def choose_tech(VE,D,j,N,energy,prices_lamp,tt,interes):
method=strategy(VE[j,2])
#returns the strategy to use over the different profiles
#(1)rational, (2) social, (3) conservative
#Rational method
if method==1: #180 hours of consume in average per month
cost=np.zeros((3,139)) #Payments over time of the different techs
cost_present=np.zeros(3) #Total present cost
#Analysis of energy consume
for i in range(139):
if VE[j,3]==1:
cost[0,i]=(-40*180/100)*energy[i+tt]*0.8
cost[1,i]=(-15*180/100)*energy[i+tt]*0.8
cost[2,i]=(-9*180/100)*energy[i+tt]*0.8
elif VE[j,3]==2:
cost[0,i]=(-40*180/100)*energy[i+tt]*1.2
cost[1,i]=(-15*180/100)*energy[i+tt]*1.2
cost[2,i]=(-9*180/100)*energy[i+tt]*1.2
#LED lamp cost
cost[2,0]=cost[2,0]-prices_lamp[2,tt]
#Fluorescent lamp cost (1:36:139)
for i in range(0,138,36):
cost[1,i]=cost[1,i]-prices_lamp[1,tt+i]
#Incandescent lamp cost (i=1:4:139)
for i in range(0,138,4):
cost[0,i]=cost[0,i]-prices_lamp[0,tt+i]
#Cost in present value
cost_present[0]=-np.npv(interes,cost[0,:])
cost_present[1]=-np.npv(interes,cost[1,:])
cost_present[2]=-np.npv(interes,cost[2,:])
#Selection of the best npv performance
tech=cost_present.argmin()
tech=tech+1
#Conservative method
if method==3:
tech=VE[j,0]
#Social method
if method==2:
S_t=np.zeros(3) #Counter of technologies in vicinity
D_N=D.neighbors(j) #Returns the list of neighbors
D_N_len=len(D_N) #Dimension of neighbors list
for k in range(D_N_len): #Iteration of evaluation
S_t[VE[D_N[k],0]-1]=S_t[VE[D_N[k],0]-1]+1
tech=S_t.argmax()+1
return(tech)
def outputRow():
#NOTE: The fields of the db row, such a loan_id, are no longer available when this is called.
discountRate = principalDiscountRates[finalStatus]
numeratorNARdiscount = prev_balance_end * discountRate
cashflowLen = len(cashflow)
while cashflow[cashflowLen-1]==0: cashflowLen -= 1
cashflow.resize(max(cashflowLen, receivedMonthIndex+1))
npv = np.npv(monthlyDiscountRate-1, cashflow)
if printCashflow: print(cashflow)
if finalStatus != 'Charged Off':
cashflow[receivedMonthIndex] += prev_balance_end * (1-discountRate) / prev_investment
if printCashflow: print(cashflow)
npvForecast = np.npv(monthlyDiscountRate-1, cashflow)
irrForecast = (1+np.irr(cashflow))**12 - 1
finalStatusIsComplete = finalStatus in ["Charged Off", "Default", "Fully Paid"]
output.append((
prev_loan_id,
finalStatus,
prev_mob,
missedPayment,
firstMissed,
dueWhenFirstMissed,
receivedAfterMissed,
riskFreeRate,
npv,
npvForecast,
firstMissedOrLastObserved,
(numeratorNAR-numeratorNARdiscount)/prev_investment,
denominatorNAR/prev_investment,
finalStatusIsComplete,
irrForecast,
#cashflow
))
def net_present_value_aggregate_for_portfolio(self):
""" Gets the portfolio's aggregate net present value.
Returns: The portfolio's aggregate net present value.
"""
npv = np.npv(self.discount_rate / 12, list(self.aggregate_flows_df['total_payments']))
return float(npv)
def net_present_value_for_loan(self, loan):
""" Calculates a loan's net present value from its cash flows.
Args:
loan: The loan which is being valued.
Returns: The net present value of the loan.
"""
cash_flows = self.cash_flows_df[self.cash_flows_df['loan_df_pk'] == loan.name]
npv = np.npv(self.discount_rate / 12, cash_flows['total_payments'])
return npv
def create_company_simulation(simulation_id, stock_id):
getcontext().prec = 4
mu = 0.02
sigma = 0.1
simulation = Simulation.objects.get(pk=simulation_id)
stock = Stock.objects.get(pk=stock_id)
ticker = Ticker.objects.get(simulation=simulation)
company = TickerCompany(ticker=ticker, stock=stock, symbol=stock.symbol, name="Company %s" % stock.symbol)
company.save()
rounds = ticker.nb_rounds + 1
brownian_motion = geometric_brownian(rounds, mu, sigma, ticker.initial_value, rounds/(ticker.nb_days*rounds), stock_id)
i = 0
simulation_dividends = []
simulation_net_income = []
for sim_round in range(0, rounds):
round_dividend = 0
round_net_income = 0
for sim_day in range(1, ticker.nb_days+1):
# We have each round/day and the corresponding dividend
daily_dividend = brownian_motion[i]
daily_net_income = brownian_motion[i] / float(ticker.dividend_payoff_rate) * 100 * stock.quantity
c = CompanyFinancial(company=company, daily_dividend=Decimal(daily_dividend), daily_net_income=Decimal(daily_net_income),
sim_round=sim_round, sim_day=sim_day, sim_date=sim_round*100+sim_day)
c.save()
round_dividend += daily_dividend
round_net_income += daily_net_income
i += 1
simulation_dividends.append(round_dividend)
simulation_net_income.append(round_net_income)
# Share price estimation
G = simulation_dividends[-1]/simulation_dividends[-2]-1
R = 0.15
g = R
drift = 0
simulation_stock_price = []
previous_company_income = None
for sim_round in range(0, rounds):
stock_price = np.npv(0.15, simulation_dividends[sim_round:rounds]) + (simulation_dividends[-1]*(1+g)/(R-G))/np.power(1+R, rounds-sim_round)
simulation_stock_price.append(stock_price)
if previous_company_income:
drift = simulation_net_income[sim_round] / float(previous_company_income.net_income) - 1
previous_company_income.drift = Decimal(drift)
previous_company_income.save()
company_share = CompanyShare(company=company, share_value=Decimal(stock_price), dividends=Decimal(simulation_dividends[sim_round]),
net_income=Decimal(simulation_net_income[sim_round]), drift=Decimal(drift), sim_round=sim_round)
company_share.save()
previous_company_income = company_share
if sim_round == 0:
stock.price = Decimal(stock_price)
stock.save()
return company.id
def metrics(price, percentDown, closingCosts,repairCosts,rate,term,fedTaxRate,stTaxRate,capitalGains,propTaxRate,insPremiumYr, HOA,hoaIncRate, repairsYr, vacancyPc, propMgmtMo, rent, rentIncRate,appreciationRate, inflation,bldgValPct,sellYear):
years = linspace(0, term, term+1)
cashflowND = [0]*len(years)
for year in years:
y = int(year)
if y <= sellYear:
payment, downPayment, cashToClose, cashToOperate = loanCalcs(price, percentDown, closingCosts, repairCosts, rate, term)
equity, mortBal, appVal, interestYr = equityCalcs(y, price, downPayment, payment, rate, appreciationRate)
capex = capexF(y, sellYear, repairsYr, cashToOperate, appVal, price)
revenue, vacancy = revenueF(rent, rentIncRate, vacancyPc, y)
opexTotal, opexLessDS, deductibles = opexF(payment, propTaxRate, insPremiumYr, HOA, hoaIncRate, propMgmtMo, price, y, interestYr, vacancy)
NOI = NOIF(revenue, opexLessDS)
if y is 1:
NOI1=NOI
deductions = deductionsF(deductibles, price, bldgValPct)
taxes = taxesF(price, appVal, revenue, deductions, capex, fedTaxRate, stTaxRate, capitalGains, y, sellYear)
cashflowND[y] = cashflowATF(revenue, opexTotal, taxes, capex, equity, y, sellYear)
capRateYr1 = price/NOI1
NFV = sum(cashflowND)
PV10 = npv(.1,cashflowND)
ROR = irr(cashflowND)
cashPayout = payoutCalc(cashflowND, cashToOperate)
cashOnCashYr1 = cashToOperate/cashflowND[1]
moCashflowYr1 = cashflowND[1]/12.
metricsDict = {
"NFV" : NFV,
"PV10" : PV10,
"ROR" : ROR,
"capRateYr1" : capRateYr1,
"cashPayout" : cashPayout,
"cashOnCashYr1" : cashOnCashYr1,
"moCashflowYr1" : moCashflowYr1,
"cashToClose" : cashToClose,
"cashToOperate" : cashToOperate,
}
# print "NFV = $%s" %"{:,}".format(int(NFV))
# print "PV10 = $%s" %"{:,}".format(int(PV10))
# print "IRR = %s" %round(ROR*100,2) + '%'
# print "Cash payout occurs in year %s" %cashPayout
# print "Cash on cash return in year 1 = %s" %int(cashOnCashYr1)+'%'
# print "Monthly cashflow in year 1 = $%s" % int(moCashflowYr1)
#return NFV, PV10, ROR, capRateYr1, cashPayout, cashOnCashYr1, moCashflowYr1, cashToClose, cashToOperate
return metricsDict
def mirr(values, finance_rate, reinvest_rate):
"""
Modified internal rate of return.
Parameters
----------
values : array_like
Cash flows (must contain at least one positive and one negative value)
or nan is returned.
finance_rate : scalar
Interest rate paid on the cash flows
reinvest_rate : scalar
Interest rate received on the cash flows upon reinvestment
Returns
-------
out : float
Modified internal rate of return
"""
values = np.asarray(values)
initial = values[0]
values = values[1:]
n = values.size
pos = values * (values>0)
neg = values * (values<0)
if not (pos.size > 0 and neg.size > 0):
return np.nan
numer = np.abs(np.npv(reinvest_rate, pos))
denom = np.abs(np.npv(finance_rate, neg))
if initial>0:
return ((initial + numer) / denom)**(1.0/n)*(1+reinvest_rate) - 1
else:
return ((numer / (-initial + denom)))**(1.0/n)*(1+reinvest_rate) - 1
def eq_subvention(montant, duree, taux):
""" Calcule l' "équivalent subvention" par rapport à un taux 0%
pour chacune des différentes combinaisons d'hypothèses possibles"""
# RAZ des résultats
equivalent_subvention = np.zeros((len(montant), len(duree), len(taux)))
part_subventionee = np.zeros((len(montant), len(duree), len(taux)))
# Calcule de l' "équivalent subvention"
for i in range(len(montant)):
for j in range(len(duree)):
# Périodicité des remboursements
per = np.arange(duree[j]) + 1
for k in range(len(taux)):
# Calcule l'échancier des intérêts perçus
echeancier_interets = -np.ipmt(taux[k], per, duree[j], montant[i])
# Calcule et enregistre l' "équivalent subvention" comparé
# à un taux 0 pour le jeu d'hypothèses considéré, les flux
# d'intérêt étant actualisés à 4%
equivalent_subvention[i, j, k] = np.npv(0.04, echeancier_interets)
# ou alternativemen, sans actualiser:
# equivalent_subvention[i,j,k]=np.sum(echeancier_interets)
part_subventionee[i, j, k] = (equivalent_subvention[i, j, k] / montant[i]) * 100
return equivalent_subvention, part_subventionee
def calc_npv(r, ubi, inputs, n):
x = np.zeros((ubi['Years Post Transfer'], n))
y = np.zeros((ubi['Years Post Transfer'], n))
# iterate through years of benefits
for j in range(1, ubi['Years Post Transfer'] + 1):
# sum benefits during program
if(j < r):
x[j - 1] += ubi['Expected baseline per capita consumption (nominal USD)']* \
np.power((1.0 + inputs['UBI']['Expected annual consumption increase (without the UBI program)']), float(j))* \
inputs['UBI']['Work participation adjustment'] + \
ubi['Annual quantity of transfer money used for immediate consumtion (pre-discounting)']
# benefits after program
else:
x[j - 1] += ubi['Expected baseline per capita consumption (nominal USD)']* \
np.power((1.0 + inputs['UBI']['Expected annual consumption increase (without the UBI program)']), float(j))
# investments calculations
for k in range(n):
if(j < r + inputs['UBI']['Duration of investment benefits (in years) - UBI'][k]):
x[j - 1][k] += ubi['Annual return for each year of transfer investments (pre-discounting)'][k]* \
np.min([j, inputs['UBI']['Duration of investment benefits (in years) - UBI'][k], \
r, (inputs['UBI']['Duration of investment benefits (in years) - UBI'][k] + r - j)])
if(j > r):
x[j - 1][k] += ubi['Value eventually returned from one years investment (pre-discounting)'][k]
# log transform and subtact baseline
y[j - 1] = np.log(x[j - 1])
y[j - 1] -= np.log(ubi['Expected baseline per capita consumption (nominal USD)']* \
np.power((1.0 + inputs['UBI']['Expected annual consumption increase (without the UBI program)']), float(j)))
# npv on yearly data
z = np.zeros(n)
for i in range(n):
z[i] = np.npv(inputs['Shared']['Discount rate'][i], y[:, i])
return z
depreciation_rate = 200
change_opex = labor_savings+forklift_savings+floor_salaries
## Gather income statements
income_0 = income_statement(name='Year 0', sales=0, cogs=0, sga=0, da=0, interest=0, tax_rate=tax_rate).get_income_statement(
depreciation_rate=0, delta_capex=equipment_buy, delta_workingcap=0)
income_1 = income_statement(name='Year 1', sales=0, cogs=0, sga=change_opex, da=depreciation_rate, interest=0, tax_rate=tax_rate).get_income_statement(
depreciation_rate=depreciation_rate, delta_capex=0, delta_workingcap=0)
income_2 = income_statement(name='Year 2', sales=0, cogs=0, sga=change_opex, da=depreciation_rate, interest=0,
tax_rate=tax_rate).get_income_statement(depreciation_rate=depreciation_rate, delta_capex=0, delta_workingcap=0)
income_3 = income_statement(name='Year 3', sales=0, cogs=0, sga=change_opex, da=depreciation_rate, interest=0,
tax_rate=tax_rate).get_income_statement(depreciation_rate=depreciation_rate, delta_capex=equipment_sell, delta_workingcap=0)
income = pd.concat([income_0, income_1, income_2, income_3], axis=1)
print(income)
NPV = np.npv(discount_rate, income.ix['FreeCashFlow'])
IRR = np.irr(income.ix['FreeCashFlow'])
payback_period = abs(income.ix['FreeCashFlow'][0]) / income.ix['FreeCashFlow'][1:].sum()
print('''
NPV ................................ %.2f
IRR ................................ %.2f
Payback Period ................................ %.2f
'''%(NPV, IRR, payback_period))
# In[167]:
income.ix['FreeCashFlow'][1:]
def robust_npv(values, rate=0.03): try: return np.npv(rate, values) except: return np.nan
def npv(flow, r=disc_fac):
sum = 0
for i in range(flow.shape[1]):
sum += np.npv(r, flow[:, i])
return sum
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
energySaleChange = sum(energySaleDRyear) - sum(energySaleArray) peakDemandRed = PeakDemandCharge - PeakDemandChargeDR # Calculating the Purchase Cost, Operation and Maint. Cost and Total Cost outData["AnnualOpCost"] = [- AnnDROM for x in lifeYears[0:]] LifetimeOperationCost = (sum(outData["AnnualOpCost"])) outData["LifetimeOperationCost"] = abs(LifetimeOperationCost) outData["lifePurchaseCosts"] = [-1.0 * DrTechCost] + [0 for x in lifeYears[1:]] outData["TotalCost"] = abs(outData["LifetimeOperationCost"] + DrTechCost) # Outputs of the Program Lifetime Cash Flow figure outData["EnergySaleChangeBenefit"] = [energySaleChange * ScalingAnnual ** x for x in range(lifeSpan)] outData["PeakDemandReduction"] = [peakDemandRed * ScalingAnnual ** x for x in range(lifeSpan)] BenefitCurve = [x+y for x,y in zip(outData["EnergySaleChangeBenefit"], outData["PeakDemandReduction"])] outData["TotalBenefit"] = sum(BenefitCurve) outData["BenefittoCostRatio"] = float(outData["TotalBenefit"] / outData["TotalCost"]) netBenefit = [x+y+z for x,y,z in zip(outData["AnnualOpCost"],outData["lifePurchaseCosts"],BenefitCurve)] outData["npv"] = npv(DiscountRate, netBenefit) outData["cumulativeNetBenefit"] = [sum(netBenefit[0:i+1]) for i,d in enumerate(netBenefit)] outData["SimplePaybackPeriod"] = DrTechCost / (outData["TotalBenefit"] / lifeSpan) # 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) except: # If input range wasn't valid delete output, write error to disk.
def work(modelDir, inputDict):
''' Run the model in its directory.'''
if inputDict['dispatch_type'] == 'prediction':
return workForecast(modelDir, inputDict)
out = {}
try:
with open(pJoin(modelDir, 'demand.csv'), 'w') as f:
f.write(inputDict['demandCurve'].replace('\r', ''))
with open(pJoin(modelDir, 'demand.csv')) as f:
demand = [float(r[0]) for r in csv.reader(f)]
assert len(demand) == 8760
with open(pJoin(modelDir, 'temp.csv'), 'w') as f:
lines = inputDict['tempCurve'].split('\n')
out["tempData"] = [float(x) if x != '999.0' else float(inputDict['setpoint']) for x in lines[:-1]]
correctData = [x+'\n' if x != '999.0' else inputDict['setpoint']+'\n' for x in lines][:-1]
f.write(''.join(correctData))
assert len(correctData) == 8760
except:
raise Exception("CSV file is incorrect format. Please see valid format "
"definition at <a target='_blank' href = 'https://github.com/dpinney/"
"omf/wiki/Models-~-storagePeakShave#demand-file-csv-format'>\nOMF Wiki "
"storagePeakShave - Demand File CSV Format</a>")
# # created using calendar = {'1': 31, '2': 28, ..., '12': 31}
# m = [calendar[key]*24 for key in calendar]
# monthHours = [(sum(m[:i]), sum(m[:i+1])) for i, _ in enumerate(m)]
monthHours = [(0, 744), (744, 1416), (1416, 2160), (2160, 2880),
(2880, 3624), (3624, 4344), (4344, 5088), (5088, 5832),
(5832, 6552), (6552, 7296), (7296, 8016), (8016, 8760)]
P_lower, P_upper, E_UL = pyVbat(modelDir, inputDict)
P_lower, P_upper, E_UL = list(P_lower), list(P_upper), list(E_UL)
out["minPowerSeries"] = [-1*x for x in P_lower]
out["maxPowerSeries"] = P_upper
out["minEnergySeries"] = [-1*x for x in E_UL]
out["maxEnergySeries"] = E_UL
VBpower, out["VBenergy"] = pulpFunc(inputDict, demand, P_lower, P_upper, E_UL, monthHours)
out["VBpower"] = VBpower
out["dispatch_number"] = [len([p for p in VBpower[s:f] if p != 0]) for (s, f) in monthHours]
peakDemand = [max(demand[s:f]) for s, f in monthHours]
energyMonthly = [sum(demand[s:f]) for s, f in monthHours]
demandAdj = [d+p for d, p in zip(demand, out["VBpower"])]
peakAdjustedDemand = [max(demandAdj[s:f]) for s, f in monthHours]
energyAdjustedMonthly = [sum(demandAdj[s:f]) for s, f in monthHours]
rms = all([x == 0 for x in P_lower]) and all([x == 0 for x in P_upper])
out["dataCheck"] = 'VBAT returns no values for your inputs' if rms else ''
out["demand"] = demand
out["peakDemand"] = peakDemand
out["energyMonthly"] = energyMonthly
out["demandAdjusted"] = demandAdj
out["peakAdjustedDemand"] = peakAdjustedDemand
out["energyAdjustedMonthly"] = energyAdjustedMonthly
cellCost = float(inputDict["unitDeviceCost"])*float(inputDict["number_devices"])
eCost = float(inputDict["electricityCost"])
dCharge = float(inputDict["demandChargeCost"])
out["VBdispatch"] = [dal-d for dal, d in zip(demandAdj, demand)]
out["energyCost"] = [em*eCost for em in energyMonthly]
out["energyCostAdjusted"] = [eam*eCost for eam in energyAdjustedMonthly]
out["demandCharge"] = [peak*dCharge for peak in peakDemand]
out["demandChargeAdjusted"] = [pad*dCharge for pad in out["peakAdjustedDemand"]]
out["totalCost"] = [ec+dcm for ec, dcm in zip(out["energyCost"], out["demandCharge"])]
out["totalCostAdjusted"] = [eca+dca for eca, dca in zip(out["energyCostAdjusted"], out["demandChargeAdjusted"])]
out["savings"] = [tot-tota for tot, tota in zip(out["totalCost"], out["totalCostAdjusted"])]
annualEarnings = sum(out["savings"]) - float(inputDict["unitUpkeepCost"])*float(inputDict["number_devices"])
cashFlowList = [annualEarnings] * int(inputDict["projectionLength"])
cashFlowList.insert(0, -1*cellCost)
out["NPV"] = npv(float(inputDict["discountRate"])/100, cashFlowList)
out["SPP"] = cellCost / annualEarnings
out["netCashflow"] = cashFlowList
out["cumulativeCashflow"] = [sum(cashFlowList[:i+1]) for i, d in enumerate(cashFlowList)]
out["stdout"] = "Success"
return out
# fv = np.fv(0.01, 5, -100, -1000) print(round(fv, 2)) # # 现值 = numpy.pv(利率, 期数, 每期支付, 终值) # 将多少钱1%的年利率存入银行5年，每年加存100元， # 到期后本息合计fv元？ # pv = np.pv(0.01, 5, -100, fv) print(pv) # # 净现值 = numpy.npv(利率, 现金流) # 将1000元以1%的年利率存入银行5年，每年加存100元， # 相当于一次存入多少钱？ # npv = np.npv(0.01, [-1000, -100, -100, -100, -100, -100]) print(round(npv, 2)) fv = np.fv(0.01, 5, 0, npv) print(round(fv, 2)) # # 内部收益率 = numpy.irr(现金流) # 将1000元存入银行5年，以后逐年提现100元、200元、300元、 # 400元、500元，银行利率达到多少，可在最后一次提现后偿 # 清全部本息？ # irr = np.irr([-1000, 100, 200, 300, 400, 500]) print(round(irr, 2)) npv = np.npv(irr, [-1000, 100, 200, 300, 400, 500]) print(round(npv, 2)) # # 每期支付 = numpy.pmt(利率, 期数, 现值)
def work(modelDir, inputDict):
''' Model processing done here. '''
dispatchStrategy = str(inputDict.get('dispatchStrategy'))
if dispatchStrategy == 'prediction':
return forecastWork(modelDir, inputDict)
out = {} # See bottom of file for out's structure
cellCapacity, dischargeRate, chargeRate, cellQuantity, demandCharge, cellCost, retailCost = \
[float(inputDict[x]) for x in ('cellCapacity', 'dischargeRate', 'chargeRate',
'cellQuantity', 'demandCharge', 'cellCost', 'retailCost')]
projYears, batteryCycleLife = [int(inputDict[x]) for x in ('projYears', 'batteryCycleLife')]
discountRate = float(inputDict.get('discountRate')) / 100.0
dodFactor = float(inputDict.get('dodFactor')) / 100.0
# Efficiency calculation temporarily removed
inverterEfficiency = float(inputDict.get('inverterEfficiency')) / 100.0
# Note: inverterEfficiency is squared to get round trip efficiency.
battEff = float(inputDict.get('batteryEfficiency')) / 100.0 * (inverterEfficiency ** 2)
with open(pJoin(modelDir, 'demand.csv'), 'w') as f:
f.write(inputDict['demandCurve'])
if dispatchStrategy == 'customDispatch':
with open(pJoin(modelDir, 'dispatchStrategy.csv'), 'w') as f:
f.write(inputDict['customDispatchStrategy'])
dc = [] # main data table
try:
dates = [(dt(2011, 1, 1) + timedelta(hours=1)*x) for x in range(8760)]
with open(pJoin(modelDir, 'demand.csv')) as f:
reader = csv.reader(f)
for row, date in zip(reader, dates):
dc.append({ 'power': float(row[0]), # row is a list of length 1
'month': date.month - 1,
'hour': date.hour })
assert len(dc) == 8760
except:
if str(sys.exc_info()[0]) != "<type 'exceptions.SystemExit'>":
raise Exception("CSV file is incorrect format. Please see valid "
"format definition at <a target='_blank' href = 'https://github.com/"
"dpinney/omf/wiki/Models-~-storagePeakShave#demand-file-csv-format'>"
"\nOMF Wiki storagePeakShave - Demand File CSV Format</a>")
# list of 12 lists of monthly demands
demandByMonth = [[t['power'] for t in dc if t['month']==x] for x in range(12)]
monthlyPeakDemand = [max(lDemands) for lDemands in demandByMonth]
battCapacity = cellQuantity * cellCapacity * dodFactor
battDischarge = cellQuantity * dischargeRate
battCharge = cellQuantity * chargeRate
SoC = battCapacity
if dispatchStrategy == 'optimal':
ps = [battDischarge] * 12
# keep shrinking peak shave (ps) until every month doesn't fully expend the battery
while True:
SoC = battCapacity
incorrect_shave = [False] * 12
for row in dc:
month = row['month']
if not incorrect_shave[month]:
powerUnderPeak = monthlyPeakDemand[month] - row['power'] - ps[month]
charge = (min(powerUnderPeak, battCharge, battCapacity - SoC) if powerUnderPeak > 0
else -1 * min(abs(powerUnderPeak), battDischarge, SoC))
if charge == -1 * SoC:
incorrect_shave[month] = True
SoC += charge
row['netpower'] = row['power'] + charge
row['battSoC'] = SoC
ps = [s-1 if incorrect else s for s, incorrect in zip(ps, incorrect_shave)]
if not any(incorrect_shave):
break
elif dispatchStrategy == 'daily':
start = int(inputDict.get('startPeakHour'))
end = int(inputDict.get('endPeakHour'))
for r in dc:
# Discharge if hour is within peak hours otherwise charge
charge = (-1*min(battDischarge, SoC) if start <= r['hour'] <= end
else min(battCharge, battCapacity - SoC))
r['netpower'] = r['power'] + charge
SoC += charge
r['battSoC'] = SoC
elif dispatchStrategy == 'customDispatch':
try:
with open(pJoin(modelDir,'dispatchStrategy.csv')) as f:
reader = csv.reader(f)
for d, r in zip(dc, reader):
d['dispatch'] = int(r[0])
assert all(['dispatch' in r for r in dc]) # ensure each row is filled
except:
if str(sys.exc_info()[0]) != "<type 'exceptions.SystemExit'>":
raise Exception("Dispatch Strategy file is in an incorrect "
"format. Please see valid format definition at <a target "
"= '_blank' href = 'https://github.com/dpinney/omf/wiki/"
"Models-~-storagePeakShave#custom-dispatch-strategy-file-"
"csv-format'>\nOMF Wiki storagePeakShave - Custom "
"Dispatch Strategy File Format</a>")
for r in dc:
# Discharge if there is a 1 in the dispatch strategy csv, otherwise charge the battery.
charge = (-1*min(battDischarge, SoC) if r['dispatch'] == 1
else min(battCharge, battCapacity-SoC))
r['netpower'] = r['power'] + charge
SoC += charge
r['battSoC'] = SoC
# ------------------------- CALCULATIONS ------------------------- #
netByMonth = [[t['netpower'] for t in dc if t['month']==x] for x in range(12)]
monthlyPeakNet = [max(net) for net in netByMonth]
ps = [h-s for h, s in zip(monthlyPeakDemand, monthlyPeakNet)]
dischargeByMonth = [[i-j for i, j in zip(k, l) if i-j < 0] for k, l in zip(netByMonth, demandByMonth)]
# Monthly Cost Comparison Table
out['monthlyDemand'] = [sum(lDemand)/1000 for lDemand in demandByMonth]
out['monthlyDemandRed'] = [t-p for t, p in zip(out['monthlyDemand'], ps)]
out['ps'] = ps
out['benefitMonthly'] = [x*demandCharge for x in ps]
# Demand Before and After Storage Graph
out['demand'] = [t['power']*1000.0 for t in dc] # kW -> W
out['demandAfterBattery'] = [t['netpower']*1000.0 for t in dc] # kW -> W
out['batteryDischargekW'] = [d-b for d, b in zip(out['demand'], out['demandAfterBattery'])]
out['batteryDischargekWMax'] = max(out['batteryDischargekW'])
# Battery State of Charge Graph
# Turn dc's SoC into a percentage, with dodFactor considered.
out['batterySoc'] = SoC = [t['battSoC']/battCapacity*100*dodFactor + (100-100*dodFactor) for t in dc]
# Estimate number of cyles the battery went through. Sums the percent of SoC.
cycleEquivalents = sum([SoC[i]-SoC[i+1] for i, x in enumerate(SoC[:-1]) if SoC[i+1] < SoC[i]]) / 100.0
out['cycleEquivalents'] = cycleEquivalents
out['batteryLife'] = batteryCycleLife / cycleEquivalents
# Cash Flow Graph
# inserting battery efficiency only into the cashflow calculation
# cashFlowCurve is $ in from peak shaving minus the cost to recharge the battery every day of the year
cashFlowCurve = [sum(ps)*demandCharge for year in range(projYears)]
cashFlowCurve.insert(0, -1 * cellCost * cellQuantity) # insert initial investment
# simplePayback is also affected by the cost to recharge the battery every day of the year
out['SPP'] = (cellCost*cellQuantity)/(sum(ps)*demandCharge)
out['netCashflow'] = cashFlowCurve
out['cumulativeCashflow'] = [sum(cashFlowCurve[:i+1]) for i, d in enumerate(cashFlowCurve)]
out['NPV'] = npv(discountRate, cashFlowCurve)
battCostPerCycle = cellQuantity * cellCost / batteryCycleLife
lcoeTotCost = cycleEquivalents*retailCost + battCostPerCycle*cycleEquivalents
out['LCOE'] = lcoeTotCost / (cycleEquivalents*battCapacity)
# Other
out['startDate'] = '2011-01-01' # dc[0]['datetime'].isoformat()
out['stderr'] = ''
# Seemingly unimportant. Ask permission to delete.
out['stdout'] = 'Success'
out['months'] = ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]
return out
def work(modelDir, inputDict):
''' Run the model in its directory. '''
# Copy spcific climate data into model directory
inputDict["climateName"] = zipCodeToClimateName(inputDict["zipCode"])
shutil.copy(pJoin(__neoMetaModel__._omfDir, "data", "Climate", inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, "file_name", modelDir + "/climate.tmy2")
ssc.ssc_data_set_number(dat, "system_size", float(inputDict.get("systemSize", 100)))
derate = float(inputDict.get("pvModuleDerate", 99.5))/100 \
* float(inputDict.get("mismatch", 99.5))/100 \
* float(inputDict.get("diodes", 99.5))/100 \
* float(inputDict.get("dcWiring", 99.5))/100 \
* float(inputDict.get("acWiring", 99.5))/100 \
* float(inputDict.get("soiling", 99.5))/100 \
* float(inputDict.get("shading", 99.5))/100 \
* float(inputDict.get("sysAvail", 99.5))/100 \
* float(inputDict.get("age", 99.5))/100 \
* float(inputDict.get("inverterEfficiency", 92))/100
ssc.ssc_data_set_number(dat, "derate", derate)
# TODO: Should we move inverter efficiency to 'inv_eff' below (as done in PVWatts?)
# Doesn't seem to affect output very much
# ssc.ssc_data_set_number(dat, "inv_eff", float(inputDict.get("inverterEfficiency", 92))/100)
ssc.ssc_data_set_number(dat, "track_mode", float(inputDict.get("trackingMode", 0)))
ssc.ssc_data_set_number(dat, "azimuth", float(inputDict.get("azimuth", 180)))
# Advanced inputs with defaults.
ssc.ssc_data_set_number(dat, "rotlim", float(inputDict.get("rotlim", 45)))
ssc.ssc_data_set_number(dat, "gamma", float(inputDict.get("gamma", 0.5))/100)
# Complicated optional inputs.
if (inputDict.get("tilt",0) == "-"):
tilt_eq_lat = 1.0
manualTilt = 0.0
else:
tilt_eq_lat = 0.0
manualTilt = float(inputDict.get("tilt",0))
ssc.ssc_data_set_number(dat, "tilt", manualTilt)
ssc.ssc_data_set_number(dat, "tilt_eq_lat", tilt_eq_lat)
# Run PV system simulation.
mod = ssc.ssc_module_create("pvwattsv1")
ssc.ssc_module_exec(mod, dat)
# Setting options for start time.
simLengthUnits = inputDict.get("simLengthUnits","hours")
simStartDate = inputDict.get("simStartDate", "2014-01-01")
# Set the timezone to be UTC, it won't affect calculation and display, relative offset handled in pvWatts.html
startDateTime = simStartDate + " 00:00:00 UTC"
# Timestamp output.
outData = {}
outData["timeStamps"] = [dt.datetime.strftime(
dt.datetime.strptime(startDateTime[0:19],"%Y-%m-%d %H:%M:%S") +
dt.timedelta(**{simLengthUnits:x}),"%Y-%m-%d %H:%M:%S") + " UTC" for x in range(int(inputDict.get("simLength", 8760)))]
# Geodata output.
outData["city"] = ssc.ssc_data_get_string(dat, "city")
outData["state"] = ssc.ssc_data_get_string(dat, "state")
outData["lat"] = ssc.ssc_data_get_number(dat, "lat")
outData["lon"] = ssc.ssc_data_get_number(dat, "lon")
outData["elev"] = ssc.ssc_data_get_number(dat, "elev")
# Weather output.
outData["climate"] = {}
outData["climate"]["Global Horizontal Radiation (W/m^2)"] = ssc.ssc_data_get_array(dat, "gh")
outData["climate"]["Plane of Array Irradiance (W/m^2)"] = ssc.ssc_data_get_array(dat, "poa")
outData["climate"]["Ambient Temperature (F)"] = ssc.ssc_data_get_array(dat, "tamb")
outData["climate"]["Cell Temperature (F)"] = ssc.ssc_data_get_array(dat, "tcell")
outData["climate"]["Wind Speed (m/s)"] = ssc.ssc_data_get_array(dat, "wspd")
# Power generation and clipping.
outData["powerOutputAc"] = ssc.ssc_data_get_array(dat, "ac")
invSizeWatts = float(inputDict.get("inverterSize", 0)) * 1000
outData["InvClipped"] = [x if x < invSizeWatts else invSizeWatts for x in outData["powerOutputAc"]]
try:
outData["percentClipped"] = 100 * (1.0 - sum(outData["InvClipped"]) / sum(outData["powerOutputAc"]))
except ZeroDivisionError:
outData["percentClipped"] = 0.0
# Cashflow outputs.
lifeSpan = int(inputDict.get("lifeSpan",30))
lifeYears = range(1, 1 + lifeSpan)
retailCost = float(inputDict.get("retailCost",0.0))
degradation = float(inputDict.get("degradation",0.5))/100
installCost = float(inputDict.get("installCost",0.0))
discountRate = float(inputDict.get("discountRate", 7))/100
outData["oneYearGenerationWh"] = sum(outData["powerOutputAc"])
outData["lifeGenerationDollars"] = [retailCost*(1.0/1000)*outData["oneYearGenerationWh"]*(1.0-(x*degradation)) for x in lifeYears]
outData["lifeOmCosts"] = [-1.0*float(inputDict["omCost"]) for x in lifeYears]
outData["lifePurchaseCosts"] = [-1.0 * installCost] + [0 for x in lifeYears[1:]]
srec = inputDict.get("srecCashFlow", "").split(",")
outData["srecCashFlow"] = map(float,srec) + [0 for x in lifeYears[len(srec):]]
outData["netCashFlow"] = [x+y+z+a for (x,y,z,a) in zip(outData["lifeGenerationDollars"], outData["lifeOmCosts"], outData["lifePurchaseCosts"], outData["srecCashFlow"])]
outData["cumCashFlow"] = map(lambda x:x, _runningSum(outData["netCashFlow"]))
outData["ROI"] = roundSig(sum(outData["netCashFlow"]), 3) / (-1*roundSig(sum(outData["lifeOmCosts"]), 3) + -1*roundSig(sum(outData["lifePurchaseCosts"], 3)))
outData["NPV"] = roundSig(npv(discountRate, outData["netCashFlow"]), 3)
outData["lifeGenerationWh"] = sum(outData["powerOutputAc"])*lifeSpan
outData["lifeEnergySales"] = sum(outData["lifeGenerationDollars"])
try:
# The IRR function is very bad.
outData["IRR"] = roundSig(irr(outData["netCashFlow"]), 3)
except:
outData["IRR"] = "Undefined"
# Monthly aggregation outputs.
months = {"Jan":0,"Feb":1,"Mar":2,"Apr":3,"May":4,"Jun":5,"Jul":6,"Aug":7,"Sep":8,"Oct":9,"Nov":10,"Dec":11}
totMonNum = lambda x:sum([z for (y,z) in zip(outData["timeStamps"], outData["powerOutputAc"]) if y.startswith(simStartDate[0:4] + "-{0:02d}".format(x+1))])
outData["monthlyGeneration"] = [[a, totMonNum(b)] for (a,b) in sorted(months.items(), key=lambda x:x[1])]
# Heatmaped hour+month outputs.
hours = range(24)
from calendar import monthrange
totHourMon = lambda h,m:sum([z for (y,z) in zip(outData["timeStamps"], outData["powerOutputAc"]) if y[5:7]=="{0:02d}".format(m+1) and y[11:13]=="{0:02d}".format(h+1)])
outData["seasonalPerformance"] = [[x,y,totHourMon(x,y) / monthrange(int(simStartDate[:4]), y+1)[1]] for x in hours for y in months.values()]
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
import numpy as np
# (1) 生成5个随机数作为现金流的取值。插入-100作为初始值。
cashflows = np.random.randint(100, size=5)
cashflows = np.insert(cashflows, 0, -100)
print("Cashflows", cashflows)
# (2) 根据上一步生成的现金流数据，调用npv函数计算净现值。利率按3%计算
print("Net present value", np.npv(0.03, cashflows))
def robust_npv(values, rate=0.03): try: return np.npv(rate, values) except: return np.nan
def work(modelDir, inputDict):
''' Run the model in a separate process. web.py calls this to run the model.
This function will return fast, but results take a while to hit the file system.'''
o = {}
(cellCapacity, dischargeRate, chargeRate, cellQuantity, cellCost) = \
[float(inputDict[x]) for x in ('cellCapacity', 'dischargeRate', 'chargeRate', 'cellQuantity', 'cellCost')]
inverterEfficiency = float(inputDict.get("inverterEfficiency")) / 100.0
battEff = float(
inputDict.get("batteryEfficiency")) / 100.0 * (inverterEfficiency**2)
discountRate = float(inputDict.get('discountRate')) / 100.0
dodFactor = float(inputDict.get('dodFactor')) / 100.0
projYears = int(inputDict.get('projYears'))
dischargePriceThreshold = float(inputDict.get('dischargePriceThreshold'))
chargePriceThreshold = float(inputDict.get('chargePriceThreshold'))
batteryCycleLife = int(inputDict.get('batteryCycleLife'))
# Put demand data in to a file for safe keeping.
with open(pJoin(modelDir, 'demand.csv'), 'w') as f:
f.write(inputDict['demandCurve'])
with open(pJoin(modelDir, 'priceCurve.csv'), 'w') as f:
f.write(inputDict['priceCurve'])
# Most of our data goes inside the dc "table"
dates = [dt(2011, 1, 1) + timedelta(hours=1) * x for x in range(8760)]
dc = []
try:
with open(pJoin(modelDir, 'demand.csv'), newline='') as f:
for row, date in zip(csv.reader(f), dates):
dc.append({'month': date.month - 1, 'power': float(row[0])})
assert len(dc) == 8760
except:
if str(sys.exc_info()[0]) != "<type 'exceptions.SystemExit'>":
raise Exception("Demand CSV file is incorrect format.")
#Add price to dc table
try:
with open(pJoin(modelDir, 'priceCurve.csv'), newline='') as f:
for row, d in zip(csv.reader(f), dc):
d['price'] = float(row[0])
assert all(['price' in r for r in dc])
except:
if str(sys.exc_info()[0]) != "<type 'exceptions.SystemExit'>":
raise Exception("Price Curve File is in an incorrect format.")
battCapacity = cellQuantity * cellCapacity * dodFactor
battSoC = battCapacity
for r in dc:
#If price of energy is above price threshold and battery has charge, discharge battery
if r['price'] >= dischargePriceThreshold:
charge = -1 * min(cellQuantity * dischargeRate, battSoC)
elif r['price'] <= chargePriceThreshold:
charge = min(cellQuantity * chargeRate, battCapacity - battSoC)
else:
charge = 0
r['netpower'] = r['power'] + charge
r['battSoC'] = battSoC
battSoC += charge
# There's definitely a nicer way to make this for loop, apologies
monthlyCharge = []
monthlyDischarge = []
monthlyDischargeSavings = []
monthlyChargeCost = []
# Calculate the monthly cost to charge and savings by discharging
for x in range(12):
chargeCost = 0
dischargeSavings = 0
chargePower = 0
dischargePower = 0
for r in dc:
if r['month'] == x:
diff = r['netpower'] - r['power']
if diff > 0:
chargePower += diff
chargeCost += diff * r['price']
if diff < 0:
dischargePower += -1 * diff
dischargeSavings += -1 * diff * r['price']
monthlyCharge.append(chargePower)
monthlyDischarge.append(dischargePower)
monthlyDischargeSavings.append(dischargeSavings)
monthlyChargeCost.append(chargeCost)
# include BattEff into calculations
monthlyCharge = [t / battEff for t in monthlyCharge]
monthlyChargeCost = [t / battEff for t in monthlyChargeCost]
# Monthly Cost Comparison Table
o['energyOffset'] = monthlyDischarge
o['dischargeSavings'] = monthlyDischargeSavings
o['kWhtoRecharge'] = monthlyCharge
o['costToRecharge'] = monthlyChargeCost
# NPV, SPP are below
# Demand Before and After Storage Graph
o['demand'] = [t['power'] * 1000.0 for t in dc]
o['demandAfterBattery'] = [t['netpower'] * 1000.0 for t in dc]
o['batteryDischargekW'] = [
d - b for d, b in zip(o['demand'], o['demandAfterBattery'])
]
o['batteryDischargekWMax'] = max(o['batteryDischargekW'])
# Price Input Graph
o['price'] = [r['price'] for r in dc]
# Battery SoC Graph
o['batterySoc'] = SoC = [
t['battSoC'] / battCapacity * 100.0 * dodFactor +
(100 - 100 * dodFactor) for t in dc
]
cycleEquivalents = sum([
SoC[i] - SoC[i + 1]
for i, x in enumerate(SoC[:-1]) if SoC[i + 1] < SoC[i]
]) / 100.0
o['cycleEquivalents'] = cycleEquivalents
o['batteryLife'] = batteryCycleLife / cycleEquivalents
# Cash Flow Graph
yearlyDischargeSavings = sum(monthlyDischargeSavings)
yearlyChargeCost = sum(monthlyChargeCost)
cashFlowCurve = [yearlyDischargeSavings - yearlyChargeCost] * projYears
cashFlowCurve.insert(0, -1 * cellCost * cellQuantity)
o['benefitNet'] = [
ds - cc for cc, ds in zip(o['costToRecharge'], o['dischargeSavings'])
]
o['netCashflow'] = cashFlowCurve
o['cumulativeCashflow'] = [
sum(cashFlowCurve[:i + 1]) for i, d in enumerate(cashFlowCurve)
]
o['NPV'] = npv(discountRate, cashFlowCurve)
o['SPP'] = (cellCost * cellQuantity) / (yearlyDischargeSavings -
yearlyChargeCost)
battCostPerCycle = cellQuantity * cellCost / batteryCycleLife
lcoeTotCost = (cycleEquivalents * cellQuantity * cellCapacity *
chargePriceThreshold) + (battCostPerCycle *
cycleEquivalents)
o['LCOE'] = lcoeTotCost / (cycleEquivalents * cellCapacity * cellQuantity)
# Other
o['startDate'] = '2011-01-01'
o["stdout"] = "Success"
o["stderr"] = ""
return o
def pv(self):
return np.npv(self.interest, [0] + self.cash_flows)
econ['expenses_variable']=(config['expenses']['variable']['oil']*econ['gross_sales_oil']+config['expenses']['variable']['gas']*econ['gross_sales_gas']+config['expenses']['variable']['ngl']*econ['gross_sales_ngl'])*config['working_interest']
econ['expenses_total']=(econ['expenses_variable']+config['expenses']['fixed'])*config['working_interest']
econ['gross_revenue_after_sevtax_and_expenses']=econ['gross_sales_total']-econ['severance_total']-econ['expenses_total']
econ['ad_valorum_tax']=econ['gross_revenue_after_sevtax_and_expenses']*config['taxes']['ad_valorum']
econ['gross_revenue_after_sevtax_and_expenses_and_advaltax']=econ['gross_revenue_after_sevtax_and_expenses']-econ['ad_valorum_tax']
econ['capital']=0.0
econ.ix[0,'capital']=(config['capital']['idc']+config['capital']['icc']+config['capital']['land'])*config['working_interest']
econ['gross_cash_flow']=econ['gross_revenue_after_sevtax_and_expenses_and_advaltax']-econ['capital']
econ['net_nondiscounted_cash_flow']=(econ['gross_revenue_after_sevtax_and_expenses_and_advaltax']*config['net_revenue_interest'])-econ['capital']
econ['cum_net_nondiscounted_cashflow']=econ['net_nondiscounted_cash_flow'].cumsum()
econ['net_discounted_cashflow']=econ['net_nondiscounted_cash_flow']/(1+annual_to_monthly_rate(config['discount_rate_annual']))**econ['standard_time']
econ['cum_net_discounted_cashflow']=econ['net_discounted_cashflow'].cumsum()
econ.to_excel(r'C:\Users\WaltN\Desktop\GitHub\curvey\output\econ.xlsx')
npv=round(np.npv(annual_to_monthly_rate(config['discount_rate_annual']),econ['net_nondiscounted_cash_flow']), 2)
irr=round(np.irr(econ['net_discounted_cashflow']), 2)
print('''
NPV: ${}
IRR: {}
'''.format(npv, irr))
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
econ_cap['cum_net_nondiscounted_cashflow'] = econ_cap[
'net_nondiscounted_cash_flow'].cumsum()
econ_cap['net_discounted_cashflow'] = econ_cap.apply(
lambda row: row['net_nondiscounted_cash_flow'] /
((1 + annual_to_monthly_rate(config['discount_rate_annual']))**
(row['standard_time'])),
axis=1)
econ_cap['cum_net_discounted_cashflow'] = econ_cap[
'net_discounted_cashflow'].cumsum()
econ_cap.to_excel(excel_output_file)
npv = round(
np.npv(annual_to_monthly_rate(config['discount_rate_annual']),
econ_cap['net_nondiscounted_cash_flow']), 2)
irr = round(np.irr(econ_cap['net_nondiscounted_cash_flow']), 2)
pv5 = round(
np.npv(annual_to_monthly_rate(0.05),
econ_cap['net_nondiscounted_cash_flow']), 2)
pv8 = round(
np.npv(annual_to_monthly_rate(0.08),
econ_cap['net_nondiscounted_cash_flow']), 2)
pv10 = round(
np.npv(annual_to_monthly_rate(0.10),
econ_cap['net_nondiscounted_cash_flow']), 2)
pv15 = round(
np.npv(annual_to_monthly_rate(0.15),
econ_cap['net_nondiscounted_cash_flow']), 2)
pv20 = round(
np.npv(annual_to_monthly_rate(0.20),
def work(modelDir, inputDict):
''' Run the model in its directory. '''
outData = {}
# Get variables.
lifeSpan = int(inputDict.get('lifeSpan',25))
lifeYears = list(range(1, 1 + lifeSpan))
hours = list(range(0, 24))
DrTechCost = float(inputDict.get('DrPurchInstallCost'))
demandCharge = float(inputDict.get('demandCharge'))
retailCost = float(inputDict.get('retailCost'))
AnnDROM = float(inputDict.get('AnnualDROperationCost'))
SubElas = float(inputDict.get('SubstitutionPriceElasticity'))
DayElas = float(inputDict.get('DailyPriceElasticity'))
wholesaleCost = float(inputDict.get('WholesaleEnergyCost'))
ManagLoad = float(inputDict.get('LoadunderManagement')) / 100.0
DiscountRate = float(inputDict.get('DiscountRate')) / 100
ScalingAnnual = float(inputDict.get('ScalingAnnual'))/ 100
PeakRate = float(inputDict.get('PeakRate'))
OffPeakRate = float(inputDict.get('OffPeakRate'))
startmonth= int(inputDict.get('startMonth'))
stopmonth = int(inputDict.get('stopMonth'))
starthour = int(inputDict.get('startHour'))
stophour = int(inputDict.get('stopHour'))
rateCPP = float(inputDict.get('rateCPP'))
rate24hourly = [float(x) for x in inputDict.get('rate24hourly').split(',')]
ratePTR = float(inputDict.get('ratePTR'))
numCPPDays = int(inputDict.get('numCPPDays'))
rateStruct = inputDict.get('rateStruct')
# Price vector creation.
OffPeakDailyPrice1 = [OffPeakRate for x in hours[0:starthour]]
PeakDailyPrice = [PeakRate for x in hours[starthour-1:stophour]]
OffPeakDailyPrice2 = [OffPeakRate for x in hours[stophour+1:24]]
ProgramPrices =[]
ProgramPrices.extend(OffPeakDailyPrice1)
ProgramPrices.extend(PeakDailyPrice)
ProgramPrices.extend(OffPeakDailyPrice2)
# Setting up the demand curve.
with open(pJoin(modelDir,"demand.csv"),"w") as demandFile:
demandFile.write(inputDict['demandCurve'])
try:
demandList = []
with open(pJoin(modelDir,"demand.csv"), newline='') as inFile:
reader = csv.reader(inFile)
for row in reader:
demandList.append(row) #######demandList.append({'datetime': parse(row['timestamp']), 'power': float(row['power'])})
if len(demandList)!=8760: raise Exception
except:
errorMessage = "CSV file is incorrect format. Please see valid format definition at <a target='_blank' href='https://github.com/dpinney/omf/wiki/Models-~-demandResponse#walkthrough'>OMF Wiki demandResponse</a>"
raise Exception(errorMessage)
demandCurve = [float(x[0]) for x in demandList]
outData['startDate'] = '2011-01-01'######demandList[0]['datetime'].isoformat()
# Run the PRISM model.
allPrismOutput = prism({
'rateStructure': rateStruct, # options: 2tier, 2tierCPP, PTR, 3tier, 24hourly
'elasticitySubWOCPP': SubElas, # Substitution elasticty during non-CPP days.
'elasticityDailyWOCPP': DayElas, # Daily elasticity during non-CPP days.
'elasticitySubWCPP': SubElas, # Substitution elasticty during CPP days. Only required for 2tierCPP
'elasticityDailyWCPP': DayElas, # Daily elasticity during non-CPP days. Only reuquired for 2tierCPP
'startMonth': startmonth, # 1-12. Beginning month of the cooling season when the DR program will run.
'stopMonth': stopmonth, # 1-12. Ending month of the cooling season when the DR program will run.
'startHour': starthour, # 0-23. Beginning hour for on-peak and CPP rates.
'stopHour': stophour, # 0-23. Ending hour for on-peak and CPP rates.
'rateFlat': retailCost, # pre-DR Time-independent rate paid by residential consumers.
'rateOffPeak': OffPeakRate,
'rateOnPeak': PeakRate, # Peak hour rate on non-CPP days.
'rateCPP': rateCPP, # Peak hour rate on CPP days. Only required for 2tierCPP
'rate24hourly': rate24hourly, #Hourly energy price, only needed for 24hourly
'ratePTR': ratePTR, # Only required for PTR. $/kWh payment to customers for demand reduction on PTR days. Value is entered as a positive value, just like the other rate values, even though it is a rebate.
'numCPPDays': numCPPDays, # Number of CPP days in a cooling season. Only required for 2tierCPP
'origLoad': demandCurve }) # 8760 load values
fullParticipationModLoad = allPrismOutput['modLoad']
modifiedLoad = [x*ManagLoad+y*(1-ManagLoad) for x,y in zip(fullParticipationModLoad,demandCurve)]
# with open('modifiedLoad.csv', 'wb') as outFile:
# for row in modifiedLoad:
# outfile.write(str(row) + '\n')
diff = [y-x for x,y in zip(modifiedLoad,demandCurve)]
# Demand Before and After Program Plot
outData['modifiedLoad'] = modifiedLoad
outData['demandLoad'] = demandCurve
outData['difference'] = diff
outData['differenceMax'] = round(max(diff),0)
outData['differenceMin'] = round(min(diff),0)
# Getting the hourly prices for the whole year (8760 prices)
ProgPricesArrayYear = ProgramPrices*365
OneYearwholesaleCost = [wholesaleCost for x in range(8760)]
AnnualEnergy = sum(demandCurve)
demandCurveJanuary = max(demandCurve[0:744])
demandCurveFebruary = max(demandCurve[745:1416])
demandCurveMarch = max(demandCurve[1417:2160])
demandCurveApril = max(demandCurve[2161:2880])
demandCurveMay = max(demandCurve[2881:3624])
demandCurveJune = max(demandCurve[3625:4344])
demandCurveJuly = max(demandCurve[4345:5088])
demandCurveAugust = max(demandCurve[5089:5832])
demandCurveSeptember = max(demandCurve[5833:6552])
demandCurveOctober = max(demandCurve[6553:7296])
demandCurveNovmber = max(demandCurve[6553:7296])
demandCurveDecember = max(demandCurve[7297:8760])
maxMontlyDemand = [demandCurveJanuary,demandCurveFebruary,demandCurveMarch,
demandCurveApril,demandCurveMay,demandCurveJune,demandCurveJuly,
demandCurveAugust,demandCurveSeptember,demandCurveOctober,
demandCurveNovmber,demandCurveDecember]
annualDemandCost = demandCharge * sum(maxMontlyDemand)
PowerCost = - (AnnualEnergy * wholesaleCost + annualDemandCost)
# Calculating the maximum montly peaks after applying DR
modifiedLoadJanuary = max(modifiedLoad[0:744])
modifiedLoadFebruary = max(modifiedLoad[745:1416])
modifiedLoadMarch = max(modifiedLoad[1417:2160])
modifiedLoadApril = max(modifiedLoad[2161:2880])
modifiedLoadMay = max(modifiedLoad[2881:3624])
modifiedLoadJune = max(modifiedLoad[3625:4344])
modifiedLoadJuly = max(modifiedLoad[4345:5088])
modifiedLoadAugust = max(modifiedLoad[5089:5832])
modifiedLoadSeptember = max(modifiedLoad[5833:6552])
modifiedLoadOctober = max(modifiedLoad[6553:7296])
modifiedLoadNovmber = max(modifiedLoad[6553:7296])
modifiedLoadDecember = max(modifiedLoad[7297:8760])
maxMontlyDemandDR = [modifiedLoadJanuary,modifiedLoadFebruary,modifiedLoadMarch,
modifiedLoadApril,modifiedLoadMay,modifiedLoadJune,modifiedLoadJuly,
modifiedLoadAugust,modifiedLoadSeptember,modifiedLoadOctober,
modifiedLoadNovmber,modifiedLoadDecember]
# Calculating the Base Case Profit
EnergySale = sum(demandCurve) * retailCost
EnergyCost = sum(demandCurve) * wholesaleCost
PeakDemandCharge = sum([x*demandCharge for x in maxMontlyDemand])
BaseCaseProfit = EnergySale - EnergyCost - PeakDemandCharge
# Calculating the DR Case Profit
EnergySaleDR = sum([z[0]*z[1] for z in zip(modifiedLoad,ProgPricesArrayYear)])
PeakDemandChargeDR = sum([x*demandCharge for x in maxMontlyDemandDR])
energyCostDR = sum(modifiedLoad) * wholesaleCost
DRCaseProfit = EnergySaleDR - PeakDemandChargeDR
# Outputs of First Year Financial Impact table.
outData["BaseCase"] = [AnnualEnergy, EnergySale, abs(EnergyCost), PeakDemandCharge, 0]
outData["DRCase"] = [sum(modifiedLoad),EnergySaleDR, abs(energyCostDR), PeakDemandChargeDR, DrTechCost]
# Calculating the Benefit Cashflow and Total benefit
energySaleDRyear = [x*y for x,y in zip(ProgPricesArrayYear, demandCurve)]
oneYearRetail = [retailCost for x in range(8760)]
energySaleArray = [x*y for x,y in zip(oneYearRetail, demandCurve)]
energySaleChange = sum(energySaleDRyear) - sum(energySaleArray)
peakDemandRed = PeakDemandCharge - PeakDemandChargeDR
# Calculating the Purchase Cost, Operation and Maint. Cost and Total Cost
outData["AnnualOpCost"] = [- AnnDROM for x in lifeYears[0:]]
LifetimeOperationCost = (sum(outData["AnnualOpCost"]))
outData["LifetimeOperationCost"] = abs(LifetimeOperationCost)
outData["lifePurchaseCosts"] = [-1.0 * DrTechCost] + [0 for x in lifeYears[1:]]
outData["TotalCost"] = abs(outData["LifetimeOperationCost"] + DrTechCost)
# Outputs of the Program Lifetime Cash Flow figure
outData["EnergySaleChangeBenefit"] = [energySaleChange * ScalingAnnual ** x for x in range(lifeSpan)]
outData["PeakDemandReduction"] = [peakDemandRed * ScalingAnnual ** x for x in range(lifeSpan)]
BenefitCurve = [x+y for x,y in zip(outData["EnergySaleChangeBenefit"], outData["PeakDemandReduction"])]
outData["TotalBenefit"] = sum(BenefitCurve)
outData["BenefittoCostRatio"] = float(outData["TotalBenefit"] / outData["TotalCost"])
netBenefit = [x+y+z for x,y,z in zip(outData["AnnualOpCost"],outData["lifePurchaseCosts"],BenefitCurve)]
outData["npv"] = npv(DiscountRate, netBenefit)
outData["cumulativeNetBenefit"] = [sum(netBenefit[0:i+1]) for i,d in enumerate(netBenefit)]
outData["SimplePaybackPeriod"] = DrTechCost / (outData["TotalBenefit"] / lifeSpan)
# Stdout/stderr.
outData["stdout"] = "Success"
outData["stderr"] = ""
return outData
def forecastWork(modelDir, ind):
''' Run the model in its directory.'''
(cellCapacity, dischargeRate, chargeRate, cellQuantity, cellCost) = \
[float(ind[x]) for x in ('cellCapacity', 'dischargeRate', 'chargeRate', 'cellQuantity', 'cellCost')]
demandCharge = float(ind['demandCharge'])
retailCost = float(ind['retailCost'])
battEff = float(ind.get("batteryEfficiency")) / 100.0
dodFactor = float(ind.get('dodFactor')) / 100.0
projYears = int(ind.get('projYears'))
batteryCycleLife = int(ind.get('batteryCycleLife'))
battCapacity = cellQuantity * float(ind['cellCapacity']) * dodFactor
o = {}
try:
with open(pJoin(modelDir, 'hist.csv'), 'w') as f:
f.write(ind['histCurve'].replace('\r', ''))
df = pd.read_csv(pJoin(modelDir, 'hist.csv'), parse_dates=['dates'])
df['month'] = df['dates'].dt.month
df['dayOfYear'] = df['dates'].dt.dayofyear
assert df.shape[0] >= 26280 # must be longer than 3 years
assert df.shape[1] == 5
except:
raise Exception("CSV file is incorrect format.")
confidence = float(ind['confidence'])/100
# ---------------------- MAKE PREDICTIONS ------------------------------- #
# train model on previous data
all_X = fc.makeUsefulDf(df)
all_y = df['load']
predictions = fc.neural_net_predictions(all_X, all_y)
dailyLoadPredictions = [predictions[i:i+24] for i in range(0, len(predictions), 24)]
weather = df['tempc'][-8760:]
dailyWeatherPredictions = [weather[i:i+24] for i in range(0, len(weather), 24)]
month = df['month'][-8760:]
dispatched = [False]*365
# decide to implement VBAT every day for a year
VB_power, VB_energy = [], []
for i, (load24, temp24, m) in enumerate(zip(dailyLoadPredictions, dailyWeatherPredictions, month)):
peak = max(load24)
if fc.shouldDispatchPS(peak, m, df, confidence):
dispatched[i] = True
vbp, vbe = fc.pulp24hrBattery(load24, dischargeRate*cellQuantity,
cellCapacity*cellQuantity, battEff)
VB_power.extend(vbp)
VB_energy.extend(vbe)
else:
VB_power.extend([0]*24)
VB_energy.extend([0]*24)
# -------------------- MODEL ACCURACY ANALYSIS -------------------------- #
o['predictedLoad'] = predictions
o['trainAccuracy'] = 0#round(model.score(X_train, y_train) * 100, 2)
o['testAccuracy'] = 0#round(model.score(X_test, y_test) * 100, 2)
# PRECISION AND RECALL
maxDays = []
for month in range(1, 13):
test = df[df['month'] == month]
maxDays.append(test.loc[test['load'].idxmax()]['dayOfYear'])
shouldHaveDispatched = [False]*365
for day in maxDays:
shouldHaveDispatched[day] = True
truePositive = len([b for b in [i and j for (i, j) in zip(dispatched, shouldHaveDispatched)] if b])
falsePositive = len([b for b in [i and (not j) for (i, j) in zip(dispatched, shouldHaveDispatched)] if b])
falseNegative = len([b for b in [(not i) and j for (i, j) in zip(dispatched, shouldHaveDispatched)] if b])
o['precision'] = round(truePositive / float(truePositive + falsePositive) * 100, 2)
o['recall'] = round(truePositive / float(truePositive + falseNegative) * 100, 2)
o['number_of_dispatches'] = len([i for i in dispatched if i])
o['MAE'] = round(sum([abs(l-m)/m*100 for l, m in zip(predictions, list(all_y[-8760:]))])/8760., 2)
# ---------------------- FINANCIAL ANALYSIS ----------------------------- #
# Calculate monthHours
year = df[-8760:].copy()
year.reset_index(inplace=True)
year['hour'] = list(year.index)
start = list(year.groupby('month').first()['hour'])
finish = list(year.groupby('month').last()['hour'])
monthHours = [(s, f+1) for (s, f) in zip(start, finish)]
demand = list(all_y[-8760:])
peakDemand = [max(demand[s:f]) for s, f in monthHours]
demandAdj = [d+p for d, p in zip(demand, VB_power)]
peakDemandAdj = [max(demandAdj[s:f]) for s, f in monthHours]
discharges = [f if f < 0 else 0 for f in VB_power]
# Monthly Cost Comparison Table
o['monthlyDemand'] = peakDemand
o['monthlyDemandRed'] = peakDemandAdj
o['ps'] = [p-s for p, s in zip(peakDemand, peakDemandAdj)]
o['benefitMonthly'] = [x*demandCharge for x in o['ps']]
# Demand Before and After Storage Graph
o['demand'] = demand
o['demandAfterBattery'] = demandAdj
o['batteryDischargekW'] = VB_power
o['batteryDischargekWMax'] = max(VB_power)
batteryCycleLife = float(ind['batteryCycleLife'])
# Battery State of Charge Graph
# Turn dc's SoC into a percentage, with dodFactor considered.
o['batterySoc'] = SoC = [100 - (e / battCapacity * 100) for e in VB_energy]
# Estimate number of cyles the battery went through. Sums the percent of SoC.
cycleEquivalents = sum([SoC[i]-SoC[i+1] for i, x in enumerate(SoC[:-1]) if SoC[i+1] < SoC[i]]) / 100.0
o['cycleEquivalents'] = cycleEquivalents
o['batteryLife'] = batteryCycleLife / cycleEquivalents
# Cash Flow Graph
# inserting battery efficiency only into the cashflow calculation
# cashFlowCurve is $ in from peak shaving minus the cost to recharge the battery every day of the year
cashFlowCurve = [sum(o['ps'])*demandCharge for year in range(projYears)]
cashFlowCurve.insert(0, -1 * cellCost * cellQuantity) # insert initial investment
# simplePayback is also affected by the cost to recharge the battery every day of the year
o['SPP'] = (cellCost*cellQuantity)/(sum(o['ps'])*demandCharge)
o['netCashflow'] = cashFlowCurve
o['cumulativeCashflow'] = [sum(cashFlowCurve[:i+1]) for i, d in enumerate(cashFlowCurve)]
o['NPV'] = npv(float(ind['discountRate']), cashFlowCurve)
battCostPerCycle = cellQuantity * cellCost / batteryCycleLife
lcoeTotCost = cycleEquivalents*retailCost + battCostPerCycle*cycleEquivalents
o['LCOE'] = lcoeTotCost / (cycleEquivalents*battCapacity)
# Other
o['startDate'] = '2011-01-01' # dc[0]['datetime'].isoformat()
o['stderr'] = ''
# Seemingly unimportant. Ask permission to delete.
o['stdout'] = 'Success'
o['months'] = ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]
return o
def __init__(self, 数据, 成本收入索引, 工程费用索引, 工程建设其他费用索引, 基本预备费, 收购费用, 建设投资, 维修费):
# ==========================
self.数据 = 数据
self.成本收入索引 = 成本收入索引
self.工程费用索引 = 工程费用索引
self.工程建设其他费用索引 = 工程建设其他费用索引
self.维修费_全年量 = self.成本收入取值(维修费, self.数据.数据索引['维修费_设定值'])
self.基本预备费 = 基本预备费
self.收购费用 = 收购费用
self.供冷收入_税前 = self.成本收入取值(self.成本收入索引['供冷收入'],
self.数据.数据索引['供冷收入_设定值'])
self.供蒸汽收入_税前 = self.成本收入取值(self.成本收入索引['蒸汽收入'],
self.数据.数据索引['供蒸汽收入_设定值'])
self.发电收入_税前 = self.成本收入取值(self.成本收入索引['售电收入'],
self.数据.数据索引['发电收入_设定值'])
self.供热收入_税前 = self.成本收入取值(self.成本收入索引['供热收入'],
self.数据.数据索引['供热收入_设定值'])
self.光伏发电收入_税前 = self.成本收入取值(self.成本收入索引['光伏发电收入'],
self.数据.数据索引['光伏发电收入_设定值'])
self.天然气销售收入_税前 = self.数据.数据索引['额外天然气销售年总收入']
self.供冷配套费收入_税前 = self.成本收入取值(0, self.数据.数据索引['供冷配套费收入_设定值'])
self.供热配套费收入_税前 = self.成本收入取值(0, self.数据.数据索引['供冷配套费收入_设定值'])
self.工资支出_全年量 = self.成本收入索引['工资及福利']
self.光伏运维成本_全年量 = self.成本收入取值(0, self.数据.数据索引['光伏运维成本_设定值'])
self.天然气支出_税前 = self.成本收入取值(
self.成本收入索引['燃气费'] + self.数据.数据索引['额外天然气销售年总成本'],
self.数据.数据索引['天然气支出_设定值'])
self.水支出_税前 = self.成本收入取值(self.成本收入索引['水费'], self.数据.数据索引['水支出_设定值'])
self.电支出_税前 = self.成本收入取值(self.成本收入索引['电费'], self.数据.数据索引['电支出_设定值'])
self.变压器容量费_税前 = self.数据.数据索引['变压器容量费']
self.系统备用容量费_税前 = self.数据.数据索引['系统备用容量费']
self.土地租金_全年量_税前 = self.数据.数据索引['土地租金']
self.设备租金_全年量_税前 = self.数据.数据索引['设备租金']
#
self.建筑费用 = 工程费用索引['工程费用']['建筑']
self.设备费用 = 工程费用索引['工程费用']['设备']
self.安装费用 = 工程费用索引['工程费用']['安装']
self.工程建设其他费用总和 = self.工程建设其他费用索引['工程建设其他费用合计']
# 土地费用
self.土地费用 = self.工程建设其他费用索引['土地费']
self.土地购置费投资 = self.工程建设其他费用索引['土地费']
self.无形资产投资 = 0
self.建设投资 = 建设投资
self.借款 = self.建设投资 * self.数据.数据索引['借款比例']
self.建设期利息 = self.借款 * self.数据.数据索引['借款利率'] * 1.1 / 2
# 等于工资一半
self.其他管理费用_全年量 = self.工资支出_全年量 * self.数据.数据索引['其他管理费用占工资比例']
self.达产率 = self.数据.数据索引['达产率']
# ========一级目录==========
self.现金流入dict = {}
self.现金流出dict = {}
self.现金流入 = [0] * 21
self.现金流出 = [0] * 21
# ========二级目录=========
self.销售收入 = {}
self.补贴收入 = [0] * 21
self.回收固定资产余值 = 0
self.回收流动资金 = 0
self.增值税销项税额 = {}
self.增值税进项税额 = dict()
self.增值税 = [0] * 21
self.流动资金 = {}
self.经营成本 = {}
# self.销售税金及附加 = {}
# self.所得税 = {}
# self.利用原有固定资产 = {}
# self.特种基金 = {}
# ========三级目录=========
# 销售收入
self.供冷收入 = [0] * 21
self.供热收入 = [0] * 21
self.发电收入 = [0] * 21
self.光伏发电收入 = [0] * 21
self.供蒸汽收入 = [0] * 21
self.天然气销售收入 = [0] * 21
self.供冷配套费收入 = [0] * 21
self.供热配套费收入 = [0] * 21
self.销售收入总和 = [0] * 21
# 固定资产余值
self.建筑类固定资产余值 = 0
self.设备及安装类固定资产余值 = 0
self.回收固定资产余值 = 0
# 销项税
self.供冷收入销项税 = [0] * 21
self.供蒸汽收入销项税 = [0] * 21
self.发电收入销项税 = [0] * 21
self.供热收入销项税 = [0] * 21
self.光伏发电收入销项税 = [0] * 21
self.天然气销售收入销项税 = [0] * 21
self.供冷配套费收入销项税 = [0] * 21
self.供热配套费收入销项税 = [0] * 21
self.增值税销项税额总和 = [0] * 21
# 进项税
self.天然气支出进项税 = [0] * 21
self.水支出进项税 = [0] * 21
self.电支出进项税 = [0] * 21
self.变压器容量费进项税 = [0] * 21
self.系统备用容量费进项税 = [0] * 21
self.增值税进项税额总和 = [0] * 21
# 增值税
self.增值税总和 = [0] * 21
# 销售税金及附加总和
self.销售税金及附加总和 = [0] * 21
# 流动资金
self.应收账款 = [0] * 21
self.天然气存货 = [0] * 21
self.电存货 = [0] * 21
self.水存货 = [0] * 21
self.现金 = [0] * 21
self.应付账款 = [0] * 21
self.流动资金总和 = [0] * 21
# 经营成本
self.天然气支出 = [0] * 21
self.水支出 = [0] * 21
self.电支出 = [0] * 21
self.变压器容量费 = [0] * 21
self.系统备用容量费 = [0] * 21
self.工资支出 = [0] * 21
self.光伏运维成本 = [0] * 21
self.土地租金 = [0] * 21
self.维修费 = [0] * 21
self.其他管理费用 = [0] * 21
self.经营成本总和 = [0] * 21
# 所得税
self.利润总和 = [0] * 21
self.EBIT = [0] * 21
self.所得税总和 = [0] * 21
# 折旧费
self.工程建设其他费用待摊费用 = 0
self.税后建筑类投资 = 0
self.税后设备及安装类投资 = 0
self.建筑类折旧费 = [0] * 21
self.设备及安装类折旧费 = [0] * 21
self.无形资产折旧费 = [0] * 21
self.土地购置费折旧费 = [0] * 21
self.折旧费 = dict()
self.折旧费总和 = [0] * 21
# 财务费用
self.财务费用 = [0] * 21
# 成本费用总和
self.成本费用总和 = [0] * 21
# 净现金流
self.税后净现金流量 = [0] * 21
self.累计税后净现金流量 = [0] * 21
# ===================计算========================
self.计算销售收入()
self.计算销项税()
self.计算经营成本()
self.计算进项税()
self.计算增值税及销售税金及附加()
self.计算流动资金()
self.计算折旧费()
self.计算财务费用()
self.计算成本费用总和()
self.计算所得税()
self.计算回收固定资产余值()
self.计算回收流动资金()
self.计算现金流入总和()
self.计算现金流出总和()
self.计算税后净现金流量()
self.计算累计税后净现金流量()
self.内部收益率 = np.irr(self.税后净现金流量)
self.计算投资回收期()
self.年均销售收入 = sum(self.销售收入总和) / 20
self.年均总成本费用 = sum(self.成本费用总和) / 20
self.年均利润总额 = sum(self.利润总和) / 20
self.净现值 = np.npv(0.12, self.税后净现金流量)
self.生成技经计算结果()
self.生成技经计算明细列表()
print('税后净现金流量', self.税后净现金流量)
print('内部收益率', self.内部收益率)
print('投资回收期', self.投资回收期)
print('建设投资', self.建设投资)
print('年均利润总额', self.年均利润总额)
print('现金流入', self.现金流入)
print('销售收入总和', self.销售收入总和)
print('增值税销项税额总和', self.增值税销项税额总和)
print('现金流出', self.现金流出)
print('增值税进项税额总和', self.增值税进项税额总和)
print('增值税总和', self.增值税总和)
print('流动资金总和', self.流动资金总和)
print('经营成本总和', self.经营成本总和)
print('销售税金及附加总和', self.销售税金及附加总和)
print('所得税总和', self.所得税总和)
print('成本费用总和', self.成本费用总和)
for key, value in self.经营成本.items():
print(key, value)
print('折旧费总和', self.折旧费总和)
print('年均总成本费用', self.年均总成本费用)
print('财务费用', self.财务费用)
print('年均销售收入', self.年均销售收入)
print('利润总和', self.利润总和)
print('维修费', self.维修费)
for key, value in self.流动资金.items():
print(key, value)
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
# 率，不考虑通胀因素。 # mirr函数计算修正后内部收益率（modified internal rate of return），是内部收益率的改进 # 版本。 # nper函数计算定期付款的期数。 # rate函数计算利率（rate of interest）。 print u"终值" #fv函数参数为利率，参数，每期支付金额，现值 print "Future value",np.fv(0.03/4, 5*4,-10,1000) print u"现值" #pv函数参数为利率，参数，每期支付金额，终值 print "Present value",np.pv(0.03/4,5*4,-10,1376.09633204) #npv参数为利率和一个表示现金流的数组. cashflows = np.random.randint(100,size=5) cashflows = np.insert(cashflows,0,-100) print "Cashflows",cashflows print "Net present value",np.npv(0.03,cashflows) print u"内部收益率" print "Internal rate of return",np.irr([-100, 38, 48,90 ,17,36]) print u"分期付款" #pmt输入为利率和期数,总价，输出每期钱数 print "Payment",np.pmt(0.10/12,12*30,100000) #nper参数为贷款利率，固定的月供和贷款额，输出付款期数 print "Number of payments", np.nper(0.10/12,-100,9000) #rate参数为付款期数，每期付款资金，现值和终值计算利率 print "Interest rate",12*np.rate(167,-100,9000,0) print u"窗函数" #bartlett函数可以计算巴特利特窗 window = np.bartlett(42) print "bartlett",window
def workForecast(modelDir, ind):
''' Run the model in its directory.'''
o = {}
# Grab data from CSV,
try:
with open(pJoin(modelDir, 'hist.csv'), 'w') as f:
f.write(ind['histCurve'].replace('\r', ''))
df = pd.read_csv(pJoin(modelDir, 'hist.csv'), parse_dates=['dates'])
df['month'] = df['dates'].dt.month
df['dayOfYear'] = df['dates'].dt.dayofyear
assert df.shape[0] >= 26280 # must be longer than 3 years
assert df.shape[1] == 5
except:
raise Exception("CSV file is incorrect format.")
# train model on previous data
all_X = fc.makeUsefulDf(df)
all_y = df['load']
X_train, y_train = all_X[:-8760], all_y[:-8760]
clf = linear_model.SGDRegressor(max_iter=10000, tol=1e-4)
clf.fit(X_train, y_train)
# ---------------------- MAKE PREDICTIONS ------------------------------- #
X_test, y_test = all_X[-8760:], all_y[-8760:]
predictions = clf.predict(X_test)
dailyLoadPredictions = [predictions[i:i+24] for i in range(0, len(predictions), 24)]
P_lower, P_upper, E_UL = vbat24hr(ind, df['tempc'][-8760:])
dailyPl = [P_lower[i:i+24] for i in range(0, len(P_lower), 24)]
dailyPu = [P_upper[i:i+24] for i in range(0, len(P_upper), 24)]
dailyEu = [E_UL[i:i+24] for i in range(0, len(E_UL), 24)]
vbp, vbe = [], []
dispatched_d = [False]*365
# Decide what days to dispatch
zipped = zip(dailyLoadPredictions, df['month'][-8760:], dailyPl, dailyPu, dailyEu)
for i, (load, m, pl, pu, eu) in enumerate(zipped):
peak = max(load)
if fc.shouldDispatchPS(peak, m, df, float(ind['confidence'])/100):
dispatched_d[i] = True
p, e = fc.pulp24hrVbat(ind, load, pl, pu, eu)
vbp.extend(p)
vbe.extend(e)
else:
vbp.extend([0]*24)
vbe.extend([0]*24)
### TESTING FOR ACCURACY ###
assert len(dailyPl) == 365
assert all([len(i) == 24 for i in dailyPl])
VB_power, VB_energy = vbp, vbe
# -------------------- MODEL ACCURACY ANALYSIS -------------------------- #
o['predictedLoad'] = list(clf.predict(X_test))
o['trainAccuracy'] = round(clf.score(X_train, y_train) * 100, 2)
o['testAccuracy'] = round(clf.score(X_test, y_test) * 100, 2)
# PRECISION AND RECALL
maxDays = []
for month in range(1, 13):
test = df[df['month'] == month]
maxDays.append(test.loc[test['load'].idxmax()]['dayOfYear'])
shouldHaveDispatched = [False]*365
for day in maxDays:
shouldHaveDispatched[day] = True
truePositive = len([b for b in [i and j for (i, j) in zip(dispatched_d, shouldHaveDispatched)] if b])
falsePositive = len([b for b in [i and (not j) for (i, j) in zip(dispatched_d, shouldHaveDispatched)] if b])
falseNegative = len([b for b in [(not i) and j for (i, j) in zip(dispatched_d, shouldHaveDispatched)] if b])
o['confidence'] = ind['confidence']
o['precision'] = round(truePositive / float(truePositive + falsePositive) * 100, 2)
o['recall'] = round(truePositive / float(truePositive + falseNegative) * 100, 2)
o['number_of_dispatches'] = len([i for i in dispatched_d if i])
o['MAE'] = round(sum([abs(l-m)/m*100 for l, m in zip(predictions, list(y_test))])/8760., 2)
# ---------------------- FINANCIAL ANALYSIS ----------------------------- #
o['VBpower'], o['VBenergy'] = list(VB_power), list(VB_energy)
# Calculate monthHours
year = df[-8760:].copy()
year.reset_index(inplace=True)
year['hour'] = list(year.index)
start = list(year.groupby('month').first()['hour'])
finish = list(year.groupby('month').last()['hour'])
monthHours = [(s, f+1) for (s, f) in zip(start, finish)]
demand = list(y_test)
peakDemand = [max(demand[s:f]) for s, f in monthHours]
energyMonthly = [sum(demand[s:f]) for s, f in monthHours]
demandAdj = [d+p for d, p in zip(demand, o['VBpower'])]
peakAdjustedDemand = [max(demandAdj[s:f]) for s, f in monthHours]
energyAdjustedMonthly = [sum(demandAdj[s:f]) for s, f in monthHours]
o['demand'] = demand
o['peakDemand'] = peakDemand
o['energyMonthly'] = energyMonthly
o['demandAdjusted'] = demandAdj
o['peakAdjustedDemand'] = peakAdjustedDemand
o['energyAdjustedMonthly'] = energyAdjustedMonthly
cellCost = float(ind['unitDeviceCost'])*float(ind['number_devices'])
eCost = float(ind['electricityCost'])
dCharge = float(ind['demandChargeCost'])
o['VBdispatch'] = [dal-d for dal, d in zip(demandAdj, demand)]
o['energyCost'] = [em*eCost for em in energyMonthly]
o['energyCostAdjusted'] = [eam*eCost for eam in energyAdjustedMonthly]
o['demandCharge'] = [peak*dCharge for peak in peakDemand]
o['demandChargeAdjusted'] = [pad*dCharge for pad in o['peakAdjustedDemand']]
o['totalCost'] = [ec+dcm for ec, dcm in zip(o['energyCost'], o['demandCharge'])]
o['totalCostAdjusted'] = [eca+dca for eca, dca in zip(o['energyCostAdjusted'], o['demandChargeAdjusted'])]
o['savings'] = [tot-tota for tot, tota in zip(o['totalCost'], o['totalCostAdjusted'])]
annualEarnings = sum(o['savings']) - float(ind['unitUpkeepCost'])*float(ind['number_devices'])
cashFlowList = [annualEarnings] * int(ind['projectionLength'])
cashFlowList.insert(0, -1*cellCost)
o['NPV'] = np.npv(float(ind['discountRate'])/100, cashFlowList)
o['SPP'] = cellCost / annualEarnings
o['netCashflow'] = cashFlowList
o['cumulativeCashflow'] = [sum(cashFlowList[:i+1]) for i, d in enumerate(cashFlowList)]
o['stdout'] = 'Success'
return o
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)])
def value_can_afford(monthly_payment):
# this is a 10 year average freddie mac interest rate
ten_year_average_interest = .055
return np.npv(ten_year_average_interest/12, [monthly_payment]*30*12)
def work(modelDir, ind):
#print(ind)
''' Run the model in its directory.'''
# drop inverter efficiency
# drop DoD
(cellCapacity, dischargeRate, chargeRate, cellQuantity, cellCost) = \
[float(ind[x]) for x in ('cellCapacity', 'dischargeRate', 'chargeRate', 'cellQuantity', 'cellCost')]
battEff = float(ind.get("batteryEfficiency")) / 100.0
dodFactor = float(ind.get('dodFactor')) / 100.0
projYears = int(ind.get('projectionLength'))
batteryCycleLife = int(ind.get('batteryCycleLife'))
o = {}
try:
with open(pJoin(modelDir, 'hist.csv'), 'w') as f:
f.write(ind['historicalData'])#.replace('\r', ''))
df = pd.read_csv(pJoin(modelDir, 'hist.csv'), parse_dates=['dates'])
df['month'] = df['dates'].dt.month
df['dayOfYear'] = df['dates'].dt.dayofyear
assert df.shape[0] >= 26280 # must be longer than 3 years
assert df.shape[1] == 5
except ZeroDivisionError:
raise Exception("CSV file is incorrect format.")
# retrieve goal
goal = ind['goal']
threshold = float(ind['transformerThreshold'])*1000
confidence = float(ind['confidence'])/100
# train model on previous data
all_X = fc.makeUsefulDf(df)
all_y = df['load']
X_train, y_train = all_X[:-8760], all_y[:-8760]
clf = linear_model.SGDRegressor(max_iter=10000, tol=1e-4)
clf.fit(X_train, y_train)
# ---------------------- MAKE PREDICTIONS ------------------------------- #
X_test, y_test = all_X[-8760:], all_y[-8760:]
# Collect data necessary for dispatch calculations
predictions = clf.predict(X_test)
dailyLoadPredictions = [predictions[i:i+24] for i in range(0, len(predictions), 24)]
weather = df['tempc'][-8760:]
dailyWeatherPredictions = [weather[i:i+24] for i in range(0, len(weather), 24)]
month = df['month'][-8760:]
dispatched = [False]*365
# decide to implement VBAT every day for a year
VB_power, VB_energy = [], []
for i, (load24, temp24, m) in enumerate(zip(dailyLoadPredictions, dailyWeatherPredictions, month)):
peak = max(load24)
if fc.shouldDispatchDeferral(peak, df, confidence, threshold):
dispatched[i] = True
vbp, vbe = fc.pulp24hrBattery(load24, dischargeRate*cellQuantity,
cellCapacity*cellQuantity, battEff)
VB_power.extend(vbp)
VB_energy.extend(vbe)
else:
VB_power.extend([0]*24)
VB_energy.extend([0]*24)
# -------------------- MODEL ACCURACY ANALYSIS -------------------------- #
o['predictedLoad'] = list(clf.predict(X_test))
o['trainAccuracy'] = round(clf.score(X_train, y_train) * 100, 2)
o['testAccuracy'] = round(clf.score(X_test, y_test) * 100, 2)
# PRECISION AND RECALL
maxDays = []
for month in range(1, 13):
test = df[df['month'] == month]
maxDays.append(test.loc[test['load'].idxmax()]['dayOfYear'])
shouldHaveDispatched = [False]*365
for day in maxDays:
shouldHaveDispatched[day] = True
truePositive = len([b for b in [i and j for (i, j) in zip(dispatched, shouldHaveDispatched)] if b])
falsePositive = len([b for b in [i and (not j) for (i, j) in zip(dispatched, shouldHaveDispatched)] if b])
falseNegative = len([b for b in [(not i) and j for (i, j) in zip(dispatched, shouldHaveDispatched)] if b])
o['precision'] = round(truePositive / float(truePositive + falsePositive) * 100, 2)
o['recall'] = round(truePositive / float(truePositive + falseNegative) * 100, 2)
o['number_of_dispatches'] = len([i for i in dispatched if i])
o['MAE'] = round(sum([abs(l-m)/m*100 for l, m in zip(predictions, list(y_test))])/8760., 2)
# ---------------------- FINANCIAL ANALYSIS ----------------------------- #
o['VBpower'], o['VBenergy'] = list(VB_power), list(VB_energy)
# Calculate monthHours
year = df[-8760:].copy()
year.reset_index(inplace=True)
year['hour'] = list(year.index)
start = list(year.groupby('month').first()['hour'])
finish = list(year.groupby('month').last()['hour'])
monthHours = [(s, f+1) for (s, f) in zip(start, finish)]
demand = list(y_test)
peakDemand = [max(demand[s:f]) for s, f in monthHours]
energyMonthly = [sum(demand[s:f]) for s, f in monthHours]
demandAdj = [d+p for d, p in zip(demand, o['VBpower'])]
peakAdjustedDemand = [max(demandAdj[s:f]) for s, f in monthHours]
energyAdjustedMonthly = [sum(demandAdj[s:f]) for s, f in monthHours]
o['demand'] = demand
o['peakDemand'] = peakDemand
o['energyMonthly'] = energyMonthly
o['demandAdjusted'] = demandAdj
o['peakAdjustedDemand'] = peakAdjustedDemand
o['energyAdjustedMonthly'] = energyAdjustedMonthly
initInvestment = cellCost*cellQuantity
eCost = float(ind['electricityCost'])
dCharge = float(ind['demandChargeCost'])
o['VBdispatch'] = [dal-d for dal, d in zip(demandAdj, demand)]
o['energyCost'] = [em*eCost for em in energyMonthly]
o['energyCostAdjusted'] = [eam*eCost for eam in energyAdjustedMonthly]
o['demandCharge'] = [peak*dCharge for peak in peakDemand]
o['demandChargeAdjusted'] = [pad*dCharge for pad in o['peakAdjustedDemand']]
o['totalCost'] = [ec+dcm for ec, dcm in zip(o['energyCost'], o['demandCharge'])]
o['totalCostAdjusted'] = [eca+dca for eca, dca in zip(o['energyCostAdjusted'], o['demandChargeAdjusted'])]
o['savings'] = [tot-tota for tot, tota in zip(o['totalCost'], o['totalCostAdjusted'])]
annualEarnings = sum(o['savings']) # - something!
cashFlowList = [annualEarnings] * int(ind['projectionLength'])
cashFlowList.insert(0, -1*initInvestment)
o['NPV'] = np.npv(float(ind['discountRate'])/100, cashFlowList)
o['SPP'] = initInvestment / annualEarnings
o['netCashflow'] = cashFlowList
o['cumulativeCashflow'] = [sum(cashFlowList[:i+1]) for i, d in enumerate(cashFlowList)]
o['dataCheck'] = 'Threshold exceeded' if any([threshold > i for i in demandAdj]) and goal == 'deferral' else ''
o['transformerThreshold'] = threshold if goal == 'deferral' else None
o['stdout'] = 'Success'
return o
import numpy as np cashflows = [-45.45, 50] npv = np.npv(0.1, cashflows) print(round(50 / (1 + 0.1), 2)) print(round(npv, 2))
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"] = omf.weather.zipCodeToClimateName(
inputDict["zipCode"])
inverterSizeAC = float(inputDict.get("inverterSize", 0))
if (inputDict.get("systemSize", 0) == "-"):
arraySizeDC = 1.3908 * inverterSizeAC
else:
arraySizeDC = float(inputDict.get("systemSize", 0))
numberPanels = (arraySizeDC * 1000 / 305)
# Set constants
panelSize = 305
trackingMode = 0
rotlim = 45.0
gamma = 0.45
if (inputDict.get("tilt", 0) == "-"):
tilt_eq_lat = 1.0
manualTilt = 0.0
else:
tilt_eq_lat = 0.0
manualTilt = float(inputDict.get("tilt", 0))
numberInverters = math.ceil(inverterSizeAC / 1000 / 0.5)
# Copy specific climate data into model directory
shutil.copy(
pJoin(__neoMetaModel__._omfDir, "data", "Climate",
inputDict["climateName"] + ".tmy2"),
pJoin(modelDir, "climate.tmy2"))
# Set up SAM data structures.
ssc = nrelsam2013.SSCAPI()
dat = ssc.ssc_data_create()
# Required user inputs.
ssc.ssc_data_set_string(dat, b'file_name',
bytes(modelDir + '/climate.tmy2', 'ascii'))
ssc.ssc_data_set_number(dat, b'system_size', arraySizeDC)
ssc.ssc_data_set_number(
dat, b'derate',
float(inputDict.get('inverterEfficiency', 96)) / 100 *
float(inputDict.get('nonInverterEfficiency', 87)) / 100)
ssc.ssc_data_set_number(dat, b'track_mode', float(trackingMode))
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(rotlim))
ssc.ssc_data_set_number(dat, b'gamma', float(-gamma / 100))
ssc.ssc_data_set_number(dat, b'tilt', manualTilt)
ssc.ssc_data_set_number(dat, b'tilt_eq_lat', 0.0)
# Run PV system simulation.
mod = ssc.ssc_module_create(b'pvwattsv1')
ssc.ssc_module_exec(mod, dat)
# Timestamp output.
outData = {}
outData["timeStamps"] = [
dt.datetime.strftime(
dt.datetime.strptime(startDateTime[0:19], "%Y-%m-%d %H:%M:%S") +
dt.timedelta(**{simLengthUnits: x}), "%Y-%m-%d %H:%M:%S") + " UTC"
for x in range(simLength)
]
# Geodata output.
outData["minLandSize"] = round(
(arraySizeDC / 1390.8 * 5 + 1) * math.cos(math.radians(22.5)) /
math.cos(math.radians(30.0)), 0)
landAmount = float(inputDict.get("landAmount", 6.0))
outData['city'] = ssc.ssc_data_get_string(dat, 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')
# Calculate clipping.
outData['powerOutputAc'] = ssc.ssc_data_get_array(dat, b'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] + list(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 heavyProcessing(modelDir, inputDict):
''' Run the model in a separate process. web.py calls this to run the model.
This function will return fast, but results take a while to hit the file system.'''
# Delete output file every run if it exists
try:
# Ready to run.
startTime = datetime.datetime.now()
outData = {}
# Get variables.
cellCapacity = float(inputDict['cellCapacity'])
(cellCapacity, dischargeRate, chargeRate, cellQuantity, demandCharge, cellCost) = \
[float(inputDict[x]) for x in ('cellCapacity', 'dischargeRate', 'chargeRate', 'cellQuantity', 'demandCharge', 'cellCost')]
battEff = float(inputDict.get("batteryEfficiency", 92)) / 100.0 * float(inputDict.get("inverterEfficiency", 92)) / 100.0 * float(inputDict.get("inverterEfficiency", 92)) / 100.0
discountRate = float(inputDict.get('discountRate', 2.5)) / 100.0
retailCost = float(inputDict.get('retailCost', 0.07))
dodFactor = float(inputDict.get('dodFactor', 85)) / 100.0
projYears = int(inputDict.get('projYears',10))
startPeakHour = int(inputDict.get('startPeakHour',18))
endPeakHour = int(inputDict.get('endPeakHour',24))
dispatchStrategy = str(inputDict.get('dispatchStrategy'))
batteryCycleLife = int(inputDict.get('batteryCycleLife',5000))
# Put demand data in to a file for safe keeping.
with open(pJoin(modelDir,"demand.csv"),"w") as demandFile:
demandFile.write(inputDict['demandCurve'])
# If dispatch is custom, write the strategy to a file in the model directory
if dispatchStrategy == 'customDispatch':
with open(pJoin(modelDir,"dispatchStrategy.csv"),"w") as customDispatchFile:
customDispatchFile.write(inputDict['customDispatchStrategy'])
# Start running battery simulation.
battCapacity = cellQuantity * cellCapacity * dodFactor
battDischarge = cellQuantity * dischargeRate
battCharge = cellQuantity * chargeRate
# Most of our data goes inside the dc "table"
try:
dc = []
with open(pJoin(modelDir,"demand.csv")) as inFile:
reader = csv.DictReader(inFile)
for row in reader:
dc.append({'datetime': parse(row['timestamp']), 'power': float(row['power'])})
if len(dc)!=8760: raise Exception
except:
e = sys.exc_info()[0]
if str(e) == "<type 'exceptions.SystemExit'>":
pass
else:
errorMessage = "CSV file is incorrect format. Please see valid format definition at <a target='_blank' href = 'https://github.com/dpinney/omf/wiki/Models-~-storagePeakShave#demand-file-csv-format'>\nOMF Wiki storagePeakShave - Demand File CSV Format</a>"
raise Exception(errorMessage)
for row in dc:
row['month'] = row['datetime'].month-1
row['hour'] = row['datetime'].hour
# row['weekday'] = row['datetime'].weekday() # TODO: figure out why we care about this.
if dispatchStrategy == "optimal":
outData['startDate'] = dc[0]['datetime'].isoformat()
ps = [battDischarge for x in range(12)]
dcGroupByMonth = [[t['power'] for t in dc if t['datetime'].month-1==x] for x in range(12)]
monthlyPeakDemand = [max(dcGroupByMonth[x]) for x in range(12)]
capacityLimited = True
while capacityLimited:
battSoC = battCapacity # Battery state of charge; begins full.
battDoD = [battCapacity for x in range(12)] # Depth-of-discharge every month, depends on dodFactor.
for row in dc:
month = int(row['datetime'].month)-1
powerUnderPeak = monthlyPeakDemand[month] - row['power'] - ps[month]
isCharging = powerUnderPeak > 0
isDischarging = powerUnderPeak <= 0
charge = isCharging * min(
powerUnderPeak * battEff, # Charge rate <= new monthly peak - row['power']
battCharge, # Charge rate <= battery maximum charging rate.
battCapacity - battSoC) # Charge rage <= capacity remaining in battery.
discharge = isDischarging * min(
abs(powerUnderPeak), # Discharge rate <= new monthly peak - row['power']
abs(battDischarge), # Discharge rate <= battery maximum charging rate.
abs(battSoC+.001)) # Discharge rate <= capacity remaining in battery.
# (Dis)charge battery
battSoC += charge
battSoC -= discharge
# Update minimum state-of-charge for this month.
battDoD[month] = min(battSoC,battDoD[month])
row['netpower'] = row['power'] + charge/battEff - discharge
row['battSoC'] = battSoC
capacityLimited = min(battDoD) < 0
ps = [ps[month]-(battDoD[month] < 0) for month in range(12)]
peakShaveSum = sum(ps)
elif dispatchStrategy == "daily":
outData['startDate'] = dc[0]['datetime'].isoformat()
battSoC = battCapacity
for row in dc:
month = int(row['datetime'].month)-1
discharge = min(battDischarge,battSoC)
charge = min(battCharge, battCapacity-battSoC)
#If hour is within peak hours and the battery has charge
if row['hour'] >= startPeakHour and row['hour'] <= endPeakHour and battSoC >= 0:
row['netpower'] = row['power'] - discharge
battSoC -= discharge
else:
#If hour is outside peak hours and the battery isnt fully charged, charge it
if battSoC < battCapacity:
battSoC += charge
row['netpower'] = row['power'] + charge/battEff
else:
row['netpower'] = row['power']
row['battSoC'] = battSoC
dcGroupByMonth = [[t['power'] for t in dc if t['datetime'].month-1==x] for x in range(12)]
simpleDCGroupByMonth = [[t for t in dc if t['datetime'].month-1==x] for x in range(12)]
#Finding rows with max power
monthlyPeakDemand = [max(dVals, key=lambda x: x['power']) for dVals in simpleDCGroupByMonth]
ps = []
#Determining monthly peak shave
for row in monthlyPeakDemand:
ps.append(row['power']-row['netpower'])
peakShaveSum = sum(ps)
else:
try:
with open(pJoin(modelDir,'dispatchStrategy.csv')) as strategyFile:
reader = csv.DictReader(strategyFile)
rowCount = 0
for i, row in enumerate(reader):
dc[i]['dispatch'] = int(row['dispatch'])
rowCount+=1
if rowCount!= 8760: raise Exception
except:
e = sys.exc_info()[0]
if str(e) == "<type 'exceptions.SystemExit'>":
pass
else:
errorMessage = "Dispatch Strategy file is in an incorrect format. Please see valid format definition at <a target = '_blank' href = 'https://github.com/dpinney/omf/wiki/Models-~-storagePeakShave#custom-dispatch-strategy-file-csv-format'>\nOMF Wiki storagePeakShave - Custom Dispatch Strategy File Format</a>"
raise Exception(errorMessage)
outData['startDate'] = dc[0]['datetime'].isoformat()
battSoC = battCapacity
for row in dc:
month = int(row['datetime'].month)-1
discharge = min(battDischarge,battSoC)
charge = min(battCharge, battCapacity-battSoC)
#If there is a 1 in the dispatch strategy csv, the battery discharges
if row['dispatch']==1:
row['netpower'] = row['power'] - discharge
battSoC -= discharge
else:
if battSoC < battCapacity:
battSoC += charge
row['netpower'] = row['power'] + charge/battEff
else:
row['netpower'] = row['power']
row['battSoC'] = battSoC
dcGroupByMonth = [[t['power'] for t in dc if t['datetime'].month-1==x] for x in range(12)]
#Calculating how much the battery discharges each month
dischargeGroupByMonth = [[t['netpower']-t['power'] for t in dc if t['datetime'].month-1==x] for x in range(12)]
simpleDCGroupByMonth = [[t for t in dc if t['datetime'].month-1==x] for x in range(12)]
monthlyPeakDemand = [max(dVals, key=lambda x: x['power']) for dVals in simpleDCGroupByMonth]
ps = []
for row in monthlyPeakDemand:
ps.append(row['power']-row['netpower'])
peakShaveSum = sum(ps)
chargePerMonth = []
#Calculate how much the battery charges per year for cashFlowCurve, SPP calculation, kWhToRecharge
for row in dischargeGroupByMonth:
total = 0
for num in row:
if num > 0:
total += num
chargePerMonth.append(total)
totalYearlyCharge = sum(chargePerMonth)
#Calculations
dcThroughTheMonth = [[t for t in iter(dc) if t['datetime'].month-1<=x] for x in range(12)]
hoursThroughTheMonth = [len(dcThroughTheMonth[month]) for month in range(12)]
if peakShaveSum == 0:
peakShaveSum = -1
#peakShave of 0 means no benefits, so make it -1
if dispatchStrategy == 'optimal':
cashFlowCurve = [peakShaveSum * demandCharge for year in range(projYears)]
outData['SPP'] = (cellCost*cellQuantity)/(peakShaveSum*demandCharge)
elif dispatchStrategy == 'daily':
#cashFlowCurve is $ in from peak shaving minus the cost to recharge the battery every day of the year
cashFlowCurve = [(peakShaveSum * demandCharge)-(battCapacity*365*retailCost) for year in range(projYears)]
#simplePayback is also affected by the cost to recharge the battery every day of the year
outData['SPP'] = (cellCost*cellQuantity)/((peakShaveSum*demandCharge)-(battCapacity*365*retailCost))
else:
cashFlowCurve = [(peakShaveSum * demandCharge)-(totalYearlyCharge*retailCost) for year in range(projYears)]
outData['SPP'] = (cellCost*cellQuantity)/((peakShaveSum*demandCharge)-(totalYearlyCharge*retailCost))
cashFlowCurve[0]-= (cellCost * cellQuantity)
outData['netCashflow'] = cashFlowCurve
outData['cumulativeCashflow'] = [sum(cashFlowCurve[0:i+1]) for i,d in enumerate(cashFlowCurve)]
outData['NPV'] = npv(discountRate, cashFlowCurve)
outData['demand'] = [t['power']*1000.0 for t in dc]
outData['demandAfterBattery'] = [t['netpower']*1000.0 for t in dc]
outData['batterySoc'] = [t['battSoC']/battCapacity*100.0*dodFactor + (100-100*dodFactor) for t in dc]
# Estimate number of cyles the battery went through.
SoC = outData['batterySoc']
cycleEquivalents = sum([SoC[i]-SoC[i+1] for i,x in enumerate(SoC[0:-1]) if SoC[i+1] < SoC[i]]) / 100.0
outData['cycleEquivalents'] = cycleEquivalents
outData['batteryLife'] = batteryCycleLife/cycleEquivalents
# # Output some matplotlib results as well.
# plt.plot([t['power'] for t in dc])
# plt.plot([t['netpower'] for t in dc])
# plt.plot([t['battSoC'] for t in dc])
# for month in range(12):
# plt.axvline(hoursThroughTheMonth[month])
# plt.savefig(pJoin(modelDir,"plot.png"))
# Summary of results
outData['months'] = ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]
totMonNum = []
monthlyDemand = []
for x in range (0, len(dcGroupByMonth)):
totMonNum.append(sum(dcGroupByMonth[x])/1000)
monthlyDemand.append([outData['months'][x], totMonNum[x]])
outData['monthlyDemand'] = totMonNum
outData['ps'] = ps
outData['monthlyDemandRed'] = [totMonNum - ps for totMonNum, ps in zip(totMonNum, ps)]
outData['benefitMonthly'] = [x * demandCharge for x in outData['ps']]
if dispatchStrategy == 'optimal':
outData['kWhtoRecharge'] = [battCapacity - x for x in outData['ps']]
elif dispatchStrategy == 'daily':
#Battery is dispatched and charged everyday, ~30 days per month
kWhtoRecharge = [battCapacity * 30 -x for x in range(12)]
outData['kWhtoRecharge'] = kWhtoRecharge
else:
#
kWhtoRecharge = []
for num in chargePerMonth:
kWhtoRecharge.append(num)
outData['kWhtoRecharge'] = kWhtoRecharge
outData['costtoRecharge'] = [retailCost * x for x in outData['kWhtoRecharge']]
benefitMonthly = outData['benefitMonthly']
costtoRecharge = outData['costtoRecharge']
outData['benefitNet'] = [benefitMonthly - costtoRecharge for benefitMonthly, costtoRecharge in zip(benefitMonthly, costtoRecharge)]
# Battery KW
demandAfterBattery = outData['demandAfterBattery']
demand = outData['demand']
outData['batteryDischargekW'] = [demand - demandAfterBattery for demand, demandAfterBattery in zip(demand, demandAfterBattery)]
outData['batteryDischargekWMax'] = max(outData['batteryDischargekW'])
battCostPerCycle = cellQuantity * cellCapacity * cellCost / batteryCycleLife
lcoeTotCost = (cycleEquivalents * cellQuantity * cellCapacity * retailCost) + (battCostPerCycle * cycleEquivalents)
loceTotEnergy = cycleEquivalents * cellQuantity * cellCapacity
LCOE = lcoeTotCost / loceTotEnergy
outData['LCOE'] = LCOE
# 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)
try:
os.remove(pJoin(modelDir,"PPID.txt"))
except:
pass
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)
timeRemaining = int(np.floor((5*240) - (exDrillTime + prodDrillTime))) # 5 years - exploration and production drill time
randomWalk = gmr(40.3, timeRemaining, 1) # Generate spot oil prices for time remaining days
# Arrays to be reset each simulation
qProd =[]
revProd = []
cProd = []
netProd = []
oilVal = []
for x in range(timeRemaining):
prodQ = prodQt(i+1)
qProd.append(prodQ)
revProd.append(randomWalk[x]*prodQ)
cProd.append(prodQ*prodCost)
netProd.append(revProd[x]-cProd[x])
oilVal.append(randomWalk[x])
npvProd.append(np.npv(dailyRate, netProd))
NPVcProd.append(np.npv(dailyRate, cProd))
NPVrevProd.append(np.npv(dailyRate, revProd))
SUMqProd.append(np.sum(qProd))
npvOilVal.append(np.npv(dailyRate, oilVal))
finalRev.append(np.npv(dailyRate, netProd)-1000.0*(cPreDiscover[i]+cDryWell[i]+cDrill[i]+cLogging[i]+cBlowout[i]))
else:
finalRev.append(-1000.0*(cPreDiscover[i]+cDryWell[i]+cDrill[i]+cLogging[i]+cBlowout[i]))
npvProd.append(0)
print("Pre Discovery")
print(descriptiveStats(cPreDiscover))
print(percentilesC(cPreDiscover))
print("Dry Well")
print(descriptiveStats(cDryWell))
print(percentilesC(cDryWell))
def test_npv_decimal(self):
assert_equal(
np.npv(Decimal('0.05'), [-15000, 1500, 2500, 3500, 4500, 6000]),
Decimal('122.894854950942692161628715'))
def heavyProcessing(modelDir, inputDict):
''' Run the model in a separate process. web.py calls this to run the model.
This function will return fast, but results take a while to hit the file system.'''
# Delete output file every run if it exists
try:
# Ready to run.
startTime = datetime.datetime.now()
outData = {}
# Get variables.
cellCapacity = float(inputDict['cellCapacity'])
(cellCapacity, dischargeRate, chargeRate, cellQuantity, demandCharge, cellCost) = \
[float(inputDict[x]) for x in ('cellCapacity', 'dischargeRate', 'chargeRate', 'cellQuantity', 'demandCharge', 'cellCost')]
battEff = float(inputDict.get("batteryEfficiency", 92)) / 100.0 * float(inputDict.get("inverterEfficiency", 92)) / 100.0 * float(inputDict.get("inverterEfficiency", 92)) / 100.0
discountRate = float(inputDict.get('discountRate', 2.5)) / 100.0
retailCost = float(inputDict.get('retailCost', 0.07))
dodFactor = float(inputDict.get('dodFactor', 85)) / 100.0
projYears = int(inputDict.get('projYears',10))
# Put demand data in to a file for safe keeping.
with open(pJoin(modelDir,"demand.csv"),"w") as demandFile:
demandFile.write(inputDict['demandCurve'])
# Start running battery simulation.
battCapacity = cellQuantity * cellCapacity * dodFactor
battDischarge = cellQuantity * dischargeRate
battCharge = cellQuantity * chargeRate
# Most of our data goes inside the dc "table"
try:
dc = []
with open(pJoin(modelDir,"demand.csv")) as inFile:
reader = csv.DictReader(inFile)
for row in reader:
dc.append({'datetime': parse(row['timestamp']), 'power': float(row['power'])})
if len(dc)<8760: raise Exception
except:
errorMessage = "CSV file is incorrect format. Please see valid format definition at\n <a target='_blank' href = 'https://github.com/dpinney/omf/wiki/Models-~-energyStorage#demand-file-csv-format'>OMF Wiki energyStorage</a>"
raise Exception(errorMessage)
for row in dc:
row['month'] = row['datetime'].month-1
row['weekday'] = row['datetime'].weekday
outData['startDate'] = dc[0]['datetime'].isoformat()
ps = [battDischarge for x in range(12)]
dcGroupByMonth = [[t['power'] for t in dc if t['datetime'].month-1==x] for x in range(12)]
monthlyPeakDemand = [max(dcGroupByMonth[x]) for x in range(12)]
capacityLimited = True
while capacityLimited:
battSoC = battCapacity # Battery state of charge; begins full.
battDoD = [battCapacity for x in range(12)] # Depth-of-discharge every month, depends on dodFactor.
for row in dc:
month = int(row['datetime'].month)-1
powerUnderPeak = monthlyPeakDemand[month] - row['power'] - ps[month]
isCharging = powerUnderPeak > 0
isDischarging = powerUnderPeak <= 0
charge = isCharging * min(
powerUnderPeak * battEff, # Charge rate <= new monthly peak - row['power']
battCharge, # Charge rate <= battery maximum charging rate.
battCapacity - battSoC) # Charge rage <= capacity remaining in battery.
discharge = isDischarging * min(
abs(powerUnderPeak), # Discharge rate <= new monthly peak - row['power']
abs(battDischarge), # Discharge rate <= battery maximum charging rate.
abs(battSoC+.001)) # Discharge rate <= capacity remaining in battery.
# (Dis)charge battery
battSoC += charge
battSoC -= discharge
# Update minimum state-of-charge for this month.
battDoD[month] = min(battSoC,battDoD[month])
row['netpower'] = row['power'] + charge/battEff - discharge
row['battSoC'] = battSoC
capacityLimited = min(battDoD) < 0
ps = [ps[month]-(battDoD[month] < 0) for month in range(12)]
dcThroughTheMonth = [[t for t in iter(dc) if t['datetime'].month-1<=x] for x in range(12)]
hoursThroughTheMonth = [len(dcThroughTheMonth[month]) for month in range(12)]
peakShaveSum = sum(ps)
outData['SPP'] = (cellCost*cellQuantity)/(peakShaveSum*demandCharge)
cashFlowCurve = [peakShaveSum * demandCharge for year in range(projYears)]
cashFlowCurve[0]-= (cellCost * cellQuantity)
outData['netCashflow'] = cashFlowCurve
outData['cumulativeCashflow'] = [sum(cashFlowCurve[0:i+1]) for i,d in enumerate(cashFlowCurve)]
outData['NPV'] = npv(discountRate, cashFlowCurve)
outData['demand'] = [t['power']*1000.0 for t in dc]
outData['demandAfterBattery'] = [t['netpower']*1000.0 for t in dc]
outData['batterySoc'] = [t['battSoC']/battCapacity*100.0*dodFactor + (100-100*dodFactor) for t in dc]
# Estimate number of cyles the battery went through.
SoC = outData['batterySoc']
outData['cycleEquivalents'] = sum([SoC[i]-SoC[i+1] for i,x in enumerate(SoC[0:-1]) if SoC[i+1] < SoC[i]]) / 100.0
# # Output some matplotlib results as well.
# plt.plot([t['power'] for t in dc])
# plt.plot([t['netpower'] for t in dc])
# plt.plot([t['battSoC'] for t in dc])
# for month in range(12):
# plt.axvline(hoursThroughTheMonth[month])
# plt.savefig(pJoin(modelDir,"plot.png"))
# Summary of results
outData['months'] = ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]
totMonNum = []
monthlyDemand = []
for x in range (0, len(dcGroupByMonth)):
totMonNum.append(sum(dcGroupByMonth[x])/1000)
monthlyDemand.append([outData['months'][x], totMonNum[x]])
outData['monthlyDemand'] = totMonNum
outData['ps'] = ps
outData['monthlyDemandRed'] = [totMonNum - ps for totMonNum, ps in zip(totMonNum, ps)]
outData['benefitMonthly'] = [x * demandCharge for x in outData['ps']]
outData['kWhtoRecharge'] = [battCapacity - x for x in outData['ps']]
outData['costtoRecharge'] = [retailCost * x for x in outData['kWhtoRecharge']]
benefitMonthly = outData['benefitMonthly']
costtoRecharge = outData['costtoRecharge']
outData['benefitNet'] = [benefitMonthly - costtoRecharge for benefitMonthly, costtoRecharge in zip(benefitMonthly, costtoRecharge)]
# Battery KW
demandAfterBattery = outData['demandAfterBattery']
demand = outData['demand']
outData['batteryDischargekW'] = [demand - demandAfterBattery for demand, demandAfterBattery in zip(demand, demandAfterBattery)]
outData['batteryDischargekWMax'] = max(outData['batteryDischargekW'])
# 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)
try:
os.remove(pJoin(modelDir,"PPID.txt"))
except:
pass
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 test_npv(self):
assert_almost_equal(np.npv(0.05,[-15000,1500,2500,3500,4500,6000]),
117.04, 2)
def get_roe_date(code, coefficient, required_rate_of_return, ref_year,
df_stock, type):
# ref_year = datetime.date.today() # date type
# ref_year = ref_year.year - 1 # 현재 년도 실적 ROE는 없음으로 직전 년도를 기준으로 함.
# required_rate_of_return는 현재 BBB- 5년를 사용하지만, 업종별, 종목별, 시장환경에 맞게 변경하여 판단이 필요함.
try:
year_capital = df_stock.loc['capital', ref_year]
year_roe = df_stock.loc['roe', ref_year] / 100 # 단위 % 환산
spread = year_roe - required_rate_of_return # 단위 % - % 계산임으로 spread변수도 값이 %가 됨.
# 테이블 계산을 위한 기본 setting
index = ['coefficient', 'roe', 'net_profit', 'earing', 'capital']
df_table = pd.DataFrame({ref_year: [1, year_roe, 0, 0, year_capital]},
index=index)
# 적정가치 구하기
roe_max_len = len(df_stock.columns) # 년도가 있는 컬럼의 최대 길이 구함
year_index = df_stock.columns.values.tolist() #array자료형을 list형으로 변환
year_index = year_index.index(ref_year) # ref_year를 기준으로 컬럼에서 위치 찾음
year_roe_count = roe_max_len - year_index
year = copy(ref_year) # ref_year를 year로 copy하여 별도 변수로 사용 a is b로 테스트
calculated_coefficient = 0
calculated_roe = 0
calculated_net_profit = 0
calculated_earing = 0
calculated_capital = 0
for year_count in range(1, 11): #10년 동안 계산
calculated_coefficient = df_table.loc[
'coefficient', year] - coefficient # dataframe에서 직전년도 roe와 계산
if calculated_coefficient < 0.0: #roe 0 이하이면 0으로 세팅
calculated_coefficient = 0.0
# 기준년을 참조하여 table상의 roe그대로 사용하고, roe참조가 끝나면 계산함.
'''
if year_roe_count >= year_count:
calculated_roe = df_stock.loc['roe',year] / 100 # 단위 % 환산
else:
calculated_roe = spread * calculated_coefficient + required_rate_of_return # spread와 required_rate_of_return는 %단위로 환산되어있음.
'''
calculated_roe = spread * calculated_coefficient + required_rate_of_return # spread와 required_rate_of_return는 %단위로 환산되어있음.
calculated_net_profit = df_table.loc['capital',
year] * calculated_roe
calculated_earing = calculated_net_profit - df_table.loc[
'capital', year] * required_rate_of_return # 단위 % 환산
calculated_capital = df_table.loc['capital',
year] + calculated_net_profit
if type == 'year':
year = year + 1 # 1년 증가
elif type == 'quarter':
year_month = datetime.strptime(year, '%Y%m')
year_month = year_month + relativedelta(months=+3)
year = year_month.strftime('%Y%m')
seri_table = pd.Series([
calculated_coefficient, calculated_roe, calculated_net_profit,
calculated_earing, calculated_capital
],
name=year,
index=index)
df_table = pd.concat([df_table, seri_table], axis=1)
# NPV구하기
pv_val = df_table.loc['earing'].values.tolist()
pv_val = pv_val[1:]
# pv = get_npv(required_rate_of_return, pv_val) #초기 투자금액은 0,np.npv(0.281,[-0, 39, 59, 55, 20])
pv = np.npv(required_rate_of_return, pv_val)
rim = year_capital + pv
# 적정주가 구하기
'''
stock_date = stock.loc['date',ref_year] # date type
today = datetime.date.today() # date type
date_delta = stock_date - today # timedelta type
date_delta = date_delta.days # int type
stock_value를 구할때 할인율의 산출일 기준으로 자승으로 곱하여 남은 날짜만큼 구하고 있음.
그러나, 이것은 과거데이타 이용 및 현재 가치 판단시에 과한것으로 판단되어 일단, 사용하지 않음
또한, 기준날짜 - 현재날짜 일때 ...컨센서스 데이타가 없을 때는 (-)값이 나와 엉뚱하게 과하게 나옴.
stock_value1 = rim / (1 + required_rate_of_return)**(date_delta/365)
'''
# RIM 값을 구한후 미래가치에 대해 spread값만큼 자승하여 현재 가치를 구함
# calculated_rim = rim / (1 + required_rate_of_return) roe를 해당 Year것을 사용하여 할인율 미적용함.
share_outstanding = df_stock.loc['share_outstanding',
ref_year] # ROE가 계산되는 year의 보통주식수
value_per_share = int(
(rim / share_outstanding) * 100000000) # 단위:억원 환산, 정수처리
'''
# 자승하여 현재가지치구하지 않고 현재 year 기준으로 모든것을 구했음으로 미래가치 -> 현재가지 부분을 제외
share_outstanding = df_stock.loc['share_outstanding',ref_year] # ROE가 계산되는 year의 보통주식수
value_per_share = int((rim / share_outstanding) * 100000000) # 단위:억원 환산, 정수처리
'''
except:
return (000000) # 종목정보 오류는 000000으로 세팅
else:
return (value_per_share)
