  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.
