Learning the Optimal Power Flow: Environment Design Matters

Document detail

ID

oai:arXiv.org:2403.17831

Topic

Computer Science - Machine Learnin... Electrical Engineering and Systems...

Author

Wolgast, Thomas Nieße, Astrid

Year

2024

listing date

4/3/2024

Keywords

regarding learning design

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Abstract

To solve the optimal power flow (OPF) problem, reinforcement learning (RL) emerges as a promising new approach.

However, the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment.

In this work, we collect and implement diverse environment design decisions from the literature regarding training data, observation space, episode definition, and reward function choice.

In an experimental analysis, we show the significant impact of these environment design options on RL-OPF training performance.

Further, we derive some first recommendations regarding the choice of these design decisions.

The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.

Wolgast, Thomas,Nieße, Astrid, 2024, Learning the Optimal Power Flow: Environment Design Matters

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