Gurobi backend interface

[brynpickering]

Fixes #349

Summary of changes in this pull request:

• Duplicated pyomo backend interface as a pure gurobipy interface
• Added basic tests of the interface methods

How to use:

1. Install gurobipy into your calliope env: mamba install gurobi::gurobi
2. Build optimisation problem with gurobi backend:

import calliope

model = calliope.Model(...)
model.build(backend="gurobi")
model.solve()

NOTE: without a license, one can only work with very small models. Our example models are already too big (maybe if run over ~2 timesteps it would work?).

Reviewer checklist:

• Test(s) added to cover contribution
• Documentation updated
• Changelog updated
• Coverage maintained or improved

[codecov]

Codecov Report

Attention: Patch coverage is 97.14286% with 11 lines in your changes are missing coverage. Please review.

Project coverage is 96.56%. Comparing base (872978d) to head (39099ed).
Report is 13 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #551      +/-   ##
==========================================
+ Coverage   95.85%   96.56%   +0.70%     
==========================================
  Files          24       25       +1     
  Lines        3619     3900     +281     
  Branches      788      771      -17     
==========================================
+ Hits         3469     3766     +297     
+ Misses         86       76      -10     
+ Partials       64       58       -6     

Files	Coverage Δ
src/calliope/backend/backend_model.py	98.99% <100.00%> (+1.08%)	⬆️
src/calliope/backend/latex_backend_model.py	97.79% <ø> (ø)
src/calliope/backend/parsing.py	98.64% <100.00%> (+0.88%)	⬆️
src/calliope/backend/pyomo_backend_model.py	98.60% <100.00%> (+3.18%)	⬆️
src/calliope/model.py	94.53% <100.00%> (+0.88%)	⬆️
src/calliope/backend/expression_parser.py	97.63% <95.23%> (+0.44%)	⬆️
src/calliope/backend/gurobi_backend_model.py	95.95% <95.95%> (ø)

... and 2 files with indirect coverage changes

[brynpickering]

I have added tests, but we don't have gurobipy installed in the CI builds so those tests are being skipped. Hence the massive coverage drop.

[brynpickering]

I have run the urban scale model for a full year using the pyomo (with gurobi solver) and gurobi interfaces and get the attached results.

Headline: Gurobi interface peaks at ~30% less memory than pyomo.
I know you got different results when running locally @sjpfenninger. This could be because I'm using a cloud license for Gurobi which I think means it offloads the actual solving onto a remote machine.

memray_flamegraphs.zip

---

gurobi run:

import calliope

calliope.set_log_verbosity("info")
m = calliope.examples.urban_scale(time_subset=None)
m.build(backend="gurobi")
m.solve(solver_options={"Threads": 1})

pyomo run:

import calliope

calliope.set_log_verbosity("info")
m = calliope.examples.urban_scale(time_subset=None)
m.build(backend="pyomo")
m.solve(
solver="gurobi", solver_options={"Threads": 1})

Profiling using memray and setting [out] and [runfile] separately for pyomo and gurobi runs:

memray run --trace-python-allocators -o [out].bin [runfile].py    
memray flamegraph [out].bin -o [out].html    

[brynpickering]

Last week's new pyomo release has caused an issue in one of our tests (see #597). I've pinned Pyomo upper bound to avoid this issue for now.