expert-op4grid-recommender 0.1.1

Expert system recommender for power grid contingency analysis based on ExpertOp4Grid principles.

ExpertOp4Grid Recommender

Expert system recommender for power grid contingency analysis based on ExpertOp4Grid principles. This tool analyzes N-1 contingencies in Grid2Op/pypowsybl environments, builds overflow graphs, applies expert rules to filter potential actions, and identifies relevant corrective measures to alleviate line overloads.

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Features

• Contingency Simulation: Simulates N-1 contingencies in a Grid2Op environment.
• Overflow Graph Generation: Builds and visualizes overflow graphs using alphaDeesp and networkx.
• Expert Rule Engine: Filters potential grid actions (line switching, topology changes) based on predefined rules derived from operator expertise.
• Action Prioritization: Identifies and scores relevant corrective actions (line reconnections, disconnections, node splitting/merging).
• Modular Structure: Organized code for better maintainability and testing.

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Installation

1. Clone the repository:

   git clone https://github.com/marota/Expert_op4grid_recommender.git
   cd Expert_op4grid_recommender
   

2. Recommended: Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install the package and dependencies:

   • Core dependencies: Make sure you have the necessary libraries installed. If alphaDeesp or specific grid2op versions are not on PyPI, you might need to install them manually first according to their own instructions.
   • Install this package: For development (recommended), use editable mode:

     pip install -e .
     

     Or for a standard installation:

     pip install .
     

   • Install test dependencies (optional):

     pip install -e .[test]
     

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Usage Example

Configure the desired scenario in expert_op4grid_recommender/config.py (Date, Timestep, Contingency Lines, etc.).

Then, run the main analysis script from the project root directory:

python expert_op4grid_recommender/main.py --date 2024-08-28 --timestep 36 --lines-defaut FRON5L31LOUHA P.SAOL31RONCI

The script will:

1. Set up the Grid2Op environment.
2. Simulate the specified contingency.
3. Build and save an overflow graph visualization in the Overflow_Graph/ directory.
4. Apply expert rules to filter actions loaded from the action space file.
5. Identify and print a list of prioritized corrective actions.

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An option that can be activated for specific use is to rebuild an action space from one segmentation of a grid to another or the full grid:

python expert_op4grid_recommender/main.py --rebuild-actions --repas-file allLogics.json --grid-snapshot-file data/snapshot/pf_20240828T0100Z_20240828T0100Z.xiidm

From all known logics on the full grid, and targeted action ids in the ACTION_FILE, it rebuilds the actions to be applied on the grid snapshot (in detailed topology format with switches) at the date of interest.

Configuration

Key parameters can be adjusted in expert_op4grid_recommender/config.py:

• DATE, TIMESTEP, LINES_DEFAUT: Define the specific case to analyze.
• ENV_FOLDER, ENV_NAME: Specify the Grid2Op environment location.
• ACTION_FILE_PATH: Path to the JSON file containing the action space.
• USE_DC_LOAD_FLOW: Set to True to use DC power flow if AC flow fails.
• PARAM_OPTIONS_EXPERT_OP: Thresholds and parameters for the overflow graph analysis.

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Dependencies

This project relies on several external libraries, including:

• numpy
• pandas
• networkx
• pypowsybl
• grid2op (Ensure you have a compatible version installed)
• alphaDeesp (Ensure this library is installed in your environment)
• expertop4grid>=0.2.8

See pyproject.toml for the full list.

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Testing

To run the unit and integration tests, navigate to the project root and use pytest:

pytest

Note: Some integration tests (@pytest.mark.slow) require the Grid2Op environment data to be present and may take longer to run.

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License

This project is licensed under the Mozilla Public License 2.0 (MPL 2.0). See the LICENSE file for details.