PowerIO

Rust Python Julia binding crates.io docs.rs PyPI MSRV 1.88 License: MIT OR Apache-2.0

PowerIO format and matrix flow

PowerIO reads power system data into typed values, writes those values back out in the formats other tools read, and builds the sparse matrices and graph data that solvers consume. The core is Rust, and the Python package, the Julia package PowerIO.jl, and the C ABI expose the same operations under the same names.

A parse returns a PioModule, which is one typed value together with its sources, diagnostics, source map, and history. Depending on what the source declares, the value is a network, a calculation instance, a solution, a time series, a scenario set, or a geographic layer.

Install

cargo add powerio                 # parsing, emission, PowerIO IR
cargo add powerio -F matrix       # and sparse matrices, sensitivities, graph data
pip install powerio               # pip install 'powerio[all]' adds SciPy, NetworkX, Polars
julia -e 'using Pkg; Pkg.add("PowerIO")'
cargo install powerio-cli         # the powerio command

Parse and emit

The examples use case9.m, the nine-bus MATPOWER case included in this repository. Save it in your working directory first, or replace "case9.m" with the path to your own case.

Rust:

use powerio::{PioValue, emit, parse};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let module = parse("case9.m")?;
    let PioValue::BalancedNetwork(network) = module.value() else {
        panic!("expected a balanced network");
    };
    assert_eq!(network.buses().len(), 9);
    emit(&module, "matpower", "copy.m")?;   // the source bytes, unchanged
    let result = emit(&module, "psse", "case9.raw")?;
    for finding in result.diagnostics() {
        eprintln!("{}", finding.code());    // what PSS/E cannot carry
    }
    Ok(())
}

Python:

import powerio

module = powerio.parse("case9.m")
network = module.value
assert isinstance(network, powerio.BalancedNetwork)
module.diagnostics                          # the reader's findings
powerio.emit(module, "matpower", "copy.m")  # the source bytes, unchanged
result = powerio.emit(module, "psse")       # text in memory plus findings

Julia:

using PowerIO

module_ = parse("case9.m")            # PioModule{BalancedNetwork}
net = module_.value
length(net.buses)                     # 9
module_.diagnostics
emit(module_, "matpower", "copy.m")

Command line:

powerio convert case9.m --to psse -o case9.raw
powerio serialize case9.m -o case9.pio.json
powerio verify case9.m --kind bdoubleprime
powerio sensitivities case9.m -o out

If you write an unchanged module back to its own format, you get the source back byte for byte. If you write it to another format, PowerIO keeps what that format can represent and reports each loss as a diagnostic with a stable code, so you can see what the conversion dropped. serialize and deserialize write and read PowerIO IR, the JSON document that stores a complete module for another PowerIO consumer to read back. It preserves the typed data and its history; retained source bytes stay in the running process. After deserialization, emit writes fresh output from the stored value.

Upgrading from 0.10? Start with the migration guide. PowerIO 0.11 uses PowerIO IR generation 2 and C ABI 7; these version numbers describe separate interfaces.

Formats

Format Token Read Write
MATPOWER .m matpower yes yes
PSS/E RAW psse, psse34, psse35 revisions 32 to 35 33, 34, 35
PSS/E RAWX psse-rawx revision 35 35
PowSybl XIIDM xiidm 1.0 to 1.17 1.17
PowSybl JIIDM jiidm 1.0 to 1.17 1.17
CIM CGMES profile set cgmes 2.4.15, 3.0 3.0
ENTSO-E UCTE-DEF .uct ucte 2003.09.01, 2007.05.01 2007.05.01
PowerWorld .aux powerworld yes yes
PowerWorld .pwb pwb yes no
PowerWorld .pwd display powerworld-pwd as a geographic layer no
GE PSLF .epc pslf yes yes
IEEE Common Data Format ieee-cdf yes no
PowerModels JSON powermodels-json yes yes
Egret JSON egret-json yes yes
pandapower JSON pandapower-json yes yes
PyPSA CSV directory pypsa-csv yes yes
Surge JSON surge-json yes yes
GridFM Parquet directory gridfm yes yes
DOE GO Challenge 3 JSON goc3-json problem, or problem with solution a complete solution
DeepMind OPFData JSON opfdata-json yes no
OpenDSS .dss dss yes yes
PowerModelsDistribution engineering JSON pmd-json yes yes
BMOPF JSON bmopf-json 0.1.0, 0.2.0 0.2.0
Geographic layer .geo.json geo-json yes yes

The balanced transmission formats parse to BalancedNetwork, and OpenDSS, PMD, and BMOPF parse to MulticonductorNetwork. A GO Challenge 3 problem parses to AcScucInstance, a problem with its solution to AcScucSolution, an OPFData file to AcOpfSolution, a GridFM dataset to ScenarioSet<BalancedNetwork>, and a PyPSA directory with several snapshots to a TimeSeries. The format guide says what each reader keeps, what each writer reports, and how each is checked against its reference implementation.

Packages

Start with powerio in an application. The component crates let libraries depend on just the layer they use.

Crate Provides
powerio PioValue, parse, emit, serialize, deserialize
powerio-core Source, PioModule, diagnostics, collections, destinations
powerio-tx BalancedNetwork and balanced format readers and writers
powerio-dist MulticonductorNetwork and OpenDSS, PMD, BMOPF converters
powerio-prob Operating points, updates, calculation instances and solutions
powerio-matrix Sparse matrices, sensitivities, DC OPF bundles and graph data
powerio-cli The powerio command and its terminal interface
powerio-py The native extension used by the Python package
powerio-capi C ABI 7, used by Julia and other FFI bindings

powerio-tx and powerio-dist share powerio-core and nothing else, so a distribution consumer pulls in no transmission code. powerio-prob is matrix free. The powerio facade re-exports the component crates, with powerio-matrix behind its matrix feature.

Documentation

License

Apache License 2.0 or MIT License, at your option.

Project links

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GitHub Statistics

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License

Apache Software License, MIT License (MIT OR Apache-2.0)

Requires

Python >=3.9

Provides Extra

all bench graph gridfm matrix mcp pandas

Tags

power systems matpower psse powermodels sparse ptdf dc-opf

Classifiers

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