CODEWITHSUNDEEP
pythonoptimizationconstraintspyomonon-linear
lucassculp
lucassculp

Reputation: 71

PYOMO Constraints - setting constraints over indexed variables

I have been trying to get into python optimization, and I have found that pyomo is probably the way to go; I had some experience with GUROBI as a student, but of course that is no longer possible, so I have to look into the open source options.

I basically want to perform an non-linear mixed integer problem in which I will minimized a certain ratio. The problem itself is setting up a power purchase agreement (PPA) in a renewable energy scenario. Depending on the electricity generated, you will have to either buy or sell electricity acording to the PPA.

The only starting data is the generation; the PPA is the main decision variable, but I will need others. "buy", "sell", "b1" and "b2" are unknown without the PPA value. These are the equations:

Equations that rule the problem (by hand).

Using pyomo, I was trying to set up the problem as:

# Dataframe with my Generation information: 

January = Data['Full_Data'][(Data['Full_Data']['Month'] == 1) & (Data['Full_Data']['Year'] == 2011)]
                                                                  
Gen = January['Producible (MWh)']
Time = len(Generacion)
M=100
# Model variables and definition: 

m = ConcreteModel()
m.IDX = range(time)
m.PPA = Var(initialize = 2.0, bounds =(1,7))
m.compra = Var(m.IDX, bounds = (0, None))
m.venta = Var(m.IDX, bounds = (0, None))
m.b1 = Var(m.IDX, within = Binary)
m.b2 = Var(m.IDX, within = Binary)

And then, the constraint; only the first one, as I was already getting errors:

m.b1_rule = Constraint( 
    expr = (((Gen[i] - PPA)/M for i in m.IDX)  <= m.b1[i])
 )

which gives me the error:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-5-5d5f5584ebca> in <module>
      1 m.b1_rule = Constraint( 
----> 2     expr = (((Generacion[i] - PPA)/M for i in m.IDX)  <= m.b1[i])
      3  )

pyomo\core\expr\numvalue.pyx in pyomo.core.expr.numvalue.NumericValue.__ge__()

pyomo\core\expr\logical_expr.pyx in pyomo.core.expr.logical_expr._generate_relational_expression()

AttributeError: 'generator' object has no attribute 'is_expression_type'

I honestly have no idea what this means. I feel like this should be a simple problem, but I am strugling with the syntax. I basically have to apply a constraint to each individual data from "Generation", there is no sum involved; all constraints are 1-to-1 contraints set so that the physical energy requirements make sense.

How do I set up the constraints like this?

Thank you very much

Upvotes: 0

Views: 1646

Answers (1)

AirSquid
AirSquid

Reputation: 11883

You have a couple things to fix. First, the error you are getting is because you have "extra parenthesis" around an expression that python is trying to convert to a generator. So, step 1 is to remove the outer parenthesis, but that will not solve your issue.

You said you want to generate this constraint "for each" value of your index. Any time you want to generate copies of a constraint "for each" you will need to either do that by making a constraint list and adding to it with some kind of loop, or use a function-rule combination. There are examples of each in the pyomo documentation and plenty on this site (I have posted a ton if you look at some of my posts.) I would suggest the function-rule combo and you should end up with something like:

def my_constr(m, i):
    return m.Gen[i] - m.PPA <= m.b1[i] * M
m.C1 = Constraint(m.IDX, rule=my_constr)

Upvotes: 1

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