Scipy.optimise differential evolution made NPV value lower than original value. Is this normal? - numpy

Question

Ran a scipy.optimise with differential evolution on my script below and got a result that says my optimal value is lower than my original value. Is this normal?
enter image description here
Have posted all my code here for any answers that may help me.
# from centralUncertainConstants import *
import pandas as pd
import numpy as np
from statistics import NormalDist
import random
import math
from scipy.stats import norm
import matplotlib.pyplot as plt;
demandyr0 = 0.0240232711111154
MDemLimit = 1.2042811301817
aTransParam = 49.1297731108936
bsharpParam = 0.20118446680713
annVol = 0.15
volyr0 = 0.5
volM = 0.5
volb = 0.7
scaleFactor = 1468000
BlueH2MarketShare = 0.4
SFMarketShare = 0.18
thou = 1000
mill = 1000000
daysyear = 365
cons1 = 8760
cons2 = 16.92
plantDesignCap = 190950;
plantOperationalCap = 0.95;
CO2emissionRate = 1.06; # Kg of CO2 per Kg of H2
CO2captureRate = 8.60; # Kg of CO2 per Kg of H2
CO2emissionRatefeedstock = 0.28;
CO2emissionRatefuel = 1.18;
NatGasConsumption = 33411;
plantDesignCapBase = 190950;
plantDesignCapScaled = 190000;
lifetime = 25
workingHours = 8322
dailyprodrate = 190000;
yearlyprodrate = dailyprodrate * plantOperationalCap * 365/1000;
discountrate = 0.1; # Nine percent per annum
th = 25
statetax = 0.0725;
fedtax = 0.21;
#Hprice stuff
Hpricelow = 8
Hpricehigh = 15
Hpriceave = 11.5
#H2 delivery cost stuff
H2dpave = 1.07
H2dplow = H2dpave*(1+0.3)
H2dohigh = H2dpave*(1-0.3)
#CO2 transport storage (ts) stuff
CO2low = 10
CO2high = 30
CO2ave = 15
capInvestment = 591730751/1000000
basecostFF = 67836966;
basecostWM = 104434;
basecostCC = 625712;
#CO2 gas prices constants
drift = 0.00234
volatility = 0.19
CO2captradeprice = 29.15
initialCapCostsFixed = 591.73
initialCapCostsPhased = 230.53
#Other Phased stuff
iniProdRateDay = 47500
iniProdRateYear = 16470.63
expansionTimes = 3
expansionIncrement = 16470.63
expansionThresh = 0.75
reductionDuringExpansion = 0.5
dict = {
16470.63: 6.59,
32941.26: 10.56,
49411.89: 13.91,
65882.52: 16.92,
#82353.13: 19.69
}
dict2 = {
16470.63: 137.12,
32941.26: 137.12,
49411.89: 137.12,
65882.52: 137.12,
}
#For plots using matplotlib
a = 49.1298;
b = 0.2012;
demand = 1468000; # raw data demand in 2050 from source. required to calc demandCAL
MACRSfixed = [22,43,40,37,34,31,29,27,26,26,26,26,26,26,26,26,26,26,26,26,13,0,0,0,0]
MACRSphased = [10,19,18,16,15,19,23,21,20,25,29,28,27,31,35,34,33,32,31,31,24,18,18,18,18]
MACRSflexible = [10,19,18,16,15,14,13,12,12,17,22,21,20,25,29,33,36,35,34,33,26,19,18,18,18]
q = [46.96, 50, 54.25, 58.86, 63.86, 69.29, 75.18, 81.57, 88.51, 96.03, 104.19, 113.05, 122.66, 133.08, 144.40, 156.67, 169.99, 184.44, 200.11, 217.12, 235.58, 255.60, 277.33, 300.90, 326.48]
nomyear = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25]
nomyear2 = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]
nums = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]
year = [2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050]
ngprices = [130.48,109.52,118.95,225.84,207.50,177.11,286.63,308.64,455.36,352.65,365.23,464.26,206.46,228.99,209.60,144.10,195.45,228.99,137.29,132.05,156.68,165.06,134.14,106.37,203.84]
elecprices = [7.27,6.67,6.81,6.92,6.88,6.76,6.91,7.10,6.89,6.67,6.82,6.77,6.83,6.96,6.39,6.16,5.73,5.25,5.11,4.88,5.05,4.64,4.43,4.48,4.53]
def test(help):
yearlyprodrate = help[0]
def npv():
availableRate = pd.Series(index=nums, dtype='float64')
finalprodrate = pd.Series(index=nums, dtype='float64')
ProdCO2emissions = pd.Series(index=nums, dtype='float64')
CO2capture = pd.Series(index=nums, dtype='float64')
upstreamCO2emissions = pd.Series(index=nums, dtype='float64')
revenue = pd.Series(index=nums, dtype='float64')
fixedOPEX = pd.Series(index=nums, dtype='float64')
varOPEXcost = pd.Series(index=nums, dtype='float64')
OpCost = pd.Series(index=nums, dtype='float64')
Depreciation = pd.Series(index=nums, dtype='float64')
# expansionInvestment = pd.Series(index=nums, dtype='float64')
# expansionInvestment2 = pd.Series(index=nums, dtype='float64')
demandprojection = pd.Series(index=nomyear2, dtype='float64').array
normDemProjGrowth = pd.Series(index=nums, dtype='float64')
randomDraw = pd.Series(index=nums, dtype='float64')
realisedGrowth = pd.Series(index=nums, dtype='float64')
realisedNormalisedDemand = pd.Series(index=nums, dtype='float64')
realisedDemand = pd.Series(index=nums, dtype='float64')
demandSF = pd.Series(index=nums, dtype='float64')
sv = 0
cf = pd.Series(index=nums, dtype='float64')
dcf = pd.Series(index=nums, dtype='float64')
npv1 = 0
npv = 0
# Demand Projection
realisedDemandyr0 = (1-volyr0)*(demandyr0)+2*demandyr0*volyr0*random.uniform(0,1)
stoM = (1-volM)*MDemLimit+2*volM*MDemLimit*random.uniform(0,1)
stoa = stoM/realisedDemandyr0-1
stob = (1-volb)*bsharpParam+2*volb*bsharpParam*random.uniform(0,1)
for i in range(0,len(nomyear2)):
demandprojection[i] = stoM/(1+stoa*np.exp(-nomyear2[i]*stob))
for i in range(0,len(nomyear2)-1):
normDemProjGrowth[i] = (demandprojection[i+1]-demandprojection[i])/demandprojection[i]
for i in range(0,len(nomyear2)):
randomDraw[i] = NormalDist(mu=0, sigma=1).inv_cdf(random.uniform(0,1))
for i in range(0, len(nomyear2)-1):
realisedGrowth[i] = normDemProjGrowth[i]+randomDraw[i+1]*annVol
for i in range(0, len(nomyear2)-1):
realisedNormalisedDemand[i] = demandprojection[i+1]*(1+realisedGrowth[i])
for i in range(0, len(nomyear2)-1):
realisedDemand[i] = realisedNormalisedDemand[i]*(scaleFactor*BlueH2MarketShare*SFMarketShare)
demandSF = realisedDemand[0:th]
for i in range(0, th):
availableRate[i] = yearlyprodrate
# finalprodrate = []
for i in range(0, th):
finalprodrate[i] = min(demandSF[i], availableRate[i])
# for i, v in enumerate(demandSF):
# finalprodrate.append(min(v, availableRate[i]))
# if i < yearlyprodrate:
# finalprodrate.append(i)
# else:
# finalprodrate.append(yearlyprodrate)
# ProdCO2emissions = [];
# CO2capture = []
# upstreamCO2emissions = [];
# for j in finalprodrate:
# ems = CO2emissionRate*(j*1000)/1000
# capture = CO2captureRate*(j*1000)/1000
# upstream = (CO2emissionRatefeedstock/1000)*(NatGasConsumption*8760)*(j/(plantDesignCapBase*plantOperationalCap*365/1000))
# ProdCO2emissions.append(ems)
# CO2capture.append(capture)
# upstreamCO2emissions.append(upstream)
for i in range(0, th):
ProdCO2emissions[i] = CO2emissionRate*(finalprodrate[i]*thou)/thou
CO2capture[i] = CO2captureRate*(finalprodrate[i]*thou)/thou
upstreamCO2emissions[i] = (CO2emissionRatefeedstock/thou)*(NatGasConsumption*cons1)*(finalprodrate[i]/(plantDesignCap*plantOperationalCap*daysyear/thou))
#Hydrogen price
lowModeHighMode = (Hpriceave-Hpricelow)/(Hpricehigh-Hpricelow)
randomDraw2 = random.uniform(0, 1)
def findHprice():
if randomDraw2 < lowModeHighMode:
Hprice = Hpricelow+math.sqrt((Hpriceave-Hpricelow)*(Hpricehigh-Hpricelow)*randomDraw2)
else:
Hprice = Hpricehigh-math.sqrt((Hpricehigh-Hpricelow)*(Hpricehigh-Hpriceave)*(1-randomDraw2))
return Hprice
lowModeHighMode2 = (H2dpave-H2dplow)/(H2dohigh-H2dpave)
def findH2deliveryprice():
if randomDraw2 < lowModeHighMode2:
H2dprice = H2dplow+math.sqrt((H2dpave-H2dplow)*(H2dohigh-H2dplow)*randomDraw2)
else:
H2dprice = H2dohigh-math.sqrt((H2dohigh-H2dplow)*(H2dohigh-H2dpave)*(1-randomDraw2))
return H2dprice
lowModeHighMode3 = (CO2ave-CO2low)/(CO2high-CO2ave)
def priceCO2TS():
if randomDraw2 < lowModeHighMode3:
CO2ts = CO2low+math.sqrt((CO2ave-CO2low)*(CO2high-CO2low)*randomDraw2)
else:
CO2ts = CO2high-math.sqrt((CO2high-CO2low)*(CO2high-CO2low)*(1-randomDraw2))
return CO2ts
#Revenues
#natural gas price
result = [];
for i in range(1000): #1000
result.append(random.choices(ngprices, weights=None, cum_weights=None, k = 25)) #k=25
samplemean = [];
for i in result:
samplemean.append(np.mean(i))
totalmean = [];
totalmean.append(np.mean(samplemean))
totalst = []
totalst.append(np.std(samplemean))
ngprice = norm.ppf(random.uniform(0,1), totalmean, totalst)
# rev = []
for i in range(0,th):
revenue[i] = findHprice()*(finalprodrate[i]*thou)/mill
# for j in finalprodrate:
# rev.append(findHprice()*(j*1000)/1000000);
# NG = [];
# WM = [];
# CC = [];
# CO2tax = [];
# CO2TS = [];
# H2delivery = [];
# fixedOPEX = [];
NG = pd.Series(index=nums, dtype='float64')
WM = pd.Series(index=nums, dtype='float64')
CC = pd.Series(index=nums, dtype='float64')
CO2tax = pd.Series(index=nums, dtype='float64')
CO2TS = pd.Series(index=nums, dtype='float64')
H2delivery = pd.Series(index=nums, dtype='float64')
CO2tax = pd.Series(index=nums, dtype='float64')
CO2TS = pd.Series(index=nums, dtype='float64')
for i in range(0,th):
NG[i] = (NatGasConsumption*workingHours)/thou*ngprice[0]*(finalprodrate[i]/(plantDesignCapScaled*plantOperationalCap*daysyear/thou))/mill
WM[i] = basecostWM*(finalprodrate[i]/(plantDesignCapBase*plantOperationalCap*daysyear/thou))/mill
CC[i] = basecostCC*(finalprodrate[i]/(plantDesignCapBase*plantOperationalCap*daysyear/thou))/mill
H2delivery[i] = findH2deliveryprice()*(finalprodrate[i]*thou)/mill
fixedOPEX[i] = cons2
# for j in finalprodrate:
# #NG.append(basecostFF*(j/(plantDesignCap*plantOperationalCap*365/1000))/1000000);
# NG.append((NatGasConsumption*workingHours)/1000*ngpricedraw[0]*(j/(plantDesignCapScaled*plantOperationalCap*365/1000))/1000000);
# WM.append(basecostWM*(j/(plantDesignCapBase*plantOperationalCap*365/1000))/1000000);
# CC.append(basecostCC*(j/(plantDesignCapBase*plantOperationalCap*365/1000))/1000000);
# H2delivery.append(findH2deliveryprice()*(j*1000)/1000000)
# fixedOPEX.append(cons2);
# print(NG)
#finding CO2 price
result2 = []
for i in range(108): #108
result2.append(drift+(norm.ppf(random.uniform(0,1), 0, 1))*volatility)
CO2tradeprices = []
CO2tradeprices.append(CO2captradeprice)
for i, v in enumerate(result2):
CO2tradeprices.append(CO2tradeprices[i]*(1+result2[i]))
result3 = []
for i in range(12, 16):
result3.append(CO2tradeprices[i])
CO2price = np.mean(result3)
for i in range(0,th):
CO2tax[i] = CO2price*ProdCO2emissions[i]/mill
CO2TS[i] = priceCO2TS()*CO2capture[i]/mill
# for k in ProdCO2emissions:
# #CO2tax.append(22.43*k/1000000)
# CO2tax.append(CO2price*k/mill)
# for l in CO2capture:
# #CO2TS.append(15*l/1000000)
# CO2TS.append(priceCO2TS()*l/mill)
temp1 = np.add(NG, WM);
temp2 = np.add(CC, CO2tax)
temp3 = np.add(CO2TS, H2delivery)
temp4 = np.add(temp1, temp2)
varOPEXcost = np.add(temp4, temp3)
# for i in NG:
# varOPEXcost.append(NG[i] + WM[i])
OpCost = np.add(fixedOPEX, varOPEXcost);
#Depreciation
# MACRSfixed = df.iloc[2:27, 1].array
# Depreciation = MACRSfixed*(statetax+fedtax);
for i in range(0, th):
Depreciation[i] = MACRSfixed[i]*(statetax+fedtax);
#Salvage Value + decommissioning
#sv = df.iloc[2:27, 3].array; #capInvestment - sum(Depreciation)
# sv = initialCapCostsFixed - sum(Depreciation)
# decom = []
# for i in range(24):
# decom.append(0)
# decom.append(sv)
decom = pd.Series(index=nums, dtype='float64')
for i in range(0, th):
if i <=24:
decom[i] = 0
else:
decom[i] = sv
#45Q tax credit
# tc = []
# q = df.iloc[2:27, 2].array #tax credit data from csv file
# tc = [];
# for i, m in enumerate(CO2capture):
# if m >= 100000:
# tc.append(q[i] * m/1000000)
# else:
# tc.append(0)
tc = pd.Series(index=nums, dtype='float64')
for i in range(0, th):
if CO2capture[i] >= 100000:
tc[i] = q[i] * CO2capture[i]/mill
else:
tc[i] = 0
#cashflow
# cf = (revenue-OpCost)*(1-statetax-fedtax)+Depreciation+tc
#Discounted Cashflow
# nomyear = df.iloc[2:27, 4].array
# dcf = cf/(1+discountrate)**nomyear
#npv
# npv = dcf.sum()-initialCapCostsFixed
for i in range(0, th):
cf[i] = (revenue[i]-OpCost[i])*(1-statetax-fedtax)+Depreciation[i]+tc[i]
dcf[i] = cf[i]/(1+discountrate)**nomyear[i]
npv1 += dcf[i]
npv = npv1 - initialCapCostsFixed
return -npv #should this be negative and if so should the counter and enpv values also be negative?
counter = 0
for i in range(0, 2000): #2000 runs
counter += npv()
enpv = counter/2000 #2000 runs
return enpv
print(test([65882.50]))
from scipy.optimize import differential_evolution
from centralised.centralFixedUncertain import *
# Define the bounds
bounds = [(0, 60000)] #centralised Fixed Certain and Uncertain
# Define the genetic algorithm
# result = differential_evolution(test, bounds)
# # Print the results
# print("Optimal solution: ", result.x)
# print("Objective function value: ", result.fun)
if __name__ == "__main__":
... # Prepare all the arguments
# result = scipy.optimize.differential_evolution(minimize_me, bounds=function_bounds, args=extraargs,
# disp=True, polish=False, updating='deferred', workers=-1)
result = differential_evolution(test, bounds, workers=-1)
print("Optimal solution: ", result.x)
print("Objective function value: ", result.fun)
In 2nd code block, set workers = -1 to utilise all cores but scipy always runs on the CPU. If any answers can help me force the apple silicon GPU or neural engine to run the process instead it would be greatly appreciated. Am using base model M1 mac mini version with 8 cores CPU.