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"], 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
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(__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", 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)
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):
# 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 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 samRun(temp_dir):
'''
Run NREL's System Advisor Model with the specified parameters and return output vectors and floats in JSON.
Form parameters:
:param tmy2: a tmy2 file.
:param system_size: nameplate capacity.
:param derate: system derate value.
:param track_mode: tracking mode.
:param azimuth: azimuth angle.
:param tilt: tilt angle.
There are 40 other additional optional input parameters to the pvwattsv1 module of S.A.M.
Details:
:OMF function: omf.solvers.sam.run().
:run-time: should only be a couple of seconds.
See pvwattsv1-variable-info.txt for details on other 40 possible inputs to this route.
'''
tmy2_path = os.path.join(temp_dir, "in.tmy2")
request.files["tmy2"].save(tmy2_path)
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Set the inputs.
ssc.ssc_data_set_string(dat, b'file_name', bytes(tmy2_path, 'ascii'))
for key in request.form.keys():
ssc.ssc_data_set_number(dat, bytes(key, 'ascii'),
float(request.form.get(key)))
# Enter required parameters
system_size = int(request.form.get('system_size', 4))
ssc.ssc_data_set_number(dat, b'system_size', system_size)
derate = float(request.form.get('derate', .77))
ssc.ssc_data_set_number(dat, b'derate', derate)
track_mode = int(request.form.get('track_mode', 0))
ssc.ssc_data_set_number(dat, b'track_mode', track_mode)
azimuth = float(request.form.get('azimuth', 180))
ssc.ssc_data_set_number(dat, b'azimuth', azimuth)
tilt = float(request.form.get('tilt', 30))
ssc.ssc_data_set_number(dat, b'tilt', tilt)
# Run PV system simulation.
mod = ssc.ssc_module_create(b'pvwattsv1')
ssc.ssc_module_exec(mod, dat)
# Geodata output.
outData = {}
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'][
'Plane of Array Irradiance (W/m^2)'] = ssc.ssc_data_get_array(
dat, b'poa')
outData['climate'][
'Beam Normal Irradiance (W/m^2)'] = ssc.ssc_data_get_array(dat, b'dn')
outData['climate']['Diffuse Irradiance (W/m^2)'] = ssc.ssc_data_get_array(
dat, b'df')
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['Consumption'] = {}
outData['Consumption']['Power'] = ssc.ssc_data_get_array(dat, b'ac')
outData['Consumption']['Losses'] = ssc.ssc_data_get_array(dat, b'ac')
outData['Consumption']['DG'] = ssc.ssc_data_get_array(dat, b'ac')
with open(os.path.join(temp_dir, filenames['samrun']), 'w') as f:
json.dump(outData, f)
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. '''
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
