The ongoing transition to renewable energy is increasing the share of fluctuating power sources like wind and solar, raising power grid volatility and making grid operation increasingly complex and costly. In our prior work, we have introduced a congestion management approach consisting of a redispatching optimizer combined with a machine learning-based topology optimization agent. Compared to a typical redispatching-only agent, it was able to keep a simulated grid in operation longer while at the same time reducing operational cost. Our approach also ranked 1st in the L2RPN 2022 competition initiated by RTE, Europe's largest grid operator. The aim of this paper is to bring this promising technology closer to the real world of power grid operation. We deploy RL-based agents in two settings resembling established workflows, AI-assisted day-ahead planning and realtime control, in an attempt to show the benefits and caveats of this new technology. We then analyse congestion, redispatching and switching profiles, and elementary sensitivity analysis providing a glimpse of operation robustness. While there is still a long way to a real control room, we believe that this paper and the associated prototypes help to narrow the gap and pave the way for a safe deployment of RL agents in tomorrow's power grids.

翻译：向可再生能源的持续转型正在增加风能和太阳能等波动性电源的占比，加剧了电网波动性，使电网运行日益复杂且成本高昂。在先前工作中，我们提出了一种由再调度优化器与基于机器学习的拓扑优化智能体相结合的拥塞管理方法。与仅执行再调度的典型智能体相比，该方法能在降低运行成本的同时，使模拟电网更长时间保持运行。我们的方法还在由欧洲最大电网运营商RTE发起的L2RPN 2022竞赛中获得第一名。本文旨在将这一前景广阔的技术更贴近电网运营的实际情况。我们将基于强化学习的智能体部署在两种类似现有工作流程的设置中——AI辅助日前规划与实时控制，试图展示这一新技术的优势与潜在问题。随后，我们分析了拥塞、再调度及开关动作的剖面图，并通过基础敏感性分析提供了运行鲁棒性的初步视角。尽管距离实际控制室仍有很长的路要走，但我们相信，本文及其相关原型有助于缩小差距，为安全部署面向未来电网的强化学习智能体铺平道路。