Sequential STEP

Runs transmission expansion one investment period at a time: each step is a single static transmission_expansion() (or, for sequential_MS_STEP, a multi_scenario_TEP()) solve, with the grid state carried forward to the next period. This is an alternative to the multi-period formulations (multi_period_transmission_expansion(), multi_period_MS_TEP()) when you prefer a series of linked single-expansion solves over one large MP model.

See Multi-period Transmission Expansion Planning (MP TEP and MP+MS TEP) for workflow examples using pyf.cases['case24_MP']().

Requires OPF/pyomo (pip install pyflow-acdc[OPF]).

Functions are found in pyflow_acdc.ACDC_sequential_STEP.

sequential_STEP(grid, inv_data=None, mix_data=None, n_years=10, Hy=8760, discount_rate=0.02, ObjRule=None, solver='bonmin', time_limit=None, tee=False, callback=False, solver_options=None, obj_scaling=1.0, export_dir=None, svg_prefix='sequential_STEP', save_svgs=False, export_steps=False, nlp_warmstart=False)

Sequentially solve static transmission expansion one investment period at a time.

Each period calls transmission_expansion() on the current grid; results are linked across periods as an alternative to multi_period_transmission_expansion().

sequential_MS_STEP(grid, inv_data=None, mix_data=None, n_years=10, Hy=8760, discount_rate=0.02, clustering_options=None, ObjRule=None, solver='bonmin', tee=False, callback=False, solver_options=None, obj_scaling=1.0, export_dir=None, svg_prefix='sequential_MS_STEP', save_svgs=False, export_steps=False, alpha=None, limit_flow_rate=True, nlp_warmstart=False, clustering_cache_json_path=None, reuse_clustering_cache=True)

Sequentially solve multi-scenario transmission expansion one investment period at a time.

Each period calls multi_scenario_TEP() on the current grid; results are linked across periods as an alternative to multi_period_MS_TEP().