学习应对电网挑战：回顾性分析
Learning to run a Power Network Challenge: a Retrospective Analysis
摘要：
电力网络负责在大的地理区域传输电力，是现代生活严重依赖的复杂基础设施。随着可再生能源整合的增加以及高压网络技术的发展，需求和生产概况的变化对人类运营商在优化电力运输同时避免停电时构成了真正的挑战。为了研究人工智能方法在电网运行中的适应性潜力，我们设计了一个L2RPN挑战赛，以鼓励开发强化学习解决方案，以解决下一代电网中存在的关键问题。NeurIPS 2020竞赛深受国际社会欢迎，吸引了全球300多名参与者。这一挑战的主要贡献是我们提出了全面的“Grid2Op”框架，以及相关的基准，它发挥了现实的顺序网络操作场景。Grid2Op框架是开源的，易于重用，允许用户使用其配套的GridAlive生态系统定义新的环境。Grid2Op依赖于现有的非线性物理电力网络模拟器，并让用户创建一系列扰动和挑战，这些扰动和挑战代表了两个重要问题：a）由于不可预测的可再生能源的使用增加而导致的不确定性，以及b）应急线路断开所需的鲁棒性。在本文中，我们给出了竞争的亮点。我们介绍了基准测试套件并分析了获胜的解决方案，包括一个超人的性能演示。我们提出了我们的组织见解，一个成功的竞争，并得出结论，开放的研究途径。鉴于挑战的成功，我们希望我们的工作将促进研究，为电网运营创造更可持续的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
