Learning the Optimal Power Flow: Environment Design Matters

TL;DR

This paper investigates how different environment design choices affect reinforcement learning performance in solving the optimal power flow problem, providing guidelines and an open-source benchmark for future research.

Contribution

It systematically analyzes environment design decisions in RL-OPF, offering practical recommendations and an open-source framework for standardized evaluation.

Findings

Environment design significantly impacts RL-OPF training performance

Recommendations for environment configuration improve learning outcomes

Open-source benchmark facilitates future research in RL-OPF

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.