psforge-grid 0.1.1

Core data models and I/O for the psforge power system analysis ecosystem. LLM-friendly design for AI-assisted analysis.

psforge-grid

Hub data model for the psforge power system analysis ecosystem

Core data models and I/O for power system analysis with LLM-friendly design.

Quick Start

pip install psforge-grid

from psforge_grid import System

# Load a PSS/E RAW file
system = System.from_raw("ieee14.raw")

# Explore the system
print(f"Buses: {len(system.buses)}, Branches: {len(system.branches)}")

# Get LLM-friendly summary
print(system.to_summary())

# Or use the CLI
psforge-grid info ieee14.raw
psforge-grid show ieee14.raw buses -f json

Why psforge-grid?

Feature	psforge-grid	Others
LLM-friendly output	Built-in JSON/summary formats	Manual formatting
Educational design	Rich docstrings, clear naming	Varies
Type hints	Complete type annotations	Often missing
CLI included	Yes, with multiple output formats	Usually separate
PSS/E RAW support	v33/v34 core sections	Varies

Overview

psforge-grid serves as the Hub of the psforge ecosystem, providing:

• Common data classes (System, Bus, Branch, Generator, Load, Shunt)
• PSS/E RAW file parser (v33/v34 partial support)
• Shared utilities for power system analysis

LLM Affinity Design

"Pickaxe in the Gold Rush" - psforge is designed for seamless LLM integration.

psforge-grid implements LLM-friendly data structures and CLI:

Feature	Description
Explicit Units	Field names include units (voltage_pu, power_mw)
Semantic Status	Enum-based status annotations (VoltageStatus.LOW)
Self-Documenting	Rich docstrings explaining physical meaning
to_description()	Human/LLM-readable output methods

# Example: LLM-friendly bus description
bus = system.get_bus(14)
print(bus.to_description())
# Output: "Bus 14 (LOAD_BUS): 13.8 kV, PQ type"

CLI for LLM Integration

psforge-grid includes a CLI designed for LLM-friendly output:

# System summary in different formats
psforge-grid info ieee14.raw              # Table format
psforge-grid info ieee14.raw -f json      # JSON for API/LLM
psforge-grid info ieee14.raw -f summary   # Compact for tokens

# Display element details
psforge-grid show ieee14.raw buses
psforge-grid show ieee14.raw branches -f json

# Validate system data
psforge-grid validate ieee14.raw
psforge-grid validate ieee14.raw --strict

Output Formats:

• table: Human-readable tables (default)
• json: Structured JSON for LLM/API processing
• summary: Compact text for token-efficient LLM usage
• csv: Comma-separated values for data analysis

See CLAUDE.md for detailed AI development guidelines.

PSS/E RAW Format Support

Current Status

The parser supports core power flow data required for basic AC power flow analysis:

Section	v33	v34	Notes
Case Identification	Yes	Yes	Base MVA, system info
Bus Data	Yes	Yes	All bus types (PQ, PV, Slack, Isolated)
Load Data	Yes	Yes	Constant power loads
Fixed Shunt Data	Yes	Yes	Capacitors and reactors
Generator Data	Yes	Yes	P, Q, voltage setpoint, Q limits
Branch Data	Yes	Yes	Transmission lines
Transformer Data	Yes	Yes	Two-winding transformers only

Not Yet Supported

The following sections are parsed but ignored (data is skipped):

• Area Data, Zone Data, Owner Data
• Two-Terminal DC Data, Multi-Terminal DC Data
• VSC DC Line Data, FACTS Device Data
• Switched Shunt Data (use Fixed Shunt instead)
• Multi-Section Line Data, Impedance Correction Data
• GNE Data, Induction Machine Data, Substation Data
• Three-winding Transformers

Test Data Sources

Parser has been validated with IEEE test cases from multiple sources:

• IEEE 9-bus (v34): GitHub - todstewart1001
• IEEE 14-bus (v33): GitHub - ITI/models
• IEEE 118-bus (v33): GitHub - powsybl

Future Plans

1. Phase 2: Three-winding transformer support
2. Phase 3: Switched shunt data support
3. Future: HVDC, FACTS device support (as needed)

Installation

# Install the package
pip install psforge-grid

# Or install from source
pip install -e .

Development Setup

Prerequisites

• Python 3.9+
• uv (recommended) or pip

Install Development Dependencies

# Using uv (recommended)
uv pip install -e ".[dev]"

# Or using pip
pip install -e ".[dev]"

Setup Pre-commit Hooks

Pre-commit hooks automatically run ruff and mypy checks before each commit.

Option 1: Global Install with pipx (Recommended)

Using pipx for global installation is recommended, especially when using git worktree for parallel development. This ensures pre-commit is available across all worktrees without additional setup.

# Install pipx if not already installed
brew install pipx  # macOS
# or: pip install --user pipx

# Install pre-commit globally
pipx install pre-commit

# Install hooks (only needed once per repository)
pre-commit install

# Run hooks manually on all files
pre-commit run --all-files

Why pipx?

• Works across all git worktrees without per-worktree setup
• Isolated environment prevents dependency conflicts
• Single installation, works everywhere

Option 2: Local Install in Virtual Environment

# Install pre-commit in your virtual environment
pip install pre-commit

# Install hooks
pre-commit install

# Run hooks manually on all files
pre-commit run --all-files

Note: CI runs ruff and mypy checks via GitHub Actions (.github/workflows/test.yml), so code quality is enforced on push/PR even if local hooks are skipped.

Manual Code Quality Checks

# Lint with ruff
ruff check src/ tests/

# Format with ruff
ruff format src/ tests/

# Type check with mypy
mypy src/

Run Tests

pytest tests/ -v

Editor Setup (VSCode/Cursor)

This project includes .vscode/ configuration for seamless development:

• Format on Save: Automatically formats code with ruff
• Organize Imports: Automatically sorts imports
• Type Checking: Mypy extension provides real-time type checking

Recommended Extensions:

• charliermarsh.ruff - Ruff linter and formatter
• ms-python.mypy-type-checker - Mypy type checker
• ms-python.python - Python language support

Related Projects

psforge is a modular power system analysis ecosystem:

Package	Description	Status
psforge-grid (this)	Core data models and I/O (Hub)	Active
psforge-flow	AC power flow calculation	Active
psforge-stability	Transient stability analysis	Planned
psforge-schedule	Unit commitment optimization	Planned

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Run tests (pytest tests/)
4. Commit your changes (git commit -m 'Add amazing feature')
5. Push to the branch (git push origin feature/amazing-feature)
6. Open a Pull Request

See CLAUDE.md for AI development guidelines.

License

MIT License - see LICENSE for details.

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Developed by Manabe Lab LLC