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：全球范围内的电力网络通过为众多工业、商业及家庭用户提供无中断、高可靠、无暂态干扰的电力供应，发挥着重要的社会与经济作用。随着可再生能源发电与电动汽车的普及导致发电不确定性增强和负荷需求高度动态化，通过合理治理暂态稳定性问题并定位停电事件来确保电力网络鲁棒运行变得前所未有地重要。鉴于现代电网基础设施与运行人员面临日益严峻的压力，本文提出一个名为PowRL的强化学习（RL）框架，旨在缓解突发网络事件的影响，同时可靠地维持电网各节点始终处于供电状态。PowRL采用一种新颖的超负荷管理启发式方法，结合基于RL的拓扑优化决策机制，确保电网在无过载条件下安全可靠运行。该框架在L2RPN（学习运行电力网络）组织的多个竞赛数据集上进行了基准测试。尽管行动空间经过精简，PowRL在L2RPN NeurIPS 2020挑战赛（鲁棒性赛道）的综合排名中位列榜首，同时成为L2RPN WCCI 2020挑战赛表现最佳的智能体。此外，详细分析表明PowRL智能体在部分测试场景中展现出业界领先的性能。