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run_COIN_basecase.py
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run_COIN_basecase.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Jul 12 14:53:40 2020
@author: kcaldeira
"""
from plot_utilities import *
from io_utilities import *
import cProfile
from DICE_diffeqs import DICE_instance
#%%
# starting point has learning curve of 10 k$ at 10,000 tCO2, and a learning rate of 12% per doubling.
if __name__ == "__main__":
"""
#initCostList = [10000,8000,6300,5000,4000,3200,2500,2000,1600,1300,1000,800,630,500,400,320,250,200,160,130,100,80,63,50,40,32,25,20,16,13,10]
#initCostList = [8900,7100,5600,4500,3500,2800,2200,1800,1400,1100,890,710,560,450,350,280,220,180,140,110,89,71,56,45,35,28,22,18,14,11,7900,790,79]
#initCostList = [28,22,18,14,11,7900,790,79]
#rampOpts = ['0by2050','welfare','max']
#rampOpts = ['max']
#shiftOpts = ['shift','follow']
shiftOpts = ["follow"]
initCostList = [10000]
rampOpts = ['welfare']
rateOptDic = {"12pct":0.18442457113742744}
maxEval = 25000
#initCostList = [1.e4,9.e3,8.e3,7.e3,6.e3,5.e3,4.e3,3.e3,2.e3,1.5e3,1.e3,900,800,700,600,500,400,300,200,100,50,0,1.e20]
#initCostList = [1e4,1e3,1e2]
#initCostList = [1.e20,1.e4,1.e3,100,0]
#initCostList = [1.e20,1.e3,900,800,700,600,500,400,300,200,100,50,0]
for initCost in initCostList:
for shiftOpt in shiftOpts:
for rateOpt in rateOptDic.keys():
if shiftOpt == 'shift':
initAmount = 1e4
else:
if initCost > 0:
initAmount = 1.e4*(initCost/1.e4)**(-1./rateOptDic[rateOpt])
else:
initAmount = 1.e80
for rampOpt in rampOpts:
if rampOpt == '0by2050':
limLower = [0.,0.,0.16666666666666666, 0.3333333333333333, 0.5, 0.6666666666666666, 0.8333333333333334, 1.,
1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.]
limUpper = limLower
elif rampOpt == 'max':
limLower = 1.0
limUpper = limLower
else:
limLower = 0.0
limUpper = 1.0
# note that the miu = 0 case is identical to the one technology miu = 0 case.
caseName = 'checkCOIN_noDamage1_'+rateOpt+"_"+str(initCost)+'_'+shiftOpt+'_'+rampOpt+'_'+str(maxEval)
# If no arg is given, run vanilla DICE
print (caseName)
resultCentral = DICE_instance(
COINmode = True, # simple version
dt = 1, # dt time step for integration
nTechs = 1, # number of technologies considered
decisionTimes =[0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100,110,120,140,160,200,240,260,280,300], # times for miu decisions
# NOTE: <decisionTimes> are also the times assumed for specified limits on miu
#limMiuLower = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # lower limit on miu values (= sum across all techs)
limMiuLower = limLower,
#limMiuLower = [0.,0.,0.16666666666666666, 0.3333333333333333, 0.5, 0.6666666666666666, 0.8333333333333334, 1.,
# 1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.],
#limMiuLower = 0, # can be scalar or vector of len(decisionTimes)
limMiuUpper = limUpper, # upper limit on miu values (= sum across all techs)
#limMiuUpper = [1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2], # upper limit on miu values (= sum across all techs)
#limMiuUpper = 1.0, # can be scalar or vector of len(decisionTimes)
optSavings = True, # True means to optimize on savings rate, False means to use default value (different for COINmode)
savingDecisionTimes =[0,20,40,60,80,100,140,200,260,280,300], # times for savings rate decisions
techLearningCurve = [False], # does this technology have a learning curve (True) or a specified cost function (False)
# NOTE: <learningCurveTech> must have a length of <nTechs>
techInitCost = [0.2], # Initial cost for learning curve. Must be same shape as nTechs.
#techInitCost = [550, 1e4], # Initial cost for learning curve. Must be same shape as nTechs.
# If no learning curve, then this value is the initial backstop cost (pback)
techInitAmount = [0], # Initial cost for learning curve. Must be same shape as nTechs, but value if no learning curve is unimportant
techLearningRate = [0.005], # 15% per doubling. Must be same shape as nTechs.
#techLearningRate = [0.005050763379468082, 0.23446525363702297], # 15% per doubling. Must be same shape as nTechs.
# techLearningRate = [0.005050763379468082, 0.18442457113742744], # 12% per doubling. Must be same shape as nTechs.
# If no learning curve, then value is fractional cost improvement per year
firstUnitFractionalCost = [0.0], # Marginal cost at miuX = 0 compared to marginal cost at miuX = 1.
utilityOption = 1, # utilityOption == 0 --> DICE utility function; 1 --> assume consumption == utility
prstp = 0.03, # pure rate of time preference (0.015 is DICE default ; for default, just comment out and don't define )
parallel = 15, # number of cores to use, 0 or 1 is single core; Serial: 0 or 1, Parallel: 2,3,4,5,6,7,8...
#parallel = 1, # number of cores to use, 0 or 1 is single core; Serial: 0 or 1, Parallel: 2,3,4,5,6,7,8...
maxeval = maxEval, # maxeval maximum number of iterations for solver
#maxeval = 100, # maxeval maximum number of iterations for solver
FOCUS = 100, # FOCUS parameter for midaco solver
damageCostRatio = 0.0, # scaling on climate damage
abatementCostRatio = 1.0 # scaling on abatement costs (multiplies costs above for all techs)
)
pickle_results('../dice-diffeqs_analyze/output',caseName,filter_dic(resultCentral.out))
write_CSV_from_pickle('../dice-diffeqs_analyze/output',caseName)
====================================================================================================================
====================================================================================================================
====================================================================================================================
"""
#initCostList = [10000,8000,6300,5000,4000,3200,2500,2000,1600,1300,1000,800,630,500,400,320,250,200,160,130,100,80,63,50,40,32,25,20,16,13,10]
#initCostList = [8900,7100,5600,4500,3500,2800,2200,1800,1400,1100,890,710,560,450,350,280,220,180,140,110,89,71,56,45,35,28,22,18,14,11,7900,790,79]
#initCostList = [28,22,18,14,11,7900,790,79]
rampOpts = ['0by2050','welfare','max']
#rampOpts = ['max']
shiftOpts = ['shift','follow']
#shiftOpts = ["shift"]
#initCostList = [1,0.8,0.6,0.4,0.2,0]
initCostRef = 1 # base initCost
#initCostList = np.insert(np.round(10.**-np.arange(0,2.05,0.05),6),0,10)
initCostList = np.round(10.**-np.arange(1.3,2.05,0.05),6)
#initCostList = np.array([10])
#rampOpts = ['welfare',"max","0by2050"]
#rampOpts = ['0by2050']
#rateOptDic = {"20pct":0.2630344058337938,"15pct":0.1634987322828795,"12pct":0.15055967657538144,"10pct":0.13750352374993496}
rateOptDic = {"10pct":0.13750352374993496}
maxEval = 100000
initAmounts = [1e-6]
#initCostList = [1.e4,9.e3,8.e3,7.e3,6.e3,5.e3,4.e3,3.e3,2.e3,1.5e3,1.e3,900,800,700,600,500,400,300,200,100,50,0,1.e20]
#initCostList = [1e4,1e3,1e2]
#initCostList = [1.e20,1.e4,1.e3,100,0]
#initCostList = [1.e20,1.e3,900,800,700,600,500,400,300,200,100,50,0]
for initCost in initCostList:
for shiftOpt in shiftOpts:
for initAmt in initAmounts:
for rateOpt in rateOptDic.keys():
if shiftOpt == 'shift':
initAmount = initAmt
else:
if initCost > 0:
initAmount = initAmt*(initCost/initCostRef)**(-1./rateOptDic[rateOpt])
else:
initAmount = 1.e80
for rampOpt in rampOpts:
if rampOpt == '0by2050':
limLower = [0.,0.16666666666666666, 0.3333333333333333, 0.5, 0.6666666666666666, 0.8333333333333334, 1.,
1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.]
limUpper = limLower
elif rampOpt == 'max':
limLower = 1.0
limUpper = limLower
else:
limLower = 0.0
limUpper = 1.0
# note that the miu = 0 case is identical to the one technology miu = 0 case.
caseName = 'COIN_001_'+rateOpt+"_"+str(initCost)+'_'+str(initAmt)+'_'+shiftOpt+'_'+rampOpt+'_'+str(maxEval)
# If no arg is given, run vanilla DICE
print (caseName)
resultCentral = DICE_instance(
COINmode = True, # simple version
dt = 1, # dt time step for integration
nTechs = 2, # number of technologies considered
decisionTimes =[0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100,105,110,115,120,125,130,135,140,145,150,170,200,240,260,280,300], # times for miu decisions
# NOTE: <decisionTimes> are also the times assumed for specified limits on miu
#limMiuLower = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # lower limit on miu values (= sum across all techs)
limMiuLower = limLower,
#limMiuLower = [0.,0.,0.16666666666666666, 0.3333333333333333, 0.5, 0.6666666666666666, 0.8333333333333334, 1.,
# 1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.],
#limMiuLower = 0, # can be scalar or vector of len(decisionTimes)
limMiuUpper = limUpper, # upper limit on miu values (= sum across all techs)
#limMiuUpper = [1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2], # upper limit on miu values (= sum across all techs)
#limMiuUpper = 1.0, # can be scalar or vector of len(decisionTimes)
optSavings = True, # True means to optimize on savings rate, False means to use default value (different for COINmode)
savingDecisionTimes =[0,30,60,90,120,150,200,260,280,300], # times for miu decisions
techLearningCurve = [False,True], # does this technology have a learning curve (True) or a specified cost function (False)
# NOTE: <learningCurveTech> must have a length of <nTechs>
techInitCost = [0.2,initCost], # Initial cost for learning curve. Must be same shape as nTechs.
#techInitCost = [550, 1e4], # Initial cost for learning curve. Must be same shape as nTechs.
# If no learning curve, then this value is the initial backstop cost (pback)
techInitAmount = [0,initAmount], # Initial cost for learning curve. Must be same shape as nTechs, but value if no learning curve is unimportant
techLearningRate = [0.005,rateOptDic[rateOpt]], # 10% per doubling (1 + 0.10)**-1. Must be same shape as nTechs.
#techLearningRate = [0.005050763379468082, 0.23446525363702297], # 15% per doubling. Must be same shape as nTechs.
# techLearningRate = [0.005050763379468082, 0.18442457113742744], # 12% per doubling. Must be same shape as nTechs.
# If no learning curve, then value is fractional cost improvement per year
firstUnitFractionalCost = [0.0,0.5], # Marginal cost at miuX = 0 compared to marginal cost at miuX = 1.
utilityOption = 1, # utilityOption == 0 --> DICE utility function; 1 --> assume consumption == utility
prstp = 0.03, # pure rate of time preference (0.015 is DICE default ; for default, just comment out and don't define )
parallel = 15, # number of cores to use, 0 or 1 is single core; Serial: 0 or 1, Parallel: 2,3,4,5,6,7,8...
#parallel = 1, # number of cores to use, 0 or 1 is single core; Serial: 0 or 1, Parallel: 2,3,4,5,6,7,8...
maxeval = maxEval, # maxeval maximum number of iterations for solver
#maxeval = 100, # maxeval maximum number of iterations for solver
FOCUS = 100, # FOCUS parameter for midaco solver
damageCostRatio = 1.0, # scaling on climate damage
abatementCostRatio = 1.0 # scaling on abatement costs (multiplies costs above for all techs)
)
pickle_results('../dice-diffeqs_analyze/output',caseName,filter_dic(resultCentral.out))
write_CSV_from_pickle('../dice-diffeqs_analyze/output',caseName)