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grid2op.Chronics

Time series (formerly called "chronics")

This page is organized as follow:

Table of Contents

Objectives

This module is present to handle everything related to input data that are not structural.

In the Grid2Op vocabulary a "GridValue" or "Chronics" is something that provides data to change the input parameter of a power flow between 1 time step and the other.

It is a more generic terminology. Modification that can be performed by GridValue object includes, but are not limited to:

  • injections such as:
    • generators active production setpoint
    • generators voltage setpoint
    • loads active consumption
    • loads reactive consumption
  • structural informations such as:
    • planned outage: powerline disconnection anticipated in advance
    • hazards: powerline disconnection that cannot be anticipated, for example due to a windstorm.

All powergrid modification that can be performed using an grid2op.Action.BaseAction can be implemented as form of a GridValue.

The same mechanism than for grid2op.Action.BaseAction or grid2op.Observation.BaseObservation is pursued here. All state modifications made by the grid2op.Environment must derived from the GridValue. It is not recommended to create them directly, but rather to use the ChronicsHandler for such a purpose.

Note that the values returned by a GridValue are backend dependant. A GridValue object should always return the data in the order expected by the grid2op.Backend, regardless of the order in which data are given in the files or generated by the data generator process.

This implies that changing the backend will change the output of GridValue. More information about this is given in the description of the GridValue.initialize method.

Finally, compared to other Reinforcement Learning problems, is the possibility to use "forecast". This optional feature can be accessed via the grid2op.Observation.BaseObservation and mainly the grid2op.Observation.BaseObservation.simulate method. The data that are used to generate this forecasts come from the grid2op.GridValue and are detailed in the GridValue.forecasts method.

More control on the chronics

We explained, in the description of the grid2op.Environment in sections environment-module-chronics-info and following how to have more control on which chronics is used, with steps are used within a chronics etc. We will not detailed here again, please refer to this page for more information.

However, know that you can have a very detailed control on which chronics are used:

  • use env.set_id(THE_CHRONIC_ID) (see grid2op.Environment.Environment.set_id) to set the id of the chronics you want to use
  • use env.chronics_handler.set_filter(a_function) (see grid2op.Chronics.GridValue.set_filter) to only use certain chronics
  • use env.chronics_handler.sample_next_chronics(probas) (see grid2op.Chronics.GridValue.sample_next_chronics) to draw at random some chronics
  • use env.fast_forward_chronics(nb_time_steps) (see grid2op.Environment.BaseEnv.fast_forward_chronics) to skip initial number of steps of a given chronics
  • use env.chronics_handler.set_max_iter(nb_max_iter) (see grid2op.Chronics.ChronicsHandler.set_max_iter) to limit the number of steps within an episode

Chosing the right chronics can also lead to some large advantage in terms of computation time. This is particularly true if you want to benefit the most from HPC for example. More detailed is given in the environment-module-data-pipeline section. In summary:

  • set the "chunk" size (amount of data read from the disk, instead of reading an entire scenarios, you read from the hard drive only a certain amount of data at a time, see grid2op.Chronics.ChronicsHandler.set_chunk_size) you can use it with env.chronics_handler.set_chunk_size(100)
  • cache all the chronics and use them from memory (instead of reading them from the hard drive, see grid2op.Chronics.MultifolderWithCache) you can do this with env = grid2op.make(..., chronics_class=MultifolderWithCache)

Finally, if you need to study machine learning in a "regular" fashion, with a train / validation / set you can use the env.train_val_split or env.train_val_split_random functions to do that. See an example usage in the section environment-module-train-val-test.

Detailed Documentation by class

grid2op.Chronics