A Reinforcement Learning-based Volt-VAR Control Dataset and Testing Environment

TL;DR

This paper provides open-source datasets and testing environments for reinforcement learning-based Volt-VAR control in power distribution systems, enabling efficient, safe, and realistic algorithm development and comparison.

Contribution

It introduces a comprehensive dataset and testing platform for RL-based VVC, facilitating research and benchmarking in a realistic power distribution context.

Findings

Dataset covers IEEE-13, 123, and 8500-bus feeders

Supports offline RL training and testing

Enables fair comparison of algorithms

Abstract

To facilitate the development of reinforcement learning (RL) based power distribution system Volt-VAR control (VVC), this paper introduces a suite of open-source datasets for RL-based VVC algorithm research that is sample efficient, safe, and robust. The dataset consists of two components: 1. a Gym-like VVC testing environment for the IEEE-13, 123, and 8500-bus test feeders and 2. a historical operational dataset for each of the feeders. Potential users of the dataset and testing environment could first train an sample-efficient off-line (batch) RL algorithm on the historical dataset and then evaluate the performance of the trained RL agent on the testing environments. This dataset serves as a useful testbed to conduct RL-based VVC research mimicking the real-world operational challenges faced by electric utilities. Meanwhile, it allows researchers to conduct fair performance comparisons between different algorithms.