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v0.5.0 - (10th December 2024)

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@maurerle maurerle released this 10 Dec 07:46
· 28 commits to main since this release
v0.5.0
e4b492f
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Learn about vigilant mode.

New Features:

  • Learning rate and noise scheduling: Added the possibility to schedule the learning rate and action noise in the learning process. This feature
    enables streamlining the learning progress. Currently, only "linear" decay available by setting the learning_rate_schedule and
    action_noise_schedule in the learning config to "linear". Defaults to no decay if not provided. It decays learning_rate/ noise_dt
    linearly from starting value to 0 over given training_episodes which can be adjusted by the user. The schedule parameters (e.g. end value
    and end fraction) are not adjustable in the config file, but can be set in the code.
  • Hydrogen Plant: A new demand side unit representing a hydrogen plant has been added. The hydrogen plant consists of an
    electrolyzer and a seasonal hydrogen storage unit. The electrolyzer converts electricity into hydrogen, which can be
    stored in the hydrogen storage unit and later used.
  • Seasonal Hydrogen Storage: A new storage unit representing a seasonal hydrogen storage has been added. The seasonal hydrogen
    storage unit can store hydrogen over long periods and release it when needed. It has specific constraints to avoid charging or
    discharging during off-season or on-season time as well as a target level to be reached at the end of the season.

Improvements:

  • Timeseries Performance Optimization: Switched to a custom FastIndex and FastSeries class, which is based on the pandas Series
    but utilizes NumPy arrays for internal data storage and indexing. This change significantly improves the
    performance of read and write operations, achieving an average speedup of 2x to 3x compared to standard
    pandas Series. The FastSeries class retains a close resemblance to the pandas Series, including core
    functionalities like indexing, slicing, and arithmetic operations. This ensures seamless integration,
    allowing users to work with the new class without requiring significant code adaptation.
  • Outputs Role Performance Optimization: Output role handles dict data directly and only converts to DataFrame on Database write.
  • Overall Performance Optimization: The overall performance of the framework has been improved by a factor of 5x to 12x
    depending on the size of the simulation (number of units, markets, and time steps).

Bugfixes:

  • Tutorials: General fixes of the tutorials, to align with updated functionalitites of Assume
  • Tutorial 07: Aligned Amiris loader with changes in format in Amiris compare (https://gitlab.com/fame-framework/fame-io/-/issues/203 and https://gitlab.com/fame-framework/fame-io/-/issues/208)
  • Powerplant: Remove duplicate Powerplant.set_dispatch_plan() which broke multi-market bidding
  • CSV scenario loader: Fixed issue when one extra day was being added to the index, which lead to an error in the simulation when additional data was not available in the input data.
  • Market opening schedule: Fixed issue where the market opening was scheduled even though the simulation was ending before the required products. Now the market opening is only scheduled
    if the total duration of the market products plus first delivery time fits before the simulation end.
  • Loader fixes: Fixes for PyPSA, OEDS and AMIRIS loaders
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