Mutable parameter in Pyomo causes a problem

Question

I am defining a Pyomo model that should have some mutable Big-M parameters whose values should be dynamically assigned (once). However, I am having a problem with a difference equation as described in this post Error message in difference equation in Pyomo. Here you can see my extremel simplified model in pyomo (that does not make much sense from a logical point of view, but I just want to adress the error):

import pyomo.environ as pyo
import pandas as pd
from pyomo.util.infeasible import log_infeasible_constraints


#Define the model
model = pyo.ConcreteModel()


#Define the sets
model.set_timeslots = pyo.RangeSet(1, 288)


#Define the bigM parameter
model.param_BigM_Surplus_Positive = pyo.Param(model.set_timeslots, mutable=True, default =0)


#Define the variables
model.variable_temperatureBufferStorage = pyo.Var(model.set_timeslots, bounds=(20, 22))
model.variable_surplusPowerTotal = pyo.Var(model.set_timeslots)



#Equations for the Big-M parameters
'''When commenting this Big-M parameter equation out, the model can be solved. Otherwise I get an error for each value of the variable model.variable_temperatureBufferStorage
   variable ERROR: evaluating object as numeric value:
    variable_temperatureBufferStorage[274]
    (object: <class 'pyomo.core.base.var._GeneralVarData'>)
    No value for uninitialized NumericValue object
    variable_temperatureBufferStorage[274]
'''    
def BigM_Surplus_PositiveRule (model, t):
    return model.param_BigM_Surplus_Positive [t] == 10

model.constraint_BigM_Surplus_Positive = pyo.Constraint(model.set_timeslots, rule = BigM_Surplus_PositiveRule)



# Defining the constraints 


#Temperature constraint for the buffer storage (space heating) with energetic difference equation
def temperatureBufferStorageConstraintRule(model, t):
    if t == model.set_timeslots.first():
        return model.variable_temperatureBufferStorage[t] == 21
    return model.variable_temperatureBufferStorage[t] == model.variable_temperatureBufferStorage[t-1] 

model.constraint_temperatureBufferStorage = pyo.Constraint (model.set_timeslots, rule=temperatureBufferStorageConstraintRule)


#Equations for the surplus power
def surplusPowerTotalRule (model, t):
    return model.variable_surplusPowerTotal [t] == 1

model.constraint_surplusPowerTotal = pyo.Constraint(model.set_timeslots, rule = surplusPowerTotalRule)



#Objectives

def objectiveRule_combined_general (model):
    return  sum(model.variable_surplusPowerTotal[t] for t in model.set_timeslots)
    
model.objective_combined_general = pyo.Objective( rule=objectiveRule_combined_general, sense =pyo.minimize)



print("Start of solving")
solver = pyo.SolverFactory('gurobi')
solver.options['MIPGap'] = 1
solver.options['TimeLimit'] = 20
solution = solver.solve(model, tee=True)

log_infeasible_constraints(model)

The problematic part is when I define the constraints for the mutable Big-M parameter model.param_BigM_Surplus_Positive = pyo.Param(model.set_timeslots, mutable=True, default =0). When they are active and not commented out I get an error from Gurobi telling that the problem is infeasible or unbounded:

Infeasible or unbounded model
WARNING: Loading a SolverResults object with a warning status into
    model.name="unknown";
      - termination condition: infeasibleOrUnbounded
      - message from solver: Problem proven to be infeasible or unbounded.

When looking what is causing this problem with from pyomo.util.infeasible import log_infeasible_constraints I can see an error for each value of the variable model.variable_temperatureBufferStorage

ERROR: evaluating object as numeric value:
    variable_temperatureBufferStorage[2]
        (object: <class 'pyomo.core.base.var._GeneralVarData'>)
    No value for uninitialized NumericValue object
    variable_temperatureBufferStorage[2]
ERROR: evaluating object as numeric value:
    variable_temperatureBufferStorage[3]
        (object: <class 'pyomo.core.base.var._GeneralVarData'>)
    No value for uninitialized NumericValue object
    variable_temperatureBufferStorage[3]
ERROR: evaluating object as numeric value:
    variable_temperatureBufferStorage[4]
        (object: <class 'pyomo.core.base.var._GeneralVarData'>)
    No value for uninitialized NumericValue object
    variable_temperatureBufferStorage[4]`

Without the Big-M parameter constraints the model is solvable. So my question is why does this error occur when having a mutable parameter and how can I tackle this problem?

Answer 1

Based on given model section, model.param_BigM_Surplus_Positive parameter is used in only constraint which is:

model.constraint_BigM_Surplus_Positive = pyo.Constraint(model.set_timeslots, rule = BigM_Surplus_PositiveRule)

You are using BigM_Surplus_PositiveRule for generating constraint which is:

model.param_BigM_Surplus_Positive[t] == 10

But this parameter is defined as 0, therefore model is infeasible. If you want to assign 10 to this parameter, you should use initialize function.

Note: On your model there isn't any big-m constraint. In this question you can find an example.