Grid2Op

Overview

  • Getting started
  • Grid2Op module

Learn with examples

  • Using “optimization” technique
  • Compatibility with gymnasium / gym
  • Model Free Reinforcement Learning
  • Model Based / Planning methods

Models

  • Dive into grid2op sequential decision process
  • Elements modeled in this environment and their main properties
  • A grid, a graph: grid2op representation of the powergrid
  • Dive into the topology “modeling” in grid2op
  • Dive into the detailed topology “modeling” in grid2op

Focus on an "environment"

  • Available environments
  • Make: Using pre defined Environments
  • Input data of an environment
  • Optimize the data pipeline
  • Known issues and workarounds

Plot

  • Grid2Op Plotting capabilities (beta)

Technical documentation for grid2op users

  • Action
  • Agent
  • Backend
  • Time series (formerly called “chronics”)
  • Converters
  • Environment
  • Episode
  • Exception
  • Observation
  • Opponent Modeling
  • Parameters
  • Reward
  • Rules of the Game
  • Runner
  • Simulator
  • Space
  • Time Series Handlers
  • Utility classes
  • Voltage Controler

Technical documentation for grid2op "external" contributions

  • Content of an environment
  • Possible workflow to create an environment from existing time series
  • Creating a new backend

Technical documentation for grid2op developers

  • How to add a new type of action
  • How to add a new attribute to the observation
Grid2Op
  • Search

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