Recent Releases of opfgym
opfgym -
First major release. - Stable benchmark environments - Add more docs (still not complete)
Full Changelog: https://github.com/Digitalized-Energy-Systems/opfgym/compare/v0.3.2...v1.0.0
- Python
Published by thomaswolgast 11 months ago
opfgym - v0.3.1
What's Changed
- Bugfix: Correct sampling in LoadShedding and MaxRenewable environments (only relevant for uniform and normal sampling)
- Introduce explicit state space definition (for data sampling)
- Simplify creation of multi-stage and security-constrained envs by adding separate classes for these cases to inherit from
Full Changelog: https://github.com/Digitalized-Energy-Systems/opfgym/compare/v0.3.0...v0.3.1
- Python
Published by thomaswolgast about 1 year ago
opfgym - v0.3.0
What's Changed
- Make base class API clearer
- Simplify base class
- Add example for custom constrain definition
- Add Reward class to enable arbitrary reward functions and simplify the base class
- Enable pure constraint satisfaction problems without objective function
- Add option to use custom power flow and OPF solvers
- Add lots of type hinting
- Set-up CI/CD
- Replace setup.py with pyproject.toml
- Update to pandas 2.x (mainly reduce warnings)
- Add lightsim2grid dependency for faster power flow
Full Changelog: https://github.com/Digitalized-Energy-Systems/opfgym/compare/v0.2.0...v0.3.0
- Python
Published by thomaswolgast over 1 year ago
opfgym - v0.2.0
What's Changed
- First version of serious documentation on readthedocs
- Add new Constraint class for easier adding of custom constraints
- Add support for piece-wise linear pandapower costs
Full Changelog: https://github.com/Digitalized-Energy-Systems/opfgym/compare/v0.1.1...v0.2.0
- Python
Published by thomaswolgast over 1 year ago
opfgym - Version 0.1.1
Minor bugfix: Add __init__ to util.
- Python
Published by thomaswolgast over 1 year ago
opfgym - Initial Release - Version 0.1.0
Initial Release - Version 0.1.0
This is the first official release and first stable version of the opfgym environment framework for learning the optimal power flow (OPF) with reinforcement learning (RL).
Features
- Gymnasium-compatible base class
OpfEnv, which allows for easy creation of RL environments that represent OPF problems. - Five benchmark RL-OPF environments, representing different OPF problems (Economic dispatch, voltage control, etc.)
- Various pre-implemented choosable environment design options, like different reward functions, observation spaces, etc.
- Several more advanced OPF features like multi-stage OPF, stochastic OPF, discrete actions, etc. (see examples)
- Allows for easy creation of labeled datasets for supervised learning from any OpfEnv environment.
- Fully compatible with the Gymnasium API.
Future Work
- Add more example environments to demonstrate the more advanced features.
- Add more convenience functionality to simplify tasks (e.g. action space definition or adding constraints).
- Add an advanced baseline OPF solver that can deal with discrete actions, multi-stage OPF, etc.
- Improve seeding according to Gymnasium API.
- Python
Published by thomaswolgast over 1 year ago