Dominik Köhler, Mohamed Hassouna, Dmitry Degtyar, Jonas Krauß, Kurt Brendlinger und Christoph Scholz stellen im Rahmen des Workshop on Machine Learning for Sustainable Power Systems (ML4SPS 2025) auf der ECML PKDD ihr Paper PPTopoGym: Towards an RL Environment for Topology Actions on Power Grids vor.

Abstract: Power grids are facing growing challenges, arising from the increasing share of renewable energy sources and highly variable loads, in- troducing significant variability into both power generation and demand. This variability can create transmission line bottlenecks, which in turn lead to overheating. Transmission system operators address this prob- lem by rerouting the power flow via topological control, which is more cost-effective than redispatch measures. While reinforcement learning has shown promise in optimizing such topology changes, existing reinforce- ment learning environments exhibit notable differences from real-world operations. To support research and bridge the gap to real-world ap- plications, we present PPTopoGym—a reinforcement learning environ- ment for evaluating the impact of topology actions on the power flow in pandapower-based grids. It allows for simulations with variable load and generation profiles, and supports direct computation of key grid security metrics, i.e., N−0 and N−1 security constraints for each time step. PP- TopoGym addresses key limitations of existing environments by enabling realistic grid modeling and the integration of real-world data. The code is publicly available.