Getting Started

Manual Mode

To execute in manual mode, type: expertop4grid -l 9 -s 0 -c 0 -t 0

–ltc | -l int

Integer representing the line to cut. For the moment, only one line to cut is handled

–snapshot | -s int

If 1, will generate plots of the different grid topologies managed by alphadeesp and store it in alphadeesp/ressources/output

–chronicscenario | -c string

Name of the folder containing the chronic scenario to consider By default, the first available folder will be chosen

–timestep | -t int

Integer representing the timestep number at which we want to run alphadeesp simulation

–fileconfig | -f string

Path to .ini file that provides detailed configuration of the module. If none is provided, a default config.ini is provided in package

In any case, an end result dataframe is written in root folder.

If you run the same command with ‘-s 1’ to print the plots, you will indeed see that:

  • On the intial state, you had an overflow to solve

_images/g_pow_grid2op_ltc9.PNG

  • The expert system indeed finds a solution topology for it at substation 4

_images/example_4_score_ltc9.PNG

See Algorithm Description section to learn more about the workflow and results.

In manual mode, further configuration is made through alphadeesp/config.ini

  • simulatorType - you can chose Grid2op or Pypownet

  • gridPath - path to folder containing files representing the grid. If no value is provided, a default grid will be loaded (l2rpn_2019) containing one chronic as a simple usecase example

  • outputPath - path to write outputs in case snapshot mode is activated. If no path is provided, ExpertOp4Grid will write image results in the current working directory (folder named output/grid/linetocut/scenario/timestep)

  • CustomLayout - list of couples reprenting coordinates of grid nodes. If not provided, grid2op will load grid_layout.json in grid folder

  • grid2opDifficulty - “0”, “1”, “2” or “competition”. Be careful: grid datasets should have a difficulty_levels.json

  • 7 other constants for alphadeesp computation can be set in config.ini, with comments within the file

Agent Mode

To execute in agent mode, please refer to ExpertAgent available in l2rpn-baseline repository

https://github.com/mjothy/l2rpn-baselines/tree/mj-devs/l2rpn_baselines/ExpertAgent

Instead of configuring through config.ini, you can pass a similar python dictionary to the API

Tests

To launch the test suite in git repo: pipenv run python -m pytest --verbose --continue-on-collection-errors -p no:warnings

Debug Help

  • To force specific hubs

in AlphaDeesp.compute_best_topo() function, one can force override the hubs result. Check in code, there are commented examples.

  • To force specific combinations for hubs

If one wants a specific hub, a user can “force” a specific node combination. Check in the code, there are commented examples