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. '''
# 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.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, b'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, b'track_mode',
float(inputDict.get('trackingMode', 0)))
ssc.ssc_data_set_number(dat, b'azimuth',
float(inputDict.get('azimuth', 180)))
# Advanced inputs with defaults.
ssc.ssc_data_set_number(dat, b'rotlim', float(inputDict.get('rotlim', 45)))
ssc.ssc_data_set_number(dat, b'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, b'tilt', manualTilt)
ssc.ssc_data_set_number(dat, b'tilt_eq_lat', tilt_eq_lat)
# Run PV system simulation.
mod = ssc.ssc_module_create(b'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, 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 and clipping.
outData['powerOutputAc'] = ssc.ssc_data_get_array(dat, b'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 = list(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"] = list(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"] = [x for x in _runningSum(outData["netCashFlow"])]
outData["ROI"] = __neoMetaModel__.roundSig(
sum(outData["netCashFlow"]), 3
) / (-1 * __neoMetaModel__.roundSig(sum(outData["lifeOmCosts"]), 3) +
-1 * __neoMetaModel__.roundSig(sum(outData["lifePurchaseCosts"], 3)))
outData["NPV"] = __neoMetaModel__.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"] = __neoMetaModel__.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 = list(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