# ERAD (Equitable Resilience Analysis For Power Distribution System)
<p align="center">
<img src="logo.svg" width="250" style="display:flex;justify-content:center;">
<p align="center">Graph based python tool for computing equitable resilience. </p>
</p>
[Visit full documentation here.](https://nrel.github.io/erad/)
Understanding the impact of disaster events on people's ability to access critical service is key to designing appropriate programs to minimize the overall impact. Flooded roads, downed power lines, flooded power substation etc. could impact access to critical servies like electricity, food, health and more. The field of disaster modeling is still evolving and so is our understanding of how these events would impact our critical infrastrctures such power grid, hospitals, groceries, banks etc.
ERAD is a free, open-source Python toolkit for computing equity and resilience measures in the face of hazards like earthquakes and flooding. It uses graph database to store data and perform computation at the household level for a variety of critical services that are connected by power distribution network. It uses asset fragility curves, which are functions that relate hazard severity to survival probability for power system assets including cables, transformers, substations, roof-mounted solar panels, etc. recommended in top literature. Programs like undergrounding, microgrid, and electricity backup units for critical infrastructures may all be evaluated using metrics and compared across different neighborhoods to assess their effects on equity and resilience.
ERAD is designed to be used by researchers, students, community stakeholders, distribution utilities to understand and possibly evaluate effectiveness of different post disaster programs to improve resilience and equity. It was funded by National Renewable Energy Laboratory (NREL) and made publicy available with open license.
Raw data
{
"_id": null,
"home_page": "https://github.com/nrel/erad",
"name": "NREL-erad",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "",
"keywords": "Resilience,Equity,Python,Power Distribution Systems,Earthquake,Flooding,Fire",
"author": "Kapil Duwadi, Aadil Latif, Kwami Sedzro, Sherin Ann Abraham, Bryan Palmintier",
"author_email": "kapil.duwadi@nrel.gov, aadil.altif@nrel.gov, sherinann.abraham@nrel.gov, kwami.sedzro@nrel.gov, bryan.palmintier@nrel.gov",
"download_url": "https://files.pythonhosted.org/packages/7e/ad/b6bd4dbdfd00dedb13f1635c948c64a86b5b8eadbc34b991ba29dbb3657c/NREL-erad-0.0.0a0.tar.gz",
"platform": null,
"description": "# ERAD (Equitable Resilience Analysis For Power Distribution System)\r\n<p align=\"center\"> \r\n<img src=\"logo.svg\" width=\"250\" style=\"display:flex;justify-content:center;\">\r\n<p align=\"center\">Graph based python tool for computing equitable resilience. </p>\r\n</p>\r\n\r\n\r\n\r\n[Visit full documentation here.](https://nrel.github.io/erad/)\r\n\r\nUnderstanding the impact of disaster events on people's ability to access critical service is key to designing appropriate programs to minimize the overall impact. Flooded roads, downed power lines, flooded power substation etc. could impact access to critical servies like electricity, food, health and more. The field of disaster modeling is still evolving and so is our understanding of how these events would impact our critical infrastrctures such power grid, hospitals, groceries, banks etc.\r\n\r\nERAD is a free, open-source Python toolkit for computing equity and resilience measures in the face of hazards like earthquakes and flooding. It uses graph database to store data and perform computation at the household level for a variety of critical services that are connected by power distribution network. It uses asset fragility curves, which are functions that relate hazard severity to survival probability for power system assets including cables, transformers, substations, roof-mounted solar panels, etc. recommended in top literature. Programs like undergrounding, microgrid, and electricity backup units for critical infrastructures may all be evaluated using metrics and compared across different neighborhoods to assess their effects on equity and resilience.\r\n\r\nERAD is designed to be used by researchers, students, community stakeholders, distribution utilities to understand and possibly evaluate effectiveness of different post disaster programs to improve resilience and equity. It was funded by National Renewable Energy Laboratory (NREL) and made publicy available with open license.\r\n",
"bugtrack_url": null,
"license": "",
"summary": "Graph based scalable tool for computing equitable resilience metrics for distribution systems.",
"version": "0.0.0a0",
"project_urls": {
"Homepage": "https://github.com/nrel/erad"
},
"split_keywords": [
"resilience",
"equity",
"python",
"power distribution systems",
"earthquake",
"flooding",
"fire"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "8bf588289f2a443baa4d90cc148fbca691e0541ee23e31e1b7ab6d2d64ddb92b",
"md5": "29f174eeca7bef0f108d6ed938852738",
"sha256": "d699b467894de68a24e7fe29f6867886d8e943b09938b85ecb1096198366ffb5"
},
"downloads": -1,
"filename": "NREL_erad-0.0.0a0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "29f174eeca7bef0f108d6ed938852738",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 656069,
"upload_time": "2023-07-18T20:49:28",
"upload_time_iso_8601": "2023-07-18T20:49:28.552237Z",
"url": "https://files.pythonhosted.org/packages/8b/f5/88289f2a443baa4d90cc148fbca691e0541ee23e31e1b7ab6d2d64ddb92b/NREL_erad-0.0.0a0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "7eadb6bd4dbdfd00dedb13f1635c948c64a86b5b8eadbc34b991ba29dbb3657c",
"md5": "37af52ae8904c8202d55d208dc88d777",
"sha256": "512199daaf9e057672d445803a1b703143beebd324ed050866ba4d37e9acfb06"
},
"downloads": -1,
"filename": "NREL-erad-0.0.0a0.tar.gz",
"has_sig": false,
"md5_digest": "37af52ae8904c8202d55d208dc88d777",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 623466,
"upload_time": "2023-07-18T20:49:31",
"upload_time_iso_8601": "2023-07-18T20:49:31.434369Z",
"url": "https://files.pythonhosted.org/packages/7e/ad/b6bd4dbdfd00dedb13f1635c948c64a86b5b8eadbc34b991ba29dbb3657c/NREL-erad-0.0.0a0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-07-18 20:49:31",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "nrel",
"github_project": "erad",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [
{
"name": "pytest",
"specs": []
},
{
"name": "networkx",
"specs": []
},
{
"name": "pyyaml",
"specs": []
},
{
"name": "geojson",
"specs": []
},
{
"name": "neo4j-driver",
"specs": []
},
{
"name": "python-dotenv",
"specs": []
},
{
"name": "ditto.py",
"specs": []
},
{
"name": "boto3",
"specs": []
},
{
"name": "botocore",
"specs": []
},
{
"name": "OpenDSSDirect.py",
"specs": []
},
{
"name": "pandas",
"specs": []
},
{
"name": "matplotlib",
"specs": []
},
{
"name": "plotly",
"specs": []
},
{
"name": "shapely",
"specs": []
},
{
"name": "jupyter",
"specs": []
},
{
"name": "geopandas",
"specs": []
},
{
"name": "stateplane",
"specs": []
},
{
"name": "graphdatascience",
"specs": []
},
{
"name": "scipy",
"specs": []
},
{
"name": "geopy",
"specs": []
},
{
"name": "rasterio",
"specs": []
},
{
"name": "xmltodict",
"specs": []
},
{
"name": "pydantic",
"specs": []
},
{
"name": "requests",
"specs": []
}
],
"lcname": "nrel-erad"
}
JSON
