.97)
ssc.ssc_data_set_number(dat, "track_mode", 0)
ssc.ssc_data_set_number(dat, "azimuth", 180)
ssc.ssc_data_set_number(dat, "tilt_eq_lat", 1)
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = "2013-01-01 00:00:00 UTC"
# Timestamp output.
outData = {}
outData["timeStamps"] = [dt.strftime(
dt.strptime(startDateTime[0:19],"%Y-%m-%d %H:%M:%S") +
td(**{"hours":x}),"%Y-%m-%d %H:%M:%S") + " UTC" for x in range(int(8760))]
# HACK: makes it easier to calculate some things later.
outData["pythonTimeStamps"] = [dt(2012,1,1,0) + x*td(hours=1) for x in range(8760)]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Global Horizontal Radiation (W/m^2)"] = ssc.ssc_data_get_array(dat, "gh")
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = ssc.ssc_data_get_array(dat, "poa")
outData["climate"]["Ambient Temperature (F)"] = ssc.ssc_data_get_array(dat, "tamb")
outData["climate"]["Cell Temperature (F)"] = ssc.ssc_data_get_array(dat, "tcell")
outData["climate"]["Wind Speed (m/s)"] = ssc.ssc_data_get_array(dat, "wspd")
# Power generation.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
# TODO: INSERT TJ CODE BELOW
tjCode(inputDict, outData)
del outData["pythonTimeStamps"]
# TODO: INSERT TJ CODE ABOVE
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
def work(modelDir, inputDict):
''' Run the model in its directory. '''
# Copy spcific climate data into model directory
inputDict["climateName"] = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", float(inputDict["SystemSize"]))
# SAM options where we take defaults.
ssc.ssc_data_set_number(dat, "derate", 0.97)
ssc.ssc_data_set_number(dat, "track_mode", 0)
ssc.ssc_data_set_number(dat, "azimuth", 180)
ssc.ssc_data_set_number(dat, "tilt_eq_lat", 1)
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = "2013-01-01 00:00:00 UTC"
# Timestamp output.
outData = {}
outData["timeStamps"] = [dt.strftime(
dt.strptime(startDateTime[0:19],"%Y-%m-%d %H:%M:%S") +
td(**{"hours":x}),"%Y-%m-%d %H:%M:%S") + " UTC" for x in range(int(8760))]
# HACK: makes it easier to calculate some things later.
outData["pythonTimeStamps"] = [dt(2012,1,1,0) + x*td(hours=1) for x in range(8760)]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Global Horizontal Radiation (W/m^2)"] = ssc.ssc_data_get_array(dat, "gh")
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = ssc.ssc_data_get_array(dat, "poa")
outData["climate"]["Ambient Temperature (F)"] = ssc.ssc_data_get_array(dat, "tamb")
outData["climate"]["Cell Temperature (F)"] = ssc.ssc_data_get_array(dat, "tcell")
outData["climate"]["Wind Speed (m/s)"] = ssc.ssc_data_get_array(dat, "wspd")
# Power generation.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
# TODO: INSERT TJ CODE BELOW
tjCode(inputDict, outData)
del outData["pythonTimeStamps"]
# TODO: INSERT TJ CODE ABOVE
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
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
hazardPath = pJoin(modelDir, inputDict['weatherImpactsFileName'])
with open(hazardPath, '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
critLoads = inputDict['criticalLoads']
gfmInputTemplate = {
'phase_variation': float(inputDict['phaseVariation']),
'chance_constraint': float(inputDict['chanceConstraint']),
'critical_load_met': float(inputDict['criticalLoadMet']),
'total_load_met': float(inputDict['nonCriticalLoadMet']),
'maxDGPerGenerator': float(inputDict['maxDGPerGenerator']),
'dgUnitCost': float(inputDict['dgUnitCost']),
'generatorCandidates': inputDict['generatorCandidates'],
'criticalLoads': inputDict['criticalLoads']
}
gfmJson = convertToGFM(gfmInputTemplate, feederModel)
gfmInputFilename = 'gfmInput.json'
with open(pJoin(modelDir, gfmInputFilename), 'w') as outFile:
json.dump(gfmJson, outFile, indent=4)
# Check for overlap between hazard field and GFM circuit input:
hazard = HazardField(hazardPath)
if circuitOutsideOfHazard(hazard, gfmJson):
outData[
'warning'] = 'Warning: the hazard field does not overlap with the circuit.'
# Draw hazard field if needed.
if inputDict['showHazardField'] == 'Yes':
hazard.drawHeatMap(show=False)
plt.title('') #Hack: remove plot title.
# Run GFM
gfmBinaryPath = pJoin(__neoMetaModel__._omfDir, 'solvers', 'gfm',
'Fragility.jar')
rdtInputName = 'rdtInput.json'
if platform.system() == 'Darwin':
#HACK: force use of Java8 on MacOS.
javaCmd = '/Library/Java/JavaVirtualMachines/jdk1.8.0_181.jdk/Contents/Home/bin/java'
else:
javaCmd = 'java'
proc = subprocess.Popen([
javaCmd, '-jar', gfmBinaryPath, '-r', gfmInputFilename, '-wf',
inputDict['weatherImpactsFileName'], '-num',
inputDict['scenarioCount'], '-ro', rdtInputName
],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=modelDir)
(stdout, stderr) = proc.communicate()
with open(pJoin(modelDir, "gfmConsoleOut.txt"), "w") as gfmConsoleOut:
gfmConsoleOut.write(stdout)
rdtInputFilePath = pJoin(modelDir, 'rdtInput.json')
# 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)
rdtJson["power_flow"] = inputDict["power_flow"]
rdtJson["solver_iteration_timeout"] = 300.0
rdtJson["algorithm"] = "miqp"
# 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
# Insert user-specified scenarios block into RDT input
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 1ST GLD RUN FOR", modelDir
omdPath = pJoin(modelDir, feederName + ".omd")
with open(omdPath, "r") as omd:
omd = json.load(omd)
# Remove new line candidates to get normal system powerflow results.
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 JSON_dump_line.json;\n\twrite_system_info true;\n\twrite_per_unit true;\n\tsystem_base 100.0 MVA;\n};\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 = zipCodeToClimateName(inputDict["simulationZipCode"])
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
climateFileName + ".tmy2"), pJoin(modelDir, 'climate.tmy2'))
# Platform specific binaries for GridLAB-D First Run.
if platform.system() == "Linux":
myEnv = os.environ.copy()
myEnv['GLPATH'] = omf.omfDir + '/solvers/gridlabdv990/'
commandString = omf.omfDir + '/solvers/gridlabdv990/gridlabd.bin feeder.glm'
elif platform.system() == "Windows":
myEnv = os.environ.copy()
commandString = '"' + pJoin(omf.omfDir, "solvers", "gridlabdv990",
"gridlabd.exe") + '"' + " feeder.glm"
elif platform.system() == "Darwin":
myEnv = os.environ.copy()
myEnv['GLPATH'] = omf.omfDir + '/solvers/gridlabdv990/MacRC4p1_std8/'
commandString = '"' + omf.omfDir + '/solvers/gridlabdv990/MacRC4p1_std8/gld.sh" feeder.glm'
# Run GridLAB-D First Time.
proc = subprocess.Popen(commandString,
stdout=subprocess.PIPE,
shell=True,
cwd=modelDir,
env=myEnv)
(out, err) = proc.communicate()
with open(pJoin(modelDir, "gldConsoleOut.txt"), "w") as gldConsoleOut:
gldConsoleOut.write(out)
with open(pJoin(modelDir, "JSON_dump_line.json"), "r") as gldOut:
gld_json_line_dump = json.load(gldOut)
outData['gridlabdRawOut'] = gld_json_line_dump
# Add GridLAB-D line objects and line codes in to the RDT model.
rdtJson["line_codes"] = gld_json_line_dump["properties"]["line_codes"]
rdtJson["lines"] = gld_json_line_dump["properties"]["lines"]
hardCands = list(
set(gfmJson['lineLikeObjs']) - set(inputDict['hardeningCandidates']))
newLineCands = inputDict['newLineCandidates'].strip().replace(
' ', '').split(',')
switchCands = inputDict['switchCandidates'].strip().replace(' ',
'').split(',')
for line in rdtJson["lines"]:
line_id = line.get('id',
'') # this is equal to name in the OMD objects.
object_type = line.get('object', '')
line['node1_id'] = line['node1_id'] + "_bus"
line['node2_id'] = line['node2_id'] + "_bus"
line_code = line["line_code"]
# Getting ratings from OMD
tree = omd['tree']
nameToIndex = {tree[key].get('name', ''): key for key in tree}
treeOb = tree[nameToIndex[line_id]]
config_name = treeOb.get('configuration', '')
config_ob = tree.get(nameToIndex[config_name], {})
full_rating = 0
for phase in ['A', 'B', 'C']:
cond_name = config_ob.get('conductor_' + phase, '')
cond_ob = tree.get(nameToIndex.get(cond_name, ''), '')
rating = cond_ob.get('rating.summer.continuous', '')
try:
full_rating = int(rating) #TODO: replace with avg of 3 phases.
except:
pass
if full_rating != 0:
line['capacity'] = full_rating
else:
line['capacity'] = 10000
# Setting other line parameters.
line['construction_cost'] = float(inputDict['lineUnitCost'])
line['harden_cost'] = float(inputDict['hardeningUnitCost'])
line['switch_cost'] = float(inputDict['switchCost'])
if line_id in hardCands:
line['can_harden'] = True
if line_id in switchCands:
line['can_add_switch'] = True
if line_id in newLineCands:
line['is_new'] = True
if object_type in ['transformer', 'regulator']:
line['is_transformer'] = True
if object_type == 'switch':
line['has_switch'] = True
with open(rdtInputFilePath, "w") as outFile:
json.dump(rdtJson, outFile, indent=4)
# Run RDT.
print "RUNNING RDT FOR", modelDir
rdtOutFile = modelDir + '/rdtOutput.json'
rdtSolverFolder = pJoin(__neoMetaModel__._omfDir, 'solvers', 'rdt')
rdtJarPath = pJoin(rdtSolverFolder, 'micot-rdt.jar')
# TODO: modify path, don't assume SCIP installation.
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 2ND GLD RUN FOR", modelDir
feederCopy = copy.deepcopy(feederModel)
lineSwitchList = []
edgeLabels = {}
generatorList = []
for gen in rdtOut['design_solution']['generators']:
generatorList.append(gen['id'][:-4])
damagedLoads = {}
for scenario in rdtOut['scenario_solution']:
for load in scenario['loads']:
if load['id'] in damagedLoads.keys():
damagedLoads[load['id'][:-4]] += 1
else:
damagedLoads[load['id'][:-4]] = 1
for line in rdtOut['design_solution']['lines']:
if ('switch_built' in line and 'hardened' in line):
lineSwitchList.append(line['id'])
if (line['switch_built'] == True and line['hardened'] == True):
edgeLabels[line['id']] = "SH"
elif (line['switch_built'] == True):
edgeLabels[line['id']] = "S"
elif (line['hardened'] == True):
edgeLabels[line['id']] = "H"
elif ('switch_built' in line):
lineSwitchList.append(line['id'])
if (line['switch_built'] == True):
edgeLabels[line['id']] = "S"
elif ('hardened' in line):
if (line['hardened'] == True):
edgeLabels[line['id']] = "H"
# 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
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.
damageDict = {}
for scenario in rdtJson["scenarios"]:
for line in scenario["disable_lines"]:
if line in damageDict:
damageDict[line] = damageDict[line] + 1
else:
damageDict[line] = 1
genDiagram(modelDir, feederModel, damageDict, critLoads, damagedLoads,
edgeLabels, generatorList)
with open(pJoin(modelDir, "feederChart.png"), "rb") as inFile:
outData["oneLineDiagram"] = inFile.read().encode("base64")
# And we're done.
return outData
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
''' Run the model in its directory. '''
# Delete output file every run if it exists
try:
os.remove(pJoin(modelDir,"allOutputData.json"))
except Exception, e:
pass
try:
# Check whether model exist or not
if not os.path.isdir(modelDir):
os.makedirs(modelDir)
inputDict["created"] = str(dt.datetime.now())
# MAYBEFIX: remove this data dump. Check showModel in web.py and renderTemplate()
with open(pJoin(modelDir, "allInputData.json"),"w") as inputFile:
json.dump(inputDict, inputFile, indent = 4)
# Copy spcific climate data into model directory
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__metaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Ready to run
startTime = dt.datetime.now()
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", float(inputDict.get("systemSize", 100)))
derate = float(inputDict.get("pvModuleDerate", 99.5))/100 \
* float(inputDict.get("mismatch", 99.5))/100 \
* float(inputDict.get("diodes", 99.5))/100 \
* float(inputDict.get("dcWiring", 99.5))/100 \
* float(inputDict.get("acWiring", 99.5))/100 \
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
dssName = [x for x in os.listdir(modelDir) if x.endswith('.dss')][0][:-4]
inputDict["dssName1"] = dssName
zipCode = "59001" #TODO get zip code from the PV and Load input file
#Value check for attackVariable
if inputDict.get("attackVariable", "None") == "None":
attackAgentType = "None"
else:
attackAgentType = inputDict['attackVariable']
# Value check for train
if inputDict.get("trainAgent", "") == "True":
trainAgentValue = True
else:
trainAgentValue = False
# create solarPVLengthValue to represent number of steps in simulation - will be manipulated by number of rows in load solar data csv file
solarPVLengthValue = 0
#create startStep to represent which step pyCigar should start on - default = 100
startStep = 100
#None check for simulation length
if inputDict.get("simLength", "None") == "None":
simLengthValue = None
else:
simLengthValue = int(inputDict['simLength'])
#None check for simulation length units
if inputDict.get("simLengthUnits", "None") == "None":
simLengthUnitsValue = None
else:
simLengthUnitsValue = inputDict["simLengthUnits"]
#None check for simulation start date
if inputDict.get("simStartDate", "None") == "None":
simStartDateTimeValue = None
simStartDateValue = None
simStartTimeValue = None
else:
simStartDateTimeValue = inputDict["simStartDate"]
simStartDateValue = simStartDateTimeValue.split('T')[0]
simStartTimeValue = simStartDateTimeValue.split('T')[1]
# None check for defenseVariable
if inputDict.get("defenseVariable", "None") == "None":
defenseAgentName = None
else:
defenseAgentName = inputDict['defenseVariable']
#Check to make sure that defenseAgent selected by user exists, otherwise return a warning and set defenseAgentName to None
if os.path.isdir(pJoin(modelDir, "pycigarOutput",
defenseAgentName)) == False:
errorMessage = "ERROR: Defense Agent named " + defenseAgentName + " could not be located."
defenseAgentName = None
raise Exception(errorMessage)
inputDict["climateName"] = weather.zipCodeToClimateName(zipCode)
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
def convertInputs():
#create the PyCIGAR_inputs folder to store the input files to run PyCIGAR
try:
os.mkdir(pJoin(modelDir, "PyCIGAR_inputs"))
except FileExistsError:
print("PyCIGAR_inputs folder already exists!")
pass
except:
print("Error occurred creating PyCIGAR_inputs folder")
#create misc_inputs.csv file in folder
with open(pJoin(modelDir, "PyCIGAR_inputs", "misc_inputs.csv"),
"w") as miscFile:
#Populate misc_inputs.csv
# miscFile.write(misc_inputs)
# for key in misc_dict.keys():
# miscFile.write("%s,%s\n"%(key,misc_dict[key]))
miscFile.write(inputDict['miscFile'])
#create dss file in folder
dss_filename = "circuit.dss"
with open(pJoin(modelDir, "PyCIGAR_inputs", dss_filename),
"w") as dssFile:
dssFile.write(inputDict['dssFile'])
#create load_solar_data.csv file in folder
rowCount = 0
with open(pJoin(modelDir, "PyCIGAR_inputs", "load_solar_data.csv"),
"w") as loadPVFile:
loadPVFile.write(inputDict['loadPV'])
#Open load and PV input file
try:
with open(pJoin(modelDir, "PyCIGAR_inputs", "load_solar_data.csv"),
newline='') as inFile:
reader = csv.reader(inFile)
for row in reader:
rowCount = rowCount + 1
#Check to see if the simulation length matches the load and solar csv
# if (rowCount-1)*misc_dict["load file timestep"] != simLengthValue:
# errorMessage = "Load and PV Output File does not match simulation length specified by user"
# raise Exception(errorMessage)
solarPVLengthValue = rowCount - 1
except:
#TODO change to an appropriate warning message
errorMessage = "CSV file is incorrect format."
raise Exception(errorMessage)
#create breakpoints.csv file in folder
# f1Name = "breakpoints.csv"
# with open(pJoin(omf.omfDir, "static", "testFiles", "pyCIGAR", f1Name)) as f1:
# breakpoints_inputs = f1.read()
with open(pJoin(modelDir, "PyCIGAR_inputs", "breakpoints.csv"),
"w") as breakpointsFile:
breakpointsFile.write(inputDict['breakpoints'])
return solarPVLengthValue
solarPVLengthValue = convertInputs()
#create simLengthAdjusted to represent simLength accounting for start step offset
simLengthAdjusted = 0
if simLengthValue != None:
if simLengthValue + startStep > solarPVLengthValue:
#raise error message that simLengthValue is too large for given Load Solar csv and given timestep (set to 100)
simLengthAdjusted = solarPVLengthValue - startStep
else:
#simLengthValue is equal to the value entered by the user
simLengthAdjusted = simLengthValue
else:
#simLengthAdjusted accounts for the offset by startStep
simLengthAdjusted = solarPVLengthValue - startStep
# #hard-coding simLengthAdjusted for testing purposes
# simLengthAdjusted = 750
# create value to represent the timestep in which the hack starts and adjust it to make sure it is within the bounds or the simulation length
defaultHackStart = 250
if defaultHackStart > simLengthAdjusted:
defaultHackStart = simLengthAdjusted / 5
# attackVars = dict of attack types and their corresponding parameter values
# to add new attack: attackVars[attackAgentType_name] = {"hackStart": val, "hackEnd": val, "percentHack": val}
# MAKE SURE to add attackVars entry when adding another Attack Agent option to the html dropdown list and the name must match the value passed back from the form (inputDict["attackVariable"])!
attackVars = {}
attackVars["None"] = {
"hackStart": defaultHackStart,
"hackEnd": None,
"percentHack": 0.0
}
attackVars["curveSwitch"] = {
"hackStart": defaultHackStart,
"hackEnd": None,
"percentHack": 0.45
}
#check to make sure attackAgentType is in the attackVars dictionary, otherwise set it to None. This shouldn't ever be a problem since the user selects attackAgentType from a preset HTML dropdown.
if attackAgentType not in attackVars:
attackAgentType = "None"
outData = {}
# Std Err and Std Out
outData['stderr'] = "This should be stderr" #rawOut['stderr']
outData['stdout'] = "This should be stdout" #rawOut['stdout']
# Create list of timestamps for simulation steps
outData['timeStamps'] = []
start_time = dt_parser.isoparse(simStartDateTimeValue)
start_time = start_time + timedelta(seconds=startStep)
for single_datetime in (start_time + timedelta(seconds=n)
for n in range(simLengthAdjusted)):
single_datetime_str = single_datetime.strftime("%Y-%m-%d %H:%M:%S%z")
outData['timeStamps'].append(single_datetime_str)
# Day/Month Aggregation Setup:
stamps = outData.get('timeStamps', [])
level = inputDict.get('simLengthUnits', 'seconds')
# TODO: Create/populate Climate data without gridlab-d
outData['climate'] = {}
outData['allMeterVoltages'] = {}
outData['allMeterVoltages']['Min'] = [0] * int(simLengthAdjusted)
outData['allMeterVoltages']['Mean'] = [0] * int(simLengthAdjusted)
outData['allMeterVoltages']['StdDev'] = [0] * int(simLengthAdjusted)
outData['allMeterVoltages']['Max'] = [0] * int(simLengthAdjusted)
# Power Consumption
outData['Consumption'] = {}
# Set default value to be 0, avoiding missing value when computing Loads
outData['Consumption']['Power'] = [0] * int(simLengthAdjusted)
outData['Consumption']['Losses'] = [0] * int(simLengthAdjusted)
outData['Consumption']['DG'] = [0] * int(simLengthAdjusted)
outData['swingTimestamps'] = []
outData['swingTimestamps'] = outData['timeStamps']
# 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')
#create the pycigarOutput folder to store the output file(s) generated by PyCIGAR
try:
os.mkdir(pJoin(modelDir, "pycigarOutput"))
except FileExistsError:
print("pycigarOutput folder already exists!")
pass
except:
print("Error occurred creating pycigarOutput folder")
def runPyCIGAR():
#import and run pycigar
import pycigar
#Set up runType scenarios
#runType of 2 implies the base scenario - not training a defense agent, nor is there a defense agent entered
runType = 2
defenseAgentPath = None
#set default values for attack variables
hackStartVal = defaultHackStart
hackEndVal = None
percentHackVal = 0.0
#set pycigar attack variables
hackStartVal = attackVars[attackAgentType]["hackStart"]
hackEndVal = attackVars[attackAgentType][
"hackEnd"] #TODO: see if we need to change from a hard-coded value
percentHackVal = attackVars[attackAgentType][
"percentHack"] #TODO: see if we need to change from a hard-coded value
# check to see if we are trying to train a defense agent
if trainAgentValue:
#runType of 0 implies the training scenario - runs to train a defense agent and outputs a zip containing defense agent files
# runType = 0
pycigar.main(modelDir + "/PyCIGAR_inputs/misc_inputs.csv",
modelDir + "/PyCIGAR_inputs/circuit.dss",
modelDir + "/PyCIGAR_inputs/load_solar_data.csv",
modelDir + "/PyCIGAR_inputs/breakpoints.csv",
0,
defenseAgentPath,
modelDir + "/pycigarOutput/",
start=startStep,
duration=simLengthAdjusted,
hack_start=hackStartVal,
hack_end=hackEndVal,
percentage_hack=percentHackVal)
#check to see if user entered a defense agent file
elif defenseAgentName != None:
defenseAgentPath = pJoin(modelDir, "pycigarOutput",
defenseAgentName)
#runType of 1 implies the defense scenario - not training a defense agent, but a defense agent zip was uploaded
runType = 1
# TODO how to factor attackAgentType into pycigar inputs
# if there is no training selected and no attack variable, run without a defense agent
pycigar.main(modelDir + "/PyCIGAR_inputs/misc_inputs.csv",
modelDir + "/PyCIGAR_inputs/circuit.dss",
modelDir + "/PyCIGAR_inputs/load_solar_data.csv",
modelDir + "/PyCIGAR_inputs/breakpoints.csv",
runType,
defenseAgentPath,
modelDir + "/pycigarOutput/",
start=startStep,
duration=simLengthAdjusted,
hack_start=hackStartVal,
hack_end=hackEndVal,
percentage_hack=percentHackVal)
#print("Got through pyCigar!!!")
def convertOutputs():
#set outData[] values to those from modelDir/pycigarOutput/pycigar_output_specs_.json
#read in the pycigar-outputed json
with open(
pJoin(modelDir, "pycigarOutput", "pycigar_output_specs.json"),
'r') as f:
pycigarJson = json.load(f)
#convert "allMeterVoltages"
outData["allMeterVoltages"] = pycigarJson["allMeterVoltages"]
#convert "Consumption"."Power"
# HACK! Units are actually kW. Needs to be fixed in pyCigar.
outData["Consumption"]["Power"] = pycigarJson["Consumption"][
"Power Substation (W)"]
#convert "Consumption"."Losses"
outData["Consumption"]["Losses"] = pycigarJson["Consumption"][
"Losses Total (W)"]
#convert "Consumption"."DG"
outData["Consumption"]["DG"] = [
-1.0 * x for x in pycigarJson["Consumption"]["DG Output (W)"]
]
#convert "powerFactors"
outData["powerFactors"] = pycigarJson["Substation Power Factor (%)"]
#convert "swingVoltage"
outData["swingVoltage"] = pycigarJson["Substation Top Voltage(V)"]
#convert "downlineNodeVolts"
outData["downlineNodeVolts"] = pycigarJson[
"Substation Bottom Voltage(V)"]
#convert "minVoltBand"
outData["minVoltBand"] = pycigarJson[
"Substation Regulator Minimum Voltage(V)"]
#convert "maxVoltBand"
outData["maxVoltBand"] = pycigarJson[
"Substation Regulator Maximum Voltage(V)"]
#create lists of circuit object names
regNameList = []
capNameList = []
for key in pycigarJson:
if key.startswith('Regulator_'):
regNameList.append(key)
elif key.startswith('Capacitor_'):
capNameList.append(key)
#convert regulator data
for reg_name in regNameList:
outData[reg_name] = {}
regPhaseValue = pycigarJson[reg_name]["RegPhases"]
if regPhaseValue.find('A') != -1:
outData[reg_name]["RegTapA"] = pycigarJson[reg_name]["creg1a"]
if regPhaseValue.find('B') != -1:
outData[reg_name]["RegTapB"] = pycigarJson[reg_name]["creg1b"]
if regPhaseValue.find('C') != -1:
outData[reg_name]["RegTapC"] = pycigarJson[reg_name]["creg1c"]
outData[reg_name]["RegPhases"] = regPhaseValue
#convert inverter data
inverter_output_dict = {}
for inv_dict in pycigarJson["Inverter Outputs"]:
#create a new dictionary to represent the single inverter
new_inv_dict = {}
#get values from pycigar output for given single inverter
inv_name = inv_dict["Name"]
inv_volt = inv_dict["Voltage (V)"]
inv_pow_real = inv_dict["Power Output (W)"]
inv_pow_imag = inv_dict["Reactive Power Output (VAR)"]
#populate single inverter dict with pycigar values
new_inv_dict["Voltage"] = inv_volt
new_inv_dict["Power_Real"] = inv_pow_real
new_inv_dict["Power_Imag"] = inv_pow_imag
#add single inverter dict to dict of all the inverters using the inverter name as the key
inverter_output_dict[inv_name] = new_inv_dict
outData["Inverter_Outputs"] = inverter_output_dict
#convert capacitor data - Need one on test circuit first!
for cap_name in capNameList:
outData[cap_name] = {}
capPhaseValue = pycigarJson[cap_name]["CapPhases"]
if capPhaseValue.find('A') != -1:
outData[cap_name]['Cap1A'] = [0] * int(simLengthValue)
outData[cap_name]['Cap1A'] = pycigarJson[cap_name]['switchA']
if capPhaseValue.find('B') != -1:
outData[cap_name]['Cap1B'] = [0] * int(simLengthValue)
outData[cap_name]['Cap1B'] = pycigarJson[cap_name]['switchB']
if capPhaseValue.find('C') != -1:
outData[cap_name]['Cap1C'] = [0] * int(simLengthValue)
outData[cap_name]['Cap1C'] = pycigarJson[cap_name]['switchC']
outData[cap_name]["CapPhases"] = capPhaseValue
outData["stdout"] = pycigarJson["stdout"]
runPyCIGAR()
# Report out the agent paths.
defAgentFolders = os.listdir(pJoin(modelDir, "pycigarOutput"))
outData['defenseAgents'] = [
x for x in defAgentFolders if x.startswith('policy_')
]
convertOutputs()
return outData
def run(modelDir, inputDict):
try:
''' Run the model in its directory. '''
# Check whether model exist or not
if not os.path.isdir(modelDir):
os.makedirs(modelDir)
inputDict["created"] = str(dt.now())
# MAYBEFIX: remove this data dump. Check showModel in web.py and renderTemplate()
with open(pJoin(modelDir, "allInputData.json"), "w") as inputFile:
json.dump(inputDict, inputFile, indent=4)
# Copy spcific climate data into model directory
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(
inputDict["zipCode"])
shutil.copy(
pJoin(__metaModel__._omfDir, "data", "Climate",
inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Ready to run
startTime = dt.now()
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size",
float(inputDict["SystemSize"]))
# SAM options where we take defaults.
ssc.ssc_data_set_number(dat, "derate", 0.97)
ssc.ssc_data_set_number(dat, "track_mode", 0)
ssc.ssc_data_set_number(dat, "azimuth", 180)
ssc.ssc_data_set_number(dat, "tilt_eq_lat", 1)
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = "2013-01-01 00:00:00 UTC"
# Timestamp output.
outData = {}
outData["timeStamps"] = [
dt.strftime(
dt.strptime(startDateTime[0:19], "%Y-%m-%d %H:%M:%S") +
td(**{"hours": x}), "%Y-%m-%d %H:%M:%S") + " UTC"
for x in range(int(8760))
]
# HACK: makes it easier to calculate some things later.
outData["pythonTimeStamps"] = [
dt(2012, 1, 1, 0) + x * td(hours=1) for x in range(8760)
]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"][
"Global Horizontal Radiation (W/m^2)"] = ssc.ssc_data_get_array(
dat, "gh")
outData["climate"][
"Plane of Array Irradiance (W/m^2)"] = ssc.ssc_data_get_array(
dat, "poa")
outData["climate"]["Ambient Temperature (F)"] = ssc.ssc_data_get_array(
dat, "tamb")
outData["climate"]["Cell Temperature (F)"] = ssc.ssc_data_get_array(
dat, "tcell")
outData["climate"]["Wind Speed (m/s)"] = ssc.ssc_data_get_array(
dat, "wspd")
# Power generation.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
# TODO: INSERT TJ CODE BELOW
tjCode(inputDict, outData)
del outData["pythonTimeStamps"]
# TODO: INSERT TJ CODE ABOVE
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
# Write the output.
with open(pJoin(modelDir, "allOutputData.json"), "w") as outFile:
json.dump(outData, outFile, indent=4)
# Update the runTime in the input file.
endTime = dt.now()
inputDict["runTime"] = str(
td(seconds=int((endTime - startTime).total_seconds())))
with open(pJoin(modelDir, "allInputData.json"), "w") as inFile:
json.dump(inputDict, inFile, 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)
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):
''' Run the model in its directory. '''
#Set static input data
simLength = 8760
simStartDate = "2013-01-01"
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = simStartDate + " 00:00:00 UTC"
simLengthUnits = "hours"
# Associate zipcode to climate data
inputDict["climateName"] = zipCodeToClimateName(inputDict["zipCode"])
inverterSizeAC = float(inputDict.get("inverterSize",0))
if (inputDict.get("systemSize",0) == "-"):
arraySizeDC = 1.3908 * inverterSizeAC
else:
arraySizeDC = float(inputDict.get("systemSize",0))
numberPanels = (arraySizeDC * 1000/305)
# Set constants
panelSize = 305
trackingMode = 0
rotlim = 45.0
gamma = 0.45
if (inputDict.get("tilt",0) == "-"):
tilt_eq_lat = 1.0
manualTilt = 0.0
else:
tilt_eq_lat = 0.0
manualTilt = float(inputDict.get("tilt",0))
numberInverters = math.ceil(inverterSizeAC/1000/0.5)
# Copy specific climate data into model directory
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", arraySizeDC)
ssc.ssc_data_set_number(dat, "derate", float(inputDict.get("inverterEfficiency", 96))/100 * float(inputDict.get("nonInverterEfficiency", 87))/100)
ssc.ssc_data_set_number(dat, "track_mode", float(trackingMode))
ssc.ssc_data_set_number(dat, "azimuth", float(inputDict.get("azimuth", 180)))
# Advanced inputs with defaults.
ssc.ssc_data_set_number(dat, "rotlim", float(rotlim))
ssc.ssc_data_set_number(dat, "gamma", float(-gamma/100))
ssc.ssc_data_set_number(dat, "tilt", manualTilt)
ssc.ssc_data_set_number(dat, "tilt_eq_lat", 0.0)
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Timestamp output.
outData = {}
outData["timeStamps"] = [dt.datetime.strftime(
dt.datetime.strptime(startDateTime[0:19],"%Y-%m-%d %H:%M:%S") +
dt.timedelta(**{simLengthUnits:x}),"%Y-%m-%d %H:%M:%S") + " UTC" for x in range(simLength)]
# Geodata output.
outData["minLandSize"] = round((arraySizeDC/1390.8*5 + 1)*math.cos(math.radians(22.5))/math.cos(math.radians(30.0)),0)
landAmount = float(inputDict.get("landAmount", 6.0))
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Global Horizontal Radiation (W/m^2)"] = ssc.ssc_data_get_array(dat, "gh")
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = ssc.ssc_data_get_array(dat, "poa")
outData["climate"]["Ambient Temperature (F)"] = ssc.ssc_data_get_array(dat, "tamb")
outData["climate"]["Cell Temperature (F)"] = ssc.ssc_data_get_array(dat, "tcell")
outData["climate"]["Wind Speed (m/s)"] = ssc.ssc_data_get_array(dat, "wspd")
# Power generation.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
# Calculate clipping.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
invSizeWatts = inverterSizeAC * 1000
outData["powerOutputAcInvClipped"] = [x if x < invSizeWatts else invSizeWatts for x in outData["powerOutputAc"]]
try:
outData["percentClipped"] = 100 * (1.0 - sum(outData["powerOutputAcInvClipped"]) / sum(outData["powerOutputAc"]))
except ZeroDivisionError:
outData["percentClipped"] = 0.0
#One year generation
outData["oneYearGenerationWh"] = sum(outData["powerOutputAcInvClipped"])
#Annual generation for all years
loanYears = 25
outData["allYearGenerationMWh"] = {}
outData["allYearGenerationMWh"][1] = float(outData["oneYearGenerationWh"])/1000000
# outData["allYearGenerationMWh"][1] = float(2019.576)
for i in range (2, loanYears+1):
outData["allYearGenerationMWh"][i] = float(outData["allYearGenerationMWh"][i-1]) * (1 - float(inputDict.get("degradation", 0.8))/100)
# Summary of Results.
######
### Total Costs (sum of): Hardware Costs, Design/Engineering/PM/EPC/Labor Costs, Siteprep Costs, Construction Costs, Installation Costs, Land Costs
######
### Hardware Costs
pvModules = arraySizeDC * float(inputDict.get("moduleCost",0))*1000 #off by 4000
racking = arraySizeDC * float(inputDict.get("rackCost",0))*1000
inverters = numberInverters * float(inputDict.get("inverterCost",0))
inverterSize = inverterSizeAC
if (inverterSize <= 250):
gear = 15000
elif (inverterSize <= 600):
gear = 18000
else:
gear = inverterSize/1000 * 22000
balance = inverterSizeAC * 1.3908 * 134
combiners = math.ceil(numberPanels/19/24) * float(1800) #*
wireManagement = arraySizeDC * 1.5
transformer = 1 * 28000
weatherStation = 1 * 12500
shipping = 1.02
hardwareCosts = (pvModules + racking + inverters + gear + balance + combiners + wireManagement + transformer + weatherStation) * shipping
### Design/Engineering/PM/EPC/Labor Costs
EPCmarkup = float(inputDict.get("EPCRate",0))/100 * hardwareCosts
#designCosts = float(inputDict.get("mechLabor",0))*160 + float(inputDict.get("elecLabor",0))*75 + float(inputDict.get("pmCost",0)) + EPCmarkup
hoursDesign = 160*math.sqrt(arraySizeDC/1390)
hoursElectrical = 80*math.sqrt(arraySizeDC/1391)
designLabor = 65*hoursDesign
electricalLabor = 75*hoursElectrical
laborDesign = designLabor + electricalLabor + float(inputDict.get("pmCost",0)) + EPCmarkup
materialDesign = 0
designCosts = materialDesign + laborDesign
### Siteprep Costs
surveying = 2.25 * 4 * math.sqrt(landAmount*43560)
concrete = 8000 * math.ceil(numberInverters/2)
fencing = 6.75 * 4 * math.sqrt(landAmount*43560)
grading = 2.5 * 4 * math.sqrt(landAmount*43560)
landscaping = 750 * landAmount
siteMaterial = 8000 + 600 + 5500 + 5000 + surveying + concrete + fencing + grading + landscaping + 5600
blueprints = float(inputDict.get("mechLabor",0))*12
mobilization = float(inputDict.get("mechLabor",0))*208
mobilizationMaterial = float(inputDict.get("mechLabor",0))*19.98
siteLabor = blueprints + mobilization + mobilizationMaterial
sitePrep = siteMaterial + siteLabor
### Construction Costs (Office Trailer, Skid Steer, Storage Containers, etc)
constrEquip = 6000 + math.sqrt(landAmount)*16200
### Installation Costs
moduleAndRackingInstall = numberPanels * (15.00 + 12.50 + 1.50)
pierDriving = 1 * arraySizeDC*20
balanceInstall = 1 * arraySizeDC*100
installCosts = moduleAndRackingInstall + pierDriving + balanceInstall + float(inputDict.get("elecLabor",0)) * (72 + 60 + 70 + 10 + 5 + 30 + 70)
### Land Costs
if (str(inputDict.get("landOwnership",0)) == "Owned" or (str(inputDict.get("landOwnership",0)) == "Leased")):
landCosts = 0
else:
landCosts = float(inputDict.get("costAcre",0))*landAmount
######
### Total Costs
######
totalCosts = hardwareCosts + designCosts + sitePrep + constrEquip + installCosts + landCosts
totalFees= float(inputDict.get("devCost",0))/100 * totalCosts
outData["totalCost"] = totalCosts + totalFees + float(inputDict.get("interCost",0))
# Add to Pie Chart
outData["costsPieChart"] = [["Land", landCosts],
["Design/Engineering/PM/EPC", designCosts],
["PV Modules", pvModules*shipping],
["Racking", racking*shipping],
["Inverters & Switchgear", (inverters+gear)*shipping],
["BOS", hardwareCosts - pvModules*shipping - racking*shipping - (inverters+gear)*shipping],
["Site Prep, Constr. Eq. and Installation", (siteMaterial + constrEquip) + (siteLabor + installCosts)]]
# Cost per Wdc
outData["costWdc"] = (totalCosts + totalFees + float(inputDict.get("interCost",0))) / (arraySizeDC * 1000)
outData["capFactor"] = float(outData["oneYearGenerationWh"])/(inverterSizeAC*1000*365.25*24) * 100
######
### Loans calculations for Direct, NCREB, Lease, Tax-equity, and PPA
######
### Full Ownership, Direct Loan
#Output - Direct Loan [C]
projectCostsDirect = 0
#Output - Direct Loan [D]
netFinancingCostsDirect = 0
#Output - Direct Loan [E]
OMInsuranceETCDirect = []
#Output - Direct Loan [F]
distAdderDirect = []
#Output - Direct Loan [G]
netCoopPaymentsDirect = []
#Output - Direct Loan [H]
costToCustomerDirect = []
#Output - Direct Loan [F53]
Rate_Levelized_Direct = 0
## Output - Direct Loan Formulas
projectCostsDirect = 0
#Output - Direct Loan [D]
payment = pmt(float(inputDict.get("loanRate",0))/100, loanYears, outData["totalCost"])
interestDirectPI = outData["totalCost"] * float(inputDict.get("loanRate",0))/100
principleDirectPI = (-payment - interestDirectPI)
patronageCapitalRetiredDPI = 0
netFinancingCostsDirect = -(principleDirectPI + interestDirectPI - patronageCapitalRetiredDPI)
#Output - Direct Loan [E] [F] [G] [H]
firstYearOPMainCosts = (1.25 * arraySizeDC * 12)
firstYearInsuranceCosts = (0.37 * outData["totalCost"]/100)
if (inputDict.get("landOwnership",0) == "Leased"):
firstYearLandLeaseCosts = float(inputDict.get("costAcre",0))*landAmount
else:
firstYearLandLeaseCosts = 0
for i in range (1, len(outData["allYearGenerationMWh"])+1):
OMInsuranceETCDirect.append(-firstYearOPMainCosts*math.pow((1 + .01),(i-1)) - firstYearInsuranceCosts*math.pow((1 + .025),(i-1)) - firstYearLandLeaseCosts*math.pow((1 + .01),(i-1)))
distAdderDirect.append(float(inputDict.get("distAdder",0))*outData["allYearGenerationMWh"][i])
netCoopPaymentsDirect.append(OMInsuranceETCDirect[i-1] + netFinancingCostsDirect)
costToCustomerDirect.append((netCoopPaymentsDirect[i-1] - distAdderDirect[i-1]))
#Output - Direct Loan [F53]
NPVLoanDirect = npv(float(inputDict.get("discRate",0))/100, [0,0] + costToCustomerDirect)
NPVallYearGenerationMWh = npv(float(inputDict.get("discRate",0))/100, [0,0] + outData["allYearGenerationMWh"].values())
Rate_Levelized_Direct = -NPVLoanDirect/NPVallYearGenerationMWh
#Master Output [Direct Loan]
outData["levelCostDirect"] = Rate_Levelized_Direct
outData["costPanelDirect"] = abs(NPVLoanDirect/numberPanels)
outData["cost10WPanelDirect"] = (float(outData["costPanelDirect"])/panelSize)*10
### NCREBs Financing
ncrebsRate = float(inputDict.get("NCREBRate",4.060))/100
ncrebBorrowingRate = 1.1 * ncrebsRate
ncrebPaymentPeriods = 44
ncrebCostToCustomer = []
# TODO ASAP: FIX ARRAY OFFSETS START 0
for i in range (1, len(outData["allYearGenerationMWh"])+1):
coopLoanPayment = 2 * pmt(ncrebBorrowingRate/2.0, ncrebPaymentPeriods, outData["totalCost"]) if i <= ncrebPaymentPeriods / 2 else 0
ncrebsCredit = -0.7 * (ipmt(ncrebsRate / 2, 2 * i - 1, ncrebPaymentPeriods, outData["totalCost"])
+ ipmt(ncrebsRate / 2, 2 * i, ncrebPaymentPeriods, outData["totalCost"])) if i <= ncrebPaymentPeriods / 2 else 0
financingCost = ncrebsCredit + coopLoanPayment
omCost = OMInsuranceETCDirect[i - 1]
netCoopPayments = financingCost + omCost
distrAdder = distAdderDirect[i - 1]
costToCustomer = netCoopPayments + distrAdder
ncrebCostToCustomer.append(costToCustomer)
NPVLoanNCREB = npv(float(inputDict.get("discRate", 0))/100, [0,0] + ncrebCostToCustomer)
Rate_Levelized_NCREB = -NPVLoanNCREB/NPVallYearGenerationMWh
outData["levelCostNCREB"] = Rate_Levelized_NCREB
outData["costPanelNCREB"] = abs(NPVLoanNCREB/numberPanels)
outData["cost10WPanelNCREB"] = (float(outData["costPanelNCREB"])/panelSize)*10
### Lease Buyback Structure
#Output - Lease [C]
projectCostsLease = outData["totalCost"]
#Output - Lease [D]
leasePaymentsLease = []
#Output - Lease [E]
OMInsuranceETCLease = OMInsuranceETCDirect
#Output - Lease [F]
distAdderLease = distAdderDirect
#Output - Lease [G]
netCoopPaymentsLease = []
#Output - Lease [H]
costToCustomerLease = []
#Output - Lease [H44]
NPVLease = 0
#Output - Lease [H49]
Rate_Levelized_Lease = 0
## Tax Lease Formulas
#Output - Lease [D]
for i in range (0, 12):
leaseRate = float(inputDict.get("taxLeaseRate",0))/100.0
if i>8: # Special behavior in later years:
leaseRate = leaseRate - 0.0261
leasePaymentsLease.append(-1*projectCostsLease/((1.0-(1.0/(1.0+leaseRate)**12))/(leaseRate)))
# Last year is different.
leasePaymentsLease[11] += -0.2*projectCostsLease
for i in range (12, 25):
leasePaymentsLease.append(0)
#Output - Lease [G] [H]
for i in range (1, len(outData["allYearGenerationMWh"])+1):
netCoopPaymentsLease.append(OMInsuranceETCLease[i-1]+leasePaymentsLease[i-1])
costToCustomerLease.append(netCoopPaymentsLease[i-1]-distAdderLease[i-1])
#Output - Lease [H44]. Note the extra year at the zero point to get the discounting right.
NPVLease = npv(float(inputDict.get("discRate", 0))/100, [0,0]+costToCustomerLease)
#Output - Lease [H49] (Levelized Cost Three Loops)
Rate_Levelized_Lease = -NPVLease/NPVallYearGenerationMWh
#Master Output [Lease]
outData["levelCostTaxLease"] = Rate_Levelized_Lease
outData["costPanelTaxLease"] = abs(NPVLease/numberPanels)
outData["cost10WPanelTaxLease"] = (float(outData["costPanelTaxLease"])/float(panelSize))*10
### Tax Equity Flip Structure
# Tax Equity Flip Function
def taxEquityFlip(PPARateSixYearsTE, discRate, totalCost, allYearGenerationMWh, distAdderDirect, loanYears, firstYearLandLeaseCosts, firstYearOPMainCosts, firstYearInsuranceCosts, numberPanels):
#Output Tax Equity Flip [C]
coopInvestmentTaxEquity = -totalCost*(1-0.53)
#Output Tax Equity Flip [D]
financeCostCashTaxEquity = 0
#Output Tax Equity Flip [E]
cashToSPEOForPPATE = []
#Output Tax Equity Flip [F]
derivedCostEnergyTE = 0
#Output Tax Equity Flip [G]
OMInsuranceETCTE = []
#Output Tax Equity Flip [H]
cashFromSPEToBlockerTE = []
#Output Tax Equity Flip [I]
cashFromBlockerTE = 0
#Output Tax Equity Flip [J]
distAdderTaxEquity = distAdderDirect
#Output Tax Equity Flip [K]
netCoopPaymentsTaxEquity = []
#Output Tax Equity Flip [L]
costToCustomerTaxEquity = []
#Output Tax Equity Flip [L64]
NPVLoanTaxEquity = 0
#Output Tax Equity Flip [F72]
Rate_Levelized_Equity = 0
## Tax Equity Flip Formulas
#Output Tax Equity Flip [D]
#TEI Calcs [E]
financeCostOfCashTE = 0
coopFinanceRateTE = 2.7/100
if (coopFinanceRateTE == 0):
financeCostOfCashTE = 0
else:
payment = pmt(coopFinanceRateTE, loanYears, -coopInvestmentTaxEquity)
financeCostCashTaxEquity = payment
#Output Tax Equity Flip [E]
SPERevenueTE = []
for i in range (1, len(allYearGenerationMWh)+1):
SPERevenueTE.append(PPARateSixYearsTE * allYearGenerationMWh[i])
if ((i>=1) and (i<=6)):
cashToSPEOForPPATE.append(-SPERevenueTE[i-1])
else:
cashToSPEOForPPATE.append(0)
#Output Tax Equity Flip [F]
derivedCostEnergyTE = cashToSPEOForPPATE[0]/allYearGenerationMWh[1]
#Output Tax Equity Flip [G]
#TEI Calcs [F] [U] [V]
landLeaseTE = []
OMTE = []
insuranceTE = []
for i in range (1, len(allYearGenerationMWh)+1):
landLeaseTE.append(firstYearLandLeaseCosts*math.pow((1 + .01),(i-1)))
OMTE.append(-firstYearOPMainCosts*math.pow((1 + .01),(i-1)))
insuranceTE.append(- firstYearInsuranceCosts*math.pow((1 + .025),(i-1)) )
if (i<7):
OMInsuranceETCTE.append(float(landLeaseTE[i-1]))
else:
OMInsuranceETCTE.append(float(OMTE[i-1]) + float(insuranceTE[i-1]) + float(landLeaseTE[i-1]))
#Output Tax Equity Flip [H]
#TEI Calcs [T]
SPEMgmtFeeTE = []
EBITDATE = []
EBITDATEREDUCED = []
managementFee = 10000
for i in range (1, len(SPERevenueTE)+1):
SPEMgmtFeeTE.append(-managementFee*math.pow((1 + .01),(i-1)))
EBITDATE.append(float(SPERevenueTE[i-1]) + float(OMTE[i-1]) + float(insuranceTE[i-1]) + float(SPEMgmtFeeTE[i-1]))
if (i<=6):
cashFromSPEToBlockerTE.append(float(EBITDATE[i-1]) * .01)
else:
cashFromSPEToBlockerTE.append(0)
EBITDATEREDUCED.append(EBITDATE[i-1])
#Output Tax Equity Flip [I]
#TEI Calcs [Y21]
cashRevenueTE = -totalCost * (1 - 0.53)
buyoutAmountTE = 0
for i in range (1, len(EBITDATEREDUCED) + 1):
buyoutAmountTE = buyoutAmountTE + EBITDATEREDUCED[i-1]/(math.pow(1+0.12,i))
buyoutAmountTE = buyoutAmountTE * 0.05
cashFromBlockerTE = - (buyoutAmountTE) + 0.0725 * cashRevenueTE
#Output Tax Equity Flip [K] [L]
for i in range (1, len(allYearGenerationMWh)+1):
if (i==6):
netCoopPaymentsTaxEquity.append(financeCostCashTaxEquity + cashToSPEOForPPATE[i-1] + cashFromSPEToBlockerTE[i-1] + OMInsuranceETCTE[i-1] + cashFromBlockerTE)
else:
netCoopPaymentsTaxEquity.append(financeCostCashTaxEquity + cashFromSPEToBlockerTE[i-1] + cashToSPEOForPPATE[i-1] + OMInsuranceETCTE[i-1])
costToCustomerTaxEquity.append(netCoopPaymentsTaxEquity[i-1] - distAdderTaxEquity[i-1])
#Output Tax Equity Flip [L37]
NPVLoanTaxEquity = npv(float(inputDict.get("discRate",0))/100, [0, 0] + costToCustomerTaxEquity)
#Output - Tax Equity [F42]
Rate_Levelized_TaxEquity = -NPVLoanTaxEquity/NPVallYearGenerationMWh
#TEI Calcs - Achieved Return [AW 21]
#[AK]
MACRDepreciation = []
MACRDepreciation.append(-0.99*0.2*(totalCost-totalCost*0.5*0.9822*0.3))
MACRDepreciation.append(-0.99*0.32*(totalCost-totalCost*0.5*0.9822*0.3))
MACRDepreciation.append(-0.99*0.192*(totalCost-totalCost*0.5*0.9822*0.3))
MACRDepreciation.append(-0.99*0.1152*(totalCost-totalCost*0.5*0.9822*0.3))
MACRDepreciation.append(-0.99*0.1152*(totalCost-totalCost*0.5*0.9822*0.3))
MACRDepreciation.append(-0.99*0.0576*(totalCost-totalCost*0.5*0.9822*0.3))
#[AI] [AL] [AN]
cashRevenueTEI = [] #[AI]
slDepreciation = [] #[AL]
totalDistributions = [] #[AN]
cashRevenueTEI.append(-totalCost*0.53)
for i in range (1,7):
cashRevenueTEI.append(EBITDATE[i-1]*0.99)
slDepreciation.append(totalCost/25)
totalDistributions.append(-cashRevenueTEI[i])
#[AJ]
ITC = totalCost*0.9822*0.3*0.99
#[AM]
taxableIncLoss = [0]
taxableIncLoss.append(cashRevenueTEI[1]+MACRDepreciation[0])
#[AO]
capitalAcct = []
capitalAcct.append(totalCost*0.53)
condition = capitalAcct[0] - 0.5*ITC + taxableIncLoss[1] + totalDistributions[0]
if condition > 0:
capitalAcct.append(condition)
else:
capitalAcct.append(0)
#[AQ]
ratioTE = [0]
#[AP]
reallocatedIncLoss = []
#AO-1 + AN + AI + AK + AJ
for i in range (0, 5):
reallocatedIncLoss.append(capitalAcct[i+1] + totalDistributions[i+1] + MACRDepreciation[i+1] + cashRevenueTEI[i+2])
ratioTE.append(reallocatedIncLoss[i]/(cashRevenueTEI[i+2] + MACRDepreciation[i+1]))
taxableIncLoss.append(cashRevenueTEI[i+2]+MACRDepreciation[i+1]-ratioTE[i+1]*(MACRDepreciation[i+1]-totalDistributions[i+1]))
condition = capitalAcct[i+1] + taxableIncLoss[i+2] + totalDistributions[i+1]
if condition > 0:
capitalAcct.append(condition)
else:
capitalAcct.append(0)
#[AR]
taxesBenefitLiab = [0]
for i in range (1, 7):
taxesBenefitLiab.append(-taxableIncLoss[i]*0.35)
#[AS] [AT]
buyoutAmount = 0
taxFromBuyout = 0
for i in range (0, len(EBITDATEREDUCED)):
buyoutAmount = buyoutAmount + .05*EBITDATEREDUCED[i]/(math.pow(1.12,(i+1)))
taxFromBuyout = -buyoutAmount*0.35
#[AU] [AV]
totalCashTax = []
cumulativeCashTax = [0]
for i in range (0, 7):
if i == 1:
totalCashTax.append(cashRevenueTEI[i] + ITC + taxesBenefitLiab[i] + 0 + 0)
cumulativeCashTax.append(cumulativeCashTax[i] + totalCashTax[i])
elif i == 6:
totalCashTax.append(cashRevenueTEI[i] + 0 + taxesBenefitLiab[i] + buyoutAmount + taxFromBuyout)
cumulativeCashTax.append(cumulativeCashTax[i] + totalCashTax[i] + buyoutAmount + taxFromBuyout)
else:
totalCashTax.append(cashRevenueTEI[i] + 0 + taxesBenefitLiab[i] + 0 + 0)
cumulativeCashTax.append(cumulativeCashTax[i] + totalCashTax[i])
#[AW21]
if (cumulativeCashTax[7] > 0):
cumulativeIRR = round(irr(totalCashTax), 4)
else:
cumulativeIRR = 0
# Deleteme: Variable Dump for debugging
# variableDump = {}
# variableDump["TaxEquity"] = {}
# variableDump["TaxEquity"]["coopInvestmentTaxEquity"] = coopInvestmentTaxEquity
# variableDump["TaxEquity"]["financeCostCashTaxEquity"] = financeCostCashTaxEquity
# variableDump["TaxEquity"]["cashToSPEOForPPATE"] = cashToSPEOForPPATE
# variableDump["TaxEquity"]["derivedCostEnergyTE"] = derivedCostEnergyTE
# variableDump["TaxEquity"]["OMInsuranceETCTE"] = OMInsuranceETCTE
# variableDump["TaxEquity"]["cashFromSPEToBlockerTE"] = cashFromSPEToBlockerTE
# variableDump["TaxEquity"]["cashFromBlockerTE"] = cashFromBlockerTE
# variableDump["TaxEquity"]["distAdderTaxEquity"] = distAdderTaxEquity
# variableDump["TaxEquity"]["netCoopPaymentsTaxEquity"] = netCoopPaymentsTaxEquity
# variableDump["TaxEquity"]["NPVLoanTaxEquity"] = NPVLoanTaxEquity
return cumulativeIRR, Rate_Levelized_TaxEquity, NPVLoanTaxEquity
# Function Calls Mega Sized Tax Equity Function Above
z = 0
PPARateSixYearsTE = z / 100
nGoal = float(inputDict.get("taxEquityReturn",0))/100
nValue = 0
for p in range (0, 3):
while ((z < 50000) and (nValue < nGoal)):
achievedReturnTE, Rate_Levelized_TaxEquity, NPVLoanTaxEquity = taxEquityFlip(PPARateSixYearsTE, inputDict.get("discRate", 0), outData["totalCost"], outData["allYearGenerationMWh"], distAdderDirect, loanYears, firstYearLandLeaseCosts, firstYearOPMainCosts, firstYearInsuranceCosts, numberPanels)
nValue = achievedReturnTE
z = z + math.pow(10,p)
PPARateSixYearsTE = z/100.0
z = z - math.pow(10,p)
PPARateSixYearsTE = z/100
#Master Output [Tax Equity]
outData["levelCostTaxEquity"] = Rate_Levelized_TaxEquity
outData["costPanelTaxEquity"] = abs(NPVLoanTaxEquity/numberPanels)
outData["cost10WPanelTaxEquity"] = (float(outData["costPanelTaxEquity"])/panelSize)*10
### PPA Comparison
#Output - PPA [F]
distAdderPPA = distAdderDirect
#Output - PPA [G]
netCoopPaymentsPPA = []
#Output - PPA [H]
costToCustomerPPA = []
#Output - PPA [I]
costToCustomerPPA = []
#Output - PPA [H40]
NPVLoanPPA = 0
#Output - PPA [I40]
Rate_Levelized_PPA = 0
## PPA Formulas
#Output - PPA [G] [H]
for i in range (1, len(outData["allYearGenerationMWh"])+1):
netCoopPaymentsPPA.append(-outData["allYearGenerationMWh"][i]*float(inputDict.get("firstYearEnergyCostPPA",0))*math.pow((1 + float(inputDict.get("annualEscRatePPA", 0))/100),(i-1)))
costToCustomerPPA.append(netCoopPaymentsPPA[i-1]-distAdderPPA[i-1])
#Output - PPA [H58]
NPVLoanPPA = npv(float(inputDict.get("discRate", 0))/100, [0,0]+costToCustomerPPA)
#Output - PPA [F65]
Rate_Levelized_PPA = -NPVLoanPPA/NPVallYearGenerationMWh
#Master Output [PPA]
outData["levelCostPPA"] = Rate_Levelized_PPA
outData["firstYearCostKWhPPA"] = float(inputDict.get("firstYearEnergyCostPPA",0))
outData["yearlyEscalationPPA"] = float(inputDict.get("annualEscRatePPA", 0))
# Add all Levelized Costs to Output
outData["LevelizedCosts"] = [["Direct Loan", Rate_Levelized_Direct],
["NCREBs Financing", Rate_Levelized_NCREB],
["Lease Buyback", Rate_Levelized_Lease],
["Tax Equity Flip", Rate_Levelized_TaxEquity]]
outData["LevelizedCosts"].append({"name":"PPA Comparison", "y":Rate_Levelized_PPA, "color":"gold"})
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
def run(modelDir, inputDict):
try:
''' Run the model in its directory. '''
# Check whether model exist or not
if not os.path.isdir(modelDir):
os.makedirs(modelDir)
inputDict["created"] = str(dt.now())
# MAYBEFIX: remove this data dump. Check showModel in web.py and renderTemplate()
with open(pJoin(modelDir, "allInputData.json"),"w") as inputFile:
json.dump(inputDict, inputFile, indent = 4)
# Copy spcific climate data into model directory
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__metaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Ready to run
startTime = dt.now()
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", float(inputDict["SystemSize"]))
# SAM options where we take defaults.
ssc.ssc_data_set_number(dat, "derate", 0.97)
ssc.ssc_data_set_number(dat, "track_mode", 0)
ssc.ssc_data_set_number(dat, "azimuth", 180)
ssc.ssc_data_set_number(dat, "tilt_eq_lat", 1)
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = "2013-01-01 00:00:00 UTC"
# Timestamp output.
outData = {}
outData["timeStamps"] = [dt.strftime(
dt.strptime(startDateTime[0:19],"%Y-%m-%d %H:%M:%S") +
td(**{"hours":x}),"%Y-%m-%d %H:%M:%S") + " UTC" for x in range(int(8760))]
# HACK: makes it easier to calculate some things later.
outData["pythonTimeStamps"] = [dt(2012,1,1,0) + x*td(hours=1) for x in range(8760)]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Global Horizontal Radiation (W/m^2)"] = ssc.ssc_data_get_array(dat, "gh")
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = ssc.ssc_data_get_array(dat, "poa")
outData["climate"]["Ambient Temperature (F)"] = ssc.ssc_data_get_array(dat, "tamb")
outData["climate"]["Cell Temperature (F)"] = ssc.ssc_data_get_array(dat, "tcell")
outData["climate"]["Wind Speed (m/s)"] = ssc.ssc_data_get_array(dat, "wspd")
# Power generation.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
# TODO: INSERT TJ CODE BELOW
tjCode(inputDict, outData)
del outData["pythonTimeStamps"]
# TODO: INSERT TJ CODE ABOVE
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
# Write the output.
with open(pJoin(modelDir,"allOutputData.json"),"w") as outFile:
json.dump(outData, outFile, indent=4)
# Update the runTime in the input file.
endTime = dt.now()
inputDict["runTime"] = str(td(seconds=int((endTime - startTime).total_seconds())))
with open(pJoin(modelDir,"allInputData.json"),"w") as inFile:
json.dump(inputDict, inFile, 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)
def work(modelDir, inputDict):
''' Run the model in its directory. '''
inputDict["climateName"] = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
# TODO: FIX THIS!!!! IT SHOULD BE AVGSYS*PEN*RESCUSTOMERS
ssc.ssc_data_set_number(dat, "system_size", float(inputDict["systemSize"]))
# SAM options where we take defaults.
ssc.ssc_data_set_number(dat, "derate", 0.97)
ssc.ssc_data_set_number(dat, "track_mode", 0)
ssc.ssc_data_set_number(dat, "azimuth", 180)
ssc.ssc_data_set_number(dat, "tilt_eq_lat", 1)
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = "2013-01-01 00:00:00 UTC"
# Timestamp output.
outData = {}
outData["timeStamps"] = [dt.datetime.strftime(
dt.datetime.strptime(startDateTime[0:19],"%Y-%m-%d %H:%M:%S") +
dt.timedelta(**{"hours":x}),"%Y-%m-%d %H:%M:%S") + " UTC" for x in range(int(8760))]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Global Horizontal Radiation (W/m^2)"] = ssc.ssc_data_get_array(dat, "gh")
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = ssc.ssc_data_get_array(dat, "poa")
outData["climate"]["Ambient Temperature (F)"] = ssc.ssc_data_get_array(dat, "tamb")
outData["climate"]["Cell Temperature (F)"] = ssc.ssc_data_get_array(dat, "tcell")
outData["climate"]["Wind Speed (m/s)"] = ssc.ssc_data_get_array(dat, "wspd")
# Power generation.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
# Monthly aggregation outputs.
months = {"Jan":0,"Feb":1,"Mar":2,"Apr":3,"May":4,"Jun":5,"Jul":6,"Aug":7,"Sep":8,"Oct":9,"Nov":10,"Dec":11}
totMonNum = lambda x:sum([z for (y,z) in zip(outData["timeStamps"], outData["powerOutputAc"]) if y.startswith(startDateTime[0:4] + "-{0:02d}".format(x+1))])
outData["monthlyGeneration"] = [[a, roundSig(totMonNum(b),2)] for (a,b) in sorted(months.items(), key=lambda x:x[1])]
monthlyNoConsumerServedSales = []
monthlyKWhSold = []
monthlyRevenue = []
totalKWhSold = []
totalRevenue = []
for key in inputDict:
# MAYBEFIX: data in list may not be ordered by month.
if key.endswith("Sale"):
monthlyNoConsumerServedSales.append([key[:3].title(),float(inputDict.get(key, 0))])
elif key.endswith("KWh"):# the order of calculation matters
monthlyKWhSold.append([key[:3].title(),float(inputDict.get(key, 0))])
elif key.endswith("Rev"):
monthlyRevenue.append([key[:3].title(),float(inputDict.get(key, 0))])
elif key.endswith("KWhT"):
totalKWhSold.append([key[:3].title(),float(inputDict.get(key, 0))])
elif key.endswith("RevT"):
totalRevenue.append([key[:3].title(),float(inputDict.get(key, 0))])
outData["monthlyNoConsumerServedSales"] = sorted(monthlyNoConsumerServedSales, key=lambda x:months[x[0]])
outData["monthlyKWhSold"] = sorted(monthlyKWhSold, key=lambda x:months[x[0]])
outData["monthlyRevenue"] = sorted(monthlyRevenue, key=lambda x:months[x[0]])
outData["totalKWhSold"] = sorted(totalKWhSold, key=lambda x:months[x[0]])
outData["totalRevenue"] = sorted(totalRevenue, key=lambda x:months[x[0]])
outData["totalGeneration"] = [[sorted(months.items(), key=lambda x:x[1])[i][0], outData["monthlyGeneration"][i][1]*outData["monthlyNoConsumerServedSales"][i][1]*(float(inputDict.get("resPenetration", 5))/100/1000)] for i in range(12)]
outData["totalSolarSold"] = [[sorted(months.items(), key=lambda x:x[1])[i][0], outData["totalKWhSold"][i][1] - outData["totalGeneration"][i][1]] for i in range(12)]
##################
# TODO: add retailCost to the calculation.
##################
## Flow Diagram Calculations, and order of calculation matters
# BAU case
outData["BAU"] = {}
# E23 = E11
outData["BAU"]["totalKWhPurchased"] = float(inputDict.get("totalKWhPurchased", 1))
# E24 = SUM(E19:P19)
outData["BAU"]["totalKWhSales"] = sum([x[1] for x in totalKWhSold])
# E25 = E23-E24
outData["BAU"]["losses"] = float(inputDict.get("totalKWhPurchased", 0)) - sum([totalKWhSold[i][1] for i in range(12)])
# E26 = E25/E23
outData["BAU"]["effectiveLossRate"] = outData["BAU"]["losses"] / outData["BAU"]["totalKWhPurchased"]
# E27 = 0
outData["BAU"]["annualSolarGen"] = 0
# E28 = SUM(E17:P17)
outData["BAU"]["resNonSolarKWhSold"] = sum([monthlyKWhSold[i][1] for i in range(12)])
# E29 = 0
outData["BAU"]["solarResDemand"] = 0
# E30 = 0
outData["BAU"]["solarResSold"] = 0
# E31 = E24-E28
outData["BAU"]["nonResKWhSold"] = outData["BAU"]["totalKWhSales"] - outData["BAU"]["resNonSolarKWhSold"]
# E32 = 0
outData["BAU"]["costSolarGen"] = 0
# E33 = SUM(E20:P20)-SUM(E18:P18)+E10
outData["BAU"]["nonResRev"] = sum([totalRevenue[i][1] for i in range(12)]) - sum([monthlyRevenue[i][1] for i in range(12)]) + float(inputDict.get("otherElecRevenue"))
# E34 = (SUM(E18:P18)-SUM(E16:P16)*E6)/SUM(E17:P17)
outData["BAU"]["effectiveResRate"] = (sum ([monthlyRevenue[i][1] for i in range(12)]) - sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])*float(inputDict.get("customServiceCharge", 0)))/sum([monthlyKWhSold[i][1] for i in range(12)])
# E35 = E34*E28+SUM(E16:P16)*E6
outData["BAU"]["resNonSolarRev"] = outData["BAU"]["effectiveResRate"] * outData["BAU"]["resNonSolarKWhSold"] + sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])*float(inputDict.get("customServiceCharge", 0))
# E36 = E30*E34
outData["BAU"]["solarResRev"] = 0
# E37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71), update after Form 7 model
outData["BAU"]["nonPowerCosts"] = 0
# E38 = E23-E25-E28-E30-E31
outData["BAU"]["energyAllBal"] = 0
# E39 = E36+E33+E35-E47-E72-E37
outData["BAU"]["dollarAllBal"] = 0
# E40 = 0
outData["BAU"]["avgMonthlyBillSolarCus"] = 0
# E41 = E35/SUM(E16:P16)
avgCustomerCount = (sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])/12)
outData["BAU"]["avgMonthlyBillNonSolarCus"] = outData["BAU"]["resNonSolarRev"] / sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])
# E42 = E63/E24, update after Form 7 model
outData["BAU"]["costofService"] = 0
# Solar case
outData["Solar"] = {}
# F27 = SUM(E15:P15)
outData["Solar"]["annualSolarGen"] = sum([outData["totalGeneration"][i][1] for i in range(12)])
# F24 = E24-F27
outData["Solar"]["totalKWhSales"] = sum([totalKWhSold[i][1] for i in range(12)]) - outData["Solar"]["annualSolarGen"]
# F23 =F24/(1-E26)
outData["Solar"]["totalKWhPurchased"] = outData["Solar"]["totalKWhSales"]/ (1-outData["BAU"]["effectiveLossRate"])
outData["totalsolarmonthly"] = [[sorted(months.items(), key=lambda x:x[1])[i][0], outData["totalSolarSold"][i][1] / (1-outData["BAU"]["effectiveLossRate"])] for i in range(12)]
# F25 = F23-F24
outData["Solar"]["losses"] = (outData["Solar"]["totalKWhPurchased"] - outData["Solar"]["totalKWhSales"])
# F26 = E26
outData["Solar"]["effectiveLossRate"] = outData["BAU"]["effectiveLossRate"]
# F28 = (1-E5)*E28
outData["Solar"]["resNonSolarKWhSold"] = (1-float(inputDict.get("resPenetration", 0))/100)*outData["BAU"]["resNonSolarKWhSold"]
# F29 = E5*E28
outData["Solar"]["solarResDemand"] = float(inputDict.get("resPenetration", 0))/100*outData["BAU"]["resNonSolarKWhSold"]
# F30 = F29-F27
outData["Solar"]["solarResSold"] = outData["Solar"]["solarResDemand"] - outData["Solar"]["annualSolarGen"]
# F31 = E31
outData["Solar"]["nonResKWhSold"] = outData["BAU"]["nonResKWhSold"]
# F32 = E9*F27
outData["Solar"]["costSolarGen"] = float(inputDict.get("solarLCoE", 0.07))*outData["Solar"]["annualSolarGen"]
# F33 = E33
outData["Solar"]["nonResRev"] = outData["BAU"]["nonResRev"]
# F34 = E34
outData["Solar"]["effectiveResRate"] = outData["BAU"]["effectiveResRate"]
# F35 = E35*(1-E5)
outData["Solar"]["resNonSolarRev"] = outData["BAU"]["resNonSolarRev"] * (1 - float(inputDict.get("resPenetration", 0.05))/100)
# F30*E34 = Solar revenue from selling at residential rate
solarSoldRateRev = outData["Solar"]["solarResSold"] * outData["Solar"]["effectiveResRate"]
# (E6+E7)*SUM(E16:P16)*E5 = Solar revenue from charges
solarChargesRev = (float(inputDict.get("customServiceCharge", 0))+float(inputDict.get("solarServiceCharge", 0)))*sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])*float(inputDict.get("resPenetration", 0.05))/100
# F36 = F30*E34+(E6+E7)*SUM(E16:P16)*E5 = solarSoldRate + solarChargesRev
outData["Solar"]["solarResRev"] = solarSoldRateRev + solarChargesRev
# F37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71) = E37, update after Form 7 model
outData["Solar"]["nonPowerCosts"] = 0
# F38 = F23-F25-F28-F30-E31
outData["Solar"]["energyAllBal"] = 0
# F39 = F36+E33+F35-F47-F72-E37
outData["Solar"]["dollarAllBal"] = 0
if (float(inputDict.get("resPenetration", 0.05)) > 0):
# F41 = (F35)/(SUM(E16:P16)*(1-E5))
outData["Solar"]["avgMonthlyBillNonSolarCus"] = outData["Solar"]["resNonSolarRev"] / (sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])* (1 - float(inputDict.get("resPenetration", 0.05))/100))
# F42 = F30*E34/(SUM(E16:P16)*E5)+E6+E7
outData["Solar"]["avgMonthlyBillSolarCus"] = outData["Solar"]["solarResSold"] * outData["BAU"]["effectiveResRate"] / (sum([monthlyNoConsumerServedSales[i][1] for i in range(12)]) * float(inputDict.get("resPenetration", 0.05))/100) + float(inputDict.get("customServiceCharge", 0))+float(inputDict.get("solarServiceCharge", 0))
# F43 = (F27/(SUM(E16:P16)*E5))*E9
outData["Solar"]["avgMonthlyBillSolarSolarCus"] = (outData["Solar"]["annualSolarGen"] / (sum([monthlyNoConsumerServedSales[i][1] for i in range(12)]) * float(inputDict.get("resPenetration", 0.05))/100)) * float(inputDict.get("solarLCoE", 0.07))
else:
outData["Solar"]["avgMonthlyBillNonSolarCus"] = 0
outData["Solar"]["avgMonthlyBillSolarCus"] = 0
outData["Solar"]["avgMonthlyBillSolarSolarCus"] = 0
# Net Average Monthly Bill
avgMonthlyBillSolarNet = outData["Solar"]["avgMonthlyBillSolarCus"] + outData["Solar"]["avgMonthlyBillSolarSolarCus"]
outData["Solar"]["avgMonthlyBillSolarCus"] = avgMonthlyBillSolarNet
# F45 = F63/F24, update after Form 7 model
outData["Solar"]["costofService"] = 0
## Form 7 Model
# E46
outData["Solar"]["powerProExpense"] = outData["BAU"]["powerProExpense"] = float(inputDict.get("powerProExpense", 0))
# E47 != F47
outData["BAU"]["costPurchasedPower"] = float(inputDict.get("costPurchasedPower", 0))
# E48
outData["Solar"]["transExpense"] = outData["BAU"]["transExpense"] = float(inputDict.get("transExpense", 0))
# E49
outData["Solar"]["distriExpenseO"] = outData["BAU"]["distriExpenseO"] = float(inputDict.get("distriExpenseO", 0))
# E50
outData["Solar"]["distriExpenseM"] = outData["BAU"]["distriExpenseM"] = float(inputDict.get("distriExpenseM", 0))
# E51
outData["Solar"]["customerAccountExpense"] = outData["BAU"]["customerAccountExpense"] = float(inputDict.get("customerAccountExpense", 0))
# E52
outData["Solar"]["customerServiceExpense"] = outData["BAU"]["customerServiceExpense"] = float(inputDict.get("customerServiceExpense", 0))
# E53
outData["Solar"]["salesExpense"] = outData["BAU"]["salesExpense"] = float(inputDict.get("salesExpense", 0))
# E54
outData["Solar"]["adminGeneralExpense"] = outData["BAU"]["adminGeneralExpense"] = float(inputDict.get("adminGeneralExpense", 0))
# E56
outData["Solar"]["depreAmortiExpense"] = outData["BAU"]["depreAmortiExpense"] = float(inputDict.get("depreAmortiExpense", 0))
# E57
outData["Solar"]["taxExpensePG"] = outData["BAU"]["taxExpensePG"] = float(inputDict.get("taxExpensePG", 0))
# E58
outData["Solar"]["taxExpense"] = outData["BAU"]["taxExpense"] = float(inputDict.get("taxExpense", 0))
# E59
outData["Solar"]["interestLongTerm"] = outData["BAU"]["interestLongTerm"] = float(inputDict.get("interestLongTerm", 0))
# E60
outData["Solar"]["interestConstruction"] = outData["BAU"]["interestConstruction"] = float(inputDict.get("interestConstruction", 0))
# E61
outData["Solar"]["interestExpense"] = outData["BAU"]["interestExpense"] = float(inputDict.get("interestExpense", 0))
# E62
outData["Solar"]["otherDeductions"] = outData["BAU"]["otherDeductions"] = float(inputDict.get("otherDeductions", 0))
# E65
outData["Solar"]["nonOpMarginInterest"] = outData["BAU"]["nonOpMarginInterest"] = float(inputDict.get("nonOpMarginInterest", 0))
# E66
outData["Solar"]["fundsUsedConstruc"] = outData["BAU"]["fundsUsedConstruc"] = float(inputDict.get("fundsUsedConstruc", 0))
# E67
outData["Solar"]["incomeEquityInvest"] = outData["BAU"]["incomeEquityInvest"] = float(inputDict.get("incomeEquityInvest", 0))
# E68
outData["Solar"]["nonOpMarginOther"] = outData["BAU"]["nonOpMarginOther"] = float(inputDict.get("nonOpMarginOther", 0))
# E69
outData["Solar"]["genTransCapCredits"] = outData["BAU"]["genTransCapCredits"] = float(inputDict.get("genTransCapCredits", 0))
# E70
outData["Solar"]["otherCapCreditsPatroDivident"] = outData["BAU"]["otherCapCreditsPatroDivident"] = float(inputDict.get("otherCapCreditsPatroDivident", 0))
# E71
outData["Solar"]["extraItems"] = outData["BAU"]["extraItems"] = float(inputDict.get("extraItems", 0))
# Calculation
# E45 = SUM(E20:P20)+E10
outData["BAU"]["operRevPatroCap"] = sum([totalRevenue[i][1] for i in range(12)])+float(inputDict.get("otherElecRevenue", 0))
# E55 = SUM(E46:E54)
outData["BAU"]["totalOMExpense"] = float(inputDict.get("powerProExpense")) \
+ float(inputDict.get("costPurchasedPower")) \
+ float(inputDict.get("transExpense")) \
+ float(inputDict.get("distriExpenseO")) \
+ float(inputDict.get("distriExpenseM")) \
+ float(inputDict.get("customerAccountExpense")) \
+ float(inputDict.get("customerServiceExpense")) \
+ float(inputDict.get("salesExpense")) \
+ float(inputDict.get("adminGeneralExpense"))
# E63 = SUM(E55:E62)
outData["BAU"]["totalCostElecService"] = outData["BAU"]["totalOMExpense"] \
+ float(inputDict.get("depreAmortiExpense"))\
+ float(inputDict.get("taxExpensePG"))\
+ float(inputDict.get("taxExpense"))\
+ float(inputDict.get("interestLongTerm"))\
+ float(inputDict.get("interestExpense"))\
+ float(inputDict.get("interestConstruction"))\
+ outData["BAU"]["otherDeductions"]
# E64 = E45-E63
outData["BAU"]["patCapOperMargins"] = outData["BAU"]["operRevPatroCap"] - outData["BAU"]["totalCostElecService"]
# E72 = SUM(E64:E71)
outData["BAU"]["patCapital"] = outData["BAU"]["patCapOperMargins"]\
+ float(inputDict.get("nonOpMarginInterest"))\
+ float(inputDict.get("fundsUsedConstruc"))\
+ float(inputDict.get("incomeEquityInvest"))\
+ float(inputDict.get("nonOpMarginOther"))\
+ float(inputDict.get("genTransCapCredits"))\
+ float(inputDict.get("otherCapCreditsPatroDivident"))\
+ float(inputDict.get("extraItems"))
# F48 = E48-F27*E34+SUM(E16:P16)*E5*E7
outData["Solar"]["operRevPatroCap"] = outData["BAU"]["operRevPatroCap"] - outData["BAU"]["effectiveResRate"]*outData["Solar"]["annualSolarGen"] + sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])*float(inputDict.get("resPenetration", 0.05))/100*float(inputDict.get("solarServiceCharge", 0))
# F47 = (F23)*E8
inputDict["costofPower"] = float(inputDict.get("costPurchasedPower", 0)) / float(inputDict.get("totalKWhPurchased", 0))
outData["Solar"]["costPurchasedPower"] = outData["Solar"]["totalKWhPurchased"] * float(inputDict.get("costofPower", 0))
inputDict["costofPower"] = round(inputDict["costofPower"],3)
# F55 = SUM(F46:F54)
outData["Solar"]["totalOMExpense"] = outData["Solar"]["powerProExpense"]\
+ outData["Solar"]["costPurchasedPower"]\
+ outData["Solar"]["transExpense"]\
+ outData["Solar"]["distriExpenseO"]\
+ outData["Solar"]["distriExpenseM"]\
+ outData["Solar"]["customerAccountExpense"]\
+ outData["Solar"]["customerServiceExpense"]\
+ outData["Solar"]["salesExpense"]\
+ outData["Solar"]["adminGeneralExpense"]
# F63 = E63
outData["Solar"]["totalCostElecService"] = outData["Solar"]["totalOMExpense"]\
+ outData["Solar"]["depreAmortiExpense"]\
+ outData["Solar"]["taxExpensePG"]\
+ outData["Solar"]["taxExpense"]\
+ outData["Solar"]["interestLongTerm"]\
+ outData["Solar"]["interestConstruction"]\
+ outData["Solar"]["interestExpense"]\
+ outData["Solar"]["otherDeductions"]
# F64 = F45 - F63
outData["Solar"]["patCapOperMargins"] = outData["Solar"]["operRevPatroCap"] - outData["Solar"]["totalCostElecService"]
# F72 = SUM(F64:F71)
outData["Solar"]["patCapital"] = outData["Solar"]["patCapOperMargins"]\
+ outData["Solar"]["nonOpMarginInterest"]\
+ outData["Solar"]["fundsUsedConstruc"]\
+ outData["Solar"]["incomeEquityInvest"]\
+ outData["Solar"]["nonOpMarginOther"]\
+ outData["Solar"]["genTransCapCredits"]\
+ outData["Solar"]["otherCapCreditsPatroDivident"]\
+ outData["Solar"]["extraItems"]
# E37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71), update after Form 7 model
outData["BAU"]["nonPowerCosts"] = outData["BAU"]["transExpense"] \
+ outData["BAU"]["distriExpenseO"] \
+ outData["BAU"]["distriExpenseM"] \
+ outData["BAU"]["customerAccountExpense"] \
+ outData["BAU"]["customerServiceExpense"] \
+ outData["BAU"]["salesExpense"] \
+ outData["BAU"]["adminGeneralExpense"] \
+ outData["BAU"]["depreAmortiExpense"] \
+ outData["BAU"]["taxExpensePG"] \
+ outData["BAU"]["taxExpense"] \
+ outData["BAU"]["interestLongTerm"] \
+ outData["BAU"]["interestConstruction"] \
+ outData["BAU"]["interestExpense"] \
+ outData["BAU"]["otherDeductions"] \
- (outData["BAU"]["nonOpMarginInterest"] \
+ outData["BAU"]["fundsUsedConstruc"] \
+ outData["BAU"]["incomeEquityInvest"] \
+ outData["BAU"]["nonOpMarginOther"] \
+ outData["BAU"]["genTransCapCredits"] \
+ outData["BAU"]["otherCapCreditsPatroDivident"] \
+ outData["BAU"]["extraItems"])
# E42 = E63/E24, update after Form 7 model
outData["BAU"]["costofService"] = outData["BAU"]["totalCostElecService"] / outData["BAU"]["totalKWhSales"]
# F37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71) = E37, update after Form 7 model
outData["Solar"]["nonPowerCosts"] = outData["BAU"]["nonPowerCosts"]
# F42 = F63/F24, update after Form 7 model
outData["Solar"]["costofService"] = outData["Solar"]["totalCostElecService"] / outData["Solar"]["totalKWhSales"]
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
def work(modelDir, inputDict):
#plotly imports. Here for now so web server starts.
import plotly
# from plotly import __version__
# from plotly.offline import download_plotlyjs, plot
# from plotly import tools
import plotly.graph_objs as go
# Copy specific climate data into model directory
inputDict["climateName"] = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", float(inputDict["systemSize"]))
ssc.ssc_data_set_number(dat, "derate",
0.01 * float(inputDict["nonInverterEfficiency"]))
ssc.ssc_data_set_number(dat, "track_mode",
float(inputDict["trackingMode"]))
ssc.ssc_data_set_number(dat, "azimuth", float(inputDict["azimuth"]))
# Advanced inputs with defaults.
if (inputDict.get("tilt", 0) == "-"):
tilt_eq_lat = 1.0
manualTilt = 0.0
else:
tilt_eq_lat = 0.0
manualTilt = float(inputDict.get("tilt", 0))
ssc.ssc_data_set_number(dat, "tilt_eq_lat", tilt_eq_lat)
ssc.ssc_data_set_number(dat, "tilt", manualTilt)
ssc.ssc_data_set_number(dat, "rotlim", float(inputDict["rotlim"]))
ssc.ssc_data_set_number(dat, "gamma", -1 * float(inputDict["gamma"]))
ssc.ssc_data_set_number(dat, "inv_eff",
0.01 * float(inputDict["inverterEfficiency"]))
ssc.ssc_data_set_number(dat, "w_stow", float(inputDict["w_stow"]))
# Complicated optional inputs that we could enable later.
# ssc.ssc_data_set_array(dat, 'shading_hourly', ...) # Hourly beam shading factors
# ssc.ssc_data_set_matrix(dat, 'shading_mxh', ...) # Month x Hour beam shading factors
# ssc.ssc_data_set_matrix(dat, 'shading_azal', ...) # Azimuth x altitude beam shading factors
# ssc.ssc_data_set_number(dat, 'shading_diff', ...) # Diffuse shading factor
# ssc.ssc_data_set_number(dat, 'enable_user_poa', ...) # Enable user-defined POA irradiance input = 0 or 1
# ssc.ssc_data_set_array(dat, 'user_poa', ...) # User-defined POA irradiance in W/m2
# ssc.ssc_data_set_number(dat, 'tilt', 999)
# ssc.ssc_data_set_number(dat, "t_noct", float(inputDict["t_noct"]))
# ssc.ssc_data_set_number(dat, "t_ref", float(inputDict["t_ref"]))
# ssc.ssc_data_set_number(dat, "fd", float(inputDict["fd"]))
# ssc.ssc_data_set_number(dat, "i_ref", float(inputDict["i_ref"]))
# ssc.ssc_data_set_number(dat, "poa_cutin", float(inputDict["poa_cutin"]))
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Setting options for start time.
simLengthUnits = inputDict.get("simLengthUnits", "")
simStartDate = inputDict["simStartDate"]
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = simStartDate + " 00:00:00 UTC"
# Set aggregation function constants.
agg = lambda x, y: _aggData(x, y, inputDict["simStartDate"],
int(inputDict["simLength"]), inputDict[
"simLengthUnits"], ssc, dat)
avg = lambda x: sum(x) / len(x)
# Timestamp output.
outData = {}
outData["timeStamps"] = [
datetime.datetime.strftime(
datetime.datetime.strptime(startDateTime[0:19],
"%Y-%m-%d %H:%M:%S") +
datetime.timedelta(**{simLengthUnits: x}), "%Y-%m-%d %H:%M:%S") +
" UTC" for x in range(int(inputDict["simLength"]))
]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = agg("poa", avg)
outData["climate"]["Beam Normal Irradiance (W/m^2)"] = agg("dn", avg)
outData["climate"]["Diffuse Irradiance (W/m^2)"] = agg("df", avg)
outData["climate"]["Ambient Temperature (F)"] = agg("tamb", avg)
outData["climate"]["Cell Temperature (F)"] = agg("tcell", avg)
outData["climate"]["Wind Speed (m/s)"] = agg("wspd", avg)
# Power generation.
outData["Consumption"] = {}
outData["Consumption"]["Power"] = [x for x in agg("ac", avg)]
outData["Consumption"]["Losses"] = [0 for x in agg("ac", avg)]
outData["Consumption"]["DG"] = agg("ac", avg)
#Plotly data sets for power generation graphs
convertedDateStrings = [
datetime.datetime.strptime(x, "%Y-%m-%d %H:%M:%S %Z")
for x in outData["timeStamps"]
]
powerGeneration = go.Scatter(x=convertedDateStrings,
y=outData["Consumption"]["Power"],
line=dict(color=('red')),
name="Power Generated")
chartInverter = None
if float(inputDict["inverterSize"]) == 0:
chartInverter = float(inputDict["systemSize"])
else:
chartInverter = float(inputDict["inverterSize"])
panelsNameplate = go.Scatter(x=convertedDateStrings,
y=[
float(inputDict['systemSize']) * 1000
for x in range(len(convertedDateStrings))
],
line=dict(dash='dash', color='orange'),
name="Panels Nameplate")
inverterNameplate = go.Scatter(
x=convertedDateStrings,
y=[chartInverter * 1000 for x in range(len(convertedDateStrings))],
line=dict(dash='dash', color='orange'),
name="inverter Nameplate")
#Set Power generation plotly layout
powerGenerationLayout = go.Layout(width=1000,
height=375,
xaxis=dict(showgrid=False, ),
legend=dict(x=0, y=1.25,
orientation="h"))
#Combine all datasets for plotly graph
powerGenerationData = [powerGeneration, panelsNameplate, inverterNameplate]
#Example updating go object
powerGenerationLayout['yaxis'].update(title='Power (W-AC)')
#fig = go.Figure(data=powerGenerationData, layout=powerGenerationLayout)
#inlinePlot = plotly.offline.plot(fig, include_plotlyjs=False, output_type='div')
#outData["plotlyDiv"] = html.escape(json.dumps(inlinePlot, cls=plotly.utils.PlotlyJSONEncoder))
#Plotly power generation outputs
outData["powerGenerationData"] = json.dumps(
powerGenerationData, cls=plotly.utils.PlotlyJSONEncoder)
outData["powerGenerationLayout"] = json.dumps(
powerGenerationLayout, cls=plotly.utils.PlotlyJSONEncoder)
#Irradiance plotly data
poaIrradiance = go.Scatter(
x=convertedDateStrings,
y=outData["climate"]["Plane of Array Irradiance (W/m^2)"],
line=dict(color='yellow'),
name="Plane of Array Irradiance (W/m^2)")
beamNormalIrradiance = go.Scatter(
x=convertedDateStrings,
y=outData["climate"]["Beam Normal Irradiance (W/m^2)"],
line=dict(color='gold'),
name="Beam Normal Irradiance (W/m^2)")
diffuseIrradiance = go.Scatter(
x=convertedDateStrings,
y=outData["climate"]["Diffuse Irradiance (W/m^2)"],
line=dict(color='lemonchiffon'),
name="Diffuse Irradiance (W/m^2)")
irradianceData = [poaIrradiance, beamNormalIrradiance, diffuseIrradiance]
#Set Power generation plotly layout
irradianceLayout = go.Layout(width=1000,
height=375,
xaxis=dict(showgrid=False, ),
yaxis=dict(title="Climate Units", ),
legend=dict(x=0, y=1.25, orientation="h"))
outData["irradianceData"] = json.dumps(irradianceData,
cls=plotly.utils.PlotlyJSONEncoder)
outData["irradianceLayout"] = json.dumps(
irradianceLayout, cls=plotly.utils.PlotlyJSONEncoder)
#Other Climate Variables plotly data
ambientTemperature = go.Scatter(
x=convertedDateStrings,
y=outData["climate"]["Ambient Temperature (F)"],
line=dict(color='dimgray'),
name="Ambient Temperature (F)")
cellTemperature = go.Scatter(x=convertedDateStrings,
y=outData["climate"]["Cell Temperature (F)"],
line=dict(color='gainsboro'),
name="Cell Temperature (F)")
windSpeed = go.Scatter(x=convertedDateStrings,
y=outData["climate"]["Wind Speed (m/s)"],
line=dict(color='darkgray'),
name="Wind Speed (m/s)")
otherClimateData = [ambientTemperature, cellTemperature, windSpeed]
#Set Power generation plotly layout
otherClimateLayout = go.Layout(width=1000,
height=375,
xaxis=dict(showgrid=False, ),
yaxis=dict(title="Climate Units", ),
legend=dict(x=0, y=1.25, orientation="h"))
outData["otherClimateData"] = json.dumps(
otherClimateData, cls=plotly.utils.PlotlyJSONEncoder)
outData["otherClimateLayout"] = json.dumps(
otherClimateLayout, cls=plotly.utils.PlotlyJSONEncoder)
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
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
hazardPath = pJoin(modelDir,inputDict['weatherImpactsFileName'])
with open(hazardPath,'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
critLoads = inputDict['criticalLoads']
gfmInputTemplate = {
'phase_variation' : float(inputDict['phaseVariation']),
'chance_constraint' : float(inputDict['chanceConstraint']),
'critical_load_met' : float(inputDict['criticalLoadMet']),
'total_load_met' : float(inputDict['nonCriticalLoadMet']),
'maxDGPerGenerator' : float(inputDict['maxDGPerGenerator']),
'dgUnitCost' : float(inputDict['dgUnitCost']),
'generatorCandidates' : inputDict['generatorCandidates'],
'criticalLoads' : inputDict['criticalLoads']
}
gfmJson = convertToGFM(gfmInputTemplate, feederModel)
gfmInputFilename = 'gfmInput.json'
with open(pJoin(modelDir, gfmInputFilename), 'w') as outFile:
json.dump(gfmJson, outFile, indent=4)
# Check for overlap between hazard field and GFM circuit input:
hazard = HazardField(hazardPath)
if circuitOutsideOfHazard(hazard, gfmJson):
outData['warning'] = 'Warning: the hazard field does not overlap with the circuit.'
# Draw hazard field if needed.
if inputDict['showHazardField'] == 'Yes':
hazard.drawHeatMap(show=False)
plt.title('') #Hack: remove plot title.
# Run GFM
gfmBinaryPath = pJoin(__neoMetaModel__._omfDir,'solvers','gfm', 'Fragility.jar')
rdtInputName = 'rdtInput.json'
if platform.system() == 'Darwin':
#HACK: force use of Java8 on MacOS.
javaCmd = '/Library/Java/JavaVirtualMachines/jdk1.8.0_181.jdk/Contents/Home/bin/java'
else:
javaCmd = 'java'
proc = subprocess.Popen([javaCmd,'-jar', gfmBinaryPath, '-r', gfmInputFilename, '-wf', inputDict['weatherImpactsFileName'],'-num','3','-ro',rdtInputName], stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=modelDir)
(stdout,stderr) = proc.communicate()
with open(pJoin(modelDir, "gfmConsoleOut.txt"), "w") as gfmConsoleOut:
gfmConsoleOut.write(stdout)
rdtInputFilePath = pJoin(modelDir,'rdtInput.json')
# 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)
rdtJson["power_flow"] = inputDict["power_flow"]
rdtJson["solver_iteration_timeout"] = 300.0
rdtJson["algorithm"] = "miqp"
# 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
# Insert user-specified scenarios block into RDT input
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 1ST GLD RUN FOR", modelDir
omdPath = pJoin(modelDir, feederName + ".omd")
with open(omdPath, "r") as omd:
omd = json.load(omd)
# Remove new line candidates to get normal system powerflow results.
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 JSON_dump_line.json;\n\twrite_system_info true;\n\twrite_per_unit true;\n\tsystem_base 100.0 MVA;\n};\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 = zipCodeToClimateName(inputDict["simulationZipCode"])
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", climateFileName + ".tmy2"), pJoin(modelDir, 'climate.tmy2'))
# Platform specific binaries for GridLAB-D First Run.
if platform.system() == "Linux":
myEnv = os.environ.copy()
myEnv['GLPATH'] = omf.omfDir + '/solvers/gridlabdv990/'
commandString = omf.omfDir + '/solvers/gridlabdv990/gridlabd.bin feeder.glm'
elif platform.system() == "Windows":
myEnv = os.environ.copy()
commandString = '"' + pJoin(omf.omfDir, "solvers", "gridlabdv990", "gridlabd.exe") + '"' + " feeder.glm"
elif platform.system() == "Darwin":
myEnv = os.environ.copy()
myEnv['GLPATH'] = omf.omfDir + '/solvers/gridlabdv990/MacRC4p1_std8/'
commandString = '"' + omf.omfDir + '/solvers/gridlabdv990/MacRC4p1_std8/gld.sh" feeder.glm'
# Run GridLAB-D First Time.
proc = subprocess.Popen(commandString, stdout=subprocess.PIPE, shell=True, cwd=modelDir, env=myEnv)
(out, err) = proc.communicate()
with open(pJoin(modelDir, "gldConsoleOut.txt"), "w") as gldConsoleOut:
gldConsoleOut.write(out)
with open(pJoin(modelDir, "JSON_dump_line.json"), "r") as gldOut:
gld_json_line_dump = json.load(gldOut)
outData['gridlabdRawOut'] = gld_json_line_dump
# Add GridLAB-D line objects and line codes in to the RDT model.
rdtJson["line_codes"] = gld_json_line_dump["properties"]["line_codes"]
rdtJson["lines"] = gld_json_line_dump["properties"]["lines"]
hardCands = list(set(gfmJson['lineLikeObjs']) - set(inputDict['hardeningCandidates']))
newLineCands = inputDict['newLineCandidates'].strip().replace(' ', '').split(',')
switchCands = inputDict['switchCandidates'].strip().replace(' ', '').split(',')
for line in rdtJson["lines"]:
line_id = line.get('id','') # this is equal to name in the OMD objects.
object_type = line.get('object','')
line['node1_id'] = line['node1_id'] + "_bus"
line['node2_id'] = line['node2_id'] + "_bus"
line_code = line["line_code"]
# Getting ratings from OMD
tree = omd['tree']
nameToIndex = {tree[key].get('name',''):key for key in tree}
treeOb = tree[nameToIndex[line_id]]
config_name = treeOb.get('configuration','')
config_ob = tree.get(nameToIndex[config_name], {})
full_rating = 0
for phase in ['A','B','C']:
cond_name = config_ob.get('conductor_' + phase, '')
cond_ob = tree.get(nameToIndex.get(cond_name, ''), '')
rating = cond_ob.get('rating.summer.continuous','')
try:
full_rating = int(rating) #TODO: replace with avg of 3 phases.
except:
pass
if full_rating != 0:
line['capacity'] = full_rating
else:
line['capacity'] = 10000
# Setting other line parameters.
line['construction_cost'] = float(inputDict['lineUnitCost'])
line['harden_cost'] = float(inputDict['hardeningUnitCost'])
line['switch_cost'] = float(inputDict['switchCost'])
if line_id in hardCands:
line['can_harden'] = True
if line_id in switchCands:
line['can_add_switch'] = True
if line_id in newLineCands:
line['is_new'] = True
if object_type in ['transformer','regulator']:
line['is_transformer'] = True
if object_type == 'switch':
line['has_switch'] = True
with open(rdtInputFilePath, "w") as outFile:
json.dump(rdtJson, outFile, indent=4)
# Run RDT.
print "RUNNING RDT FOR", modelDir
rdtOutFile = modelDir + '/rdtOutput.json'
rdtSolverFolder = pJoin(__neoMetaModel__._omfDir,'solvers','rdt')
rdtJarPath = pJoin(rdtSolverFolder,'micot-rdt.jar')
# TODO: modify path, don't assume SCIP installation.
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 2ND GLD RUN FOR", modelDir
feederCopy = copy.deepcopy(feederModel)
lineSwitchList = []
edgeLabels = {}
generatorList = []
for gen in rdtOut['design_solution']['generators']:
generatorList.append(gen['id'][:-4])
damagedLoads = {}
for scenario in rdtOut['scenario_solution']:
for load in scenario['loads']:
if load['id'] in damagedLoads.keys():
damagedLoads[load['id'][:-4]] += 1
else:
damagedLoads[load['id'][:-4]] = 1
for line in rdtOut['design_solution']['lines']:
if('switch_built' in line and 'hardened' in line):
lineSwitchList.append(line['id'])
if (line['switch_built'] == True and line['hardened'] == True):
edgeLabels[line['id']] = "SH"
elif(line['switch_built'] == True):
edgeLabels[line['id']] = "S"
elif (line['hardened'] == True):
edgeLabels[line['id']] = "H"
elif('switch_built' in line):
lineSwitchList.append(line['id'])
if (line['switch_built'] == True):
edgeLabels[line['id']] = "S"
elif('hardened' in line):
if (line['hardened'] == True):
edgeLabels[line['id']] = "H"
# 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
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.
damageDict = {}
for scenario in rdtJson["scenarios"]:
for line in scenario["disable_lines"]:
if line in damageDict:
damageDict[line] = damageDict[line] + 1
else:
damageDict[line] = 1
genDiagram(modelDir, feederModel, damageDict, critLoads, damagedLoads, edgeLabels, generatorList)
with open(pJoin(modelDir,"feederChart.png"),"rb") as inFile:
outData["oneLineDiagram"] = inFile.read().encode("base64")
# And we're done.
return outData
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 run(modelDir, inputDict):
try:
''' Run the model in its directory. '''
# Check whether model exist or not
if not os.path.isdir(modelDir):
os.makedirs(modelDir)
inputDict["created"] = str(dt.datetime.now())
# MAYBEFIX: remove this data dump. Check showModel in web.py and renderTemplate()
with open(pJoin(modelDir, "allInputData.json"),"w") as inputFile:
json.dump(inputDict, inputFile, indent = 4)
# Copy spcific climate data into model directory
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__metaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Ready to run
startTime = dt.datetime.now()
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
# TODO: FIX THIS!!!! IT SHOULD BE AVGSYS*PEN*RESCUSTOMERS
ssc.ssc_data_set_number(dat, "system_size", float(inputDict["systemSize"]))
# SAM options where we take defaults.
ssc.ssc_data_set_number(dat, "derate", 0.97)
ssc.ssc_data_set_number(dat, "track_mode", 0)
ssc.ssc_data_set_number(dat, "azimuth", 180)
ssc.ssc_data_set_number(dat, "tilt_eq_lat", 1)
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = "2013-01-01 00:00:00 UTC"
# Timestamp output.
outData = {}
outData["timeStamps"] = [dt.datetime.strftime(
dt.datetime.strptime(startDateTime[0:19],"%Y-%m-%d %H:%M:%S") +
dt.timedelta(**{"hours":x}),"%Y-%m-%d %H:%M:%S") + " UTC" for x in range(int(8760))]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Global Horizontal Radiation (W/m^2)"] = ssc.ssc_data_get_array(dat, "gh")
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = ssc.ssc_data_get_array(dat, "poa")
outData["climate"]["Ambient Temperature (F)"] = ssc.ssc_data_get_array(dat, "tamb")
outData["climate"]["Cell Temperature (F)"] = ssc.ssc_data_get_array(dat, "tcell")
outData["climate"]["Wind Speed (m/s)"] = ssc.ssc_data_get_array(dat, "wspd")
# Power generation.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
# Monthly aggregation outputs.
months = {"Jan":0,"Feb":1,"Mar":2,"Apr":3,"May":4,"Jun":5,"Jul":6,"Aug":7,"Sep":8,"Oct":9,"Nov":10,"Dec":11}
totMonNum = lambda x:sum([z for (y,z) in zip(outData["timeStamps"], outData["powerOutputAc"]) if y.startswith(startDateTime[0:4] + "-{0:02d}".format(x+1))])
outData["monthlyGeneration"] = [[a, roundSig(totMonNum(b),2)] for (a,b) in sorted(months.items(), key=lambda x:x[1])]
monthlyNoConsumerServedSales = []
monthlyKWhSold = []
monthlyRevenue = []
totalKWhSold = []
totalRevenue = []
for key in inputDict:
# MAYBEFIX: data in list may not be ordered by month.
if key.endswith("Sale"):
monthlyNoConsumerServedSales.append([key[:3].title(),float(inputDict.get(key, 0))])
elif key.endswith("KWh"):# the order of calculation matters
monthlyKWhSold.append([key[:3].title(),float(inputDict.get(key, 0))])
elif key.endswith("Rev"):
monthlyRevenue.append([key[:3].title(),float(inputDict.get(key, 0))])
elif key.endswith("KWhT"):
totalKWhSold.append([key[:3].title(),float(inputDict.get(key, 0))])
elif key.endswith("RevT"):
totalRevenue.append([key[:3].title(),float(inputDict.get(key, 0))])
outData["monthlyNoConsumerServedSales"] = sorted(monthlyNoConsumerServedSales, key=lambda x:months[x[0]])
outData["monthlyKWhSold"] = sorted(monthlyKWhSold, key=lambda x:months[x[0]])
outData["monthlyRevenue"] = sorted(monthlyRevenue, key=lambda x:months[x[0]])
outData["totalKWhSold"] = sorted(totalKWhSold, key=lambda x:months[x[0]])
outData["totalRevenue"] = sorted(totalRevenue, key=lambda x:months[x[0]])
outData["totalGeneration"] = [[sorted(months.items(), key=lambda x:x[1])[i][0], outData["monthlyGeneration"][i][1]*outData["monthlyNoConsumerServedSales"][i][1]*(float(inputDict.get("resPenetration", 5))/100/1000)] for i in range(12)]
outData["totalSolarSold"] = [[sorted(months.items(), key=lambda x:x[1])[i][0], outData["totalKWhSold"][i][1] - outData["totalGeneration"][i][1]] for i in range(12)]
##################
# TODO: add retailCost to the calculation.
##################
## Flow Diagram Calculations, and order of calculation matters
# BAU case
outData["BAU"] = {}
# E23 = E11
outData["BAU"]["totalKWhPurchased"] = float(inputDict.get("totalKWhPurchased", 1))
# E24 = SUM(E19:P19)
outData["BAU"]["totalKWhSales"] = sum([x[1] for x in totalKWhSold])
# E25 = E23-E24
outData["BAU"]["losses"] = float(inputDict.get("totalKWhPurchased", 0)) - sum([totalKWhSold[i][1] for i in range(12)])
# E26 = E25/E23
outData["BAU"]["effectiveLossRate"] = outData["BAU"]["losses"] / outData["BAU"]["totalKWhPurchased"]
# E27 = 0
outData["BAU"]["annualSolarGen"] = 0
# E28 = SUM(E17:P17)
outData["BAU"]["resNonSolarKWhSold"] = sum([monthlyKWhSold[i][1] for i in range(12)])
# E29 = 0
outData["BAU"]["solarResDemand"] = 0
# E30 = 0
outData["BAU"]["solarResSold"] = 0
# E31 = E24-E28
outData["BAU"]["nonResKWhSold"] = outData["BAU"]["totalKWhSales"] - outData["BAU"]["resNonSolarKWhSold"]
# E32 = 0
outData["BAU"]["costSolarGen"] = 0
# E33 = SUM(E20:P20)-SUM(E18:P18)+E10
outData["BAU"]["nonResRev"] = sum([totalRevenue[i][1] for i in range(12)]) - sum([monthlyRevenue[i][1] for i in range(12)]) + float(inputDict.get("otherElecRevenue"))
# E34 = (SUM(E18:P18)-SUM(E16:P16)*E6)/SUM(E17:P17)
outData["BAU"]["effectiveResRate"] = (sum ([monthlyRevenue[i][1] for i in range(12)]) - sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])*float(inputDict.get("customServiceCharge", 0)))/sum([monthlyKWhSold[i][1] for i in range(12)])
# E35 = E34*E28+SUM(E16:P16)*E6
outData["BAU"]["resNonSolarRev"] = outData["BAU"]["effectiveResRate"] * outData["BAU"]["resNonSolarKWhSold"] + sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])*float(inputDict.get("customServiceCharge", 0))
# E36 = E30*E34
outData["BAU"]["solarResRev"] = 0
# E37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71), update after Form 7 model
outData["BAU"]["nonPowerCosts"] = 0
# E38 = E23-E25-E28-E30-E31
outData["BAU"]["energyAllBal"] = 0
# E39 = E36+E33+E35-E47-E72-E37
outData["BAU"]["dollarAllBal"] = 0
# E40 = 0
outData["BAU"]["avgMonthlyBillSolarCus"] = 0
# E41 = E35/SUM(E16:P16)
avgCustomerCount = (sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])/12)
outData["BAU"]["avgMonthlyBillNonSolarCus"] = outData["BAU"]["resNonSolarRev"] / sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])
# E42 = E63/E24, update after Form 7 model
outData["BAU"]["costofService"] = 0
# Solar case
outData["Solar"] = {}
# F27 = SUM(E15:P15)
outData["Solar"]["annualSolarGen"] = sum([outData["totalGeneration"][i][1] for i in range(12)])
# F24 = E24-F27
outData["Solar"]["totalKWhSales"] = sum([totalKWhSold[i][1] for i in range(12)]) - outData["Solar"]["annualSolarGen"]
# F23 =F24/(1-E26)
outData["Solar"]["totalKWhPurchased"] = outData["Solar"]["totalKWhSales"]/ (1-outData["BAU"]["effectiveLossRate"])
outData["totalsolarmonthly"] = [[sorted(months.items(), key=lambda x:x[1])[i][0], outData["totalSolarSold"][i][1] / (1-outData["BAU"]["effectiveLossRate"])] for i in range(12)]
# F25 = F23-F24
outData["Solar"]["losses"] = (outData["Solar"]["totalKWhPurchased"] - outData["Solar"]["totalKWhSales"])
# F26 = E26
outData["Solar"]["effectiveLossRate"] = outData["BAU"]["effectiveLossRate"]
# F28 = (1-E5)*E28
outData["Solar"]["resNonSolarKWhSold"] = (1-float(inputDict.get("resPenetration", 0))/100)*outData["BAU"]["resNonSolarKWhSold"]
# F29 = E5*E28
outData["Solar"]["solarResDemand"] = float(inputDict.get("resPenetration", 0))/100*outData["BAU"]["resNonSolarKWhSold"]
# F30 = F29-F27
outData["Solar"]["solarResSold"] = outData["Solar"]["solarResDemand"] - outData["Solar"]["annualSolarGen"]
# F31 = E31
outData["Solar"]["nonResKWhSold"] = outData["BAU"]["nonResKWhSold"]
# F32 = E9*F27
outData["Solar"]["costSolarGen"] = float(inputDict.get("solarLCoE", 0.07))*outData["Solar"]["annualSolarGen"]
# F33 = E33
outData["Solar"]["nonResRev"] = outData["BAU"]["nonResRev"]
# F34 = E34
outData["Solar"]["effectiveResRate"] = outData["BAU"]["effectiveResRate"]
# F35 = E35*(1-E5)
outData["Solar"]["resNonSolarRev"] = outData["BAU"]["resNonSolarRev"] * (1 - float(inputDict.get("resPenetration", 0.05))/100)
# F30*E34 = Solar revenue from selling at residential rate
solarSoldRateRev = outData["Solar"]["solarResSold"] * outData["Solar"]["effectiveResRate"]
# (E6+E7)*SUM(E16:P16)*E5 = Solar revenue from charges
solarChargesRev = (float(inputDict.get("customServiceCharge", 0))+float(inputDict.get("solarServiceCharge", 0)))*sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])*float(inputDict.get("resPenetration", 0.05))/100
# F36 = F30*E34+(E6+E7)*SUM(E16:P16)*E5 = solarSoldRate + solarChargesRev
outData["Solar"]["solarResRev"] = solarSoldRateRev + solarChargesRev
# F37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71) = E37, update after Form 7 model
outData["Solar"]["nonPowerCosts"] = 0
# F38 = F23-F25-F28-F30-E31
outData["Solar"]["energyAllBal"] = 0
# F39 = F36+E33+F35-F47-F72-E37
outData["Solar"]["dollarAllBal"] = 0
if (float(inputDict.get("resPenetration", 0.05)) > 0):
# F41 = (F35)/(SUM(E16:P16)*(1-E5))
outData["Solar"]["avgMonthlyBillNonSolarCus"] = outData["Solar"]["resNonSolarRev"] / (sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])* (1 - float(inputDict.get("resPenetration", 0.05))/100))
# F42 = F30*E34/(SUM(E16:P16)*E5)+E6+E7
outData["Solar"]["avgMonthlyBillSolarCus"] = outData["Solar"]["solarResSold"] * outData["BAU"]["effectiveResRate"] / (sum([monthlyNoConsumerServedSales[i][1] for i in range(12)]) * float(inputDict.get("resPenetration", 0.05))/100) + float(inputDict.get("customServiceCharge", 0))+float(inputDict.get("solarServiceCharge", 0))
# F43 = (F27/(SUM(E16:P16)*E5))*E9
outData["Solar"]["avgMonthlyBillSolarSolarCus"] = (outData["Solar"]["annualSolarGen"] / (sum([monthlyNoConsumerServedSales[i][1] for i in range(12)]) * float(inputDict.get("resPenetration", 0.05))/100)) * float(inputDict.get("solarLCoE", 0.07))
else:
outData["Solar"]["avgMonthlyBillNonSolarCus"] = 0
outData["Solar"]["avgMonthlyBillSolarCus"] = 0
outData["Solar"]["avgMonthlyBillSolarSolarCus"] = 0
# Net Average Monthly Bill
avgMonthlyBillSolarNet = outData["Solar"]["avgMonthlyBillSolarCus"] + outData["Solar"]["avgMonthlyBillSolarSolarCus"]
outData["Solar"]["avgMonthlyBillSolarCus"] = avgMonthlyBillSolarNet
# F45 = F63/F24, update after Form 7 model
outData["Solar"]["costofService"] = 0
## Form 7 Model
# E46
outData["Solar"]["powerProExpense"] = outData["BAU"]["powerProExpense"] = float(inputDict.get("powerProExpense", 0))
# E47 != F47
outData["BAU"]["costPurchasedPower"] = float(inputDict.get("costPurchasedPower", 0))
# E48
outData["Solar"]["transExpense"] = outData["BAU"]["transExpense"] = float(inputDict.get("transExpense", 0))
# E49
outData["Solar"]["distriExpenseO"] = outData["BAU"]["distriExpenseO"] = float(inputDict.get("distriExpenseO", 0))
# E50
outData["Solar"]["distriExpenseM"] = outData["BAU"]["distriExpenseM"] = float(inputDict.get("distriExpenseM", 0))
# E51
outData["Solar"]["customerAccountExpense"] = outData["BAU"]["customerAccountExpense"] = float(inputDict.get("customerAccountExpense", 0))
# E52
outData["Solar"]["customerServiceExpense"] = outData["BAU"]["customerServiceExpense"] = float(inputDict.get("customerServiceExpense", 0))
# E53
outData["Solar"]["salesExpense"] = outData["BAU"]["salesExpense"] = float(inputDict.get("salesExpense", 0))
# E54
outData["Solar"]["adminGeneralExpense"] = outData["BAU"]["adminGeneralExpense"] = float(inputDict.get("adminGeneralExpense", 0))
# E56
outData["Solar"]["depreAmortiExpense"] = outData["BAU"]["depreAmortiExpense"] = float(inputDict.get("depreAmortiExpense", 0))
# E57
outData["Solar"]["taxExpensePG"] = outData["BAU"]["taxExpensePG"] = float(inputDict.get("taxExpensePG", 0))
# E58
outData["Solar"]["taxExpense"] = outData["BAU"]["taxExpense"] = float(inputDict.get("taxExpense", 0))
# E59
outData["Solar"]["interestLongTerm"] = outData["BAU"]["interestLongTerm"] = float(inputDict.get("interestLongTerm", 0))
# E60
outData["Solar"]["interestConstruction"] = outData["BAU"]["interestConstruction"] = float(inputDict.get("interestConstruction", 0))
# E61
outData["Solar"]["interestExpense"] = outData["BAU"]["interestExpense"] = float(inputDict.get("interestExpense", 0))
# E62
outData["Solar"]["otherDeductions"] = outData["BAU"]["otherDeductions"] = float(inputDict.get("otherDeductions", 0))
# E65
outData["Solar"]["nonOpMarginInterest"] = outData["BAU"]["nonOpMarginInterest"] = float(inputDict.get("nonOpMarginInterest", 0))
# E66
outData["Solar"]["fundsUsedConstruc"] = outData["BAU"]["fundsUsedConstruc"] = float(inputDict.get("fundsUsedConstruc", 0))
# E67
outData["Solar"]["incomeEquityInvest"] = outData["BAU"]["incomeEquityInvest"] = float(inputDict.get("incomeEquityInvest", 0))
# E68
outData["Solar"]["nonOpMarginOther"] = outData["BAU"]["nonOpMarginOther"] = float(inputDict.get("nonOpMarginOther", 0))
# E69
outData["Solar"]["genTransCapCredits"] = outData["BAU"]["genTransCapCredits"] = float(inputDict.get("genTransCapCredits", 0))
# E70
outData["Solar"]["otherCapCreditsPatroDivident"] = outData["BAU"]["otherCapCreditsPatroDivident"] = float(inputDict.get("otherCapCreditsPatroDivident", 0))
# E71
outData["Solar"]["extraItems"] = outData["BAU"]["extraItems"] = float(inputDict.get("extraItems", 0))
# Calculation
# E45 = SUM(E20:P20)+E10
outData["BAU"]["operRevPatroCap"] = sum([totalRevenue[i][1] for i in range(12)])+float(inputDict.get("otherElecRevenue", 0))
# E55 = SUM(E46:E54)
outData["BAU"]["totalOMExpense"] = float(inputDict.get("powerProExpense")) \
+ float(inputDict.get("costPurchasedPower")) \
+ float(inputDict.get("transExpense")) \
+ float(inputDict.get("distriExpenseO")) \
+ float(inputDict.get("distriExpenseM")) \
+ float(inputDict.get("customerAccountExpense")) \
+ float(inputDict.get("customerServiceExpense")) \
+ float(inputDict.get("salesExpense")) \
+ float(inputDict.get("adminGeneralExpense"))
# E63 = SUM(E55:E62)
outData["BAU"]["totalCostElecService"] = outData["BAU"]["totalOMExpense"] \
+ float(inputDict.get("depreAmortiExpense"))\
+ float(inputDict.get("taxExpensePG"))\
+ float(inputDict.get("taxExpense"))\
+ float(inputDict.get("interestLongTerm"))\
+ float(inputDict.get("interestExpense"))\
+ float(inputDict.get("interestConstruction"))\
+ outData["BAU"]["otherDeductions"]
# E64 = E45-E63
outData["BAU"]["patCapOperMargins"] = outData["BAU"]["operRevPatroCap"] - outData["BAU"]["totalCostElecService"]
# E72 = SUM(E64:E71)
outData["BAU"]["patCapital"] = outData["BAU"]["patCapOperMargins"]\
+ float(inputDict.get("nonOpMarginInterest"))\
+ float(inputDict.get("fundsUsedConstruc"))\
+ float(inputDict.get("incomeEquityInvest"))\
+ float(inputDict.get("nonOpMarginOther"))\
+ float(inputDict.get("genTransCapCredits"))\
+ float(inputDict.get("otherCapCreditsPatroDivident"))\
+ float(inputDict.get("extraItems"))
# F48 = E48-F27*E34+SUM(E16:P16)*E5*E7
outData["Solar"]["operRevPatroCap"] = outData["BAU"]["operRevPatroCap"] - outData["BAU"]["effectiveResRate"]*outData["Solar"]["annualSolarGen"] + sum([monthlyNoConsumerServedSales[i][1] for i in range(12)])*float(inputDict.get("resPenetration", 0.05))/100*float(inputDict.get("solarServiceCharge", 0))
# F47 = (F23)*E8
inputDict["costofPower"] = float(inputDict.get("costPurchasedPower", 0)) / float(inputDict.get("totalKWhPurchased", 0))
outData["Solar"]["costPurchasedPower"] = outData["Solar"]["totalKWhPurchased"] * float(inputDict.get("costofPower", 0))
inputDict["costofPower"] = round(inputDict["costofPower"],3)
# F55 = SUM(F46:F54)
outData["Solar"]["totalOMExpense"] = outData["Solar"]["powerProExpense"]\
+ outData["Solar"]["costPurchasedPower"]\
+ outData["Solar"]["transExpense"]\
+ outData["Solar"]["distriExpenseO"]\
+ outData["Solar"]["distriExpenseM"]\
+ outData["Solar"]["customerAccountExpense"]\
+ outData["Solar"]["customerServiceExpense"]\
+ outData["Solar"]["salesExpense"]\
+ outData["Solar"]["adminGeneralExpense"]
# F63 = E63
outData["Solar"]["totalCostElecService"] = outData["Solar"]["totalOMExpense"]\
+ outData["Solar"]["depreAmortiExpense"]\
+ outData["Solar"]["taxExpensePG"]\
+ outData["Solar"]["taxExpense"]\
+ outData["Solar"]["interestLongTerm"]\
+ outData["Solar"]["interestConstruction"]\
+ outData["Solar"]["interestExpense"]\
+ outData["Solar"]["otherDeductions"]
# F64 = F45 - F63
outData["Solar"]["patCapOperMargins"] = outData["Solar"]["operRevPatroCap"] - outData["Solar"]["totalCostElecService"]
# F72 = SUM(F64:F71)
outData["Solar"]["patCapital"] = outData["Solar"]["patCapOperMargins"]\
+ outData["Solar"]["nonOpMarginInterest"]\
+ outData["Solar"]["fundsUsedConstruc"]\
+ outData["Solar"]["incomeEquityInvest"]\
+ outData["Solar"]["nonOpMarginOther"]\
+ outData["Solar"]["genTransCapCredits"]\
+ outData["Solar"]["otherCapCreditsPatroDivident"]\
+ outData["Solar"]["extraItems"]
# E37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71), update after Form 7 model
outData["BAU"]["nonPowerCosts"] = outData["BAU"]["transExpense"] \
+ outData["BAU"]["distriExpenseO"] \
+ outData["BAU"]["distriExpenseM"] \
+ outData["BAU"]["customerAccountExpense"] \
+ outData["BAU"]["customerServiceExpense"] \
+ outData["BAU"]["salesExpense"] \
+ outData["BAU"]["adminGeneralExpense"] \
+ outData["BAU"]["depreAmortiExpense"] \
+ outData["BAU"]["taxExpensePG"] \
+ outData["BAU"]["taxExpense"] \
+ outData["BAU"]["interestLongTerm"] \
+ outData["BAU"]["interestConstruction"] \
+ outData["BAU"]["interestExpense"] \
+ outData["BAU"]["otherDeductions"] \
- (outData["BAU"]["nonOpMarginInterest"] \
+ outData["BAU"]["fundsUsedConstruc"] \
+ outData["BAU"]["incomeEquityInvest"] \
+ outData["BAU"]["nonOpMarginOther"] \
+ outData["BAU"]["genTransCapCredits"] \
+ outData["BAU"]["otherCapCreditsPatroDivident"] \
+ outData["BAU"]["extraItems"])
# E42 = E63/E24, update after Form 7 model
outData["BAU"]["costofService"] = outData["BAU"]["totalCostElecService"] / outData["BAU"]["totalKWhSales"]
# F37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71) = E37, update after Form 7 model
outData["Solar"]["nonPowerCosts"] = outData["BAU"]["nonPowerCosts"]
# F42 = F63/F24, update after Form 7 model
outData["Solar"]["costofService"] = outData["Solar"]["totalCostElecService"] / outData["Solar"]["totalKWhSales"]
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
# Write the output.
with open(pJoin(modelDir,"allOutputData.json"),"w") as outFile:
json.dump(outData, outFile, indent=4)
# Update the runTime in the input file.
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)
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)
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 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 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 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 work(modelDir, inputDict):
# Copy specific climate data into model directory
inputDict["climateName"] = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", float(inputDict["systemSize"]))
ssc.ssc_data_set_number(dat, "derate", 0.01 * float(inputDict["nonInverterEfficiency"]))
ssc.ssc_data_set_number(dat, "track_mode", float(inputDict["trackingMode"]))
ssc.ssc_data_set_number(dat, "azimuth", float(inputDict["azimuth"]))
# Advanced inputs with defaults.
if (inputDict.get("tilt",0) == "-"):
tilt_eq_lat = 1.0
manualTilt = 0.0
else:
tilt_eq_lat = 0.0
manualTilt = float(inputDict.get("tilt",0))
ssc.ssc_data_set_number(dat, "tilt_eq_lat", tilt_eq_lat)
ssc.ssc_data_set_number(dat, "tilt", manualTilt)
ssc.ssc_data_set_number(dat, "rotlim", float(inputDict["rotlim"]))
ssc.ssc_data_set_number(dat, "gamma", -1 * float(inputDict["gamma"]))
ssc.ssc_data_set_number(dat, "inv_eff", 0.01 * float(inputDict["inverterEfficiency"]))
ssc.ssc_data_set_number(dat, "w_stow", float(inputDict["w_stow"]))
# Complicated optional inputs that we could enable later.
# ssc.ssc_data_set_array(dat, 'shading_hourly', ...) # Hourly beam shading factors
# ssc.ssc_data_set_matrix(dat, 'shading_mxh', ...) # Month x Hour beam shading factors
# ssc.ssc_data_set_matrix(dat, 'shading_azal', ...) # Azimuth x altitude beam shading factors
# ssc.ssc_data_set_number(dat, 'shading_diff', ...) # Diffuse shading factor
# ssc.ssc_data_set_number(dat, 'enable_user_poa', ...) # Enable user-defined POA irradiance input = 0 or 1
# ssc.ssc_data_set_array(dat, 'user_poa', ...) # User-defined POA irradiance in W/m2
# ssc.ssc_data_set_number(dat, 'tilt', 999)
# ssc.ssc_data_set_number(dat, "t_noct", float(inputDict["t_noct"]))
# ssc.ssc_data_set_number(dat, "t_ref", float(inputDict["t_ref"]))
# ssc.ssc_data_set_number(dat, "fd", float(inputDict["fd"]))
# ssc.ssc_data_set_number(dat, "i_ref", float(inputDict["i_ref"]))
# ssc.ssc_data_set_number(dat, "poa_cutin", float(inputDict["poa_cutin"]))
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Setting options for start time.
simLengthUnits = inputDict.get("simLengthUnits","")
simStartDate = inputDict["simStartDate"]
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = simStartDate + " 00:00:00 UTC"
# Set aggregation function constants.
agg = lambda x,y:_aggData(x,y,inputDict["simStartDate"],
int(inputDict["simLength"]), inputDict["simLengthUnits"], ssc, dat)
avg = lambda x:sum(x)/len(x)
# Timestamp output.
outData = {}
outData["timeStamps"] = [datetime.datetime.strftime(
datetime.datetime.strptime(startDateTime[0:19],"%Y-%m-%d %H:%M:%S") +
datetime.timedelta(**{simLengthUnits:x}),"%Y-%m-%d %H:%M:%S") + " UTC" for x in range(int(inputDict["simLength"]))]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = agg("poa", avg)
outData["climate"]["Beam Normal Irradiance (W/m^2)"] = agg("dn", avg)
outData["climate"]["Diffuse Irradiance (W/m^2)"] = agg("df", avg)
outData["climate"]["Ambient Temperature (F)"] = agg("tamb", avg)
outData["climate"]["Cell Temperature (F)"] = agg("tcell", avg)
outData["climate"]["Wind Speed (m/s)"] = agg("wspd", avg)
# Power generation.
outData["Consumption"] = {}
outData["Consumption"]["Power"] = [x for x in agg("ac", avg)]
outData["Consumption"]["Losses"] = [0 for x in agg("ac", avg)]
outData["Consumption"]["DG"] = agg("ac", avg)
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
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
def work(modelDir, inputDict):
# Copy specific climate data into model directory
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(
inputDict["zipCode"])
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", float(inputDict["systemSize"]))
ssc.ssc_data_set_number(dat, "derate",
0.01 * float(inputDict["nonInverterEfficiency"]))
ssc.ssc_data_set_number(dat, "track_mode",
float(inputDict["trackingMode"]))
ssc.ssc_data_set_number(dat, "azimuth", float(inputDict["azimuth"]))
# Advanced inputs with defaults.
if (inputDict.get("tilt", 0) == "-"):
tilt_eq_lat = 1.0
manualTilt = 0.0
else:
tilt_eq_lat = 0.0
manualTilt = float(inputDict.get("tilt", 0))
ssc.ssc_data_set_number(dat, "tilt_eq_lat", tilt_eq_lat)
ssc.ssc_data_set_number(dat, "tilt", manualTilt)
ssc.ssc_data_set_number(dat, "rotlim", float(inputDict["rotlim"]))
ssc.ssc_data_set_number(dat, "gamma", -1 * float(inputDict["gamma"]))
ssc.ssc_data_set_number(dat, "inv_eff",
0.01 * float(inputDict["inverterEfficiency"]))
ssc.ssc_data_set_number(dat, "w_stow", float(inputDict["w_stow"]))
# Complicated optional inputs that we could enable later.
# ssc.ssc_data_set_array(dat, 'shading_hourly', ...) # Hourly beam shading factors
# ssc.ssc_data_set_matrix(dat, 'shading_mxh', ...) # Month x Hour beam shading factors
# ssc.ssc_data_set_matrix(dat, 'shading_azal', ...) # Azimuth x altitude beam shading factors
# ssc.ssc_data_set_number(dat, 'shading_diff', ...) # Diffuse shading factor
# ssc.ssc_data_set_number(dat, 'enable_user_poa', ...) # Enable user-defined POA irradiance input = 0 or 1
# ssc.ssc_data_set_array(dat, 'user_poa', ...) # User-defined POA irradiance in W/m2
# ssc.ssc_data_set_number(dat, 'tilt', 999)
# ssc.ssc_data_set_number(dat, "t_noct", float(inputDict["t_noct"]))
# ssc.ssc_data_set_number(dat, "t_ref", float(inputDict["t_ref"]))
# ssc.ssc_data_set_number(dat, "fd", float(inputDict["fd"]))
# ssc.ssc_data_set_number(dat, "i_ref", float(inputDict["i_ref"]))
# ssc.ssc_data_set_number(dat, "poa_cutin", float(inputDict["poa_cutin"]))
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Setting options for start time.
simLengthUnits = inputDict.get("simLengthUnits", "")
simStartDate = inputDict["simStartDate"]
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = simStartDate + " 00:00:00 UTC"
# Set aggregation function constants.
agg = lambda x, y: _aggData(x, y, inputDict["simStartDate"],
int(inputDict["simLength"]), inputDict[
"simLengthUnits"], ssc, dat)
avg = lambda x: sum(x) / len(x)
# Timestamp output.
outData = {}
outData["timeStamps"] = [
datetime.datetime.strftime(
datetime.datetime.strptime(startDateTime[0:19],
"%Y-%m-%d %H:%M:%S") +
datetime.timedelta(**{simLengthUnits: x}), "%Y-%m-%d %H:%M:%S") +
" UTC" for x in range(int(inputDict["simLength"]))
]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = agg("poa", avg)
outData["climate"]["Beam Normal Irradiance (W/m^2)"] = agg("dn", avg)
outData["climate"]["Diffuse Irradiance (W/m^2)"] = agg("df", avg)
outData["climate"]["Ambient Temperature (F)"] = agg("tamb", avg)
outData["climate"]["Cell Temperature (F)"] = agg("tcell", avg)
outData["climate"]["Wind Speed (m/s)"] = agg("wspd", avg)
# Power generation.
outData["Consumption"] = {}
outData["Consumption"]["Power"] = [x for x in agg("ac", avg)]
outData["Consumption"]["Losses"] = [0 for x in agg("ac", avg)]
outData["Consumption"]["DG"] = agg("ac", avg)
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
def work(modelDir, inputDict):
''' Run the model in its directory. '''
inputDict["climateName"] = weather.zipCodeToClimateName(
inputDict["zipCode"])
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, b'file_name',
bytes(modelDir + '/climate.tmy2', 'ascii'))
systemSize = max(float(inputDict.get('systemSize', 0)), .1)
ssc.ssc_data_set_number(dat, b'system_size', systemSize)
# SAM options where we take defaults.
ssc.ssc_data_set_number(dat, b'derate', 0.97)
ssc.ssc_data_set_number(dat, b'track_mode', 0)
ssc.ssc_data_set_number(dat, b'azimuth', 180)
ssc.ssc_data_set_number(dat, b'tilt_eq_lat', 1)
# Run PV system simulation.
mod = ssc.ssc_module_create(b'pvwattsv1')
ssc.ssc_module_exec(mod, dat)
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = "2013-01-01 00:00:00 UTC"
# Timestamp output.
outData = {}
outData["timeStamps"] = [
datetime.datetime.strftime(
datetime.datetime.strptime(startDateTime[0:19],
"%Y-%m-%d %H:%M:%S") +
datetime.timedelta(**{"hours": x}), "%Y-%m-%d %H:%M:%S") + " UTC"
for x in range(int(8760))
]
# Geodata output.
outData['city'] = ssc.ssc_data_get_string(dat, b'city').decode()
outData['state'] = ssc.ssc_data_get_string(dat, b'state').decode()
outData['lat'] = ssc.ssc_data_get_number(dat, b'lat')
outData['lon'] = ssc.ssc_data_get_number(dat, b'lon')
outData['elev'] = ssc.ssc_data_get_number(dat, b'elev')
# Weather output.
outData["climate"] = {}
outData['climate'][
'Global Horizontal Radiation (W/m^2)'] = ssc.ssc_data_get_array(
dat, b'gh')
outData['climate'][
'Plane of Array Irradiance (W/m^2)'] = ssc.ssc_data_get_array(
dat, b'poa')
outData['climate']['Ambient Temperature (F)'] = ssc.ssc_data_get_array(
dat, b'tamb')
outData['climate']['Cell Temperature (F)'] = ssc.ssc_data_get_array(
dat, b'tcell')
outData['climate']['Wind Speed (m/s)'] = ssc.ssc_data_get_array(
dat, b'wspd')
# Power generation.
outData['powerOutputAc'] = ssc.ssc_data_get_array(dat, b'ac')
solarFraction = float(inputDict.get("resPenetration", .05)) / 100
fossilFraction = max(1 - solarFraction, 10**-6)
# Monthly aggregation outputs.
months = {
"Jan": 0,
"Feb": 1,
"Mar": 2,
"Apr": 3,
"May": 4,
"Jun": 5,
"Jul": 6,
"Aug": 7,
"Sep": 8,
"Oct": 9,
"Nov": 10,
"Dec": 11
}
totMonNum = lambda x: sum([
z for (y, z) in zip(outData["timeStamps"], outData["powerOutputAc"])
if y.startswith(startDateTime[0:4] + "-{0:02d}".format(x + 1))
])
outData["monthlyGeneration"] = [[
a, __neoMetaModel__.roundSig(totMonNum(b), 2)
] for (a, b) in sorted(months.items(), key=lambda x: x[1])]
monthlyConsumers = []
monthlyResidentialkWhLoad = []
monthlyResidentialRevenue = []
monthlyTotalkWhLoad = []
monthlyTotalRevenue = []
for key in inputDict:
# MAYBEFIX: data in list may not be ordered by month.
if key.endswith("Sale"):
monthlyConsumers.append(
[key[:3].title(),
float(inputDict.get(key, 0))])
elif key.endswith("KWh"): # the order of calculation matters
monthlyResidentialkWhLoad.append(
[key[:3].title(),
float(inputDict.get(key, 0))])
elif key.endswith("Rev"):
monthlyResidentialRevenue.append(
[key[:3].title(),
float(inputDict.get(key, 0))])
elif key.endswith("KWhT"):
monthlyTotalkWhLoad.append(
[key[:3].title(),
float(inputDict.get(key, 0))])
elif key.endswith("RevT"):
monthlyTotalRevenue.append(
[key[:3].title(),
float(inputDict.get(key, 0))])
outData["monthlyConsumers"] = sorted(monthlyConsumers,
key=lambda x: months[x[0]])
outData["monthlyResidentialkWhLoad"] = sorted(monthlyResidentialkWhLoad,
key=lambda x: months[x[0]])
outData["monthlyResidentialRevenue"] = sorted(monthlyResidentialRevenue,
key=lambda x: months[x[0]])
outData["monthlyTotalkWhLoad"] = sorted(monthlyTotalkWhLoad,
key=lambda x: months[x[0]])
outData["monthlyTotalRevenue"] = sorted(monthlyTotalRevenue,
key=lambda x: months[x[0]])
outData["monthlySolarkWhGenerated"] = [[
sorted(months.items(),
key=lambda x: x[1])[i][0], outData["monthlyGeneration"][i][1] /
1000 * outData["monthlyConsumers"][i][1] * solarFraction
] for i in range(12)]
outData["monthlyTotalNetLoadAfterSolar"] = [[
sorted(months.items(), key=lambda x: x[1])[i][0],
outData["monthlyTotalkWhLoad"][i][1] -
outData["monthlySolarkWhGenerated"][i][1]
] for i in range(12)]
## Flow Diagram Calculations, and order of calculation matters
retailCost = float(inputDict.get("retailCost"))
solarLCoE = float(inputDict.get('solarLCoE'))
customerServiceCharge = float(inputDict.get("customServiceCharge", 0))
solarServiceCharge = float(inputDict.get("solarServiceCharge", 0))
totalkWhLoad = sum(monthlyTotalkWhLoad[i][1] for i in range(12))
# BAU case
outData["BAU"] = {}
# E23 = E11
outData["BAU"]["totalKWhPurchased"] = float(
inputDict.get("totalKWhPurchased", 1))
# E24 = SUM(E19:P19)
outData["BAU"]["totalKWhSales"] = totalkWhLoad
# E25 = E23-E24
outData["BAU"]["losses"] = float(inputDict.get("totalKWhPurchased",
0)) - totalkWhLoad
# E26 = E25/E23
outData["BAU"]["effectiveLossRate"] = outData["BAU"]["losses"] / outData[
"BAU"]["totalKWhPurchased"]
# E27 = 0
outData["BAU"]["annualSolarGen"] = 0
# E28 = SUM(E17:P17)
outData["BAU"]["resNonSolarKWhSold"] = sum(
[monthlyResidentialkWhLoad[i][1] for i in range(12)])
# E29 = 0
outData["BAU"]["solarResDemand"] = 0
# E30 = 0
outData["BAU"]["solarResSold"] = 0
# E31 = E24-E28
outData["BAU"]["nonResKWhSold"] = outData["BAU"][
"totalKWhSales"] - outData["BAU"]["resNonSolarKWhSold"]
# E32 = 0
outData["BAU"]["costSolarGen"] = 0
# E33 = SUM(E20:P20)-SUM(E18:P18)+E10
outData["BAU"]["nonResRev"] = sum([
monthlyTotalRevenue[i][1] for i in range(12)
]) - sum([monthlyResidentialRevenue[i][1]
for i in range(12)]) + float(inputDict.get("otherElecRevenue"))
# E34 = (SUM(E18:P18)-SUM(E16:P16)*E6)/SUM(E17:P17):Buggy and complicated line calculating effectiveResRate replaced with retailCost. Was somehow messing up solar fixed charges.
# outData["BAU"]["effectiveResRate"] = (sum ([monthlyResidentialRevenue[i][1] for i in range(12)]) - sum([monthlyConsumers[i][1] for i in range(12)])*customerServiceCharge)/sum([monthlyResidentialkWhLoad[i][1] for i in range(12)])
customerMonths = sum([monthlyConsumers[i][1] for i in range(12)])
# E35 = E34*E28+SUM(E16:P16)*E6
outData["BAU"]["resNonSolarRev"] = retailCost * outData["BAU"][
"resNonSolarKWhSold"] + customerMonths * customerServiceCharge
# E36 = E30*E34
outData["BAU"]["solarResRev"] = 0
# E37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71), update after Form 7 model
outData["BAU"]["nonPowerCosts"] = 0
# E38 = E23-E25-E28-E30-E31
outData["BAU"]["energyAllBal"] = 0
# E39 = E36+E33+E35-E47-E72-E37
outData["BAU"]["dollarAllBal"] = 0
# E40 = 0
outData["BAU"]["avgMonthlyBillSolarCus"] = 0
# E41 = E35/SUM(E16:P16)
avgCustomerCount = (customerMonths / 12)
outData["BAU"]["avgMonthlyBillNonSolarCus"] = outData["BAU"][
"resNonSolarRev"] / customerMonths
# E42 = E63/E24, update after Form 7 model
outData["BAU"]["costofService"] = 0
# Solar case
outData["Solar"] = {}
# F27 = SUM(E15:P15)
outData["Solar"]["annualSolarGen"] = sum(
[outData["monthlySolarkWhGenerated"][i][1] for i in range(12)])
# F24 = E24-F27
outData["Solar"][
"totalKWhSales"] = totalkWhLoad - outData["Solar"]["annualSolarGen"]
# F23 =F24/(1-E26)
outData["Solar"]["totalKWhPurchased"] = outData["Solar"][
"totalKWhSales"] / (1 - outData["BAU"]["effectiveLossRate"])
outData["totalsolarmonthly"] = [[
sorted(months.items(), key=lambda x: x[1])[i][0],
outData["monthlyTotalNetLoadAfterSolar"][i][1] /
(1 - outData["BAU"]["effectiveLossRate"])
] for i in range(12)]
# F25 = F23-F24
outData["Solar"]["losses"] = (outData["Solar"]["totalKWhPurchased"] -
outData["Solar"]["totalKWhSales"])
# F26 = E26
outData["Solar"]["effectiveLossRate"] = outData["BAU"]["effectiveLossRate"]
# F28 = (1-E5)*E28
outData["Solar"]["resNonSolarKWhSold"] = fossilFraction * outData["BAU"][
"resNonSolarKWhSold"]
# F29 = E5*E28
outData["Solar"]["solarResDemand"] = solarFraction * outData["BAU"][
"resNonSolarKWhSold"]
# F30 = F29-F27
outData["Solar"]["solarResSold"] = outData["Solar"][
"solarResDemand"] - outData["Solar"]["annualSolarGen"]
# F31 = E31
outData["Solar"]["nonResKWhSold"] = outData["BAU"]["nonResKWhSold"]
# F32 = E9*F27
outData["Solar"][
"costSolarGen"] = solarLCoE * outData["Solar"]["annualSolarGen"]
# F33 = E33
outData["Solar"]["nonResRev"] = outData["BAU"]["nonResRev"]
# F34 = E34
outData["Solar"]["effectiveResRate"] = retailCost
# F35 = E35*(1-E5)
outData["Solar"][
"resNonSolarRev"] = outData["BAU"]["resNonSolarRev"] * fossilFraction
# F30*E34 = Solar revenue from selling at residential rate
solarSoldRateRev = outData["Solar"]["solarResSold"] * outData["Solar"][
"effectiveResRate"]
# (E6+E7)*SUM(E16:P16)*E5 = Solar revenue from charges
solarChargesRev = (customerServiceCharge +
solarServiceCharge) * customerMonths * solarFraction
# F36 = F30*E34+(E6+E7)*SUM(E16:P16)*E5 = solarSoldRate + solarChargesRev
outData["Solar"]["solarResRev"] = solarSoldRateRev + solarChargesRev
# F37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71) = E37, update after Form 7 model
outData["Solar"]["nonPowerCosts"] = 0
# F38 = F23-F25-F28-F30-E31
outData["Solar"]["energyAllBal"] = 0
# F39 = F36+E33+F35-F47-F72-E37
outData["Solar"]["dollarAllBal"] = 0
if (solarFraction > 0):
# F41 = (F35)/(SUM(E16:P16)*(1-E5))
outData["Solar"]["avgMonthlyBillNonSolarCus"] = outData["Solar"][
"resNonSolarRev"] / (customerMonths * fossilFraction)
# F42 = F30*E34/(SUM(E16:P16)*E5)+E6+E7
outData["Solar"]["avgMonthlyBillSolarCus"] = outData["Solar"][
"solarResSold"] * retailCost / (
customerMonths *
solarFraction) + customerServiceCharge + solarServiceCharge
# F43 = (F27/(SUM(E16:P16)*E5))*E9
outData["Solar"]["avgMonthlyBillSolarSolarCus"] = (
outData["Solar"]["annualSolarGen"] /
(customerMonths * solarFraction)) * solarLCoE
else:
outData["Solar"]["avgMonthlyBillNonSolarCus"] = 0
outData["Solar"]["avgMonthlyBillSolarCus"] = 0
outData["Solar"]["avgMonthlyBillSolarSolarCus"] = 0
# Net Average Monthly Bill
avgMonthlyBillSolarNet = outData["Solar"][
"avgMonthlyBillSolarCus"] + outData["Solar"][
"avgMonthlyBillSolarSolarCus"]
outData["Solar"]["avgMonthlyBillSolarCus"] = avgMonthlyBillSolarNet
# F45 = F63/F24, update after Form 7 model
outData["Solar"]["costofService"] = 0
## Form 7 Model
# E46
outData["Solar"]["powerProExpense"] = outData["BAU"][
"powerProExpense"] = float(inputDict.get("powerProExpense", 0))
# E47 != F47
outData["BAU"]["costPurchasedPower"] = float(
inputDict.get("costPurchasedPower", 0))
# E48
outData["Solar"]["transExpense"] = outData["BAU"]["transExpense"] = float(
inputDict.get("transExpense", 0))
# E49
outData["Solar"]["distriExpenseO"] = outData["BAU"][
"distriExpenseO"] = float(inputDict.get("distriExpenseO", 0))
# E50
outData["Solar"]["distriExpenseM"] = outData["BAU"][
"distriExpenseM"] = float(inputDict.get("distriExpenseM", 0))
# E51
outData["Solar"]["customerAccountExpense"] = outData["BAU"][
"customerAccountExpense"] = float(
inputDict.get("customerAccountExpense", 0))
# E52
outData["Solar"]["customerServiceExpense"] = outData["BAU"][
"customerServiceExpense"] = float(
inputDict.get("customerServiceExpense", 0))
# E53
outData["Solar"]["salesExpense"] = outData["BAU"]["salesExpense"] = float(
inputDict.get("salesExpense", 0))
# E54
outData["Solar"]["adminGeneralExpense"] = outData["BAU"][
"adminGeneralExpense"] = float(inputDict.get("adminGeneralExpense", 0))
# E56
outData["Solar"]["depreAmortiExpense"] = outData["BAU"][
"depreAmortiExpense"] = float(inputDict.get("depreAmortiExpense", 0))
# E57
outData["Solar"]["taxExpensePG"] = outData["BAU"]["taxExpensePG"] = float(
inputDict.get("taxExpensePG", 0))
# E58
outData["Solar"]["taxExpense"] = outData["BAU"]["taxExpense"] = float(
inputDict.get("taxExpense", 0))
# E59
outData["Solar"]["interestLongTerm"] = outData["BAU"][
"interestLongTerm"] = float(inputDict.get("interestLongTerm", 0))
# E60
outData["Solar"]["interestConstruction"] = outData["BAU"][
"interestConstruction"] = float(
inputDict.get("interestConstruction", 0))
# E61
outData["Solar"]["interestExpense"] = outData["BAU"][
"interestExpense"] = float(inputDict.get("interestExpense", 0))
# E62
outData["Solar"]["otherDeductions"] = outData["BAU"][
"otherDeductions"] = float(inputDict.get("otherDeductions", 0))
# E65
outData["Solar"]["nonOpMarginInterest"] = outData["BAU"][
"nonOpMarginInterest"] = float(inputDict.get("nonOpMarginInterest", 0))
# E66
outData["Solar"]["fundsUsedConstruc"] = outData["BAU"][
"fundsUsedConstruc"] = float(inputDict.get("fundsUsedConstruc", 0))
# E67
outData["Solar"]["incomeEquityInvest"] = outData["BAU"][
"incomeEquityInvest"] = float(inputDict.get("incomeEquityInvest", 0))
# E68
outData["Solar"]["nonOpMarginOther"] = outData["BAU"][
"nonOpMarginOther"] = float(inputDict.get("nonOpMarginOther", 0))
# E69
outData["Solar"]["genTransCapCredits"] = outData["BAU"][
"genTransCapCredits"] = float(inputDict.get("genTransCapCredits", 0))
# E70
outData["Solar"]["otherCapCreditsPatroDivident"] = outData["BAU"][
"otherCapCreditsPatroDivident"] = float(
inputDict.get("otherCapCreditsPatroDivident", 0))
# E71
outData["Solar"]["extraItems"] = outData["BAU"]["extraItems"] = float(
inputDict.get("extraItems", 0))
# Calculation
# E45 = SUM(E20:P20)+E10
outData["BAU"]["operRevPatroCap"] = sum(
[monthlyTotalRevenue[i][1]
for i in range(12)]) + float(inputDict.get("otherElecRevenue", 0))
# E55 = SUM(E46:E54)
outData["BAU"]["totalOMExpense"] = float(inputDict.get("powerProExpense")) \
+ float(inputDict.get("costPurchasedPower")) \
+ float(inputDict.get("transExpense")) \
+ float(inputDict.get("distriExpenseO")) \
+ float(inputDict.get("distriExpenseM")) \
+ float(inputDict.get("customerAccountExpense")) \
+ float(inputDict.get("customerServiceExpense")) \
+ float(inputDict.get("salesExpense")) \
+ float(inputDict.get("adminGeneralExpense"))
# E63 = SUM(E55:E62)
outData["BAU"]["totalCostElecService"] = outData["BAU"]["totalOMExpense"] \
+ float(inputDict.get("depreAmortiExpense"))\
+ float(inputDict.get("taxExpensePG"))\
+ float(inputDict.get("taxExpense"))\
+ float(inputDict.get("interestLongTerm"))\
+ float(inputDict.get("interestExpense"))\
+ float(inputDict.get("interestConstruction"))\
+ outData["BAU"]["otherDeductions"]
# E64 = E45-E63
outData["BAU"]["patCapOperMargins"] = outData["BAU"][
"operRevPatroCap"] - outData["BAU"]["totalCostElecService"]
# E72 = SUM(E64:E71)
outData["BAU"]["patCapital"] = outData["BAU"]["patCapOperMargins"]\
+ float(inputDict.get("nonOpMarginInterest"))\
+ float(inputDict.get("fundsUsedConstruc"))\
+ float(inputDict.get("incomeEquityInvest"))\
+ float(inputDict.get("nonOpMarginOther"))\
+ float(inputDict.get("genTransCapCredits"))\
+ float(inputDict.get("otherCapCreditsPatroDivident"))\
+ float(inputDict.get("extraItems"))
# F48 = E48-F27*E34+SUM(E16:P16)*E5*E7
outData["Solar"]["operRevPatroCap"] = outData["BAU"][
"operRevPatroCap"] - retailCost * outData["Solar"][
"annualSolarGen"] + customerMonths * solarFraction * solarServiceCharge
# F47 = (F23)*E8
inputDict["costofPower"] = float(inputDict.get(
"costPurchasedPower", 0)) / float(inputDict.get(
"totalKWhPurchased", 0))
outData["Solar"][
"costPurchasedPower"] = outData["Solar"]["totalKWhPurchased"] * float(
inputDict.get("costofPower", 0))
inputDict["costofPower"] = round(inputDict["costofPower"], 3)
# F55 = SUM(F46:F54)
outData["Solar"]["totalOMExpense"] = outData["Solar"]["powerProExpense"]\
+ outData["Solar"]["costPurchasedPower"]\
+ outData["Solar"]["transExpense"]\
+ outData["Solar"]["distriExpenseO"]\
+ outData["Solar"]["distriExpenseM"]\
+ outData["Solar"]["customerAccountExpense"]\
+ outData["Solar"]["customerServiceExpense"]\
+ outData["Solar"]["salesExpense"]\
+ outData["Solar"]["adminGeneralExpense"]
# F63 = E63
outData["Solar"]["totalCostElecService"] = outData["Solar"]["totalOMExpense"]\
+ outData["Solar"]["depreAmortiExpense"]\
+ outData["Solar"]["taxExpensePG"]\
+ outData["Solar"]["taxExpense"]\
+ outData["Solar"]["interestLongTerm"]\
+ outData["Solar"]["interestConstruction"]\
+ outData["Solar"]["interestExpense"]\
+ outData["Solar"]["otherDeductions"]
# F64 = F45 - F63
outData["Solar"]["patCapOperMargins"] = outData["Solar"][
"operRevPatroCap"] - outData["Solar"]["totalCostElecService"]
# F72 = SUM(F64:F71)
outData["Solar"]["patCapital"] = outData["Solar"]["patCapOperMargins"]\
+ outData["Solar"]["nonOpMarginInterest"]\
+ outData["Solar"]["fundsUsedConstruc"]\
+ outData["Solar"]["incomeEquityInvest"]\
+ outData["Solar"]["nonOpMarginOther"]\
+ outData["Solar"]["genTransCapCredits"]\
+ outData["Solar"]["otherCapCreditsPatroDivident"]\
+ outData["Solar"]["extraItems"]
# E37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71), update after Form 7 model
outData["BAU"]["nonPowerCosts"] = outData["BAU"]["transExpense"] \
+ outData["BAU"]["distriExpenseO"] \
+ outData["BAU"]["distriExpenseM"] \
+ outData["BAU"]["customerAccountExpense"] \
+ outData["BAU"]["customerServiceExpense"] \
+ outData["BAU"]["salesExpense"] \
+ outData["BAU"]["adminGeneralExpense"] \
+ outData["BAU"]["depreAmortiExpense"] \
+ outData["BAU"]["taxExpensePG"] \
+ outData["BAU"]["taxExpense"] \
+ outData["BAU"]["interestLongTerm"] \
+ outData["BAU"]["interestConstruction"] \
+ outData["BAU"]["interestExpense"] \
+ outData["BAU"]["otherDeductions"] \
- (outData["BAU"]["nonOpMarginInterest"] \
+ outData["BAU"]["fundsUsedConstruc"] \
+ outData["BAU"]["incomeEquityInvest"] \
+ outData["BAU"]["nonOpMarginOther"] \
+ outData["BAU"]["genTransCapCredits"] \
+ outData["BAU"]["otherCapCreditsPatroDivident"] \
+ outData["BAU"]["extraItems"])
# E42 = E63/E24, update after Form 7 model
outData["BAU"]["costofService"] = outData["BAU"][
"totalCostElecService"] / outData["BAU"]["totalKWhSales"]
# F37 = SUM(E48:E54)+SUM(E56:E62)-SUM(E65:E71) = E37, update after Form 7 model
outData["Solar"]["nonPowerCosts"] = outData["BAU"]["nonPowerCosts"]
# F42 = F63/F24, update after Form 7 model
outData["Solar"]["costofService"] = outData["Solar"][
"totalCostElecService"] / outData["Solar"]["totalKWhSales"]
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
def work(modelDir, inputDict):
''' Run the model in its directory. '''
#Set static input data
simLength = 8760
simStartDate = "2013-01-01"
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = simStartDate + " 00:00:00 UTC"
simLengthUnits = "hours"
# Associate zipcode to climate data
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(
inputDict["zipCode"])
inverterSizeAC = float(inputDict.get("inverterSize", 0))
if (inputDict.get("systemSize", 0) == "-"):
arraySizeDC = 1.3908 * inverterSizeAC
else:
arraySizeDC = float(inputDict.get("systemSize", 0))
numberPanels = (arraySizeDC * 1000 / 305)
# Set constants
panelSize = 305
trackingMode = 0
rotlim = 45.0
gamma = 0.45
if (inputDict.get("tilt", 0) == "-"):
manualTilt = latforpvwatts
else:
manualTilt = float(inputDict.get("tilt", 0))
numberInverters = math.ceil(inverterSizeAC / 1000 / 0.5)
# Copy specific climate data into model directory
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", arraySizeDC)
ssc.ssc_data_set_number(
dat, "derate",
float(inputDict.get("inverterEfficiency", 96)) / 100 *
float(inputDict.get("nonInverterEfficiency", 87)) / 100)
ssc.ssc_data_set_number(dat, "track_mode", float(trackingMode))
ssc.ssc_data_set_number(dat, "azimuth",
float(inputDict.get("azimuth", 180)))
# Advanced inputs with defaults.
ssc.ssc_data_set_number(dat, "rotlim", float(rotlim))
ssc.ssc_data_set_number(dat, "gamma", float(-gamma / 100))
ssc.ssc_data_set_number(dat, "tilt", manualTilt)
ssc.ssc_data_set_number(dat, "tilt_eq_lat", 0.0)
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Timestamp output.
outData = {}
outData["timeStamps"] = [
dt.datetime.strftime(
dt.datetime.strptime(startDateTime[0:19], "%Y-%m-%d %H:%M:%S") +
dt.timedelta(**{simLengthUnits: x}), "%Y-%m-%d %H:%M:%S") + " UTC"
for x in range(simLength)
]
# Geodata output.
# Geodata output.
outData["minLandSize"] = round(
(arraySizeDC / 1390.8 * 5 + 1) * math.cos(math.radians(22.5)) /
math.cos(math.radians(latforpvwatts)), 0)
landAmount = float(inputDict.get("landAmount", 6.0))
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"][
"Global Horizontal Radiation (W/m^2)"] = ssc.ssc_data_get_array(
dat, "gh")
outData["climate"][
"Plane of Array Irradiance (W/m^2)"] = ssc.ssc_data_get_array(
dat, "poa")
outData["climate"]["Ambient Temperature (F)"] = ssc.ssc_data_get_array(
dat, "tamb")
outData["climate"]["Cell Temperature (F)"] = ssc.ssc_data_get_array(
dat, "tcell")
outData["climate"]["Wind Speed (m/s)"] = ssc.ssc_data_get_array(
dat, "wspd")
# Power generation.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
# Calculate clipping.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
invSizeWatts = inverterSizeAC * 1000
outData["powerOutputAcInvClipped"] = [
x if x < invSizeWatts else invSizeWatts
for x in outData["powerOutputAc"]
]
try:
outData["percentClipped"] = 100 * (
1.0 - sum(outData["powerOutputAcInvClipped"]) /
sum(outData["powerOutputAc"]))
except ZeroDivisionError:
outData["percentClipped"] = 0.0
#One year generation
outData["oneYearGenerationWh"] = sum(outData["powerOutputAcInvClipped"])
#Annual generation for all years
loanYears = 25
outData["allYearGenerationMWh"] = {}
outData["allYearGenerationMWh"][1] = float(
outData["oneYearGenerationWh"]) / 1000000
# outData["allYearGenerationMWh"][1] = float(2019.576)
for i in range(2, loanYears + 1):
outData["allYearGenerationMWh"][i] = float(
outData["allYearGenerationMWh"][i - 1]) * (
1 - float(inputDict.get("degradation", 0.8)) / 100)
# Summary of Results.
######
### Total Costs (sum of): Hardware Costs, Design/Engineering/PM/EPC/Labor Costs, Siteprep Costs, Construction Costs, Installation Costs, Land Costs
######
### Hardware Costs
pvModules = arraySizeDC * float(inputDict.get("moduleCost",
0)) * 1000 #off by 4000
racking = arraySizeDC * float(inputDict.get("rackCost", 0)) * 1000
inverters = numberInverters * float(inputDict.get("inverterCost", 0))
inverterSize = inverterSizeAC
if (inverterSize <= 250):
gear = 15000
elif (inverterSize <= 600):
gear = 18000
else:
gear = inverterSize / 1000 * 22000
balance = inverterSizeAC * 1.3908 * 134
combiners = math.ceil(numberPanels / 19 / 24) * float(1800) #*
wireManagement = arraySizeDC * 1.5
transformer = 1 * 28000
weatherStation = 1 * 12500
shipping = 1.02
hardwareCosts = (pvModules + racking + inverters + gear + balance +
combiners + wireManagement + transformer +
weatherStation) * shipping
### Design/Engineering/PM/EPC/Labor Costs
EPCmarkup = float(inputDict.get("EPCRate", 0)) / 100 * hardwareCosts
#designCosts = float(inputDict.get("mechLabor",0))*160 + float(inputDict.get("elecLabor",0))*75 + float(inputDict.get("pmCost",0)) + EPCmarkup
hoursDesign = 160 * math.sqrt(arraySizeDC / 1390)
hoursElectrical = 80 * math.sqrt(arraySizeDC / 1391)
designLabor = 65 * hoursDesign
electricalLabor = 75 * hoursElectrical
laborDesign = designLabor + electricalLabor + float(
inputDict.get("pmCost", 0)) + EPCmarkup
materialDesign = 0
designCosts = materialDesign + laborDesign
### Siteprep Costs
surveying = 2.25 * 4 * math.sqrt(landAmount * 43560)
concrete = 8000 * math.ceil(numberInverters / 2)
fencing = 6.75 * 4 * math.sqrt(landAmount * 43560)
grading = 2.5 * 4 * math.sqrt(landAmount * 43560)
landscaping = 750 * landAmount
siteMaterial = 8000 + 600 + 5500 + 5000 + surveying + concrete + fencing + grading + landscaping + 5600
blueprints = float(inputDict.get("mechLabor", 0)) * 12
mobilization = float(inputDict.get("mechLabor", 0)) * 208
mobilizationMaterial = float(inputDict.get("mechLabor", 0)) * 19.98
siteLabor = blueprints + mobilization + mobilizationMaterial
sitePrep = siteMaterial + siteLabor
### Construction Costs (Office Trailer, Skid Steer, Storage Containers, etc)
constrEquip = 6000 + math.sqrt(landAmount) * 16200
### Installation Costs
moduleAndRackingInstall = numberPanels * (15.00 + 12.50 + 1.50)
pierDriving = 1 * arraySizeDC * 20
balanceInstall = 1 * arraySizeDC * 100
installCosts = moduleAndRackingInstall + pierDriving + balanceInstall + float(
inputDict.get("elecLabor", 0)) * (72 + 60 + 70 + 10 + 5 + 30 + 70)
### Land Costs
if (str(inputDict.get("landOwnership", 0)) == "Owned"
or (str(inputDict.get("landOwnership", 0)) == "Leased")):
landCosts = 0
else:
landCosts = float(inputDict.get("costAcre", 0)) * landAmount
######
### Total Costs
######
totalCosts = hardwareCosts + designCosts + sitePrep + constrEquip + installCosts + landCosts
totalFees = float(inputDict.get("devCost", 0)) / 100 * totalCosts
outData["totalCost"] = totalCosts + totalFees + float(
inputDict.get("interCost", 0))
# Add to Pie Chart
outData["costsPieChart"] = [
["Land", landCosts], ["Design/Engineering/PM/EPC", designCosts],
["PV Modules", pvModules * shipping], ["Racking", racking * shipping],
["Inverters & Switchgear", (inverters + gear) * shipping],
[
"BOS", hardwareCosts - pvModules * shipping - racking * shipping -
(inverters + gear) * shipping
],
[
"Site Prep, Constr. Eq. and Installation",
(siteMaterial + constrEquip) + (siteLabor + installCosts)
]
]
# Cost per Wdc
outData["costWdc"] = (totalCosts + totalFees + float(
inputDict.get("interCost", 0))) / (arraySizeDC * 1000)
outData["capFactor"] = float(outData["oneYearGenerationWh"]) / (
inverterSizeAC * 1000 * 365.25 * 24) * 100
######
### Loans calculations for Direct, NCREB, Lease, Tax-equity, and PPA
######
### Full Ownership, Direct Loan
#Output - Direct Loan [C]
projectCostsDirect = 0
#Output - Direct Loan [D]
netFinancingCostsDirect = 0
#Output - Direct Loan [E]
OMInsuranceETCDirect = []
#Output - Direct Loan [F]
distAdderDirect = []
#Output - Direct Loan [G]
netCoopPaymentsDirect = []
#Output - Direct Loan [H]
costToCustomerDirect = []
#Output - Direct Loan [F53]
Rate_Levelized_Direct = 0
## Output - Direct Loan Formulas
projectCostsDirect = 0
#Output - Direct Loan [D]
payment = pmt(
float(inputDict.get("loanRate", 0)) / 100, loanYears,
outData["totalCost"])
interestDirectPI = outData["totalCost"] * float(
inputDict.get("loanRate", 0)) / 100
principleDirectPI = (-payment - interestDirectPI)
patronageCapitalRetiredDPI = 0
netFinancingCostsDirect = -(principleDirectPI + interestDirectPI -
patronageCapitalRetiredDPI)
#Output - Direct Loan [E] [F] [G] [H]
firstYearOPMainCosts = (1.25 * arraySizeDC * 12)
firstYearInsuranceCosts = (0.37 * outData["totalCost"] / 100)
if (inputDict.get("landOwnership", 0) == "Leased"):
firstYearLandLeaseCosts = float(inputDict.get("costAcre",
0)) * landAmount
else:
firstYearLandLeaseCosts = 0
for i in range(1, len(outData["allYearGenerationMWh"]) + 1):
OMInsuranceETCDirect.append(
-firstYearOPMainCosts * math.pow((1 + .01), (i - 1)) -
firstYearInsuranceCosts * math.pow((1 + .025), (i - 1)) -
firstYearLandLeaseCosts * math.pow((1 + .01), (i - 1)))
distAdderDirect.append(
float(inputDict.get("distAdder", 0)) *
outData["allYearGenerationMWh"][i])
netCoopPaymentsDirect.append(OMInsuranceETCDirect[i - 1] +
netFinancingCostsDirect)
costToCustomerDirect.append(
(netCoopPaymentsDirect[i - 1] - distAdderDirect[i - 1]))
#Output - Direct Loan [F53]
NPVLoanDirect = npv(
float(inputDict.get("discRate", 0)) / 100,
[0, 0] + costToCustomerDirect)
NPVallYearGenerationMWh = npv(
float(inputDict.get("discRate", 0)) / 100,
[0, 0] + outData["allYearGenerationMWh"].values())
Rate_Levelized_Direct = -NPVLoanDirect / NPVallYearGenerationMWh
#Master Output [Direct Loan]
outData["levelCostDirect"] = Rate_Levelized_Direct
outData["costPanelDirect"] = abs(NPVLoanDirect / numberPanels)
outData["cost10WPanelDirect"] = (float(outData["costPanelDirect"]) /
panelSize) * 10
### NCREBs Financing
ncrebsRate = float(inputDict.get("NCREBRate", 4.060)) / 100
ncrebBorrowingRate = 1.1 * ncrebsRate
ncrebPaymentPeriods = 44
ncrebCostToCustomer = []
# TODO ASAP: FIX ARRAY OFFSETS START 0
for i in range(1, len(outData["allYearGenerationMWh"]) + 1):
coopLoanPayment = 2 * pmt(
ncrebBorrowingRate / 2.0, ncrebPaymentPeriods,
outData["totalCost"]) if i <= ncrebPaymentPeriods / 2 else 0
ncrebsCredit = -0.7 * (
ipmt(ncrebsRate / 2, 2 * i -
1, ncrebPaymentPeriods, outData["totalCost"]) +
ipmt(ncrebsRate / 2, 2 * i, ncrebPaymentPeriods,
outData["totalCost"])) if i <= ncrebPaymentPeriods / 2 else 0
financingCost = ncrebsCredit + coopLoanPayment
omCost = OMInsuranceETCDirect[i - 1]
netCoopPayments = financingCost + omCost
distrAdder = distAdderDirect[i - 1]
costToCustomer = netCoopPayments + distrAdder
ncrebCostToCustomer.append(costToCustomer)
NPVLoanNCREB = npv(
float(inputDict.get("discRate", 0)) / 100,
[0, 0] + ncrebCostToCustomer)
Rate_Levelized_NCREB = -NPVLoanNCREB / NPVallYearGenerationMWh
outData["levelCostNCREB"] = Rate_Levelized_NCREB
outData["costPanelNCREB"] = abs(NPVLoanNCREB / numberPanels)
outData["cost10WPanelNCREB"] = (float(outData["costPanelNCREB"]) /
panelSize) * 10
### Lease Buyback Structure
#Output - Lease [C]
projectCostsLease = outData["totalCost"]
#Output - Lease [D]
leasePaymentsLease = []
#Output - Lease [E]
OMInsuranceETCLease = OMInsuranceETCDirect
#Output - Lease [F]
distAdderLease = distAdderDirect
#Output - Lease [G]
netCoopPaymentsLease = []
#Output - Lease [H]
costToCustomerLease = []
#Output - Lease [H44]
NPVLease = 0
#Output - Lease [H49]
Rate_Levelized_Lease = 0
## Tax Lease Formulas
#Output - Lease [D]
for i in range(0, 12):
leaseRate = float(inputDict.get("taxLeaseRate", 0)) / 100.0
if i > 8: # Special behavior in later years:
leaseRate = leaseRate - 0.0261
leasePaymentsLease.append(-1 * projectCostsLease /
((1.0 - (1.0 / (1.0 + leaseRate)**12)) /
(leaseRate)))
# Last year is different.
leasePaymentsLease[11] += -0.2 * projectCostsLease
for i in range(12, 25):
leasePaymentsLease.append(0)
#Output - Lease [G] [H]
for i in range(1, len(outData["allYearGenerationMWh"]) + 1):
netCoopPaymentsLease.append(OMInsuranceETCLease[i - 1] +
leasePaymentsLease[i - 1])
costToCustomerLease.append(netCoopPaymentsLease[i - 1] -
distAdderLease[i - 1])
#Output - Lease [H44]. Note the extra year at the zero point to get the discounting right.
NPVLease = npv(
float(inputDict.get("discRate", 0)) / 100,
[0, 0] + costToCustomerLease)
#Output - Lease [H49] (Levelized Cost Three Loops)
Rate_Levelized_Lease = -NPVLease / NPVallYearGenerationMWh
#Master Output [Lease]
outData["levelCostTaxLease"] = Rate_Levelized_Lease
outData["costPanelTaxLease"] = abs(NPVLease / numberPanels)
outData["cost10WPanelTaxLease"] = (float(outData["costPanelTaxLease"]) /
float(panelSize)) * 10
### Tax Equity Flip Structure
# Tax Equity Flip Function
def taxEquityFlip(PPARateSixYearsTE, discRate, totalCost,
allYearGenerationMWh, distAdderDirect, loanYears,
firstYearLandLeaseCosts, firstYearOPMainCosts,
firstYearInsuranceCosts, numberPanels):
#Output Tax Equity Flip [C]
coopInvestmentTaxEquity = -totalCost * (1 - 0.53)
#Output Tax Equity Flip [D]
financeCostCashTaxEquity = 0
#Output Tax Equity Flip [E]
cashToSPEOForPPATE = []
#Output Tax Equity Flip [F]
derivedCostEnergyTE = 0
#Output Tax Equity Flip [G]
OMInsuranceETCTE = []
#Output Tax Equity Flip [H]
cashFromSPEToBlockerTE = []
#Output Tax Equity Flip [I]
cashFromBlockerTE = 0
#Output Tax Equity Flip [J]
distAdderTaxEquity = distAdderDirect
#Output Tax Equity Flip [K]
netCoopPaymentsTaxEquity = []
#Output Tax Equity Flip [L]
costToCustomerTaxEquity = []
#Output Tax Equity Flip [L64]
NPVLoanTaxEquity = 0
#Output Tax Equity Flip [F72]
Rate_Levelized_Equity = 0
## Tax Equity Flip Formulas
#Output Tax Equity Flip [D]
#TEI Calcs [E]
financeCostOfCashTE = 0
coopFinanceRateTE = 2.7 / 100
if (coopFinanceRateTE == 0):
financeCostOfCashTE = 0
else:
payment = pmt(coopFinanceRateTE, loanYears,
-coopInvestmentTaxEquity)
financeCostCashTaxEquity = payment
#Output Tax Equity Flip [E]
SPERevenueTE = []
for i in range(1, len(allYearGenerationMWh) + 1):
SPERevenueTE.append(PPARateSixYearsTE * allYearGenerationMWh[i])
if ((i >= 1) and (i <= 6)):
cashToSPEOForPPATE.append(-SPERevenueTE[i - 1])
else:
cashToSPEOForPPATE.append(0)
#Output Tax Equity Flip [F]
derivedCostEnergyTE = cashToSPEOForPPATE[0] / allYearGenerationMWh[1]
#Output Tax Equity Flip [G]
#TEI Calcs [F] [U] [V]
landLeaseTE = []
OMTE = []
insuranceTE = []
for i in range(1, len(allYearGenerationMWh) + 1):
landLeaseTE.append(firstYearLandLeaseCosts * math.pow((1 + .01),
(i - 1)))
OMTE.append(-firstYearOPMainCosts * math.pow((1 + .01), (i - 1)))
insuranceTE.append(-firstYearInsuranceCosts * math.pow((1 + .025),
(i - 1)))
if (i < 7):
OMInsuranceETCTE.append(float(landLeaseTE[i - 1]))
else:
OMInsuranceETCTE.append(
float(OMTE[i - 1]) + float(insuranceTE[i - 1]) +
float(landLeaseTE[i - 1]))
#Output Tax Equity Flip [H]
#TEI Calcs [T]
SPEMgmtFeeTE = []
EBITDATE = []
EBITDATEREDUCED = []
managementFee = 10000
for i in range(1, len(SPERevenueTE) + 1):
SPEMgmtFeeTE.append(-managementFee * math.pow((1 + .01), (i - 1)))
EBITDATE.append(
float(SPERevenueTE[i - 1]) + float(OMTE[i - 1]) +
float(insuranceTE[i - 1]) + float(SPEMgmtFeeTE[i - 1]))
if (i <= 6):
cashFromSPEToBlockerTE.append(float(EBITDATE[i - 1]) * .01)
else:
cashFromSPEToBlockerTE.append(0)
EBITDATEREDUCED.append(EBITDATE[i - 1])
#Output Tax Equity Flip [I]
#TEI Calcs [Y21]
cashRevenueTE = -totalCost * (1 - 0.53)
buyoutAmountTE = 0
for i in range(1, len(EBITDATEREDUCED) + 1):
buyoutAmountTE = buyoutAmountTE + EBITDATEREDUCED[i - 1] / (
math.pow(1 + 0.12, i))
buyoutAmountTE = buyoutAmountTE * 0.05
cashFromBlockerTE = -(buyoutAmountTE) + 0.0725 * cashRevenueTE
#Output Tax Equity Flip [K] [L]
for i in range(1, len(allYearGenerationMWh) + 1):
if (i == 6):
netCoopPaymentsTaxEquity.append(financeCostCashTaxEquity +
cashToSPEOForPPATE[i - 1] +
cashFromSPEToBlockerTE[i - 1] +
OMInsuranceETCTE[i - 1] +
cashFromBlockerTE)
else:
netCoopPaymentsTaxEquity.append(financeCostCashTaxEquity +
cashFromSPEToBlockerTE[i - 1] +
cashToSPEOForPPATE[i - 1] +
OMInsuranceETCTE[i - 1])
costToCustomerTaxEquity.append(netCoopPaymentsTaxEquity[i - 1] -
distAdderTaxEquity[i - 1])
#Output Tax Equity Flip [L37]
NPVLoanTaxEquity = npv(
float(inputDict.get("discRate", 0)) / 100,
[0, 0] + costToCustomerTaxEquity)
#Output - Tax Equity [F42]
Rate_Levelized_TaxEquity = -NPVLoanTaxEquity / NPVallYearGenerationMWh
#TEI Calcs - Achieved Return [AW 21]
#[AK]
MACRDepreciation = []
MACRDepreciation.append(-0.99 * 0.2 *
(totalCost - totalCost * 0.5 * 0.9822 * 0.3))
MACRDepreciation.append(-0.99 * 0.32 *
(totalCost - totalCost * 0.5 * 0.9822 * 0.3))
MACRDepreciation.append(-0.99 * 0.192 *
(totalCost - totalCost * 0.5 * 0.9822 * 0.3))
MACRDepreciation.append(-0.99 * 0.1152 *
(totalCost - totalCost * 0.5 * 0.9822 * 0.3))
MACRDepreciation.append(-0.99 * 0.1152 *
(totalCost - totalCost * 0.5 * 0.9822 * 0.3))
MACRDepreciation.append(-0.99 * 0.0576 *
(totalCost - totalCost * 0.5 * 0.9822 * 0.3))
#[AI] [AL] [AN]
cashRevenueTEI = [] #[AI]
slDepreciation = [] #[AL]
totalDistributions = [] #[AN]
cashRevenueTEI.append(-totalCost * 0.53)
for i in range(1, 7):
cashRevenueTEI.append(EBITDATE[i - 1] * 0.99)
slDepreciation.append(totalCost / 25)
totalDistributions.append(-cashRevenueTEI[i])
#[AJ]
ITC = totalCost * 0.9822 * 0.3 * 0.99
#[AM]
taxableIncLoss = [0]
taxableIncLoss.append(cashRevenueTEI[1] + MACRDepreciation[0])
#[AO]
capitalAcct = []
capitalAcct.append(totalCost * 0.53)
condition = capitalAcct[0] - 0.5 * ITC + taxableIncLoss[
1] + totalDistributions[0]
if condition > 0:
capitalAcct.append(condition)
else:
capitalAcct.append(0)
#[AQ]
ratioTE = [0]
#[AP]
reallocatedIncLoss = []
#AO-1 + AN + AI + AK + AJ
for i in range(0, 5):
reallocatedIncLoss.append(capitalAcct[i + 1] +
totalDistributions[i + 1] +
MACRDepreciation[i + 1] +
cashRevenueTEI[i + 2])
ratioTE.append(reallocatedIncLoss[i] /
(cashRevenueTEI[i + 2] + MACRDepreciation[i + 1]))
taxableIncLoss.append(
cashRevenueTEI[i + 2] + MACRDepreciation[i + 1] -
ratioTE[i + 1] *
(MACRDepreciation[i + 1] - totalDistributions[i + 1]))
condition = capitalAcct[i + 1] + taxableIncLoss[
i + 2] + totalDistributions[i + 1]
if condition > 0:
capitalAcct.append(condition)
else:
capitalAcct.append(0)
#[AR]
taxesBenefitLiab = [0]
for i in range(1, 7):
taxesBenefitLiab.append(-taxableIncLoss[i] * 0.35)
#[AS] [AT]
buyoutAmount = 0
taxFromBuyout = 0
for i in range(0, len(EBITDATEREDUCED)):
buyoutAmount = buyoutAmount + .05 * EBITDATEREDUCED[i] / (math.pow(
1.12, (i + 1)))
taxFromBuyout = -buyoutAmount * 0.35
#[AU] [AV]
totalCashTax = []
cumulativeCashTax = [0]
for i in range(0, 7):
if i == 1:
totalCashTax.append(cashRevenueTEI[i] + ITC +
taxesBenefitLiab[i] + 0 + 0)
cumulativeCashTax.append(cumulativeCashTax[i] +
totalCashTax[i])
elif i == 6:
totalCashTax.append(cashRevenueTEI[i] + 0 +
taxesBenefitLiab[i] + buyoutAmount +
taxFromBuyout)
cumulativeCashTax.append(cumulativeCashTax[i] +
totalCashTax[i] + buyoutAmount +
taxFromBuyout)
else:
totalCashTax.append(cashRevenueTEI[i] + 0 +
taxesBenefitLiab[i] + 0 + 0)
cumulativeCashTax.append(cumulativeCashTax[i] +
totalCashTax[i])
#[AW21]
if (cumulativeCashTax[7] > 0):
cumulativeIRR = round(irr(totalCashTax), 4)
else:
cumulativeIRR = 0
# Deleteme: Variable Dump for debugging
# variableDump = {}
# variableDump["TaxEquity"] = {}
# variableDump["TaxEquity"]["coopInvestmentTaxEquity"] = coopInvestmentTaxEquity
# variableDump["TaxEquity"]["financeCostCashTaxEquity"] = financeCostCashTaxEquity
# variableDump["TaxEquity"]["cashToSPEOForPPATE"] = cashToSPEOForPPATE
# variableDump["TaxEquity"]["derivedCostEnergyTE"] = derivedCostEnergyTE
# variableDump["TaxEquity"]["OMInsuranceETCTE"] = OMInsuranceETCTE
# variableDump["TaxEquity"]["cashFromSPEToBlockerTE"] = cashFromSPEToBlockerTE
# variableDump["TaxEquity"]["cashFromBlockerTE"] = cashFromBlockerTE
# variableDump["TaxEquity"]["distAdderTaxEquity"] = distAdderTaxEquity
# variableDump["TaxEquity"]["netCoopPaymentsTaxEquity"] = netCoopPaymentsTaxEquity
# variableDump["TaxEquity"]["NPVLoanTaxEquity"] = NPVLoanTaxEquity
return cumulativeIRR, Rate_Levelized_TaxEquity, NPVLoanTaxEquity
# Function Calls Mega Sized Tax Equity Function Above
z = 0
PPARateSixYearsTE = z / 100
nGoal = float(inputDict.get("taxEquityReturn", 0)) / 100
nValue = 0
for p in range(0, 3):
while ((z < 50000) and (nValue < nGoal)):
achievedReturnTE, Rate_Levelized_TaxEquity, NPVLoanTaxEquity = taxEquityFlip(
PPARateSixYearsTE, inputDict.get("discRate", 0),
outData["totalCost"], outData["allYearGenerationMWh"],
distAdderDirect, loanYears, firstYearLandLeaseCosts,
firstYearOPMainCosts, firstYearInsuranceCosts, numberPanels)
nValue = achievedReturnTE
z = z + math.pow(10, p)
PPARateSixYearsTE = z / 100.0
z = z - math.pow(10, p)
PPARateSixYearsTE = z / 100
#Master Output [Tax Equity]
outData["levelCostTaxEquity"] = Rate_Levelized_TaxEquity
outData["costPanelTaxEquity"] = abs(NPVLoanTaxEquity / numberPanels)
outData["cost10WPanelTaxEquity"] = (float(outData["costPanelTaxEquity"]) /
panelSize) * 10
### PPA Comparison
#Output - PPA [F]
distAdderPPA = distAdderDirect
#Output - PPA [G]
netCoopPaymentsPPA = []
#Output - PPA [H]
costToCustomerPPA = []
#Output - PPA [I]
costToCustomerPPA = []
#Output - PPA [H40]
NPVLoanPPA = 0
#Output - PPA [I40]
Rate_Levelized_PPA = 0
## PPA Formulas
#Output - PPA [G] [H]
for i in range(1, len(outData["allYearGenerationMWh"]) + 1):
netCoopPaymentsPPA.append(
-outData["allYearGenerationMWh"][i] *
float(inputDict.get("firstYearEnergyCostPPA", 0)) * math.pow(
(1 + float(inputDict.get("annualEscRatePPA", 0)) / 100),
(i - 1)))
costToCustomerPPA.append(netCoopPaymentsPPA[i - 1] -
distAdderPPA[i - 1])
#Output - PPA [H58]
NPVLoanPPA = npv(
float(inputDict.get("discRate", 0)) / 100, [0, 0] + costToCustomerPPA)
#Output - PPA [F65]
Rate_Levelized_PPA = -NPVLoanPPA / NPVallYearGenerationMWh
#Master Output [PPA]
outData["levelCostPPA"] = Rate_Levelized_PPA
outData["firstYearCostKWhPPA"] = float(
inputDict.get("firstYearEnergyCostPPA", 0))
outData["yearlyEscalationPPA"] = float(inputDict.get(
"annualEscRatePPA", 0))
# Add all Levelized Costs to Output
outData["LevelizedCosts"] = [["Direct Loan", Rate_Levelized_Direct],
["NCREBs Financing", Rate_Levelized_NCREB],
["Lease Buyback", Rate_Levelized_Lease],
["Tax Equity Flip", Rate_Levelized_TaxEquity]]
outData["LevelizedCosts"].append({
"name": "PPA Comparison",
"y": Rate_Levelized_PPA,
"color": "gold"
})
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
def work(modelDir, inputDict):
''' Run the model in its directory.'''
feederName = [x for x in os.listdir(modelDir)
if x.endswith('.omd')][0][:-4]
inputDict["feederName1"] = feederName
zipCode = "59001" #TODO get zip code from the PV and Load input file
#Value check for attackVariable
if inputDict.get("attackVariable", "None") == "None":
attackAgentType = "None"
else:
attackAgentType = inputDict['attackVariable']
# Value check for train
if inputDict.get("trainAgent", "") == "True":
trainAgentValue = True
else:
trainAgentValue = False
# create simLengthValue to represent number of steps in simulation - will be manipulated by number of rows in load solar data csv file
simLengthValue = 0
#create startStep to represent which step pyCigar should start on - default = 100
startStep = 100
#None check for simulation length
# if inputDict.get("simLength", "None") == "None":
# simLengthValue = None
# else:
# simLengthValue = int(simLengthValue)
#None check for simulation length units
if inputDict.get("simLengthUnits", "None") == "None":
simLengthUnitsValue = None
else:
simLengthUnitsValue = inputDict["simLengthUnits"]
#None check for simulation start date
if inputDict.get("simStartDate", "None") == "None":
simStartDateTimeValue = None
simStartDateValue = None
simStartTimeValue = None
else:
simStartDateTimeValue = inputDict["simStartDate"]
simStartDateValue = simStartDateTimeValue.split('T')[0]
simStartTimeValue = simStartDateTimeValue.split('T')[1]
inputDict["climateName"] = weather.zipCodeToClimateName(zipCode)
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
def convertInputs():
#create the PyCIGAR_inputs folder to store the input files to run PyCIGAR
try:
os.mkdir(pJoin(modelDir, "PyCIGAR_inputs"))
except FileExistsError:
print("PyCIGAR_inputs folder already exists!")
pass
except:
print("Error occurred creating PyCIGAR_inputs folder")
#create misc_inputs.csv file in folder
with open(pJoin(modelDir, "PyCIGAR_inputs", "misc_inputs.csv"),
"w") as miscFile:
#Populate misc_inputs.csv
# miscFile.write(misc_inputs)
# for key in misc_dict.keys():
# miscFile.write("%s,%s\n"%(key,misc_dict[key]))
miscFile.write(inputDict['miscFile'])
#create ieee37.dss file in folder
dss_filename = "circuit.dss"
with open(pJoin(modelDir, "PyCIGAR_inputs", dss_filename),
"w") as dssFile:
dssFile.write(inputDict['dssFile'])
#create load_solar_data.csv file in folder
rowCount = 0
with open(pJoin(modelDir, "PyCIGAR_inputs", "load_solar_data.csv"),
"w") as loadPVFile:
loadPVFile.write(inputDict['loadPV'])
#Open load and PV input file
try:
with open(pJoin(modelDir, "PyCIGAR_inputs", "load_solar_data.csv"),
newline='') as inFile:
reader = csv.reader(inFile)
for row in reader:
rowCount = rowCount + 1
#Check to see if the simulation length matches the load and solar csv
# if (rowCount-1)*misc_dict["load file timestep"] != simLengthValue:
# errorMessage = "Load and PV Output File does not match simulation length specified by user"
# raise Exception(errorMessage)
simLengthValue = rowCount - 1
except:
#TODO change to an appropriate warning message
errorMessage = "CSV file is incorrect format. Please see valid format definition at <a target='_blank' href='https://github.com/dpinney/omf/wiki/Models-~-demandResponse#walkthrough'>OMF Wiki demandResponse</a>"
raise Exception(errorMessage)
#create breakpoints.csv file in folder
# f1Name = "breakpoints.csv"
# with open(pJoin(omf.omfDir, "static", "testFiles", "pyCIGAR", f1Name)) as f1:
# breakpoints_inputs = f1.read()
with open(pJoin(modelDir, "PyCIGAR_inputs", "breakpoints.csv"),
"w") as breakpointsFile:
breakpointsFile.write(inputDict['breakpoints'])
# Open defense agent HDF5(pb?) file if it was uploaded
with open(pJoin(modelDir, "PyCIGAR_inputs", "defenseAgent.pb"),
"w") as defenseVariableFile:
if inputDict['defenseVariable'] == "":
defenseVariableFile.write(
"No Defense Agent Variable File Uploaded")
else:
defenseVariableFile.write(inputDict['defenseVariable'])
return simLengthValue
simLengthValue = convertInputs()
#simLengthAdjusted accounts for the offset by startStep
simLengthAdjusted = simLengthValue - startStep
#hard-coding simLengthAdjusted for testing purposes
simLengthAdjusted = 750
outData = {}
# Std Err and Std Out
outData['stderr'] = "This should be stderr" #rawOut['stderr']
outData['stdout'] = "This should be stdout" #rawOut['stdout']
# Create list of timestamps for simulation steps
outData['timeStamps'] = []
start_time = dt_parser.isoparse(simStartDateTimeValue)
for single_datetime in (start_time + timedelta(seconds=n)
for n in range(simLengthAdjusted)):
single_datetime_str = single_datetime.strftime("%Y-%m-%d %H:%M:%S%z")
outData['timeStamps'].append(single_datetime_str)
# Day/Month Aggregation Setup:
stamps = outData.get('timeStamps', [])
level = inputDict.get('simLengthUnits', 'seconds')
# TODO: Create/populate Climate data without gridlab-d
outData['climate'] = {}
outData['allMeterVoltages'] = {}
outData['allMeterVoltages']['Min'] = [0] * int(simLengthAdjusted)
outData['allMeterVoltages']['Mean'] = [0] * int(simLengthAdjusted)
outData['allMeterVoltages']['StdDev'] = [0] * int(simLengthAdjusted)
outData['allMeterVoltages']['Max'] = [0] * int(simLengthAdjusted)
# Power Consumption
outData['Consumption'] = {}
# Set default value to be 0, avoiding missing value when computing Loads
outData['Consumption']['Power'] = [0] * int(simLengthAdjusted)
outData['Consumption']['Losses'] = [0] * int(simLengthAdjusted)
outData['Consumption']['DG'] = [0] * int(simLengthAdjusted)
outData['swingTimestamps'] = []
outData['swingTimestamps'] = outData['timeStamps']
# 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')
def runPyCIGAR():
#create the pycigarOutput folder to store the output file(s) generated by PyCIGAR
try:
os.mkdir(pJoin(modelDir, "pycigarOutput"))
except FileExistsError:
print("pycigarOutput folder already exists!")
pass
except:
print("Error occurred creating pycigarOutput folder")
#import and run pycigar
import pycigar
#Set up runType scenarios
#runType of 2 implies the base scenario - not training a defense agent, nor is there a defense agent entered
runType = 2
tempDefenseAgent = None #TODO: change tempDefenseAgent to be !None if defense agent file is input
# check to see if we are trying to train a defense agent
if trainAgentValue:
#runType of 0 implies the training scenario - runs to train a defense agent and outputs a zip containing defense agent files
runType = 0
#check to see if user entered a defense agent file
elif tempDefenseAgent != None:
tempDefenseAgent = modelDir + "/PyCIGAR_inputs/defenseAgent.pb"
#runType of 1 implies the defense scenario - not training a defense agent, but a defense agent zip was uploaded
runType = 1
# TODO how to factor attackAgentType into pycigar inputs
# if there is no training selected and no attack variable, run without a defense agent
pycigar.main(modelDir + "/PyCIGAR_inputs/misc_inputs.csv",
modelDir + "/PyCIGAR_inputs/circuit.dss",
modelDir + "/PyCIGAR_inputs/load_solar_data.csv",
modelDir + "/PyCIGAR_inputs/breakpoints.csv",
runType,
tempDefenseAgent,
modelDir + "/pycigarOutput/",
start=startStep,
duration=simLengthAdjusted,
hack_start=250,
hack_end=None,
percentage_hack=0.45)
#print("Got through pyCigar!!!")
def convertOutputs():
#set outData[] values to those from modelDir/pycigarOutput/pycigar_output_specs_.json
#read in the pycigar-outputed json
with open(
pJoin(modelDir, "pycigarOutput", "pycigar_output_specs.json"),
'r') as f:
pycigarJson = json.load(f)
#convert "allMeterVoltages"
outData["allMeterVoltages"] = pycigarJson["allMeterVoltages"]
#convert "Consumption"."Power"
# HACK! Units are actually kW. Needs to be fixed in pyCigar.
outData["Consumption"]["Power"] = pycigarJson["Consumption"][
"Power Substation (W)"]
#convert "Consumption"."Losses"
outData["Consumption"]["Losses"] = pycigarJson["Consumption"][
"Losses Total (W)"]
#convert "Consumption"."DG"
outData["Consumption"]["DG"] = [
-1.0 * x for x in pycigarJson["Consumption"]["DG Output (W)"]
]
#convert "powerFactors"
outData["powerFactors"] = pycigarJson["Substation Power Factor (%)"]
#convert "swingVoltage"
outData["swingVoltage"] = pycigarJson["Substation Top Voltage(V)"]
#convert "downlineNodeVolts"
outData["downlineNodeVolts"] = pycigarJson[
"Substation Bottom Voltage(V)"]
#convert "minVoltBand"
outData["minVoltBand"] = pycigarJson[
"Substation Regulator Minimum Voltage(V)"]
#convert "maxVoltBand"
outData["maxVoltBand"] = pycigarJson[
"Substation Regulator Maximum Voltage(V)"]
#create lists of circuit object names
regNameList = []
capNameList = []
for key in pycigarJson:
if key.startswith('Regulator_'):
regNameList.append(key)
elif key.startswith('Capacitor_'):
capNameList.append(key)
#convert regulator data
for reg_name in regNameList:
outData[reg_name] = {}
regPhaseValue = pycigarJson[reg_name]["RegPhases"]
if regPhaseValue.find('A') != -1:
outData[reg_name]["RegTapA"] = pycigarJson[reg_name]["creg1a"]
if regPhaseValue.find('B') != -1:
outData[reg_name]["RegTapB"] = pycigarJson[reg_name]["creg1b"]
if regPhaseValue.find('C') != -1:
outData[reg_name]["RegTapC"] = pycigarJson[reg_name]["creg1c"]
outData[reg_name]["RegPhases"] = regPhaseValue
#convert inverter data
inverter_output_dict = {}
for inv_dict in pycigarJson["Inverter Outputs"]:
#create a new dictionary to represent the single inverter
new_inv_dict = {}
#get values from pycigar output for given single inverter
inv_name = inv_dict["Name"]
inv_volt = inv_dict["Voltage (V)"]
inv_pow_real = inv_dict["Power Output (W)"]
inv_pow_imag = inv_dict["Reactive Power Output (VAR)"]
#populate single inverter dict with pycigar values
new_inv_dict["Voltage"] = inv_volt
new_inv_dict["Power_Real"] = inv_pow_real
new_inv_dict["Power_Imag"] = inv_pow_imag
#add single inverter dict to dict of all the inverters using the inverter name as the key
inverter_output_dict[inv_name] = new_inv_dict
outData["Inverter_Outputs"] = inverter_output_dict
#convert capacitor data - Need one on test circuit first!
for cap_name in capNameList:
outData[cap_name] = {}
capPhaseValue = pycigarJson[cap_name]["CapPhases"]
if capPhaseValue.find('A') != -1:
outData[cap_name]['Cap1A'] = [0] * int(simLengthValue)
outData[cap_name]['Cap1A'] = pycigarJson[cap_name]['switchA']
if capPhaseValue.find('B') != -1:
outData[cap_name]['Cap1B'] = [0] * int(simLengthValue)
outData[cap_name]['Cap1B'] = pycigarJson[cap_name]['switchB']
if capPhaseValue.find('C') != -1:
outData[cap_name]['Cap1C'] = [0] * int(simLengthValue)
outData[cap_name]['Cap1C'] = pycigarJson[cap_name]['switchC']
outData[cap_name]["CapPhases"] = capPhaseValue
outData["stdout"] = pycigarJson["stdout"]
runPyCIGAR()
convertOutputs()
return outData
try:
# Check whether model exist or not
if not os.path.isdir(modelDir):
os.makedirs(modelDir)
inputDict["created"] = str(dt.datetime.now())
# MAYBEFIX: remove this data dump. Check showModel in web.py and renderTemplate()
with open(pJoin(modelDir, "allInputData.json"), "w") as inputFile:
json.dump(inputDict, inputFile, indent=4)
#Set static input data
simLength = 8760
simStartDate = "2013-01-01"
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = simStartDate + " 00:00:00 UTC"
simLengthUnits = "hours"
# Associate zipcode to climate data
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(
inputDict["zipCode"])
inverterSizeAC = float(inputDict.get("inverterSize", 0))
if (inputDict.get("systemSize", 0) == "-"):
arraySizeDC = 1.3908 * inverterSizeAC
else:
arraySizeDC = float(inputDict.get("systemSize", 0))
numberPanels = (arraySizeDC * 1000 / 305)
# Set constants
panelSize = 305
trackingMode = 0
rotlim = 45.0
gamma = 0.45
if (inputDict.get("tilt", 0) == "-"):
manualTilt = latforpvwatts
else:
manualTilt = float(inputDict.get("tilt", 0))
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 work(modelDir, inputDict):
''' Run the model in its directory. '''
# Copy spcific climate data into model directory
inputDict["climateName"], latforpvwatts = zipCodeToClimateName(
inputDict["zipCode"])
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size",
float(inputDict.get("systemSize", 100)))
derate = float(inputDict.get("pvModuleDerate", 99.5))/100 \
* float(inputDict.get("mismatch", 99.5))/100 \
* float(inputDict.get("diodes", 99.5))/100 \
* float(inputDict.get("dcWiring", 99.5))/100 \
* float(inputDict.get("acWiring", 99.5))/100 \
* float(inputDict.get("soiling", 99.5))/100 \
* float(inputDict.get("shading", 99.5))/100 \
* float(inputDict.get("sysAvail", 99.5))/100 \
* float(inputDict.get("age", 99.5))/100 \
* float(inputDict.get("inverterEfficiency", 92))/100
ssc.ssc_data_set_number(dat, "derate", derate)
# TODO: Should we move inverter efficiency to 'inv_eff' below (as done in PVWatts?)
# Doesn't seem to affect output very much
# ssc.ssc_data_set_number(dat, "inv_eff", float(inputDict.get("inverterEfficiency", 92))/100)
ssc.ssc_data_set_number(dat, "track_mode",
float(inputDict.get("trackingMode", 0)))
ssc.ssc_data_set_number(dat, "azimuth",
float(inputDict.get("azimuth", 180)))
# Advanced inputs with defaults.
ssc.ssc_data_set_number(dat, "rotlim", float(inputDict.get("rotlim", 45)))
ssc.ssc_data_set_number(dat, "gamma",
float(inputDict.get("gamma", 0.5)) / 100)
# Complicated optional inputs.
if (inputDict.get("tilt", 0) == "-"):
tilt_eq_lat = 1.0
manualTilt = 0.0
else:
tilt_eq_lat = 0.0
manualTilt = float(inputDict.get("tilt", 0))
ssc.ssc_data_set_number(dat, "tilt", manualTilt)
ssc.ssc_data_set_number(dat, "tilt_eq_lat", tilt_eq_lat)
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Setting options for start time.
simLengthUnits = inputDict.get("simLengthUnits", "hours")
simStartDate = inputDict.get("simStartDate", "2014-01-01")
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = simStartDate + " 00:00:00 UTC"
# Timestamp output.
outData = {}
outData["timeStamps"] = [
dt.datetime.strftime(
dt.datetime.strptime(startDateTime[0:19], "%Y-%m-%d %H:%M:%S") +
dt.timedelta(**{simLengthUnits: x}), "%Y-%m-%d %H:%M:%S") + " UTC"
for x in range(int(inputDict.get("simLength", 8760)))
]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"][
"Global Horizontal Radiation (W/m^2)"] = ssc.ssc_data_get_array(
dat, "gh")
outData["climate"][
"Plane of Array Irradiance (W/m^2)"] = ssc.ssc_data_get_array(
dat, "poa")
outData["climate"]["Ambient Temperature (F)"] = ssc.ssc_data_get_array(
dat, "tamb")
outData["climate"]["Cell Temperature (F)"] = ssc.ssc_data_get_array(
dat, "tcell")
outData["climate"]["Wind Speed (m/s)"] = ssc.ssc_data_get_array(
dat, "wspd")
# Power generation and clipping.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
invSizeWatts = float(inputDict.get("inverterSize", 0)) * 1000
outData["InvClipped"] = [
x if x < invSizeWatts else invSizeWatts
for x in outData["powerOutputAc"]
]
try:
outData["percentClipped"] = 100 * (
1.0 - sum(outData["InvClipped"]) / sum(outData["powerOutputAc"]))
except ZeroDivisionError:
outData["percentClipped"] = 0.0
# Cashflow outputs.
lifeSpan = int(inputDict.get("lifeSpan", 30))
lifeYears = range(1, 1 + lifeSpan)
retailCost = float(inputDict.get("retailCost", 0.0))
degradation = float(inputDict.get("degradation", 0.5)) / 100
installCost = float(inputDict.get("installCost", 0.0))
discountRate = float(inputDict.get("discountRate", 7)) / 100
outData["oneYearGenerationWh"] = sum(outData["powerOutputAc"])
outData["lifeGenerationDollars"] = [
retailCost * (1.0 / 1000) * outData["oneYearGenerationWh"] *
(1.0 - (x * degradation)) for x in lifeYears
]
outData["lifeOmCosts"] = [
-1.0 * float(inputDict["omCost"]) for x in lifeYears
]
outData["lifePurchaseCosts"] = [-1.0 * installCost
] + [0 for x in lifeYears[1:]]
srec = inputDict.get("srecCashFlow", "").split(",")
outData["srecCashFlow"] = map(float,
srec) + [0 for x in lifeYears[len(srec):]]
outData["netCashFlow"] = [
x + y + z + a for (
x, y, z,
a) in zip(outData["lifeGenerationDollars"], outData["lifeOmCosts"],
outData["lifePurchaseCosts"], outData["srecCashFlow"])
]
outData["cumCashFlow"] = map(lambda x: x,
_runningSum(outData["netCashFlow"]))
outData["ROI"] = roundSig(sum(outData["netCashFlow"]), 3) / (
-1 * roundSig(sum(outData["lifeOmCosts"]), 3) +
-1 * roundSig(sum(outData["lifePurchaseCosts"], 3)))
outData["NPV"] = roundSig(npv(discountRate, outData["netCashFlow"]), 3)
outData["lifeGenerationWh"] = sum(outData["powerOutputAc"]) * lifeSpan
outData["lifeEnergySales"] = sum(outData["lifeGenerationDollars"])
try:
# The IRR function is very bad.
outData["IRR"] = roundSig(irr(outData["netCashFlow"]), 3)
except:
outData["IRR"] = "Undefined"
# Monthly aggregation outputs.
months = {
"Jan": 0,
"Feb": 1,
"Mar": 2,
"Apr": 3,
"May": 4,
"Jun": 5,
"Jul": 6,
"Aug": 7,
"Sep": 8,
"Oct": 9,
"Nov": 10,
"Dec": 11
}
totMonNum = lambda x: sum([
z for (y, z) in zip(outData["timeStamps"], outData["powerOutputAc"])
if y.startswith(simStartDate[0:4] + "-{0:02d}".format(x + 1))
])
outData["monthlyGeneration"] = [[
a, totMonNum(b)
] for (a, b) in sorted(months.items(), key=lambda x: x[1])]
# Heatmaped hour+month outputs.
hours = range(24)
from calendar import monthrange
totHourMon = lambda h, m: sum([
z for (y, z) in zip(outData["timeStamps"], outData["powerOutputAc"])
if y[5:7] == "{0:02d}".format(m + 1) and y[11:13] == "{0:02d}".format(
h + 1)
])
outData["seasonalPerformance"] = [[
x, y,
totHourMon(x, y) / monthrange(int(simStartDate[:4]), y + 1)[1]
] for x in hours for y in months.values()]
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
def work(modelDir, inputDict):
''' Run the model in its directory. '''
# Copy spcific climate data into model directory
inputDict["climateName"] = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", float(inputDict.get("systemSize", 100)))
derate = float(inputDict.get("pvModuleDerate", 99.5))/100 \
* float(inputDict.get("mismatch", 99.5))/100 \
* float(inputDict.get("diodes", 99.5))/100 \
* float(inputDict.get("dcWiring", 99.5))/100 \
* float(inputDict.get("acWiring", 99.5))/100 \
* float(inputDict.get("soiling", 99.5))/100 \
* float(inputDict.get("shading", 99.5))/100 \
* float(inputDict.get("sysAvail", 99.5))/100 \
* float(inputDict.get("age", 99.5))/100 \
* float(inputDict.get("inverterEfficiency", 92))/100
ssc.ssc_data_set_number(dat, "derate", derate)
# TODO: Should we move inverter efficiency to 'inv_eff' below (as done in PVWatts?)
# Doesn't seem to affect output very much
# ssc.ssc_data_set_number(dat, "inv_eff", float(inputDict.get("inverterEfficiency", 92))/100)
ssc.ssc_data_set_number(dat, "track_mode", float(inputDict.get("trackingMode", 0)))
ssc.ssc_data_set_number(dat, "azimuth", float(inputDict.get("azimuth", 180)))
# Advanced inputs with defaults.
ssc.ssc_data_set_number(dat, "rotlim", float(inputDict.get("rotlim", 45)))
ssc.ssc_data_set_number(dat, "gamma", float(inputDict.get("gamma", 0.5))/100)
# Complicated optional inputs.
if (inputDict.get("tilt",0) == "-"):
tilt_eq_lat = 1.0
manualTilt = 0.0
else:
tilt_eq_lat = 0.0
manualTilt = float(inputDict.get("tilt",0))
ssc.ssc_data_set_number(dat, "tilt", manualTilt)
ssc.ssc_data_set_number(dat, "tilt_eq_lat", tilt_eq_lat)
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Setting options for start time.
simLengthUnits = inputDict.get("simLengthUnits","hours")
simStartDate = inputDict.get("simStartDate", "2014-01-01")
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = simStartDate + " 00:00:00 UTC"
# Timestamp output.
outData = {}
outData["timeStamps"] = [dt.datetime.strftime(
dt.datetime.strptime(startDateTime[0:19],"%Y-%m-%d %H:%M:%S") +
dt.timedelta(**{simLengthUnits:x}),"%Y-%m-%d %H:%M:%S") + " UTC" for x in range(int(inputDict.get("simLength", 8760)))]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Global Horizontal Radiation (W/m^2)"] = ssc.ssc_data_get_array(dat, "gh")
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = ssc.ssc_data_get_array(dat, "poa")
outData["climate"]["Ambient Temperature (F)"] = ssc.ssc_data_get_array(dat, "tamb")
outData["climate"]["Cell Temperature (F)"] = ssc.ssc_data_get_array(dat, "tcell")
outData["climate"]["Wind Speed (m/s)"] = ssc.ssc_data_get_array(dat, "wspd")
# Power generation and clipping.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
invSizeWatts = float(inputDict.get("inverterSize", 0)) * 1000
outData["InvClipped"] = [x if x < invSizeWatts else invSizeWatts for x in outData["powerOutputAc"]]
try:
outData["percentClipped"] = 100 * (1.0 - sum(outData["InvClipped"]) / sum(outData["powerOutputAc"]))
except ZeroDivisionError:
outData["percentClipped"] = 0.0
# Cashflow outputs.
lifeSpan = int(inputDict.get("lifeSpan",30))
lifeYears = range(1, 1 + lifeSpan)
retailCost = float(inputDict.get("retailCost",0.0))
degradation = float(inputDict.get("degradation",0.5))/100
installCost = float(inputDict.get("installCost",0.0))
discountRate = float(inputDict.get("discountRate", 7))/100
outData["oneYearGenerationWh"] = sum(outData["powerOutputAc"])
outData["lifeGenerationDollars"] = [retailCost*(1.0/1000)*outData["oneYearGenerationWh"]*(1.0-(x*degradation)) for x in lifeYears]
outData["lifeOmCosts"] = [-1.0*float(inputDict["omCost"]) for x in lifeYears]
outData["lifePurchaseCosts"] = [-1.0 * installCost] + [0 for x in lifeYears[1:]]
srec = inputDict.get("srecCashFlow", "").split(",")
outData["srecCashFlow"] = map(float,srec) + [0 for x in lifeYears[len(srec):]]
outData["netCashFlow"] = [x+y+z+a for (x,y,z,a) in zip(outData["lifeGenerationDollars"], outData["lifeOmCosts"], outData["lifePurchaseCosts"], outData["srecCashFlow"])]
outData["cumCashFlow"] = map(lambda x:x, _runningSum(outData["netCashFlow"]))
outData["ROI"] = roundSig(sum(outData["netCashFlow"]), 3) / (-1*roundSig(sum(outData["lifeOmCosts"]), 3) + -1*roundSig(sum(outData["lifePurchaseCosts"], 3)))
outData["NPV"] = roundSig(npv(discountRate, outData["netCashFlow"]), 3)
outData["lifeGenerationWh"] = sum(outData["powerOutputAc"])*lifeSpan
outData["lifeEnergySales"] = sum(outData["lifeGenerationDollars"])
try:
# The IRR function is very bad.
outData["IRR"] = roundSig(irr(outData["netCashFlow"]), 3)
except:
outData["IRR"] = "Undefined"
# Monthly aggregation outputs.
months = {"Jan":0,"Feb":1,"Mar":2,"Apr":3,"May":4,"Jun":5,"Jul":6,"Aug":7,"Sep":8,"Oct":9,"Nov":10,"Dec":11}
totMonNum = lambda x:sum([z for (y,z) in zip(outData["timeStamps"], outData["powerOutputAc"]) if y.startswith(simStartDate[0:4] + "-{0:02d}".format(x+1))])
outData["monthlyGeneration"] = [[a, totMonNum(b)] for (a,b) in sorted(months.items(), key=lambda x:x[1])]
# Heatmaped hour+month outputs.
hours = range(24)
from calendar import monthrange
totHourMon = lambda h,m:sum([z for (y,z) in zip(outData["timeStamps"], outData["powerOutputAc"]) if y[5:7]=="{0:02d}".format(m+1) and y[11:13]=="{0:02d}".format(h+1)])
outData["seasonalPerformance"] = [[x,y,totHourMon(x,y) / monthrange(int(simStartDate[:4]), y+1)[1]] for x in hours for y in months.values()]
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
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 work(modelDir, inputDict):
#plotly imports. Here for now so web server starts.
import plotly
# from plotly import __version__
# from plotly.offline import download_plotlyjs, plot
# from plotly import tools
import plotly.graph_objs as go
# Copy specific climate data into model directory
inputDict["climateName"] = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", float(inputDict["systemSize"]))
ssc.ssc_data_set_number(dat, "derate", 0.01 * float(inputDict["nonInverterEfficiency"]))
ssc.ssc_data_set_number(dat, "track_mode", float(inputDict["trackingMode"]))
ssc.ssc_data_set_number(dat, "azimuth", float(inputDict["azimuth"]))
# Advanced inputs with defaults.
if (inputDict.get("tilt",0) == "-"):
tilt_eq_lat = 1.0
manualTilt = 0.0
else:
tilt_eq_lat = 0.0
manualTilt = float(inputDict.get("tilt",0))
ssc.ssc_data_set_number(dat, "tilt_eq_lat", tilt_eq_lat)
ssc.ssc_data_set_number(dat, "tilt", manualTilt)
ssc.ssc_data_set_number(dat, "rotlim", float(inputDict["rotlim"]))
ssc.ssc_data_set_number(dat, "gamma", -1 * float(inputDict["gamma"]))
ssc.ssc_data_set_number(dat, "inv_eff", 0.01 * float(inputDict["inverterEfficiency"]))
ssc.ssc_data_set_number(dat, "w_stow", float(inputDict["w_stow"]))
# Complicated optional inputs that we could enable later.
# ssc.ssc_data_set_array(dat, 'shading_hourly', ...) # Hourly beam shading factors
# ssc.ssc_data_set_matrix(dat, 'shading_mxh', ...) # Month x Hour beam shading factors
# ssc.ssc_data_set_matrix(dat, 'shading_azal', ...) # Azimuth x altitude beam shading factors
# ssc.ssc_data_set_number(dat, 'shading_diff', ...) # Diffuse shading factor
# ssc.ssc_data_set_number(dat, 'enable_user_poa', ...) # Enable user-defined POA irradiance input = 0 or 1
# ssc.ssc_data_set_array(dat, 'user_poa', ...) # User-defined POA irradiance in W/m2
# ssc.ssc_data_set_number(dat, 'tilt', 999)
# ssc.ssc_data_set_number(dat, "t_noct", float(inputDict["t_noct"]))
# ssc.ssc_data_set_number(dat, "t_ref", float(inputDict["t_ref"]))
# ssc.ssc_data_set_number(dat, "fd", float(inputDict["fd"]))
# ssc.ssc_data_set_number(dat, "i_ref", float(inputDict["i_ref"]))
# ssc.ssc_data_set_number(dat, "poa_cutin", float(inputDict["poa_cutin"]))
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Setting options for start time.
simLengthUnits = inputDict.get("simLengthUnits","")
simStartDate = inputDict["simStartDate"]
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = simStartDate + " 00:00:00 UTC"
# Set aggregation function constants.
agg = lambda x,y:_aggData(x,y,inputDict["simStartDate"],
int(inputDict["simLength"]), inputDict["simLengthUnits"], ssc, dat)
avg = lambda x:sum(x)/len(x)
# Timestamp output.
outData = {}
outData["timeStamps"] = [datetime.datetime.strftime(
datetime.datetime.strptime(startDateTime[0:19],"%Y-%m-%d %H:%M:%S") +
datetime.timedelta(**{simLengthUnits:x}),"%Y-%m-%d %H:%M:%S") + " UTC" for x in range(int(inputDict["simLength"]))]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = agg("poa", avg)
outData["climate"]["Beam Normal Irradiance (W/m^2)"] = agg("dn", avg)
outData["climate"]["Diffuse Irradiance (W/m^2)"] = agg("df", avg)
outData["climate"]["Ambient Temperature (F)"] = agg("tamb", avg)
outData["climate"]["Cell Temperature (F)"] = agg("tcell", avg)
outData["climate"]["Wind Speed (m/s)"] = agg("wspd", avg)
# Power generation.
outData["Consumption"] = {}
outData["Consumption"]["Power"] = [x for x in agg("ac", avg)]
outData["Consumption"]["Losses"] = [0 for x in agg("ac", avg)]
outData["Consumption"]["DG"] = agg("ac", avg)
#Plotly data sets for power generation graphs
convertedDateStrings = [datetime.datetime.strptime(x, "%Y-%m-%d %H:%M:%S %Z") for x in outData["timeStamps"]]
powerGeneration = go.Scatter(
x=convertedDateStrings,
y=outData["Consumption"]["Power"],
line=dict(
color=('red')
),
name="Power Generated")
chartInverter = None
if float(inputDict["inverterSize"]) == 0:
chartInverter = float(inputDict["systemSize"])
else:
chartInverter = float(inputDict["inverterSize"])
panelsNameplate = go.Scatter(
x=convertedDateStrings,
y=[float(inputDict['systemSize'])*1000 for x in range(len(convertedDateStrings))],
line=dict(
dash = 'dash',
color='orange'
),
name="Panels Nameplate")
inverterNameplate = go.Scatter(
x=convertedDateStrings,
y=[chartInverter*1000 for x in range(len(convertedDateStrings))],
line=dict(
dash = 'dash',
color='orange'
),
name="inverter Nameplate")
#Set Power generation plotly layout
powerGenerationLayout = go.Layout(
width=1000,
height=375,
xaxis=dict(
showgrid=False,
),
legend=dict(
x=0,
y=1.25,
orientation="h")
)
#Combine all datasets for plotly graph
powerGenerationData = [powerGeneration, panelsNameplate, inverterNameplate]
#Example updating go object
powerGenerationLayout['yaxis'].update(title='Power (W-AC)')
#fig = go.Figure(data=powerGenerationData, layout=powerGenerationLayout)
#inlinePlot = plotly.offline.plot(fig, include_plotlyjs=False, output_type='div')
#outData["plotlyDiv"] = html.escape(json.dumps(inlinePlot, cls=plotly.utils.PlotlyJSONEncoder))
#Plotly power generation outputs
outData["powerGenerationData"] = json.dumps(powerGenerationData, cls=plotly.utils.PlotlyJSONEncoder)
outData["powerGenerationLayout"] = json.dumps(powerGenerationLayout, cls=plotly.utils.PlotlyJSONEncoder)
#Irradiance plotly data
poaIrradiance = go.Scatter(
x=convertedDateStrings,
y=outData["climate"]["Plane of Array Irradiance (W/m^2)"],
line=dict(
color='yellow'
),
name="Plane of Array Irradiance (W/m^2)")
beamNormalIrradiance = go.Scatter(
x=convertedDateStrings,
y=outData["climate"]["Beam Normal Irradiance (W/m^2)"],
line=dict(
color='gold'
),
name="Beam Normal Irradiance (W/m^2)")
diffuseIrradiance = go.Scatter(
x=convertedDateStrings,
y=outData["climate"]["Diffuse Irradiance (W/m^2)"],
line=dict(
color='lemonchiffon'
),
name="Diffuse Irradiance (W/m^2)")
irradianceData = [poaIrradiance, beamNormalIrradiance, diffuseIrradiance]
#Set Power generation plotly layout
irradianceLayout = go.Layout(
width=1000,
height=375,
xaxis=dict(
showgrid=False,
),
yaxis=dict(
title="Climate Units",
),
legend=dict(
x=0,
y=1.25,
orientation="h")
)
outData["irradianceData"] = json.dumps(irradianceData, cls=plotly.utils.PlotlyJSONEncoder)
outData["irradianceLayout"] = json.dumps(irradianceLayout, cls=plotly.utils.PlotlyJSONEncoder)
#Other Climate Variables plotly data
ambientTemperature = go.Scatter(
x=convertedDateStrings,
y= outData["climate"]["Ambient Temperature (F)"],
line=dict(
color='dimgray'
),
name="Ambient Temperature (F)")
cellTemperature = go.Scatter(
x=convertedDateStrings,
y=outData["climate"]["Cell Temperature (F)"],
line=dict(
color='gainsboro'
),
name="Cell Temperature (F)")
windSpeed = go.Scatter(
x=convertedDateStrings,
y=outData["climate"]["Wind Speed (m/s)"],
line=dict(
color='darkgray'
),
name="Wind Speed (m/s)")
otherClimateData = [ambientTemperature, cellTemperature, windSpeed]
#Set Power generation plotly layout
otherClimateLayout = go.Layout(
width=1000,
height=375,
xaxis=dict(
showgrid=False,
),
yaxis=dict(
title="Climate Units",
),
legend=dict(
x=0,
y=1.25,
orientation="h")
)
outData["otherClimateData"] = json.dumps(otherClimateData, cls=plotly.utils.PlotlyJSONEncoder)
outData["otherClimateLayout"] = json.dumps(otherClimateLayout, cls=plotly.utils.PlotlyJSONEncoder)
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
