论文标题
Powrl：强大管理电力网络的增强学习框架
PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks
论文作者
论文摘要
世界各地的电网通过为多个行业，企业和家庭消费者提供不间断，可靠和无瞬态的电力，从而发挥重要的社会和经济作用。随着可再生能源资源和电动汽车的出现，导致了不确定的产生和高度动态的负载需求，通过适当管理瞬态稳定性问题并将停电事件定位，确保强大的电力网络运行变得非常重要。鉴于对现代电网基础架构和电网运营商的压力越来越大，本文提出了增强学习（RL）框架Powrl，以减轻意外网络事件的影响，并始终可靠地维护网络中各地的电力。 Powrl利用了一种新颖的启发式启发式管理，以及对最佳拓扑选择的RL引导决策制定，以确保网格安全可靠地操作（没有过载）。 POWRL在L2RPN托管的各种竞争数据集上进行了基准测试（学习运行电源网络）。即使有了减少的动作空间，Powrl还是在L2RPN Neurips 2020挑战赛（稳健性轨道）中以总级别的排行榜上位，同时也是L2RPN WCCI 2020挑战中表现最好的代理。此外，详细的分析描述了Powrl代理在某些测试方案中的最先进性能。
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.
