Understanding Gurobi Variable Names

[Lauren Kuntz]

I have a network that is infeasible, so have been trying to use gurobi to save the model.ilp file so I can understand which constraints are making it infeasible. However, I'm struggling to interpret what the gurobi variable names relate to in the pypsa model. The .ilp file looks as follows:

Minimize
   <gurobi.LinExpr: 0.0>
Subject To
   c_l_x45807_ : <gurobi.LinExpr: x12970 + x23257> >= 0.0
   c_e_x48179_ : <gurobi.LinExpr: x23257 + x2900 + x418> = 22.70892072648328
Bounds
   -inf <= x12970 <= 859.1
   x23257  free
   1079.2570525 <= x2900 <= inf

Is there a way to understand what each x is referring to? I'm assuming there has to be some mapping as the pyomo model interprets the results from the gurobi solve, but after spending a few hours looking through the pypsa code, I couldn't track down where any such mapping was occuring. Any suggestions would be greatly appreciated.

Thanks!

Lauren

[Fabian Neumann]

Hi Lauren,

the mapping is done in pyomo internally, and you are right it's a bit
tricky to interpret the lp files. This could work:

```
import pypsa
from pyomo.core import ComponentUID

n = pypsa.Network("some_network.nc")

pypsa.opf.network_lopf_build_model(n, **kwargs)

_, smap_id = n.model.write("test.lp")
symbol_map = n.model.solutions.symbol_map[smap_id]

mapping = {}
for symbol, var_weakref in symbol_map.bySymbol.items():
mapping[symbol] = ComponentUID(var_weakref())

pypsa.opf.network_lopf_prepare_solver(n, **kwargs)
pypsa.opf.network_lopf_solve(n, **kwargs)
```

The dictionary `mapping` will contain the translation.

Probably there is a more elegant way. I'd be glad if someone could tell me.

Best wishes,

Fabian N

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[Lauren Kuntz]

Fabian - Wow thank you so much for the quick reply! That did exactly the trick - maybe not the prettiest but it worked which is all I needed. And saved me a lot of headache and confusion to boot

Many thanks!

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> <mailto:py...@googlegroups.com>.

[denyin]

Dear pypsa,

I am also trying to understand my infeasibile model.

Concerning the code snippets provided by Fabian, my infeasible model doesn't work at

_, smap_id = n.model.write("test.lp").

with the error message

File "..\lib\site-packages\pyomo\core\base\block.py", line 1786, in write

(filename, smap) = problem_writer(self,

File "pyomo\repn\plugins\cpxlp.pyx", line 162, in pyomo.repn.plugins.cpxlp.ProblemWriter_cpxlp.__call__

File "pyomo\repn\plugins\cpxlp.pyx", line 163, in pyomo.repn.plugins.cpxlp.ProblemWriter_cpxlp.__call__

File "pyomo\repn\plugins\cpxlp.pyx", line 164, in pyomo.repn.plugins.cpxlp.ProblemWriter_cpxlp.__call__

File "pyomo\repn\plugins\cpxlp.pyx", line 645, in pyomo.repn.plugins.cpxlp.ProblemWriter_cpxlp._print_model_LP

AssertionError

In my model, the constraint_data.lower and constraint_data.upper of some constrains are both nan in cpxlp.py in pyomo pkg

if constraint_data.equality:
    assert value(constraint_data.lower) == \
           value(constraint_data.upper)

Do you know how to determine where the nan value occurs in the pypsa model?

Info:

pypsa.network is created with the pyomo = False option.

pypsa 0.17.1

pyomo 5.7.3

python 3.7

Best,

Ying

[denyin]

Hi Fabian and PyPSA,

Thank you so much for your support and sorry for making you confused.

It is indeed a pyomo model for mapping. I have unnecessarily mentioned "setting pyomo=False". Basically, I solve the model by setting pyomo=False and exporting the pypsa network to a ".nc" file. Then the model is rebuilt by pypsa.opf.network_lopf_build_mode(network).

So far, my model has become feasible. I would also like to share my experience.

I detected the nan value by decreasing the model size (reduce number of nodes, add minimum components and then add more components) and debugging step by step. In my case, there is a single nan value in one of the time series.

In this, I borrowed the ideal from your code snippets and practically used the following (I use Gurobi to solve the problem):

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

def build_pyomo_model(nc_file):
    override_component_attrs = pypsa.descriptors.Dict({k: v.copy() for k, v in pypsa.components.component_attrs.items()})
    override_component_attrs["Link"].loc["bus2"] = ["string", np.nan, np.nan, "2nd bus", "Input (optional)"]
    override_component_attrs["Link"].loc["bus3"] = ["string", np.nan, np.nan, "3rd bus", "Input (optional)"]
    override_component_attrs["Link"].loc["efficiency2"] = ["static or series", "per unit", 1., "2nd bus efficiency", "Input (optional)"]
    override_component_attrs["Link"].loc["efficiency3"] = ["static or series", "per unit", 1., "3rd bus efficiency", "Input (optional)"]
    override_component_attrs["Link"].loc["p2"] = ["series", "MW", 0., "2nd bus output", "Output"]
    override_component_attrs["Link"].loc["p3"] = ["series", "MW", 0., "3rd bus output", "Output"]

    network = pypsa.Network(nc_file, override_component_attrs=override_component_attrs)
    pypsa.opf.network_lopf_build_model(network, formulation="kirchhoff")

    return network

def pyomo_parse_name(network, filename):
    instance = network.model
    instance.write(filename=filename, io_options={"symbolic_solver_labels": True})

def compute_IIS_gurobi(lp_file):
    """
    source: https://www.gurobi.com/documentation/9.1/examples/workforce2_py.html
    """
    model = gp.read(file_name)
    model.computeIIS()
    if model.IISMinimal:
        print('IIS is minimal\n')
    else:
        print('IIS is not minimal\n')
    print('\nThe following constraint(s) cannot be satisfied:')
    for c in model.getConstrs():
        if c.IISConstr:
            print('%s' % c.constrName)

nc_name = "my_model.nc"

file_name = "my_model.mps"
network = build_pyomo_model(nc_name)
pyomo_parse_name(network, file_name)
compute_IIS_gurobi(file_name)

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

The model prints the name of the constraints that cause the infeasibility. Then I go back to opf.py and check the parameters involved in the corresponding constraints.

Best,

Ying