I am trying to write some model in Pyomo. My code looks like this:
#model definition
model = ConcreteModel()
#Sets and indices
model.O = RangeSet(len(data)) #Set of representative operating conditions
model.D = RangeSet(len(data)) #Demand profile for the operating conditions
model.G = RangeSet(1) #set of existing generators
model.C = RangeSet(1) #set of candidate generators
#Parameters
model.Cg= Param(initialize=35, within=NonNegativeReals, mutable=True) #Opex of existing generators
model.Cc= Param(initialize=25, within=NonNegativeReals, mutable=True) #Opex of candidate generators
model.Ic= Param(initialize=70000, within=NonNegativeReals, mutable=True) #Annualized investment cost
#Decision Variables
model.pg = Var(model.O, model.G, bounds=(0, 400), initialize=0, within=NonNegativeReals)
model.pc = Var(model.O, model.C, bounds =(0, 500), initialize=0, within=NonNegativeReals)
model.pc_max = Var(model.C, bounds=(0, 500), initialize=0, within=NonNegativeIntegers)
#Objective function
def objective_rule(model, O):
return sum(data.loc[O-1, 'w']*(sum(model.Cg*model.pg[O,G] for G in model.G))+\
data.loc[O-1, 'w']*(sum(model.Cc*model.pc[O,C] for C in model.C)) for O in model.O)+\
sum(model.Ic*model.pc_max[C] for C in model.C)
model.objective = Objective(model, rule=objective_rule, sense=minimize)
#Constraints
#1. sign restrictions already provided for in the definition of decision variables
def con1_limit_candidate_units(model, O, C):
return model.pc[O,C] <= model.pc_max[C]
model.con1 = Constraint(model.O, model.C, rule=con1_limit_candidate_units)
def con2_power_balance(model, O, G):
return sum(model.pg[O, G] for G in model.G) + sum(model.pc[O, C] for C in model.C) == data.loc[O-1, 'd']
model.con2 = Constraint(model.O, model.G, rule = con2_power_balance)
opt = SolverFactory('cplex')
instance = model.create_instance()
results = opt.solve(instance) # solves and updates instance
print('Objective value=',value(instance.objective))
When I try running it I find this error so many times.
TypeError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_5960/507444092.py in <module>
36 sum(model.Ic*model.pc_max[C] for C in model.C)
37
---> 38 model.objective = Objective(model, rule=objective_rule, sense=minimize)
39 #Constraints
40 #1. sign restrictions already provided for in the definition of decision variables
~\anaconda3\lib\site-packages\pyomo\core\base\objective.py in __init__(self, *args, **kwargs)
279
280 kwargs.setdefault('ctype', Objective)
--> 281 ActiveIndexedComponent.__init__(self, *args, **kwargs)
282
283 self.rule = Initializer(_init)
~\anaconda3\lib\site-packages\pyomo\core\base\indexed_component.py in __init__(self, *args, **kwds)
1046
1047 def __init__(self, *args, **kwds):
-> 1048 IndexedComponent.__init__(self, *args, **kwds)
1049 # Replicate the ActiveComponent.__init__() here. We don't want
1050 # to use super, because that will run afoul of certain
~\anaconda3\lib\site-packages\pyomo\core\base\indexed_component.py in __init__(self, *args, **kwds)
289 #
290 self._implicit_subsets = None
--> 291 self._index = process_setarg(args[0])
292 else:
293 #
~\anaconda3\lib\site-packages\pyomo\core\base\set.py in process_setarg(arg)
118 % (arg.ctype.__name__, arg.name,))
119 if isinstance(arg, Component):
--> 120 raise TypeError("Cannot apply a Set operator to a non-Set "
121 "%s component (%s)"
122 % (arg.__class__.__name__, arg.name,))
TypeError: Cannot apply a Set operator to a non-Set ConcreteModel component (unknown)
What could be the issue and how can it be solved?
It seems the issue is in the portion: sum(model.Ic*model.pc_max[C] for C in model.C). I have tried removing the "for C in model.C" part but it still doesn't work). For this case, the set C only contains 1 element.
You shouldn't pass the model to the Objective constructor. Component constructors assume all non-keyword arguments are indexing sets. So, instead of
model.objective = Objective(model, rule=objective_rule, sense=minimize)
You should do
model.objective = Objective(rule=objective_rule, sense=minimize)