A Reinforcement Learning-based Volt-VAR Control Dataset and Testing Environment
This provides a standardized testbed for researchers in power systems to address real-world operational challenges, though it is incremental as it builds on existing RL and VVC methods.
The paper introduces an open-source dataset and testing environment for reinforcement learning-based Volt-VAR control in power distribution systems, enabling sample-efficient, safe, and robust algorithm development and fair performance comparisons.
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.