AIRGo - I-NERGY - Simulation Jupyter Notebook
Jupyter Notebook providing the execution of a simulation of a given Grid2Op agent using the PowSyBl backend.
This Jupyter Notebook provides the code in order to demonstrate the capacity to launch a simulation of a given Grid2Op agent using the PowSyBl Backend (https://github.com/powsybl/pypowsybl-grid2opbackend). This simulation uses the dataset provided in the AIRGo - I-NERGY - Open Dataset asset and available here: https://www.ai4europe.eu/research/ai-catalog/airgo-i-nergy-open-dataset. This dataset must be unzipped into the "src/data_set" directory.
The explanations of the code, the views and the results clarifies the use of the backend.
This Jupyter Notebook is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016508.
This Jupyter Notebook corresponds to the following user story: as an AIRGo agent evaluator, I want to simulate a set of actions (provided by an AI agent trained using the Grid2Op framework) to improve the current situation on the network and to evaluate them, so that I am able to statue toward the efficiency of the agent (i.e., toward the feasibility and the efficacity of the set of actions on the initial situation).
This AIRGo agent evaluator interacts with the Grid2Op module while launching an evaluation. The Grid2Op module is able to load the specified grid and to instantiate the PowSyBl objects model corresponding. Thereafter, the gri2Op module can launch its grid evaluation made by the PowSyBl framework and return the results.