def explore_Cost_marginal(dat):
from temoa_model import temoa_create_model
model = temoa_create_model()
model.dual = Suffix(direction=Suffix.IMPORT)
model.rc = Suffix(direction=Suffix.IMPORT)
model.slack = Suffix(direction=Suffix.IMPORT)
model.lrc = Suffix(direction=Suffix.IMPORT)
model.urc = Suffix(direction=Suffix.IMPORT)
data = DataPortal(model=model)
for d in dat:
data.load(filename=d)
instance = model.create_instance(data)
# Deactivate the DemandActivity constraint
# instance.DemandActivityConstraint.deactivate()
# instance.preprocess()
optimizer = SolverFactory('cplex')
results = optimizer.solve(instance,
keepfiles=True,
suffixes=['dual', 'urc', 'slack', 'lrc'])
instance.solutions.load_from(results)
print 'Dual and slack variables for emission caps:'
for e in instance.commodity_emissions:
for p in instance.time_optimize:
if (p, e) in instance.EmissionLimitConstraint:
print p, e, instance.dual[instance.EmissionLimitConstraint[
p, e]], '\t', instance.slack[
instance.EmissionLimitConstraint[p, e]]
def return_Temoa_model():
from temoa_model import temoa_create_model
model = temoa_create_model()
model.dual = Suffix(direction=Suffix.IMPORT)
model.rc = Suffix(direction=Suffix.IMPORT)
model.slack = Suffix(direction=Suffix.IMPORT)
model.lrc = Suffix(direction=Suffix.IMPORT)
model.urc = Suffix(direction=Suffix.IMPORT)
return model
ef_instance.solutions.store_to(ef_result)
ef_obj = value(ef_instance.EF_EXPECTED_COST.values()[0])
return ef_obj
def StochasticPointObjective_rule(M, p):
expr = (M.StochasticPointCost[p] == PeriodCost_rule(M, p))
return expr
def Objective_rule(M):
return sum(M.StochasticPointCost[pp] for pp in M.time_optimize)
M = model = temoa_create_model('TEMOA Stochastic')
M.StochasticPointCost = Var(M.time_optimize, within=NonNegativeReals)
M.StochasticPointCostConstraint = Constraint(
M.time_optimize, rule=StochasticPointObjective_rule)
del M.TotalCost
M.TotalCost = Objective(rule=Objective_rule, sense=minimize)
if __name__ == "__main__":
p_model = "./ReferenceModel.py"
temoa_options, config_flag = parse_args()
p_dot_dat = temoa_options.dot_dat[0] # must be ScenarioStructure.dat
p_data = os.path.dirname(p_dot_dat)
print(p_model, p_data)
print(solve_ef(p_model, p_data, temoa_options))
def sensitivity(dat, techs):
# This function performs break-even analysis for technologies specified in
# the argument techs. It uses suffix from Pyomo and returns the breakeven
# cost as screen outputs. Note that the Pyomo suffix sometimes returns
# anomalous values, and that's why I create another function,
# sensitivity_api() to use Python API for CPLEX.
from temoa_model import temoa_create_model
model = temoa_create_model()
model.dual = Suffix(direction=Suffix.IMPORT)
model.rc = Suffix(direction=Suffix.IMPORT)
model.slack = Suffix(direction=Suffix.IMPORT)
model.lrc = Suffix(direction=Suffix.IMPORT)
model.urc = Suffix(direction=Suffix.IMPORT)
data = DataPortal(model=model)
for d in dat:
data.load(filename=d)
instance = model.create_instance(data)
optimizer = SolverFactory('cplex')
optimizer.options['lpmethod'] = 1 # Use primal simplex
results = optimizer.solve(instance,
suffixes=['dual', 'urc', 'slack', 'lrc'])
instance.solutions.load_from(results)
coef_CAP = dict()
scal_CAP = dict()
# Break-even investment cost for this scenario, indexed by technology
years = list()
bic_s = dict()
ic_s = dict() # Raw investment costs for this scenario, indexed by tech
cap_s = dict()
for t in techs:
vintages = instance.vintage_optimize
P_0 = min(instance.time_optimize)
GDR = value(instance.GlobalDiscountRate)
MLL = instance.ModelLoanLife
MPL = instance.ModelProcessLife
LLN = instance.LifetimeLoanProcess
x = 1 + GDR # convenience variable, nothing more.
bic_s[t] = list()
ic_s[t] = list()
cap_s[t] = list()
years = vintages.value
for v in vintages:
period_available = set()
for p in instance.time_future:
if (p, t, v) in instance.CostFixed.keys():
period_available.add(p)
c_i = (instance.CostInvest[t, v] * instance.LoanAnnualize[t, v] *
(LLN[t, v] if not GDR else
(x**(P_0 - v + 1) * (1 - x**(-value(LLN[t, v]))) / GDR)))
c_s = (-1) * (value(instance.CostInvest[t, v]) *
value(instance.SalvageRate[t, v]) /
(1 if not GDR else
(1 + GDR)**(instance.time_future.last() -
instance.time_future.first() - 1)))
c_f = sum(instance.CostFixed[p, t, v] *
(MPL[p, t, v] if not GDR else
(x**(P_0 - p + 1) *
(1 - x**(-value(MPL[p, t, v]))) / GDR))
for p in period_available)
c = c_i + c_s + c_f
s = (c - instance.lrc[instance.V_Capacity[t, v]]) / c
coef_CAP[t, v] = c
scal_CAP[t, v] = s # Must reduce TO this percentage
bic_s[t].append(scal_CAP[t, v] * instance.CostInvest[t, v])
ic_s[t].append(instance.CostInvest[t, v])
cap_s[t].append(value(instance.V_Capacity[t, v]))
# print "Tech\tVintage\tL. RC\tCoef\tU .RC\tScale\tBE IC\tBE FC\tIC\tFC\tCap"
print "{:>10s}\t{:>7s}\t{:>6s}\t{:>4s}\t{:>6s}\t{:>5s}\t{:>7s}\t{:>7s}\t{:>5s}\t{:>3s}\t{:>5s}".format(
'Tech', 'Vintage', 'L. RC', 'Coef', 'U. RC', 'Scale', 'BE IC',
'BE FC', 'IC', 'FC', 'Cap')
for v in vintages:
lrc = instance.lrc[instance.V_Capacity[t, v]]
urc = instance.urc[instance.V_Capacity[t, v]]
# print "{:>s}\t{:>g}\t{:>.0f}\t{:>.0f}\t{:>.0f}\t{:>.3f}\t{:>.1f}\t{:>.1f}\t{:>.0f}\t{:>.0f}\t{:>.3f}".format(
print "{:>10s}\t{:>7g}\t{:>6.0f}\t{:>4.0f}\t{:>6.0f}\t{:>5.3f}\t{:>7.1f}\t{:>7.1f}\t{:>5.0f}\t{:>3.0f}\t{:>5.3f}".format(
t,
v,
lrc,
coef_CAP[t, v],
urc,
scal_CAP[t, v],
scal_CAP[t, v] * instance.CostInvest[t, v],
scal_CAP[t, v] *
instance.CostFixed[v, t, v], # Use the FC of the first period
instance.CostInvest[t, v],
instance.CostFixed[v, t, v],
value(instance.V_Capacity[t, v]))
print 'Dual and slack variables for emission caps:'
for e in instance.commodity_emissions:
for p in instance.time_optimize:
if (p, e) in instance.EmissionLimitConstraint:
print p, e, instance.dual[instance.EmissionLimitConstraint[
p, e]], '\t', instance.slack[
instance.EmissionLimitConstraint[p, e]]
return years, bic_s, ic_s
print 'Dual and slack variables for Commodity Demand Constraints'
for c in instance.commodity_demand:
for p in instance.time_optimize:
for s in instance.time_season:
for tod in instance.time_of_day:
print p, s, tod, instance.dual[instance.DemandConstraint[
p, s, tod,
c]], instance.slack[instance.DemandConstraint[p, s,
tod, c]]
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. A complete copy of the GNU General Public License v2 (GPLv2) is available in LICENSE.txt. Users uncompressing this from an archive may not have received this license file. If not, see <http://www.gnu.org/licenses/>. """ from temoa_lib import Var, Objective, Constraint, NonNegativeReals, minimize from temoa_model import temoa_create_model from temoa_rules import PeriodCost_rule def StochasticPointObjective_rule ( M, p ): expr = ( M.StochasticPointCost[ p ] == PeriodCost_rule( M, p ) ) return expr def Objective_rule ( M ): return sum( M.StochasticPointCost[ pp ] for pp in M.time_optimize ) M = model = temoa_create_model( 'TEMOA Stochastic' ) M.StochasticPointCost = Var( M.time_optimize, within=NonNegativeReals ) M.StochasticPointCostConstraint = Constraint( M.time_optimize, rule=StochasticPointObjective_rule ) del M.TotalCost M.TotalCost = Objective( rule=Objective_rule, sense=minimize )
# The Prices are the duals of the DemandConstraint constraints.
# The search range for the variable V_Demand, (MinDemand, MaxDemand)
# is determined using DEMAND_RANGE. The Price, MinDemand and MaxDemand
# are written to the file, price.dat.
# Usage: same as temoa_model.py.
# Addition output: writes file price.dat in the current directory.
DEMAND_RANGE = 0.3 # Max/MinDemand = (1 +/- DEMAND_RANGE)* Demand
DEMAND_SEGMENTS = 27 # Keep this an odd number to reproduce fixed demand results!
from temoa_model import temoa_create_model, temoa_create_model_container
from temoa_lib import temoa_solve
import sys
model = temoa_create_model()
model_data = temoa_create_model_container(model)
temoa_solve(model_data)
instance = model_data.instance
price_data = open("price.dat", "w")
ConstantDemandConstraint = instance.DemandConstraint
Demand = instance.Demand
print >> price_data, """\
data;
param: MinDemand MaxDemand :=
# year # min # max
"""
