Abstract
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.
Abstract (translated)
在能源转型的过程中，发电和消费的扩张将发生变化，许多技术，如光伏系统、电动汽车和热泵，将影响电力流动，特别是在配电电网。可扩展的方法需要为每个电网连接做出决策，以便在配电电网中实现无拥塞的电网运行。本文介绍了一种用深度强化学习解决配电电网拥塞的新型端到端方法。我们的架构学会了抑制电力和设置适当的反应电力来确定一个非拥塞的可行电网状态。与最优功率流（OPF）等先进方法相比，所提出的 method 可以在稀疏信息下做出决策，只需观察网格中的少数几条公交车。配电电网通常尚未完全实现数字化和观测，因此这种方法可以用于大多数低电压电网的决策。在实际低电压电网上，该方法可以完全解决电压带内的100%违规行为和98.8%的资产过载。结果表明，在保证足够质量的电网运行的情况下，也可以在实际电网上做出决策。
URL
https://arxiv.org/abs/2405.03262
