Transmission Expansion Planning (TEP and MS TEP)¶

Requires pip install pyflow-acdc[OPF] plus Bonmin/Ipopt (see Installation).

This page covers static TEP (one investment snapshot, one operating state) and multi-scenario TEP (MS TEP) (one expansion plan, several clustered operating scenarios). Multi-period planning is in Multi-period Transmission Expansion Planning (MP TEP and MP+MS TEP). Grid fundamentals are in Usage Guide.

After expandable grid setup (step 1), pick one driver for step 2: transmission_expansion() (static TEP) or multi_scenario_TEP() (MS TEP).

Workflow¶

1. Prepare an expandable grid¶

nodes and branches
add_gen() (with installation_cost)
add_RenSource() (with base_cost)
expandable table → expand_elements_from_pd()

Candidate line types — expandable, reconducting, and conductor size selection (CSS) — are modelled in AC System Modelling.

Bundled examples: pyf.cases["case118_TEP"]() (static TEP), pyf.cases["NS_MTDC_2025"](expandable="step") (MS TEP with time series). Grid CSVs and precomputed clusters live under examples/North_Sea_grid_data/ (see that folder’s README for expandable="mp" vs "step").

For MS TEP on price-zone cases, set social cost in ObjRule with the key "PZ_cost_of_generation" (not "Price_Zones"). See Optimal Power Flow Module for all objective component keys.

2. Run¶

Pick one of the following.

Static TEP — one investment snapshot, one operational state:

import pyflow_acdc as pyf

build_only = not pyf.is_pyomo_solver_available("ipopt")

grid, res = pyf.cases["case118_TEP"]()
obj = {'Energy_cost': 1}
model, results, timing, stats = pyf.transmission_expansion(
    grid,
    solver="ipopt",
    ObjRule=obj,
    build_only=build_only,
)

res.all()

The static TEP example uses solver="ipopt" for fast doc tests. For production MINLP solves, prefer solver="bonmin".

MS TEP — one expansion plan, several clustered operating scenarios. Attach time series with add_TimeSeries() (see Time Series Modifications), set clustering_options (see Time-Series Clustering), then call multi_scenario_TEP():

import pyflow_acdc as pyf

from pyflow_tests.test_constants import north_sea_ms_clustering_options

build_only = True

grid, res = pyf.cases["NS_MTDC_2025"](years_data="23,24", expandable="step", online=False)

model, model_results, timing_info, solver_stats, ts_results = pyf.multi_scenario_TEP(

    grid,

    ObjRule={"PZ_cost_of_generation": 1},

    clustering_options=north_sea_ms_clustering_options(),

    solver="ipopt",

    tee=True,

    obj_scaling=1e6,

    build_only=build_only,

)

The MS TEP example also uses solver="ipopt" and build_only=True in doc tests; prefer solver="bonmin" for production solves with binary expansion decisions, and omit build_only for a full solve.

Example cases¶

pyf.cases['case118_TEP']()
pyf.cases['case39']()
pyf.cases['case39_acdc']()
pyf.cases['NS_MTDC_2025'](years_data="23,24", expandable="step") (MS TEP; data in examples/North_Sea_grid_data/)

See Usage Guide for the full catalogue.