  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.
