  For power grid operations, a large body of research focuses on using
generation redispatching, load shedding or demand side management
flexibilities. However, a less costly and potentially more flexible option
would be grid topology reconfiguration, as already partially exploited by
Coreso (European RSC) and RTE (French TSO) operations. Beyond previous work on
branch switching, bus reconfigurations are a broader class of action and could
provide some substantial benefits to route electricity and optimize the grid
capacity to keep it within safety margins. Because of its non-linear and
combinatorial nature, no existing optimal power flow solver can yet tackle this
problem. We here propose a new framework to learn topology controllers through
imitation and reinforcement learning. We present the design and the results of
the first "Learning to Run a Power Network" challenge released with this
framework. We finally develop a method providing performance upper-bounds
(oracle), which highlights remaining unsolved challenges and suggests future
directions of improvement.
