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name: pypsa description: "Guides PyPSA power-system network modeling, data I/O, optimization, power flow, statistics, plotting, clustering, and troubleshooting workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

PyPSA repo skill

Use this skill when a task asks about PyPSA (Python for Power Systems Analysis) as a Python package for power-system or energy-system network modeling, optimization, simulation, data exchange, result analysis, plotting, or troubleshooting.

First checks

• PyPSA is primarily a Python API package; there is no package-level console script to route through.
• Start from import pypsa and pypsa.Network() unless the task is specifically about loading an existing network file.
• Base PyPSA covers network modeling, CSV/netCDF I/O, HiGHS optimization, power flow, statistics, and no-geomap plotting.
• Optional extras are feature-specific: HDF5, Excel, cloud paths, Cartopy, Gurobi, scikit-learn spatial clustering, TSAM temporal segmentation, and external converters.
• If the current repository or package version may differ from this skill, read repository provenance.

Install and environment

Read install and environment guidance before changing dependencies, selecting solvers, or relying on optional extras.

Minimal import check:

import pypsa
n = pypsa.Network()
n.add("Carrier", "AC")
n.add("Bus", "bus", carrier="AC")

Bundled helpers:

• Check PyPSA environment reports mandatory imports, optional-extra availability, and an optional tiny HiGHS solve.
• PyPSA quickstart smoke builds a tiny network and can optionally solve it.

Route by task

Build or fix a network model

Use network-modeling for:

• pypsa.Network, n.add, n.remove, snapshots, investment periods, and stochastic scenarios.
• Component tables, time-varying attributes, standard line/transformer types, carrier rows, and schema/default lookup.
• Old/new Components API migration, pypsa.options, n.consistency_check, n.sanitize, n.copy, and n.equals.

Load, save, or repair network data

Use network-io-data for:

• Network(import_name=...), CSV folders, netCDF, HDF5, Excel, URLs, and cloud object storage.
• Data layout, snapshots/time-series file alignment, metadata/CRS, round-trip validation, example cache behavior.
• PYPOWER and pandapower import converters and optional I/O dependency failures.

Optimize, solve, or run power flow

Use optimization-powerflow for:

• n.optimize, create_model, solve_model, extra_functionality, custom Linopy constraints, and solver options.
• Capacity expansion, dispatch, storage, unit commitment, global constraints, stochastic or multi-investment planning, MGA, rolling horizon, piecewise curves, and transmission losses.
• n.lpf, n.pf, optimize-then-nonlinear-power-flow validation, solver failures, infeasibility, and convergence triage.

Analyze, plot, compare, or cluster results

Use analysis-visualization for:

• n.statistics / n.stats, metric filters, groupers, time aggregation, and custom groupers.
• Static and interactive statistics plots, network maps, n.plot.map, n.plot.iplot, and n.plot.explore.
• NetworkCollection scenario comparison and temporal/spatial clustering, including optional Cartopy, scikit-learn, and TSAM behavior.

Shared references

• Package overview maps public APIs, concepts, optional extras, and sub-skill ownership.
• Install and environment guidance explains Python versions, solver defaults, extras, environment variables, options, offline use, and headless plotting.
• Troubleshooting is the cross-cutting triage map for install/import, schema, solver, power-flow, plotting, clustering, and optional dependency failures.
• Repository provenance records the source commit, package version, evidence paths, and refresh triggers.
• Routing metadata is structured metadata for managed repo-skills-router import.

Core operating loop

1. Decide whether the network should be built in code or loaded from a file; route to modeling or I/O.
2. Validate carriers, buses, component names, snapshots, time-series shapes, and optional API settings before solving.
3. For optimization, start with solver_name="highs", explicit include_objective_constant, and a small horizon before scaling.
4. For power flow, run a linear flow first when nonlinear convergence is uncertain.
5. Analyze solved outputs with statistics and plots; on unsolved networks, prefer input-side metrics such as installed capacity.
6. When optional features fail, install only the feature-specific extra or use the documented base fallback.

Do not

• Do not tell future agents to open or run original PyPSA repository docs, tests, notebooks, examples, or scripts; this generated skill bundles the reusable guidance and smoke helpers needed for operation.
• Do not install all optional extras or dev/docs dependencies unless the task explicitly needs them.
• Do not treat a CPU import as proof that optional Cartopy, Gurobi, cloud, Excel, HDF5, scikit-learn, TSAM, pandapower, or PYPOWER workflows are installed.
• Do not run large notebooks, network downloads, cloud operations, or commercial-solver IIS checks without explicit task need and credentials/license availability.