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name: optimization-powerflow description: "Route PyPSA solve, solver, custom constraint, stochastic, multi-investment, MGA, rolling-horizon, piecewise, loss, and power-flow workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

optimization-powerflow

Use this sub-skill for PyPSA tasks that need to:

• solve or re-solve a network with n.optimize(), n.optimize.create_model(), n.optimize.solve_model(), or extra_functionality
• choose a solver, pass solver_options, or diagnose solver availability and infeasibility
• work with capacity expansion, dispatch, storage, unit commitment, global/custom constraints, multi_investment_periods, stochastic scenarios, MGA, rolling horizon, piecewise curves, or transmission losses
• run n.lpf(), n.pf(), or n.optimize.optimize_and_run_non_linear_powerflow()
• debug convergence, slack seeding, big-M, modular committables, or missing optional solver backends

Do not use this sub-skill for:

• building or validating network structure from scratch — use network-modeling
• importing or exporting data — use network-io-data
• statistics, maps, or result plots — use analysis-visualization

Start here

• references/optimization-workflows.md — solve, model-edit, custom-constraint, stochastic, multi-investment, MGA, rolling-horizon, piecewise, and loss workflows.
• references/power-flow-workflows.md — linear and non-linear power-flow workflows, seeding, distributed slack, and optimize-then-PF handoff.
• references/solver-reference.md — solver defaults, solver flags, and license-aware notes.
• references/troubleshooting.md — quick diagnosis map for infeasibility, convergence, data, and backend failures.

Smoke helpers

• scripts/pypsa_optimize_smoke.py — tiny HiGHS solve plus a second create-model/solve-model pass with a custom constraint.
• scripts/pypsa_powerflow_smoke.py — tiny lpf/pf smoke with seeded distributed slack and an optional combined optimize-then-PF run.