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

• URL: http://arxiv.org/abs/2204.09500v1
• Date: Wed, 20 Apr 2022 14:44:55 GMT
• Title: A Reinforcement Learning-based Volt-VAR Control Dataset and Testing Environment
• Authors: Yuanqi Gao, Nanpeng Yu
• Abstract summary: 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.
• Score: 4.386026071380442
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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