Grid2Op is an open source framework designed to model and simulate sequential decision-making in power systems. It enables the development, training, and evaluation of control agents that operate within a power grid environment. Grid2Op is highly flexible and supports various approaches, including reinforcement learning, heuristic methods, optimization strategies, or hybrid techniques.

Use Cases

• Reinforcement Learning for Power Systems: Grid2Op provides a structured environment for training AI agents using reinforcement learning techniques to optimize grid operations.
• Power System Research and Development: The platform is widely used for research in power system operations, particularly in evaluating the impact of different control strategies.
• Learning to Run a Power Network (L2RPN) Competitions: Grid2Op serves as the backbone for L2RPN, a series of competitions focused on optimizing power grid operations using AI.
• Grid Operations Simulation: It supports real-time operations by allowing modifications such as load shedding, maintenance scheduling, and topology adjustments to ensure grid stability.
• Integration with External Powerflow Solvers: Grid2Op is compatible with PandaPower and other powerflow solvers, making it adaptable to different power system modeling needs.

Key Features

• Modularity: Built with extensibility in mind, Grid2Op allows users to customize grid operations and integrate new models easily.
• Comprehensive Power System Actions: Users can manipulate grid parameters, such as active and reactive power, generator voltage setpoints, and network topology.
• Multi-Environment Support: The framework supports training agents in multiple environments simultaneously, a feature beneficial for reinforcement learning.
• Visualization and Analysis Tools: Includes Grid2Viz, a graphical user interface for analyzing agent behavior and comparing different control strategies.
• OpenAI Gym Compatibility: Grid2Op follows a standardized interface, making it easier to integrate with other AI and machine learning frameworks.

Background

Developed by RTE France, Grid2Op was initially created as a platform for power grid research and AI-driven automation. It has since evolved into a widely adopted tool in academia and industry, supporting innovation in power system optimization. With its acceptance into LF Energy, Grid2Op will benefit from a larger open source community, fostering collaboration and advancements in sustainable energy solutions.

Getting Started

Grid2Op provides comprehensive documentation and interactive Jupyter notebooks for users to get familiar with the framework. Tutorials cover key functionalities, from setting up an environment to training and evaluating AI-driven agents.

Contributing

The project welcomes contributions from the community. Developers can contribute through GitHub by submitting pull requests, reporting issues, or enhancing documentation. Guidelines for contributing are detailed in the project’s repository. They can additionally join the project mailing list.