Skill

pandapower

Power systems analysis and optimization library (pandapower v3.4.0). Use when working with electric power networks: building network models (buses, lines, transformers, loads, generators), running AC/DC power flow, optimal power flow (OPF), short circuit calculations (IEC 60909), state estimation, time series simulations, network topology analysis, or visualizing power grids in Python.

From pandapower

Install

Run in your terminal

npx claudepluginhub datathings/marketplace --plugin pandapower

Tool Access

This skill uses the workspace's default tool permissions.

Supporting Assets

View in Repository

references/api-network.md

references/api-plotting.md

references/api-powerflow.md

references/api-toolbox.md

references/api-topology.md

references/workflows.md

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Parent Repo Stars7

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Last CommitFeb 21, 2026

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pandapower

Overview

pandapower is an open-source Python library for automated analysis and optimization of power systems. It stores network data as pandas DataFrames, provides Newton-Raphson and other power flow solvers (including C++ backends via lightsim2grid and PowerGridModel), and supports advanced studies including OPF, short circuit (IEC 60909), three-phase unbalanced flow, and state estimation.

Version: v3.4.0 Language: Python License: BSD 3-Clause Authors: University of Kassel (e2n) and Fraunhofer IEE

Quick Start

import pandapower as pp

# Create network
net = pp.create_empty_network(f_hz=50.)

# Add buses
b_hv = pp.create_bus(net, vn_kv=110., name="HV Bus")
b_mv = pp.create_bus(net, vn_kv=20., name="MV Bus")

# Add external grid (slack/reference)
pp.create_ext_grid(net, bus=b_hv, vm_pu=1.02)

# Add transformer (uses built-in standard type library)
pp.create_transformer(net, hv_bus=b_hv, lv_bus=b_mv, std_type="25 MVA 110/20 kV")

# Add load
pp.create_load(net, bus=b_mv, p_mw=10.0, q_mvar=2.0)

# Run AC power flow
pp.runpp(net)

# Inspect results (stored in net.res_* DataFrames)
print(net.res_bus[["vm_pu", "va_degree"]])
print(net.res_trafo[["loading_percent"]])
print(f"Converged: {net.converged}")

Core Concepts

Network as DataFrames: All elements stored as pandas DataFrames (net.bus, net.line, net.load, etc.); results in net.res_* tables after power flow.
Consumer sign convention: Positive p_mw means consumption for loads; positive p_mw means generation for generators/sgens.
Standard types: Built-in library of line and transformer types; custom types supported via create_std_type().
Per-unit system: Voltages in per unit (p.u.) with sn_mva as base; power in MW/Mvar; impedances in ohm/km.
In-place results: runpp() and other solvers write results to net.res_* tables; check net.converged after each run.
Modular subpackages: pandapower.topology, pandapower.plotting, pandapower.shortcircuit, pandapower.estimation, pandapower.timeseries, pandapower.control are separate namespaces.

API Reference

Domain	File	Description
Network Creation	api-network.md	Buses, lines, transformers, loads, generators, switches, standard types, predefined networks
Power Flow	api-powerflow.md	AC/DC power flow, OPF, 3-phase flow, short circuit, state estimation, result tables
Topology	api-topology.md	Graph creation, connectivity, distance, island detection
Plotting	api-plotting.md	Matplotlib simple plot, custom collections, Plotly interactive, geodata
Toolbox	api-toolbox.md	Element selection, network modification, file I/O, comparison, time series
Workflows	workflows.md	Complete working examples for common studies

Common Workflows

Build network from scratch: See api-network.md, then workflows.md
AC power flow: pp.runpp(net) — See api-powerflow.md
Optimal power flow: add cost functions + limits, then pp.runopp(net) — See workflows.md
Short circuit: pp.shortcircuit.calc_sc(net, fault="3ph") — See api-powerflow.md
Plot network: pp.plotting.simple_plot(net) — See api-plotting.md
Use benchmark network: pp.networks.case14(), pp.networks.mv_oberrhein() — See api-network.md
Time series simulation: See api-toolbox.md and workflows.md
N-1 contingency analysis: See workflows.md

Key Considerations

Convergence: Always check net.converged after running power flow. Non-convergence often indicates voltage angle issues — try init="dc" or algorithm="iwamoto_nr".
Geodata format: Networks created before v2.7 use legacy bus_geodata/line_geodata DataFrames; run pp.plotting.geo.convert_geodata_to_geojson(net) to upgrade.
OPF requires cost functions: runopp() will fail without at least one cost function (create_poly_cost or create_pwl_cost); all controlled elements need min_p_mw/max_p_mw limits.
Solver backends: Install lightsim2grid or power-grid-model for 10-100x speedup on large networks; pandapower uses them automatically when available (pip install pandapower[pgm]).
Three-phase flow: runpp_3ph() is imported from pandapower.pf.runpp_3ph, not the top-level namespace.
Short circuit: calc_sc() lives in pandapower.shortcircuit; single-phase faults require transformer zero-sequence parameters (vk0_percent, vkr0_percent).