FlexMeasures / flexmeasures / 30857916150

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Add a Pyomo-free scheduling backend (direct highspy model construction) (#2364)

* feat: direct HiGHS (highspy) implementation of the device scheduler

Add flexmeasures/data/models/planning/highspy_optimization.py, which builds
the device scheduler's LP/MILP directly with the HiGHS Python API (highspy),
bypassing Pyomo's model construction and solution-ingestion overhead. The
Pyomo implementation (linear_optimization.device_scheduler) remains the
semantic reference; the module docstring prominently documents that the two
models must be kept in sync, as well as the (verified) deviations:

- ems_flow_commitment_equalities are not built: on the Pyomo path they are
  bound-less, i.e. free rows without any effect on the solution
- rows no finite assignment can satisfy (bounds involving +/-inf quantities)
  are skipped, mirroring HiGHS rejecting such rows when appsi adds them
- solver results and model are lightweight shims exposing the attributes
  callers consume (termination_condition/status strings, commitment_costs,
  commodity_costs, costs, and indexed variable views)

The model is built with vectorized numpy arrays (addVars/addRows/
changeColsCost/changeColsIntegrality), constructing and solving typical
battery problems in milliseconds, where the Pyomo layer needs seconds.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WtuVTVfL4fQ9QSqbLXmAGD
Signed-off-by: F.N. Claessen <claessen@seita.nl>

* feat: expose the direct HiGHS backend as solver choice "highspy"

When FLEXMEASURES_LP_SOLVER is set to "highspy", device_scheduler delegates
to device_scheduler_highspy with the same inputs and the same return
contract. All other solver names keep using the Pyomo path unchanged.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WtuVTVfL4fQ9QSqbLXmAGD
Signed-off-by: F.N. Claessen <claessen@seita.nl>

* tests: prove equivalence of the highspy and Pyomo... (continued)

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