Publication record · 18.cifr/2020.li.synthetic-t-and-d-grid

Building Highly Detailed Synthetic Electric Grid Data Sets for Combined Transmission and Distribution Systems

v1.0.0

Hanyue Li (Texas A&M University), Jessica L. Wert (Texas A&M University), Adam Barlow Birchfield (Texas A&M University), Thomas J. Overbye (Texas A&M University), Tomas Gomez San Roman (Universidad Pontificia Comillas), Carlos Mateo Domingo (Universidad Pontificia Comillas), Fernando Emilio Postigo Marcos (Universidad Pontificia Comillas), Pablo Duenas Martinez (Universidad Pontificia Comillas), Tarek Elgindy (National Renewable Energy Laboratory), Bryan Palmintier (National Renewable Energy Laboratory)

RAI18.cifr/2020.li.synthetic-t-and-d-grid

IEEE Open Access Journal of Power and Energy· 2020· doi:10.1109/OAJPE.2020.3029278

This paper presents a methodology for building highly detailed synthetic electric grid datasets that combine transmission and distribution systems. The approach starts with a synthetic transmission system and attaches synthetic distribution feeders at each load bus, scaling loads to match transmission-level demand. The resulting datasets preserve statistical properties of real grids while containing no confidential information, enabling open sharing for research and education.

synthetic gridtransmission systemdistribution systemcombined T&Dpower systemsopen data

✦ Research context

What this agent contributes to the literature.

Problem solved

Real T&D grid data is confidential, blocking open research on combined transmission-distribution interactions.
No prior open methodology existed to generate detailed, statistically realistic combined T&D datasets at scale.
Researchers and educators lacked shareable grid models suitable for power flow studies across both system levels.

Novelty

Procedurally attaches synthetic distribution feeders to every load bus of a synthetic transmission system.

Related research

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Canvas contract1-in / 1-out · unpacked into transmission, distribution_params legacy ports

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sha256:43143d2e9e7a98944538a8aaa4fd426e73f3613919f6e0c8f11b98a78d4749ee

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python main.py

Inputs

input:application/json

Outputs

output:application/json

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Pre-filled with the paper's canonical scenario. Click Invoke agent to reproduce the original result, or edit the JSON below to run a counterfactual.

inputapplication/jsonoptional

Unified canvas input. The executor unpacks this JSON blob into legacy per-port files: transmission, distribution_params

Leave empty to run the paper's canonical scenario.

{
  "transmission": {
    "name": "9-bus-test-system",
    "baseMVA": 100,
    "buses": [
      {
        "id": 1,
        "type": 3,
        "base_kv": 345,
        "pd": 0,
        "qd": 0,
        "lat": 30.2,
        "lon": -97.8
      },
      {
        "id": 2,
        "type": 2,
        "base_kv": 345,
        "pd": 0,
        "qd": 0,
        "lat": 30.4,
        "lon": -97.6
      },
      {
        "id": 3,
        "type": 2,
        "base_kv": 345,
        "pd": 0,
        "qd": 0,
        "lat": 30.1,
        "lon": -97.4
      },
      {
        "id": 4,
        "type": 1,
        "base_kv": 345,
        "pd": 0,
        "qd": 0,
        "lat": 30.3,
        "lon": -97.7
      },
      {
        "id": 5,
        "type": 1,
        "base_kv": 345,
        "pd": 125,
        "qd": 50,
        "lat": 30.25,
        "lon": -97.55
      },
      {
        "id": 6,
        "type": 1,
        "base_kv": 345,
        "pd": 0,
        "qd": 0,
        "lat": 30.15,
        "lon": -97.65
      },
      {
        "id": 7,
        "type": 1,
        "base_kv": 345,
        "pd": 90,
        "qd": 30,
        "lat": 30.35,
        "lon": -97.5
      },
      {
        "id": 8,
        "type": 1,
        "base_kv": 345,
        "pd": 0,
        "qd": 0,
        "lat": 30.2,
        "lon": -97.45
      },
      {
        "id": 9,
        "type": 1,
        "base_kv": 345,
        "pd": 100,
        "qd": 35,
        "lat": 30.1,
        "lon": -97.55
      }
    ],
    "branches": [
      {
        "from_bus": 1,
        "to_bus": 4,
        "r": 0,
        "x": 0.0576,
        "b": 0,
        "ratea": 250
      },
      {
        "from_bus": 4,
        "to_bus": 5,
        "r": 0.017,
        "x": 0.092,
        "b": 0.158,
        "ratea": 250
      },
      {
        "from_bus": 5,
        "to_bus": 6,
        "r": 0.039,
        "x": 0.17,
        "b": 0.358,
        "ratea": 150
      },
      {
        "from_bus": 3,
        "to_bus": 6,
        "r": 0,
        "x": 0.0586,
        "b": 0,
        "ratea": 300
      },
      {
        "from_bus": 6,
        "to_bus": 7,
        "r": 0.0119,
        "x": 0.1008,
        "b": 0.209,
        "ratea": 150
      },
      {
        "from_bus": 7,
        "to_bus": 8,
        "r": 0.0085,
        "x": 0.072,
        "b": 0.149,
        "ratea": 250
      },
      {
        "from_bus": 8,
        "to_bus": 2,
        "r": 0,
        "x": 0.0625,
        "b": 0,
        "ratea": 250
      },
      {
        "from_bus": 8,
        "to_bus": 9,
        "r": 0.032,
        "x": 0.161,
        "b": 0.306,
        "ratea": 250
      },
      {
        "from_bus": 9,
        "to_bus": 4,
        "r": 0.01,
        "x": 0.085,
        "b": 0.176,
        "ratea": 250
      }
    ],
    "generators": [
      {
        "bus": 1,
        "pg": 0,
        "qg": 0,
        "pmax": 250,
        "pmin": 10,
        "vg": 1.04
      },
      {
        "bus": 2,
        "pg": 163,
        "qg": 0,
        "pmax": 300,
        "pmin": 10,
        "vg": 1.025
      },
      {
        "bus": 3,
        "pg": 85,
        "qg": 0,
        "pmax": 270,
        "pmin": 10,
        "vg": 1.025
      }
    ]
  },
  "distribution_params": {
    "feeders_per_substation": 4,
    "avg_customers_per_feeder": 80,
    "load_per_customer_kw": 3.5,
    "feeder_kv": 12.47,
    "branch_prob": 0.35,
    "max_depth": 6,
    "notes": "Parameters drive synthetic feeder generation. load_per_customer_kw is scaled to match transmission bus load."
  }
}

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