In the course of the energy transition, the expansion of generation and consumption will change, and many of these technologies, such as PV systems, electric cars and heat pumps, will influence the power flow, especially in the distribution grids. Scalable methods that can make decisions for each grid connection are needed to enable congestion-free grid operation in the distribution grids. This paper presents a novel end-to-end approach to resolving congestion in distribution grids with deep reinforcement learning. Our architecture learns to curtail power and set appropriate reactive power to determine a non-congested and, thus, feasible grid state. State-of-the-art methods such as the optimal power flow (OPF) demand high computational costs and detailed measurements of every bus in a grid. In contrast, the presented method enables decisions under sparse information with just some buses observable in the grid. Distribution grids are generally not yet fully digitized and observable, so this method can be used for decision-making on the majority of low-voltage grids. On a real low-voltage grid the approach resolves 100\% of violations in the voltage band and 98.8\% of asset overloads. The results show that decisions can also be made on real grids that guarantee sufficient quality for congestion-free grid operation.

翻译：在能源转型过程中，发电与用电的扩张格局将发生改变，光伏系统、电动汽车和热泵等新技术将显著影响电力潮流，尤其在配电网中。为实现配电网无阻塞运行，需要开发能为每个电网连接点做出决策的可扩展方法。本文提出一种基于深度强化学习的端到端新型方法来解决配电网阻塞问题。我们的架构通过学习限制有功功率并设置适当的无功功率，从而确定无阻塞且可行的电网状态。现有最优潮流（OPF）等方法需要高昂的计算成本以及每个母线节点的详细测量数据，而本文方法仅需观测部分母线节点即可在稀疏信息条件下做出决策。由于当前大多数配电网尚未实现完全数字化和可观测性，该方法可应用于绝大多数低压电网的决策辅助。在真实低压电网的测试中，该方法能够消除100%的电压越限问题和98.8%的设备过载问题。结果表明，该方法在真实电网中也能做出保证无阻塞运行质量的决策。