  Power networks, responsible for transporting electricity across large
geographical regions, are complex infrastructures on which modern life
critically depend. Variations in demand and production profiles, with
increasing renewable energy integration, as well as the high voltage network
technology, constitute a real challenge for human operators when optimizing
electricity transportation while avoiding blackouts. Motivated to investigate
the potential of AI methods in enabling adaptability in power network
operation, we have designed a L2RPN challenge to encourage the development of
reinforcement learning solutions to key problems present in the next-generation
power networks. The NeurIPS 2020 competition was well received by the
international community attracting over 300 participants worldwide.
  The main contribution of this challenge is our proposed comprehensive
'Grid2Op' framework, and associated benchmark, which plays realistic sequential
network operations scenarios. The Grid2Op framework, which is open-source and
easily re-usable, allows users to define new environments with its companion
GridAlive ecosystem. Grid2Op relies on existing non-linear physical power
network simulators and let users create a series of perturbations and
challenges that are representative of two important problems: a) the
uncertainty resulting from the increased use of unpredictable renewable energy
sources, and b) the robustness required with contingent line disconnections. In
this paper, we give the competition highlights. We present the benchmark suite
and analyse the winning solutions, including one super-human performance
demonstration. We propose our organizational insights for a successful
competition and conclude on open research avenues. Given the challenge success,
we expect our work will foster research to create more sustainable solutions
for power network operations.
