enable quadratic for win with scip

[davide-f]

Closes # (if applicable).

Changes proposed in this Pull Request

Test to unlock quadratic constraints for SCIP on windows.

This fix was needed for a CI not passing but couldn't find the ref to it.
The limitation on windows leads to issues in using linopy v0.6 in pypsa-earth as the quadratic optimization problem using for clustering usually defaults to scip and is not supported on windows because of this selection.
CI passes; do you see something against enabling it back?

Checklist

• Code changes are sufficiently documented; i.e. new functions contain docstrings and further explanations may be given in doc.
• Unit tests for new features were added (if applicable).
• A note for the release notes doc/release_notes.rst of the upcoming release is included.
• I consent to the release of this PR's code under the MIT license.

[FabianHofmann]

thanks @davide-f ; could you add a release note? then I could merge it in

[davide-f]

Thanks! Done

[coroa]

@davide-f Are you saying that you tested quadratic constraints on windows with scip and it worked?

[davide-f]

@davide-f Are you saying that you tested quadratic constraints on windows with scip and it worked?

Good point! Not tested; in the relevant issue it was mentioned that this was introduced because CI failed though the issue was solved mentioned the updates in xarray. I'll test and report

[davide-f]

@davide-f Are you saying that you tested quadratic constraints on windows with scip and it worked?

CI on windows is passing; do you recommend more?

[davide-f]

Update: this is ready to go in my opinion.

I've successfully tested this (basically the quadratic_model in tests:

fromlinopyimportModelfromlinopyimportGREATER_EQUALimportpandasaspdm=Model()
lower=pd.Series(0, range(10))
x=m.add_variables(lower, name="x")
y=m.add_variables(lower, name="y")
m.add_constraints(x+y, GREATER_EQUAL, 10)
m.add_objective(x*x)
m.solve("scip")

Moreover, scip is now recognized as quadratic in solver list and as such is tested in test_quadratic_model

[FabianHofmann]

thanks @davide-f !