The topology optimization of transmission networks using Deep Reinforcement Learning (DRL) has increasingly come into focus. Various researchers have proposed different DRL agents, which are often benchmarked on the Grid2Op environment from the Learning to Run a Power Network (L2RPN) challenges. The environments have many advantages with their realistic chronics and underlying power flow backends. However, the interpretation of agent survival or failure is not always clear, as there are a variety of potential causes. In this work, we focus on the failures of the power grid to identify patterns and detect them a priori. We collect the failed chronics of three different agents on the WCCI 2022 L2RPN environment, totaling about 40k data points. By clustering, we are able to detect five distinct clusters, identifying different failure types. Further, we propose a multi-class prediction approach to detect failures beforehand and evaluate five different models. Here, the Light Gradient-Boosting Machine (LightGBM) shows the best performance, with an accuracy of 86%. It also correctly identifies in 91% of the time failure and survival observations. Finally, we provide a detailed feature importance analysis that identifies critical features and regions in the grid.

In our rapidly evolving world, effectively managing power grids has become increasingly challenging, primarily due to rising penetration of renewable energy sources and the growing energy demand. While renewable sources like wind and solar power are crucial on our path towards a 100% clean energy future, they introduce considerable uncertainty in power systems, thereby challenging conventional control strategies. Transmission line congestions are often mitigated using redispatch actions, which entail adjusting the power output of various controllable generators in the network. However, these actions are costly and may not fully resolve all issues. Adaptively changing the network using topological actions, such as line switching and bus switching, is an under-utilized yet very cost-effective strategy for network operators facing rapidly shifting energy patterns and contingencies. To navigate the complex and large combinatorial space of all topological actions, we propose a Hierarchical Multi-Agent Reinforcement Learning (MARL) framework in our paper "Multi-Agent Reinforcement Learning for Power Grid Topology Optimization" [1] (a preprint submitted to PSCC 2024).