def run(modelDir, inputDict):
''' 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
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 = nrelsam.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)))
ssc.ssc_data_set_number(dat, "derate", float(inputDict.get("derate",
0.77)))
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, "t_noct", float(inputDict.get("t_noct", 45)))
ssc.ssc_data_set_number(dat, "t_ref", float(inputDict.get("t_ref", 25)))
ssc.ssc_data_set_number(dat, "gamma", float(inputDict.get("gamma", 0.5)))
ssc.ssc_data_set_number(dat, "inv_eff",
float(inputDict.get("inv_eff", 0.92)))
ssc.ssc_data_set_number(dat, "fd", float(inputDict.get("fd", 1)))
ssc.ssc_data_set_number(dat, "i_ref", float(inputDict.get("i_ref", 1000)))
ssc.ssc_data_set_number(dat, "poa_cutin",
float(inputDict.get("poa_cutin", 0)))
ssc.ssc_data_set_number(dat, "w_stow", float(inputDict.get("w_stow", 0)))
# Complicated optional inputs.
ssc.ssc_data_set_number(dat, "tilt_eq_lat", 1)
# 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)
# 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"
# Set aggregation function constants.
agg = lambda x, y: _aggData(
x, y, inputDict["simStartDate"], int(inputDict["simLength"]),
inputDict.get("simLengthUnits", "hours"), ssc, dat)
avg = lambda x: sum(x) / len(x)
# 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"]["Direct Irradiance (W/m^2)"] = agg("dn", avg)
outData["climate"]["Difuse 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["powerOutputAc"] = [x for x in agg("ac", avg)]
# 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))
outData["oneYearGenerationWh"] = sum(outData["powerOutputAc"])
outData["lifeGenerationDollars"] = [
roundSig(
retailCost * (1.0 / 1000.0) * outData["oneYearGenerationWh"] *
(1.0 - (x * degradation)), 2) 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:]]
outData["netCashFlow"] = [
roundSig(x + y + z, 2)
for (x, y,
z) in zip(outData["lifeGenerationDollars"],
outData["lifeOmCosts"], outData["lifePurchaseCosts"])
]
outData["cumCashFlow"] = map(lambda x: roundSig(x, 2),
_runningSum(outData["netCashFlow"]))
outData["ROI"] = roundSig(sum(outData["netCashFlow"]), 2)
#TODO: implement these two.
outData["NPV"] = "TBD"
outData["IRR"] = "TBD"
# 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, roundSig(
totMonNum(b),
2)] for (a, b) in sorted(months.items(), key=lambda x: x[1])]
# Heatmaped hour+month outputs.
hours = range(24)
from random import random
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)] for x in hours
for y in months.values()]
# 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)
def run(modelDir, inputDict):
''' Run the model in its directory. '''
# Check whether model exist or not
if not os.path.isdir(modelDir):
os.makedirs(modelDir)
inputDict["created"] = str(datetime.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
shutil.copy(pJoin(__metaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Ready to run
startTime = datetime.datetime.now()
# Set up SAM data structures.
ssc = nrelsam.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", float(inputDict["derate"]))
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.
ssc.ssc_data_set_number(dat, "rotlim", float(inputDict["rotlim"]))
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, "gamma", float(inputDict["gamma"]))
ssc.ssc_data_set_number(dat, "inv_eff", float(inputDict["inv_eff"]))
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"]))
ssc.ssc_data_set_number(dat, "w_stow", float(inputDict["w_stow"]))
# Complicated optional inputs.
ssc.ssc_data_set_number(dat, "tilt_eq_lat", 1)
# 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)
# 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"]["Direct Irradiance (W/m^2)"] = agg("dn", avg)
outData["climate"]["Difuse 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"] = ""
# 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 = 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)
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
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 = nrelsam.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)
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
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 = nrelsam.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["avgSystemSize"]))
# 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.
thisErr = traceback.format_exc()
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)
try:
os.remove(pJoin(modelDir, "allOutputData.json"))
except Exception, e:
pass