Grid Optimized Operation Dispatch (GOOD)

This is a Python-based power grid optimization framework for modeling energy systems with renewable integration and policy constraints.

You're encouraged to use this documentation to get inspiration, explore configurations, and understand the structure and operation of the GOOD model.

📚 good/

Core source code and optimization framework. This includes handling of network structures, assets, and policy implementations.

📂 Optimization Modules

• 📍 assets/: Definitions of asset types (plants, storage, loads).
• 📍 base/: Base classes for regions and policies.
• 📍 buses/: Network bus modeling.
• 📍 edges/: Transmission lines and edge definitions.
• 📍 policies/: Renewable and other policy implementations.
• 📄 network.py: Core network optimization management.
• 📄 exceptions.py: Custom exception handling for optimization.

⚙️ requirements.txt

Lists Python dependencies required to run the GOOD model.

📄 utilities.py: General utility functions for data handling, statistics, and JSON management.

📄 graph.py: Graph utilities extending NetworkX capabilities, including JSON handling, graph serialization/deserialization, subgraph and supergraph construction, and graph operation utilities.

📄 __init__.py: Initializes submodules for utilities, graph handling, and optimization framework components.

✅ tests/

Test suite for verifying feasibility, edge cases, and optimization logic. Uses pytest for structured testing.

🔢 Versioning

Project versioning follows standard Git tagging. Check releases and tags directly on GitHub.

📜 README.rst

This README provides a brief overview and links to key resources.

⁉️ Questions / Comments

For questions, suggestions, or comments, please open an issue on GitHub Issues.

Getting Started

We recommend setting up the model in a local Python virtual environment:

git clone <repository-url>
cd good-model
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Running the Optimization

Edit the config.py to define regions, optimization periods, and file paths:

REGIONS = ['ERC_FRNT', 'ERC_PHDL', 'ERC_REST', 'ERC_WEST']
START_HOUR = 0
NUM_HOURS = 24
INPUT_GRAPH_PATH = 'Examples/WEC.json'

Run the optimization script:

python run_network.py

Results are saved to optimization_results.json, and logs are available in optimization_output.log.

Testing the Project

Run all tests using pytest:

pytest

Run specific test files:

pytest tests/test_feasibility.py

Adapting GOOD for Your Project

If you're adapting this project for your specific use:

1. Clone or download the repository.
2. Adjust config.py according to your requirements.
3. Customize or add data files within the Data/ directory.
4. Ensure all dependencies are listed in requirements.txt.
5. Run initial tests to ensure basic functionality.
6. Optionally, extend the provided tests to cover new functionality or data scenarios.