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Pypsa Solve And Debug

skill-nimabahrami-pypsa-skills-kit-pypsa-solve-and-debug · by nimabahrami

Solve PyPSA optimization models + fix failures. Triggers: solver selection/options (HiGHS|Gurobi|CPLEX) | numerical issues | scaling | infeasibility | unboundedness | optimization fails | status infeasible/unbounded | runs too slowly | performance | temporal clustering | rolling horizon | spatial aggregation | interpret solved results | n.statistics | energy balances | shadow prices | suspicious…

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Install

$ agentstack add skill-nimabahrami-pypsa-skills-kit-pypsa-solve-and-debug

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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Reliability & compatibility

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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

PyPSA Solve & Debug

Solver selection

  • HiGHS = open-source default. LPs to ~1e7 nonzeros OK | MILP weaker.
  • Gurobi/CPLEX/COPT = licensed. USE: barrier (method=2) + crossover=0 for big planning LPs. ! barrier duals w/o crossover valid but less clean -> price analysis: enable crossover.
  • MILP (committable units | discrete expansion) -> orders-of-magnitude slowdown. SET: mip gap consciously. ! 1e-3 default gap hides real money in big objectives. ! MILP = no duals/prices -> fixed-commitment LP pricing run: pypsa-market-design.

Failure triage, in order

  1. PyPSA-Eur/Snakemake project -> READ logs/solvenetwork* + solver log first; solver config under solving:; re-run one rule w/ snakemake -call (pypsa-network-modeling/references/framework-workflows.md).
  2. RUN: n.consistency_check() + pypsa-physical-realism validator FIRST. Most "solver problems" = data problems.
  3. infeasible -> references/infeasibility.md + scripts/diagnose_infeasibility.py.
  4. unbounded -> free profitable machine: extendable w/ capital_cost1 loop. RUN: realism validator (catches all 3).
  5. numerical trouble (barrier stalls | "numerical difficulties") -> scale model. SET: cost coefficients within ~1e-2..1e6 of each other. ! avoid 1e9 "bigM" capacities -> use 'inf'-free explicit caps. Gurobi: NumericFocus=3, Aggregate=0.
  6. slow -> references/performance.md (clustering | rolling horizon | aggregation).

Native optimize() levers

! READ references/optimize-levers.md BEFORE hand-rolling any of: rolling horizon (3 traps) | two-stage via fixoptimalcapacities / pnomset | stochastic setscenarios + CVaR | IIS (Gurobi) | transmissionlosses | linearized UC (LP prices) | UC gotchas (snapshots-not-hours, uptimebefore=1) | MGA near-optimal | N-1 SCLOPF.

Result interpretation

READ: references/interpreting-results.md = n.statistics | energy balances | duals/shadow prices | curtailment | sanity battery. RUN: sanity battery on EVERY solved model before reporting numbers.

Reproducibility

  • SET: pin solver version + options in result metadata.
  • ! barrier deterministic-ish | concurrent methods not -> degenerate optima flip dispatch between runs w/ identical objectives.
  • USE: tie-breaking cost noise (1e-3 jitter) -> stabilizes plots.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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