  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 Artificial Intelligence 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 framework is open-sourced and easily re-usable to
define new environments with its companion GridAlive ecosystem. It relies on
existing non-linear physical simulators and let us 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 provide details about the competition
highlights. We present the benchmark suite and analyse the winning solutions of
the challenge, observing one super-human performance demonstration by the best
agent. We propose our organizational insights for a successful competition and
conclude on open research avenues. We expect our work will foster research to
create more sustainable solutions for power network operations.
