Skip to main content

A repository for extracting dehydrated metadata for distribution power grid model.

Project description

Getting Started with grid-meta

Build Python License Coverage

View Full Documentation..

Welcome! Follow the steps below to get grid-meta up and running locally.
We recommend using a Python virtual environment for a clean install 🔒🐍.

🧪 Step 1: Set Up a Python Environment

To avoid dependency conflicts, create and activate a virtual environment.

You can use any tool of your choice — here are a few popular options:

🟢 Option A: Using venv (Standard Library)
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
🔵 Option B: Using conda
conda create -n grid-reducer-env python=3.10
conda activate grid-reducer-env

🚀 Step 2: Install the Project Locally

Install the project.

pip install gridmeta

✅ This will also install all required dependencies.

🛠 Example CLI Usage

You can currently use this package as CLI tool. To see the available commands please use following command.

gridmeta --help
Usage: gridmeta [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  extract-opendss-dehydrated-dataset

To extract opendss model dehydrated metadata you can use following command.

gridmeta extract-opendss-dehydrated-dataset -f tests\data\opendss\ieee13\master.dss -o test.json

You can specify privacy flag with -pm option.

gridmeta extract-opendss-dehydrated-dataset -f tests\data\opendss\ieee13\master.dss -pm "low" -o test.json

Make sure to pass appropriate file paths. You can also update model year, state, region type and description from command line. Defaults will be used if these are not provided.

Example

Here is an example of extracted metadata for IEEE 13 opendss model.

{
  "metadata": {
    "state": "WA",
    "created_at": "2025-02-26T16:07:47.810263",
    "model_year": 2025,
    "info": "",
    "region_type": "Suburban"
  },
  "assets": {
    "transformers": [
      {
        "kva": 500,
        "count": 1,
        "is_regulator": false,
        "is_substation_transformer": false,
        "num_phase": 3,
        "high_kv": 4.16,
        "low_kv": 0.48,
        "avg_customers_served": 3.0,
        "min_customers_served": 3.0,
        "max_customers_served": 3.0,
        "std_customers_served": "NaN",
        "min_pct_peak_loading": 107.2857856604396,
        "avg_pct_peak_loading": 107.2857856604396,
        "max_pct_peak_loading": 107.2857856604396,
        "std_pct_peak_loading": "NaN"
      },
      {
        "kva": 1666,
        "count": 3,
        "is_regulator": true,
        "is_substation_transformer": false,
        "num_phase": 1,
        "high_kv": 2.4,
        "low_kv": 2.4,
        "avg_customers_served": 5.0,
        "min_customers_served": 0.0,
        "max_customers_served": 15.0,
        "std_customers_served": 8.660254037844387,
        "min_pct_peak_loading": 56.934951911321086,
        "avg_pct_peak_loading": 72.20336662873115,
        "max_pct_peak_loading": 81.85450680902247,
        "std_pct_peak_loading": 13.375776014643415
      },
      {
        "kva": 5000,
        "count": 1,
        "is_regulator": false,
        "is_substation_transformer": true,
        "num_phase": 3,
        "high_kv": 115.0,
        "low_kv": 4.16,
        "avg_customers_served": 15.0,
        "min_customers_served": 15.0,
        "max_customers_served": 15.0,
        "std_customers_served": "NaN",
        "min_pct_peak_loading": 80.6972565800457,
        "avg_pct_peak_loading": 80.6972565800457,
        "max_pct_peak_loading": 80.6972565800457,
        "std_pct_peak_loading": "NaN"
      }
    ],
    "feeder_sections": [
      {
        "kv": 2.40178,
        "num_phase": 1.0,
        "count": 2.0,
        "avg_feeder_miles": 0.16763999999999998,
        "min_feeder_miles": 0.09144,
        "max_feeder_miles": 0.24383999999999997,
        "std_feeder_miles": 0.10776307345282983,
        "min_ampacity": 400.0,
        "avg_ampacity": 400.0,
        "max_ampacity": 400.0,
        "std_ampacity": 0.0,
        "avg_per_unit_resistance_ohm_per_mile": 8.259058763487898e-5,
        "min_per_unit_resistance_ohm_per_mile": 4.504941143720672e-5,
        "max_per_unit_resistance_ohm_per_mile": 0.00012013176383255125,
        "std_per_unit_resistance_ohm_per_mile": 5.309124052618614e-5,
        "avg_per_unit_reactance_ohm_per_mile": 0.00017173146325459316,
        "min_per_unit_reactance_ohm_per_mile": 9.36717072297781e-5,
        "max_per_unit_reactance_ohm_per_mile": 0.00024979121927940824,
        "std_per_unit_reactance_ohm_per_mile": 0.00011039316564582839,
        "min_customers_served": 1.0,
        "avg_customers_served": 1.0,
        "max_customers_served": 1.0,
        "std_customers_served": 0.0,
        "min_pct_peak_loading": 15.674269355370459,
        "avg_pct_peak_loading": 16.73120979790217,
        "max_pct_peak_loading": 17.788150240433882,
        "std_pct_peak_loading": 1.4947395084489676
      },
      {
        "kv": 2.40178,
        "num_phase": 2.0,
        "count": 3.0,
        "avg_feeder_miles": 0.11175999999999998,
        "min_feeder_miles": 0.09144,
        "max_feeder_miles": 0.15239999999999998,
        "std_feeder_miles": 0.035195272409799576,
        "min_ampacity": 400.0,
        "avg_ampacity": 400.0,
        "max_ampacity": 400.0,
        "std_ampacity": 0.0,
        "avg_per_unit_resistance_ohm_per_mile": 0.0003202409042304056,
        "min_per_unit_resistance_ohm_per_mile": 0.0002217052413902808,
        "max_per_unit_resistance_ohm_per_mile": 0.000369508735650468,
        "std_per_unit_resistance_ohm_per_mile": 8.533438719828638e-5,
        "avg_per_unit_reactance_ohm_per_mile": 0.0007264657732177418,
        "min_per_unit_reactance_ohm_per_mile": 0.0005029378429968982,
        "max_per_unit_reactance_ohm_per_mile": 0.0008382297383281636,
        "std_per_unit_reactance_ohm_per_mile": 0.00019358086602660592,
        "min_customers_served": 1.0,
        "avg_customers_served": 1.6666666666666667,
        "max_customers_served": 2.0,
        "std_customers_served": 0.5773502691896257,
        "min_pct_peak_loading": 16.094312794909246,
        "avg_pct_peak_loading": 23.23659848839567,
        "max_pct_peak_loading": 35.82733535181359,
        "std_pct_peak_loading": 10.936738998493551
      },
      {
        "kv": 2.40178,
        "num_phase": 3.0,
        "count": 6.0,
        "avg_feeder_miles": 0.30479999999999996,
        "min_feeder_miles": 0.15239999999999998,
        "max_feeder_miles": 0.6095999999999999,
        "std_feeder_miles": 0.1788621873986338,
        "min_ampacity": 400.0,
        "avg_ampacity": 400.0,
        "max_ampacity": 400.0,
        "std_ampacity": 0.0,
        "avg_per_unit_resistance_ohm_per_mile": 0.0001431742504224452,
        "min_per_unit_resistance_ohm_per_mile": 5.54263103475702e-5,
        "max_per_unit_resistance_ohm_per_mile": 0.0002217052413902808,
        "std_per_unit_resistance_ohm_per_mile": 7.100745688573208e-5,
        "avg_per_unit_reactance_ohm_per_mile": 0.00032479046606481823,
        "min_per_unit_reactance_ohm_per_mile": 0.00012573446074922454,
        "max_per_unit_reactance_ohm_per_mile": 0.0005029378429968982,
        "std_per_unit_reactance_ohm_per_mile": 0.00016108025673573865,
        "min_customers_served": 0.0,
        "avg_customers_served": 6.333333333333333,
        "max_customers_served": 15.0,
        "std_customers_served": 5.501514942874069,
        "min_pct_peak_loading": 0.00014937875249263795,
        "avg_pct_peak_loading": 76.50571677687857,
        "max_pct_peak_loading": 147.9353947021755,
        "std_pct_peak_loading": 60.66546205074211
      }
    ],
    "capacitors": [
      { "kvar": 100.0, "num_phase": 1.0, "kv": 2.4, "count": 1.0 },
      { "kvar": 600.0, "num_phase": 3.0, "kv": 4.16, "count": 1.0 }
    ],
    "switches": [
      {
        "num_phase": 3.0,
        "kv": 2.40178,
        "is_normally_open": false,
        "count": 1.0,
        "avg_ampacity": 400.0,
        "min_ampacity": 400.0,
        "max_ampacity": 400.0,
        "std_ampacity": "NaN"
      }
    ],
    "loads": [
      {
        "kv": 0.277,
        "count": 3.0,
        "num_phase": 1.0,
        "total_customer": 3.0,
        "avg_customers_per_load": 1.0,
        "min_customers_per_load": 1.0,
        "max_customers_per_load": 1.0,
        "std_customers_per_load": 0.0,
        "avg_peak_kw": 133.33333333333334,
        "avg_peak_kvar": 96.66666666666667,
        "min_peak_kw": 120.0,
        "min_peak_kvar": 90.0,
        "max_peak_kw": 160.0,
        "max_peak_kvar": 110.0,
        "std_peak_kw": 23.094010767585033,
        "std_peak_kvar": 11.547005383792516
      },
      {
        "kv": 2.4,
        "count": 9.0,
        "num_phase": 1.0,
        "total_customer": 9.0,
        "avg_customers_per_load": 1.0,
        "min_customers_per_load": 1.0,
        "max_customers_per_load": 1.0,
        "std_customers_per_load": 0.0,
        "avg_peak_kw": 167.88888888888889,
        "avg_peak_kvar": 96.55555555555556,
        "min_peak_kw": 17.0,
        "min_peak_kvar": 10.0,
        "max_peak_kw": 485.0,
        "max_peak_kvar": 212.0,
        "std_peak_kw": 142.64768175862903,
        "std_peak_kvar": 67.38529348290899
      },
      {
        "kv": 4.16,
        "count": 2.0,
        "num_phase": 1.0,
        "total_customer": 2.0,
        "avg_customers_per_load": 1.0,
        "min_customers_per_load": 1.0,
        "max_customers_per_load": 1.0,
        "std_customers_per_load": 0.0,
        "avg_peak_kw": 200.0,
        "avg_peak_kvar": 141.5,
        "min_peak_kw": 170.0,
        "min_peak_kvar": 132.0,
        "max_peak_kw": 230.0,
        "max_peak_kvar": 151.0,
        "std_peak_kw": 42.42640687119285,
        "std_peak_kvar": 13.435028842544403
      },
      {
        "kv": 4.16,
        "count": 1.0,
        "num_phase": 3.0,
        "total_customer": 1.0,
        "avg_customers_per_load": 1.0,
        "min_customers_per_load": 1.0,
        "max_customers_per_load": 1.0,
        "std_customers_per_load": "NaN",
        "avg_peak_kw": 1155.0,
        "avg_peak_kvar": 660.0,
        "min_peak_kw": 1155.0,
        "min_peak_kvar": 660.0,
        "max_peak_kw": 1155.0,
        "max_peak_kvar": 660.0,
        "std_peak_kw": "NaN",
        "std_peak_kvar": "NaN"
      }
    ]
  },
  "voltage_metrics": [
    {
      "snapshot_category": "NetPeakLoad",
      "kv": 0.27713,
      "num_phase": 3.0,
      "avg_voltage_pu": 0.9926781594690027,
      "min_voltage_pu": 0.9824563162208678,
      "max_voltage_pu": 1.0084186207663994,
      "std_voltage_pu": 0.013832992200133702
    },
    {
      "snapshot_category": "NetPeakLoad",
      "kv": 2.40178,
      "num_phase": 1.0,
      "avg_voltage_pu": 0.968088461668005,
      "min_voltage_pu": 0.9608430966208247,
      "max_voltage_pu": 0.9753338267151852,
      "std_voltage_pu": 0.01024649351406627
    },
    {
      "snapshot_category": "NetPeakLoad",
      "kv": 2.40178,
      "num_phase": 2.0,
      "avg_voltage_pu": 0.9973335665813833,
      "min_voltage_pu": 0.9628590008697668,
      "max_voltage_pu": 1.0197285920694914,
      "std_voltage_pu": 0.022007077364035624
    },
    {
      "snapshot_category": "NetPeakLoad",
      "kv": 2.40178,
      "num_phase": 3.0,
      "avg_voltage_pu": 1.0077043602060967,
      "min_voltage_pu": 0.9629552840078276,
      "max_voltage_pu": 1.0560497133305953,
      "std_voltage_pu": 0.029312299320140376
    },
    {
      "snapshot_category": "NetPeakLoad",
      "kv": 66.39528,
      "num_phase": 3.0,
      "avg_voltage_pu": 0.9999724980882166,
      "min_voltage_pu": 0.9999501168596413,
      "max_voltage_pu": 0.9999938117414283,
      "std_voltage_pu": 2.1866994797273813e-5
    }
  ]
}

Attribution and Disclaimer

This software was created under a project sponsored by the U.S. Department of Energy’s Office of Electricity, an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights.

Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

PACIFIC NORTHWEST NATIONAL LABORATORY

operated by BATTELLE

for the UNITED STATES DEPARTMENT OF ENERGY

under Contract DE-AC05-76RL01830

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gridmeta-1.0.1.tar.gz (20.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

gridmeta-1.0.1-py3-none-any.whl (21.2 kB view details)

Uploaded Python 3

File details

Details for the file gridmeta-1.0.1.tar.gz.

File metadata

  • Download URL: gridmeta-1.0.1.tar.gz
  • Upload date:
  • Size: 20.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for gridmeta-1.0.1.tar.gz
Algorithm Hash digest
SHA256 2072ebd6cae1ecc5b7b377d78ea69eb7613de049dab28462827d69047a7fe616
MD5 965c2ed2d1204d8a121e1d11a5626963
BLAKE2b-256 12b217b17d4e40b82bba1c14653b89d7e38e9027ce1a4bb646c1b7c6816e233b

See more details on using hashes here.

Provenance

The following attestation bundles were made for gridmeta-1.0.1.tar.gz:

Publisher: publish.yml on Grid-Atlas/grid-meta

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file gridmeta-1.0.1-py3-none-any.whl.

File metadata

  • Download URL: gridmeta-1.0.1-py3-none-any.whl
  • Upload date:
  • Size: 21.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for gridmeta-1.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 314a10e3df2dd62540459f5d20b1e29a36030e3d275d6e751069d1be5f42ef83
MD5 21c51bfd5afb534094cdc481a58df02f
BLAKE2b-256 5f3bfd8563e0466aada75e8869365e3192bcfb60606a364a53fa4f22a47cca01

See more details on using hashes here.

Provenance

The following attestation bundles were made for gridmeta-1.0.1-py3-none-any.whl:

Publisher: publish.yml on Grid-Atlas/grid-meta

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page