Power grids, across the world, play an important societal and economical role by providing uninterrupted, reliable and transient-free power to several industries, businesses and household consumers. With the advent of renewable power resources and EVs resulting into uncertain generation and highly dynamic load demands, it has become ever so important to ensure robust operation of power networks through suitable management of transient stability issues and localize the events of blackouts. In the light of ever increasing stress on the modern grid infrastructure and the grid operators, this paper presents a reinforcement learning (RL) framework, PowRL, to mitigate the effects of unexpected network events, as well as reliably maintain electricity everywhere on the network at all times. The PowRL leverages a novel heuristic for overload management, along with the RL-guided decision making on optimal topology selection to ensure that the grid is operated safely and reliably (with no overloads). PowRL is benchmarked on a variety of competition datasets hosted by the L2RPN (Learning to Run a Power Network). Even with its reduced action space, PowRL tops the leaderboard in the L2RPN NeurIPS 2020 challenge (Robustness track) at an aggregate level, while also being the top performing agent in the L2RPN WCCI 2020 challenge. Moreover, detailed analysis depicts state-of-the-art performances by the PowRL agent in some of the test scenarios.
翻译:电力网络在世界范围内为多个行业、企业和家庭消费者提供连续、可靠和无暂态的电力,并在社会和经济角色中发挥着重要作用。随着可再生能源和 EV 的出现导致了不确定的发电和高度动态的负荷需求,通过适当的瞬态稳定问题管理来确保电网的稳健运作并局部化停电事件已变得越来越重要。本文提出了一个名为 PowRL 的强化学习(RL)框架,以应对现代电网基础设施和电网运营商的不断增加的压力,缓解意外网络事件的影响,同时在网络上始终可靠地维护电力。PowRL利用一种新颖的超载管理启发式方法以及 RL 引导的决策制定,以确保电网在安全、可靠(无超载)的情况下运行。PowRL在由L2RPN (Learning to Run a Power Network)主办的多个竞赛数据集上进行了基准测试。尽管其行动空间较小,PowRL在L2RPN NeurIPS 2020 挑战赛(健壮性赛道)的总体排名上排名第一,同时也是 L2RPN WCCI 2020 挑战赛中表现最好的代理。此外,详细分析描绘了 PowRL 代理在一些测试场景中的最新表现。