---
license: mit
task_categories:
  - other
tags:
  - power-systems
  - optimal-power-flow
  - energy
  - power-grid-modeling
  - openstreetmap
  - us-eia
  - us-census
pretty_name: "GridSFM US Power Grid Dataset"
size_categories:
  - 100M<n<1G
---


# GridSFM US Power Grid Dataset

## Overview

GridSFM US Power Grid Dataset is a set of geographically grounded, electrically coherent power-system network derived entirely from publicly available data. It was developed to support AC optimal power flow (AC-OPF) analysis, enabling physics-based study of congestion, capacity, and demand sitting without restricted data.

A detailed discussion of GridSFM US Power Grid Dataset, including how it was developed and evaluated, can be found in our paper at: https://arxiv.org/abs/2605.04289.

### Intended uses

The GridSFM US Power Grid Dataset is intended to support a broad range of physics-based research questions on the U.S. transmission network, covering 48 states and multi-state interconnections with realistic geographic structure, including transmission expansion potential, targeted line upgrades, and placement of large loads.

### Out-of-scope uses

GridSFM US Power Grid Dataset is not well suited for detailed operational or market-critical decision making, including real-time dispatch, contingency analysis, or regulatory planning that requires exact system parameters. 

There are few or no instances of measured electrical parameters, complete multi-circuit topology, detailed protection models, or operational control parameters in this dataset. As a result, GridSFM US Power Grid Dataset should not be used for safety-critical, financial, or regulatory decisions that depend on precise modeling of the real transmission grid.

We do not recommend using GridSFM US Power Grid Dataset in commercial or real-world applications without further testing and development. It is being released for research purposes.

We do not recommend using GridSFM US Power Grid Dataset in the context of high-risk decision making (e.g. in law enforcement, legal, finance, or healthcare). 

### Citation

If you use this dataset, please cite:

```bibtex
@article{britto2026powergrid,
  title   = {Building Power Grid Models from Open Data: A Complete Pipeline from OpenStreetMap to Optimal Power Flow},
  author  = {Britto, Andrea and Spina, Thiago and Yang, Weiwei and Fowers, Spencer and Zhang, Baosen and White, Chris},
  year    = {2026},
  url     = {https://arxiv.org/abs/2605.04289}
  note    = {Microsoft Research}
}
```

## Dataset Details

### Dataset Contents

GridSFM US Power Grid Dataset consists of 54 instances of OPF-ready transmission network models (48 contiguous U.S. states and 6 multi-state regions), derived entirely from open data.

Each instance includes bus-branch topology, estimated electrical parameters (line impedances, transformer characteristics), generator attributes (capacity, fuel type, cost functions), hourly demand allocation, DC warm-start solution (voltage angles and dispatch), and synthetic reactive compensation shunts for AC-OPF feasibility.

Each instance is associated with a label describing the geographic scope (state or multi-state region) and operating condition (peak 16h or off-peak 04h demand snapshot).

The data was generated between 2025 and 2026.

GridSFM US Power Grid Dataset does not contain links to external data sources. Links are to publicly available datasets used during generation (e.g., OpenStreetMap, U.S. EIA, U.S. Census, HIFLD), which are referenced for provenance but not dynamically queried at runtime.

### Data Creation & Processing

GridSFM US Power Grid Dataset was created from scratch, using publicly available open data sources, rather than adapting any existing power grid datasets.

The existing data that was used to create GridSFM US Power Grid Dataset consisted of geospatial descriptions of power infrastructure (transmission lines, substations, and generators), generator metadata (capacity, fuel type, heat rates), hourly demand measurements at the balancing-authority level, population-based geographic data, and boundary definitions for balancing authorities.

The existing data that was used to create GridSFM US Power Grid Dataset was originally collected by external public data providers, including OpenStreetMap contributors (crowdsourced mapping), the U.S. Energy Information Administration (EIA), the U.S. Census Bureau, and U.S. government infrastructure datasets such as HIFLD.

GridSFM US Power Grid Dataset was created by transforming these heterogeneous data sources into transmission network models through a multi-stage pipeline, including data extraction, topology reconstruction, parameter estimation, demand allocation, and optimal power flow (OPF) solving with progressive relaxation.

Dataset creation was carried out by members of the Microsoft Research Catalyst Lab team. 

GridSFM US Power Grid Dataset includes data crawled from the web. Specifically, OpenStreetMap data (power infrastructure features) was programmatically retrieved via local copy using the Overpass API, which serves publicly available, user-contributed geographic data.

#### People & Identifiers

The GridSFM US Power Grid Dataset is not related to humans in any way and thus does not include any information that could be used to identify a person.

#### Sensitive or harmful content

GridSFM US Power Grid Dataset contains only power systems information and thus no sensitive or harmful content.

#### Other processing

Duplicate/redundant information was automatically removed using software, as part of the topology reconstruction and data preprocessing pipeline.

The data was labeled with metadata describing the modeled region (state or multi-state region), operating condition (peak or off-peak hour), and solver outputs (e.g., DC/AC-OPF results and relaxation levels). The labeling was performed automatically using software, based on deterministic naming conventions and outputs from the OPF pipeline.

## How to get started

To begin using GridSFM US Power Grid Dataset, users can download and load the dataset directly from Hugging Face or use the official loader from the GridSFM repository:

- microsoft/GridSFM_US_power_grid · Datasets at Hugging Face
- microsoft/GridSFM: Small Foundation Models for the Power Grid

See section [Dataset Download, Usage, and File Specification](#dataset-download-usage-and-file-specification) for detailed code examples.

## Validation

To assess how effective GridSFM US Power Grid Dataset would be at its intended purpose, our team looked for physical plausibility, solver feasibility, and consistency with real-world power system statistics.

Specifically, we:
- Evaluated DC-OPF and AC-OPF convergence rates across all 48 states and multi-state regions
- Measured dispatch costs, system losses, and generator utilization, comparing them to expected ranges in real-world systems
- Assessed model robustness under different relaxation levels, using convergence behavior as a proxy for data quality
- Verified scaling consistency across geographic regions, from small states to continent-scale interconnections

A detailed discussion of our validation methods and results can be found in our paper at: https://arxiv.org/abs/2605.04289 

## Limitations

GridSFM US Power Grid Dataset was developed for research and experimental purposes. Further testing and validation are needed before considering its application in commercial or real-world scenarios.

GridSFM US Power Grid Dataset consists of English language instances only. 

GridSFM US Power Grid Dataset contains approximate and inferred data, including estimation noise in electrical parameters (e.g., line impedances, thermal limits), incomplete topology reconstruction (e.g., missing parallel circuits), and heuristic demand allocation.

GridSFM US Power Grid Dataset is missing utility-grade measurements and operational data, including exact network topology, measured electrical parameters, protection settings, dynamic system behavior, and time-series operational constraints.

There are few or no instances of detailed distribution-level networks, low-voltage infrastructure, or precise multi-circuit transmission representations in the dataset. As a result, GridSFM US Power Grid Dataset should not be used for high-fidelity operational studies, protection analysis, or real-time decision-making.

The ability to access external links in the dataset is beyond the control of the research team.

GridSFM US Power Grid Dataset should not be used in highly regulated domains where inaccurate or incomplete outputs could suggest actions that lead to injury or negatively impact an individual's legal, financial, or life opportunities.

## Best Practices

We recommend splitting the data into train/validation/test splits based on geographic regions (e.g., by state or multi-state region) or operating conditions (peak vs off-peak), depending on the intended use case.

It is the user’s responsibility to ensure that the use of GridSFM US Power Grid Dataset complies with relevant data protection regulations and organizational guidelines.

## License

MIT License

Nothing disclosed here, including the Out of Scope Uses section, should be interpreted as or deemed a restriction or modification to the license the code is released under.  

## Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

## Contact

This research was conducted by members of Microsoft Research. We welcome feedback and collaboration from our audience. If you have suggestions, questions, or observe unexpected/problematic data in our dataset, please contact us at gridsfm@microsoft.com.

If the team receives reports of undesired content or identifies issues independently, we will update this repository with appropriate mitigations.

---

# Dataset Content, Quick Start, and Specification

OPF-ready transmission network models for all 48 contiguous U.S. states and 6 multi-state regions, derived entirely from open data (OpenStreetMap + U.S. EIA + U.S. Census).

Each model is a self-contained JSON file compatible with [PowerModels.jl](https://github.com/lanl-ansi/PowerModels.jl) and MATPOWER-format tools. Models include bus-branch topology, line impedances, generator costs, hourly load allocation, DC warm-start voltage angles, and reactive compensation shunts.

**Tools & Viewer**: The Python loader ([`gridsfm_pg_loader.py`](https://github.com/microsoft/GridSFM/tree/main/power_grid)) and the interactive Data Viewer are available in the [GridSFM repository](https://github.com/microsoft/GridSFM/tree/main/power_grid).

## Coverage

**48 states** — all contiguous U.S. states (AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KS, KY, LA, ME, MD, MA, MI, MN, MS, MO, MT, NE, NV, NH, NJ, NM, NY, NC, ND, OH, OK, OR, PA, RI, SC, SD, TN, TX, UT, VT, VA, WA, WV, WI, WY).

**6 multi-state regions:**

| Region | States | Buses (approx.) |
|--------|--------|-----------------|
| `new_england` | CT, MA, ME, NH, RI, VT | ~640 |
| `pacific_nw` | OR, WA | ~1,100 |
| `desert_sw` | AZ, NV, UT | ~1,300 |
| `western` | AZ, CA, CO, ID, MT, NM, NV, OR, UT, WA, WY | ~5,100 |
| `eastern` | AL, AR, CT, DE, FL, GA, IA, IL, IN, KS, KY, LA, MA, MD, ME, MI, MN, MO, MS, NC, ND, NE, NH, NJ, NY, OH, OK, PA, RI, SC, SD, TN, VA, VT, WI, WV | ~21,700 |
| `pjm` | DE, IL, IN, KY, MD, MI, NC, NJ, OH, PA, TN, VA, WV | ~7,800 |

**Two operating hours per model:**
- `16h` — peak demand (4:00 PM, July 15 2024)
- `04h` — off-peak demand (4:00 AM, July 15 2024)

## File Structure

```
16h/
  alabama_model.json                 # Full model (topology + parameters + demand + shunts + DC warm-start)
  alabama_dc_results.json            # DC-OPF solution
  alabama_ac_results.json            # AC-OPF solution
  ...
  western_model.json                 # Multi-state region model
  western_dc_results.json
  western_ac_results.json
04h/
  ...                                # Same structure, off-peak hour
```

## Quick Start

### Download from HuggingFace

```bash
pip install huggingface_hub
```

**Load a single model (no extra dependencies):**

```python
from huggingface_hub import hf_hub_download
import json

path = hf_hub_download(
    repo_id="microsoft/GridSFM_US_power_grid",
    filename="16h/texas_model.json",
    repo_type="dataset",
)
with open(path) as f:
    model = json.load(f)

print(f"Buses: {len(model['bus'])}")
print(f"Branches: {len(model['branch'])}")
print(f"Generators: {len(model['gen'])}")
print(f"Loads: {len(model['load'])}")
print(f"Total load: {sum(l['pd'] for l in model['load'].values()) * model['baseMVA']:.0f} MW")
```

**Using the GridSFM loader** (from [github.com/microsoft/GridSFM/power_grid](https://github.com/microsoft/GridSFM/tree/main/power_grid)):

```python
from gridsfm_pg_loader import GridSFM_PG_Loader

# With export_dir (optional): the entire dataset is automatically downloaded
# to this directory on init. Without it, files are stored in HuggingFace's cache.
loader = GridSFM_PG_Loader(
    "microsoft/GridSFM_US_power_grid",
    export_dir="./gridsfm_data",  # optional; pre-fetches everything here
)

# To skip the automatic download, use pre_fetch_all=False (lazy download on access)
# loader = GridSFM_PG_Loader(
#     "microsoft/GridSFM_US_power_grid",
#     export_dir="./gridsfm_data",
#     pre_fetch_all=False,
# )

# Case-insensitive; state abbreviations work too
model = loader.load_model("TX", hour="16h")
ac    = loader.load_ac_results("texas", hour="16h")
dc    = loader.load_dc_results("Texas", hour="16h")

# Export a single file to a specific path
loader.export_file("TX", "model", hour="16h", dest="./my_models/texas.json")

# Save a loaded (or modified) model dict back to JSON
loader.save_json(model, "./my_models/texas_modified.json")

# Discover what's available (fetched from dataset_metadata.json)
loader.list_regions()        # all 54 regions + states
loader.list_abbreviations()  # {"AL": "alabama", "TX": "texas", ...}
loader.list_hours()          # ["04h", "16h"]
loader.list_file_types()     # ["model", "ac_results", "dc_results"]

# Export the entire dataset to a local directory at any time
loader.export_all("./gridsfm_data")
```

**Download the entire dataset (~230 MB):**

```bash
hf download --repo-type dataset microsoft/GridSFM_US_power_grid --local-dir ./gridsfm_data
```

**Download a subset (one hour only):**

```bash
hf download --repo-type dataset microsoft/GridSFM_US_power_grid --include "16h/*" --local-dir ./gridsfm_data
```

### Load a model from local files

```python
import json

with open("16h/texas_model.json") as f:
    model = json.load(f)

print(f"Buses: {len(model['bus'])}")
print(f"Branches: {len(model['branch'])}")
print(f"Generators: {len(model['gen'])}")
print(f"Loads: {len(model['load'])}")
print(f"Shunts: {len(model['shunt'])}")
print(f"HVDC lines: {len(model['dcline'])}")
print(f"Total load: {sum(l['pd'] for l in model['load'].values()) * model['baseMVA']:.0f} MW")
```

### Run OPF with PowerModels.jl

```julia
using PowerModels, Ipopt

data = PowerModels.parse_file("16h/texas_model.json")
result = solve_ac_opf(data, Ipopt.Optimizer)
println("Objective: \$(result[\"objective\"])")
println("Status: \$(result[\"termination_status\"])")
```

## Model File (`*_model.json`)

The primary artifact. A single JSON containing everything needed to run optimal power flow.

### Top-Level Metadata

| Field | Type | Description |
|-------|------|-------------|
| `name` | string | Model name (e.g., `"delaware"`) |
| `baseMVA` | float | System base power (always 100.0) |
| `per_unit` | bool | Always `true` — all values are in per-unit |
| `version` | string | Format version |
| `source_type` | string | `"matpower"` — format compatibility marker |
| `balancing_authority` | string | Primary BA serving this state (e.g., `"PJM"`) |
| `demand_source` | string | EIA data source and allocation fraction |
| `dispatch_method` | string | Generator dispatch method (`"merit_order"` or `"proportional"`) |
| `load_allocation_method` | string | How load was distributed to buses (`"census"` or `"per_ba_census"`) |
| `ba_coverage_pct` | float | Percentage of state capacity covered by detected BAs |
| `is_multi_state` | bool | Whether this is a multi-state region model |
| `target_datetime` | string | ISO 8601 timestamp for demand snapshot |

`storage` and `switch` are present but always empty (required by PowerModels.jl parser).

### `bus` — Transmission Buses

Keyed by string ID (non-sequential). One bus per voltage level per substation.

| Field | Type | Description |
|-------|------|-------------|
| `bus_i` | int | Bus number |
| `bus_type` | int | 1 = PQ, 2 = PV (generator), 3 = slack (reference) |
| `index` | int | Same as `bus_i` |
| `name` | string | Substation name from OSM |
| `area` | int | Network area |
| `zone` | int | Network zone |
| `base_kv` | float | Nominal voltage (kV) |
| `lat` | float | Latitude (WGS84) |
| `lon` | float | Longitude (WGS84) |
| `vm` | float | Voltage magnitude (p.u.) — initialized to 1.0 |
| `va` | float | Voltage angle (radians) — from DC-OPF warm-start |
| `vmax` | float | Upper voltage limit (p.u.) |
| `vmin` | float | Lower voltage limit (p.u.) |
| `pd` | float | Always 0.0 (loads are in the `load` section) |
| `qd` | float | Always 0.0 |

### `gen` — Generators

| Field | Type | Description |
|-------|------|-------------|
| `index` | int | Generator number |
| `gen_bus` | int | Bus this generator is connected to |
| `gen_status` | int | 1 = online, 0 = offline |
| `name` | string | Generator/plant name from OSM |
| `fuel_type` | string | Standardized fuel type (`"gas"`, `"nuclear"`, `"solar"`, `"wind"`, `"coal"`, `"hydro"`, etc.) |
| `pg` | float | Active power output (p.u.) — from DC warm-start dispatch |
| `pmax` | float | Maximum active power (p.u.) |
| `pmin` | float | Minimum active power (p.u.) |
| `qg` | float | Reactive power output (p.u.) |
| `qmax` | float | Maximum reactive power (p.u.) |
| `qmin` | float | Minimum reactive power (p.u.) |
| `vg` | float | Voltage setpoint (p.u.) |
| `mbase` | float | Machine base (MVA) |
| `model` | int | Cost model type (2 = polynomial) |
| `ncost` | int | Number of cost coefficients |
| `cost` | list | Cost polynomial `[c2, c1, c0]` where total_cost = c2·pg² + c1·pg + c0 (p.u.) |
| `apf` | float | Area participation factor |
| `startup` | float | Startup cost ($) |
| `shutdown` | float | Shutdown cost ($) |
| `ramp_10` | float | 10-minute ramp rate (p.u.) |
| `ramp_30` | float | 30-minute ramp rate (p.u.) |
| `ramp_agc` | float | AGC ramp rate (p.u.) |
| `ramp_q` | float | Reactive ramp rate (p.u.) |
| `startup_time` | float | Startup time (hours) |
| `min_up_time` | float | Minimum up time (hours) |
| `min_down_time` | float | Minimum down time (hours) |

**EIA-matched generators** also have:

| Field | Type | Description |
|-------|------|-------------|
| `fuel_type_eia` | string | Raw EIA fuel code (`"NG"`, `"SUN"`, `"NUC"`, etc.) |
| `prime_mover` | string | EIA prime mover code (`"CT"`, `"PV"`, `"ST"`, etc.) |
| `eia_plant_id` | string | EIA plant ID |
| `eia_generator_id` | string | EIA generator ID |
| `eia_match_score` | float | Match confidence (0–1) |
| `eia_match_distance_km` | float | Distance from OSM location to EIA plant (km) |
| `ref_us_eia` | string | EIA reference ID |
| `pmax_eia` | float | EIA nameplate capacity (p.u.) |
| `heat_rate_eia` | float | EIA heat rate (BTU/kWh) — thermal generators only |
| `capacity_factor` | float | Hourly capacity factor (solar/wind derating) |
| `pmax_nameplate` | float | Nameplate pmax before capacity factor derating (p.u.) |
| `qmax_nameplate` | float | Nameplate qmax before any adjustments (p.u.) |
| `qmin_nameplate` | float | Nameplate qmin before any adjustments (p.u.) |

**Injected generators** (from distant EIA plants with no OSM match) also have:

| Field | Type | Description |
|-------|------|-------------|
| `eia_injected` | bool | Always `true` — generator was injected, not matched to OSM |

### `branch` — Transmission Lines and Transformers

| Field | Type | Description |
|-------|------|-------------|
| `index` | int | Branch number |
| `f_bus` | int | From bus |
| `t_bus` | int | To bus |
| `br_r` | float | Series resistance (p.u.) |
| `br_x` | float | Series reactance (p.u.) |
| `b_fr` | float | From-side shunt susceptance (p.u.) |
| `b_to` | float | To-side shunt susceptance (p.u.) |
| `g_fr` | float | From-side shunt conductance (p.u.) |
| `g_to` | float | To-side shunt conductance (p.u.) |
| `br_status` | int | 1 = in service |
| `rate_a` | float | Long-term thermal rating (p.u.) |
| `rate_b` | float | Short-term rating (p.u.) |
| `rate_c` | float | Emergency rating (p.u.) |
| `angmin` | float | Minimum angle difference (radians) |
| `angmax` | float | Maximum angle difference (radians) |
| `tap` | float | Tap ratio (1.0 for lines; off-nominal for transformers) |
| `tap_min` | float | Minimum tap ratio |
| `tap_max` | float | Maximum tap ratio |
| `shift` | float | Phase shift angle (radians) |
| `transformer` | bool | `true` if this is a transformer |
| `circuit_key` | string | Internal circuit identifier |
| `length_km` | float | Line length in km (0.0 for transformers) |

**Transformer branches** also have:

| Field | Type | Description |
|-------|------|-------------|
| `transformer_hv_kv` | float | High-voltage side (kV) |
| `transformer_lv_kv` | float | Low-voltage side (kV) |

### `load` — Bus Loads

| Field | Type | Description |
|-------|------|-------------|
| `index` | int | Load number |
| `load_bus` | int | Bus this load is attached to |
| `pd` | float | Active power demand (p.u.) |
| `qd` | float | Reactive power demand (p.u.) |
| `status` | int | 1 = active |

### `shunt` — Reactive Compensation

Derived from DC-OPF solution to provide reactive power support for AC-OPF convergence. These are synthetic shunts — not from OSM or EIA.

| Field | Type | Description |
|-------|------|-------------|
| `index` | int | Shunt number |
| `shunt_bus` | int | Bus this shunt is attached to |
| `gs` | float | Shunt conductance (p.u.) — always 0.0 |
| `bs` | float | Shunt susceptance (p.u.) — positive = capacitor, negative = reactor |
| `status` | int | 1 = active |

### `dcline` — HVDC Lines

| Field | Type | Description |
|-------|------|-------------|
| `index` | int | DC line number |
| `f_bus` | int | From bus |
| `t_bus` | int | To bus |
| `br_status` | int | 1 = in service |
| `pf` / `pt` | float | Active power at from/to end (p.u.) |
| `qf` / `qt` | float | Reactive power at from/to end (p.u.) |
| `vf` / `vt` | float | Voltage at from/to end (p.u.) |
| `pmaxf` / `pmaxt` | float | Max active power at from/to (p.u.) |
| `pminf` / `pmint` | float | Min active power at from/to (p.u.) |
| `qmaxf` / `qmaxt` | float | Max reactive power at from/to (p.u.) |
| `qminf` / `qmint` | float | Min reactive power at from/to (p.u.) |
| `loss0` | float | Constant loss coefficient |
| `loss1` | float | Linear loss coefficient |
| `circuit_key` | string | Internal circuit identifier |
| `length_km` | float | Line length (km) |
| `model` / `ncost` / `cost` | | Cost model (same format as generators) |

### `_warm_start` — DC Warm-Start Metadata

Present in all released model files. Contains metadata about the DC-OPF warm-start that was applied before AC-OPF solving (voltage angles injected into buses, reactive shunts added).

| Field | Type | Description |
|-------|------|-------------|
| `warm_start_applied` | bool | Whether DC solution was injected |
| `dc_objective` | float | DC-OPF optimal cost ($/h) |
| `dc_solved_level` | int | Relaxation level at which DC converged (0 = strict) |
| `vm_init` | float | Initial voltage magnitude used (always 1.0) |
| `n_dc_shunts` | int | Number of shunts derived from DC solution |
| `total_shunts` | int | Total shunts in model |

## Results Files

### DC Results (`*_dc_results.json`)

Linear DC-OPF solution. Does not solve for reactive power or voltage magnitudes.

| Field | Type | Description |
|-------|------|-------------|
| `formulation` | string | `"dc"` |
| `termination_status` | string | `"LOCALLY_SOLVED"` or `"LOCALLY_INFEASIBLE"` |
| `objective` | float | Total generation cost ($/h) |
| `solve_time` | float | Solver time (seconds) |
| `relaxation_level` | int | 0 = strict, 1–5 = progressively relaxed |
| `relaxation_label` | string | Short label (e.g., `"L0"`, `"AC1"`) |
| `relaxation_name` | string | Human-readable relaxation level (e.g., `"Strict"`) |
| `total_gen_mw` | float | Total generation (MW) |
| `total_load_mw` | float | Total load (MW) |
| `n_buses` / `n_branches` / `n_gens` / `n_loads` | int | Element counts |
| `n_shunts` | int | Number of shunts in model |
| `n_decommitted` | int | Generators decommitted by unit commitment |
| `solution` | dict | Per-element solutions (see below) |

**`solution.bus`**: `va` (voltage angle, rad), `vm` (always 1.0 for DC)

**`solution.gen`**: `pg` (active power, p.u.), `pg_cost` (generation cost, $/h)

**`solution.branch`**: `pf` (from-end active flow, p.u.), `pt` (to-end active flow, p.u.)

**`solution.dcline`**: `pf`, `pt`, `p_dc_cost`

### AC Results (`*_ac_results.json`)

Full nonlinear AC-OPF solution. Same top-level fields as DC results, plus:

| Field | Type | Description |
|-------|------|-------------|
| `n_interfaces` | int | Number of inter-BA interface constraints |

**`solution.bus`**: `va`, `vm` (solved voltage magnitude)

**`solution.gen`**: `pg`, `qg` (reactive power, p.u.), `pg_cost`

**`solution.branch`**: `pf`, `pt`, `qf`, `qt`

**`solution.dcline`**: `pf`, `pt`, `qf`, `qt`, `p_dc_cost`

## Interface Constraints

Models spanning multiple Balancing Authorities include an `interface` section with inter-BA transfer limits. Only present in multi-BA states/regions (31 of 54 datasets).

### `interface` — Inter-BA Transfer Limits

Keyed by string ID. Each entry describes one directional interface between two BAs.

| Field | Type | Description |
|-------|------|-------------|
| `name` | string | Interface name (e.g., `"PNM_to_SWPP"`) |
| `from_ba` | string | Source BA code |
| `to_ba` | string | Destination BA code |
| `branch_ids` | list | Branch IDs forming this interface |
| `n_lines` | int | Number of EHV lines in interface |
| `n_all_lines` | int | Total cross-BA lines (including lower voltage) |
| `limit` | float | Transfer limit (p.u.) |
| `limit_factor` | float | Fraction of total capacity used as limit |
| `limit_method` | string | `"known"` (NERC/WECC paths) or `"heuristic"` |
| `total_rate_a` | float | Sum of branch ratings (p.u.) |


## Data Sources

- **Topology**: [OpenStreetMap](https://www.openstreetmap.org/) power infrastructure (lines, substations, generators)
- **Generator data**: [U.S. EIA-860](https://www.eia.gov/electricity/data/eia860/) (capacity, fuel type, location), [U.S. EIA-923](https://www.eia.gov/electricity/data/eia923/) (heat rates)
- **Demand**: [U.S. EIA-930](https://www.eia.gov/electricity/gridmonitor/) (hourly BA-level demand)
- **Gas prices**: [Henry Hub spot price](https://www.eia.gov/dnav/ng/ng_pri_fut_s1_d.htm) via EIA API
- **Load allocation**: [U.S. Census](https://www.census.gov/) tract-level population as spatial proxy
- **BA boundaries**: [HIFLD Electric Planning Areas](https://services5.arcgis.com/HDRa0B57OVrv2E1q/arcgis/rest/services/Electric_Planning_Areas/FeatureServer/0) (ArcGIS FeatureServer)

## Per-Unit Convention

All power quantities use system base `baseMVA = 100 MVA`:
- Powers (pg, pd, pmax, rate_a, etc.): multiply by 100 to get MW or MVA
- Impedances (br_r, br_x): in per-unit on system base
- Voltages (vm, vmax, vmin): in per-unit on bus `base_kv`
- Angles (va, angmin, angmax): in radians
- Cost coefficients: scaled for per-unit pg (i.e., `cost[1]` is $/h per unit of pg in p.u.)

## Relaxation Levels

Models that don't converge at strict limits are progressively relaxed. For DC-OPF the solver tries L0 → L1 → … → L5. For AC-OPF the solver tries L0 → AC1 → L1 → … → L5; if AC1 alone doesn't solve it, its V/Q relaxation is kept as a base layer for L1–L5.

The OPF solver with relaxation support is available in the [GridSFM repository](https://github.com/microsoft/GridSFM/tree/main/power_grid). These models can be used directly as input.

| Level | Label | Description |
|-------|-------|-------------|
| 0 | L0 — Strict | Model as-is from pipeline |
| 1 | L1 — Widen angles | Branch angles widened to ±60° |
| 2 | L2 — Thermal headroom | Branch ratings ×1.2, angles ±60° |
| 3 | L3 — Aggressive | Branch ratings ×1.5, angles ±90°, pmin ×0.5 |
| 4 | L4 — Load shedding | Cap load at 70%, ratings ×1.5, angles ±90°, pmin = 0 |
| 5 | L5 — Full relaxation | Remove thermal limits, angles ±90°, V [0.85, 1.15], Q ×2.0, load cap 70%, pmin = 0 |
| AC1 | AC1 — Voltage + Q | Voltage [0.90, 1.10], Q limits ×1.5 (AC-OPF only) |

The `relaxation_level` and `relaxation_label` fields in results files indicate which level was needed.