The operation of electricity grids has become increasingly complex due to the
current upheaval and the increase in renewable energy production. As a
consequence, active grid management is reaching its limits with conventional
approaches. In the context of the Learning to Run a Power Network challenge, it
has been shown that Reinforcement Learning (RL) is an efficient and reliable
approach with considerable potential for automatic grid operation. In this
article, we analyse the submitted agent from Binbinchen and provide novel
strategies to improve the agent, both for the RL and the rule-based approach.
The main improvement is a N-1 strategy, where we consider topology actions that
keep the grid stable, even if one line is disconnected. More, we also propose a
topology reversion to the original grid, which proved to be beneficial. The
improvements are tested against reference approaches on the challenge test sets
and are able to increase the performance of the rule-based agent by 27%. In
direct comparison between rule-based and RL agent we find similar performance.
However, the RL agent has a clear computational advantage. We also analyse the
behaviour in an exemplary case in more detail to provide additional insights.
Here, we observe that through the N-1 strategy, the actions of the agents
become more diversified.

评估了 Learning to Run a Power Network 竞赛中 Binbinchen 代理的表现，并提供了改进策略，其中最主要的是 N-1 策略，这种策略可使得电网保持稳定性，同时提高了规则代理的性能达到了 27%。

通过拓扑操作管理电网：高级基于规则的和强化学习智能体的比较研究

Managing power grids through topology actions: A comparative study  between advanced rule-based and reinforcement learning agents

Electricity grids have become an essential part of daily life, even if they
are often not noticed in everyday life. We usually only become particularly
aware of this dependence by the time the electricity grid is no longer
available. However, significant changes, such as the transition to renewable
energy (photovoltaic, wind turbines, etc.) and an increasing number of energy
consumers with complex load profiles (electric vehicles, home battery systems,
etc.), pose new challenges for the electricity grid. To address these
challenges, we propose two first-of-its-kind datasets based on measurements in
a broadband powerline communications (PLC) infrastructure. Both datasets FiN-1
and FiN-2, were collected during real practical use in a part of the German
low-voltage grid that supplies around 4.4 million people and show more than 13
billion datapoints collected by more than 5100 sensors. In addition, we present
different use cases in asset management, grid state visualization, forecasting,
predictive maintenance, and novelty detection to highlight the benefits of
these types of data. For these applications, we particularly highlight the use
of novel machine learning architectures to extract rich information from
real-world data that cannot be captured using traditional approaches. By
publishing the first large-scale real-world dataset, we aim to shed light on
the previously largely unrecognized potential of PLC data and emphasize
machine-learning-based research in low-voltage distribution networks by
presenting a variety of different use cases.

提出了基于功率线通信 (PLC) 基础设施测量的两个数据集，主要研究低压配电网络的使用案例，包括资产管理、电网状态可视化、预测性维护和新颖性检测等多个方面，强调了机器学习在现实数据中实现对电力网的提取和应用的潜力。