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.

翻译：随着能源结构变革和可再生能源发电量的增加，电网运行日益复杂。传统方法在主动电网管理中逐渐达到能力上限。在"学习运行电力网络"挑战赛的背景下，强化学习已被证明是一种高效可靠的自动化电网运行方法，具有巨大发展潜力。本文分析了Binbinchen团队提交的智能体，并提出了改进该智能体的创新策略，涵盖强化学习和规则驱动两种方法。主要改进在于N-1策略，该策略通过拓扑动作确保即使单条线路断开时电网仍保持稳定。此外，我们还提出了一种恢复原始电网拓扑的方案，经验证具有显著优势。在挑战赛测试集上的基准对比显示，改进措施使规则驱动代理的性能提升了27%。直接比较规则驱动代理与强化学习代理时，两者性能相近，但强化学习代理具有明显的计算效率优势。通过典型案例的深入行为分析，我们发现N-1策略有效地增加了代理动作的多样性。