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.

翻译：为促进发展基于强化学习(RL)的动力分配系统Volt-VAR控制(VVC),本文件介绍了一套基于RL的VVC算法研究的开放源数据集,这些数据集是高效、安全和稳健的样本。该数据集由两个部分组成:1. 为IEEE-13、123和8500-bus测试进料器建立类似Gym VVC的测试环境,2. 为每个进料器提供历史操作数据集。数据集和测试环境的潜在用户可以首先对历史数据集进行抽样高效的离线(batch) RL算法培训,然后评估经过培训的RL代理商在测试环境中的性能。该数据集作为进行基于RL的VVC研究的有用测试平台,以模拟电力设施所面临的现实世界操作挑战。与此同时,它允许研究人员对不同算法进行公平的业绩比较。