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.

中文翻译：

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学习运行电力网络挑战：回顾性分析

负责跨大地理区域输送电力的电力网络是现代生活至关重要的复杂基础设施。随着可再生能源整合的增加以及高压网络技术的出现，需求和生产状况的变化对操作人员在优化电力运输同时避免停电的过程中构成了真正的挑战。为了研究人工智能方法在实现电网运行适应性方面的潜力，我们设计了L2RPN挑战，以鼓励针对下一代电网中存在的关键问题开发强化学习解决方案。NeurIPS 2020竞赛获得了国际社会的广泛好评，吸引了全球300多名参赛者。这一挑战的主要贡献是我们提出的全面的Grid2Op框架以及相关的基准，该框架具有现实的顺序网络操作方案。该框架是开源的，可轻松地通过其配套的GridAlive生态系统来重新定义新环境。它依赖于现有的非线性物理仿真器，让我们创建一系列代表两个重要问题的扰动和挑战：a）不可预测的可再生能源使用量增加导致的不确定性； b）偶然性要求的鲁棒性线路断开。在本文中，我们提供了有关比赛亮点的详细信息。我们展示了基准套件，并分析了挑战的胜出方案，并观察了最佳代理商的一次超人表现演示。我们为成功的竞争提出我们的组织见解，并就开放的研究途径得出结论。我们希望我们的工作将促进研究，为电力网络运营创造更多可持续的解决方案。