论文标题
增强基于学习的伏特控制数据集和测试环境
A Reinforcement Learning-based Volt-VAR Control Dataset and Testing Environment
论文作者
论文摘要
为了促进基于加固学习（RL）的功率分配系统VAR控制（VVC）的开发，本文介绍了一套开源数据集，用于基于RL的VVC算法研究，这是样本效率，安全和稳健的样本。该数据集由两个组件组成：1。IEEE-13、123和8500-BUS测试馈线的类似体育馆的VVC测试环境和2个。每个馈线的历史操作数据集。数据集和测试环境的潜在用户可以首先在历史数据集中训练样本效率高线（批量）RL算法，然后评估训练有素的RL代理在测试环境中的性能。该数据集是一种有用的测试台，以进行基于RL的VVC研究，模仿了电力公司面临的现实运营挑战。同时，它允许研究人员在不同算法之间进行公平的性能比较。
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.
