Researchers from the University of Kassel introduce a machine learning approach that presents specific target topologies (Tts) as actions

The power generation landscape has changed significantly in recent years due to urgent global climate change trends. This change has led to a significant increase in renewable energy (RE) generation and, as a result, the grid has become increasingly exposed to input fluctuations. The rise of heat pumps and electric vehicles is further increasing consumer demand for electricity, while users are also beginning to contribute to the grid by generating their own electricity through solar PV systems.

Transmission system operators (TSOs) must adapt their power infrastructure in innovative ways to deal with unpredictability. Changing the topology of the power grid by switching buses at the substation level is a promising method that is receiving increasing attention in academic circles. As mentioned in , the grid can be stabilized to some extent by intelligent switching in key parts. In particular, DRL, which stands for deep reinforcement learning, scholars have proposed using deep learning technology to solve this problem, as deep learning technology has the potential to significantly reduce computational costs. . His TSO RTE in France was the first to test such a method in the L2RPN challenge. As a result of its realistic depiction of power grids, ongoing developments, and challenges, L2RPN has emerged as the community's go-to standard for DRL-based power grid simulations.

The problem arises when these behaviors are frequently examined individually. These may be useful in the next step, but a non-ideal topology may emerge. Contrary to popular belief, grid operations do not take autonomous substation activities into account. As an alternative, we are considering switching over multiple substations in stages. Nevertheless, these comprehensive topology techniques are rarely mentioned in DRL research aimed at grid optimization. This could be due to the expensive computations required to determine the combination, or it could be a limitation of the L2RPN Grid2Op environment design, which only allows one substation change per time step. .

In a recent study, researchers at the University of Kassel have developed a new direction that focuses on the topology of power grids, focusing on the placement of all buses in all substations, rather than on the switching operations of individual substations. I'm exploring sexuality. The basic assumption is that some topologies (TT) are more stable than others. If the current topology state is not durable enough, attempts to reach nearby TTs are prioritized. The target topology (TT) can be reached from almost any topology configuration, so there is no need to understand a specific combination of substation activities. This is an advantage and is especially useful in more complex grids, as TTs can perform a large number of substation actions in succession.

In this study, we introduce a search method for TTs that meet the criteria. The findings show that using this technique, the TT is stable against instability, given the existing set of substation activities. Furthermore, the researchers incorporated a greedy search component using TT into his previously reported CAgent technique to create a topology agent (TopoAgent85-95%). The team ran the agent on a validation grid for the WCCI 2022 L2RPN Challenge and validated that the method helps optimize the grid. A multi-seed evaluation with 500 TT was used to evaluate the impact of the proposed topology agent on his WCCI 2022 L2RPN environment. The TopoAgent85-95% agent achieved a 10% higher score and 25% longer median survival than the benchmark. Additional investigation revealed that TopoAgent85-95% was close to the base topology, underscoring its performance resiliency.

Overall, this study shows that using TT as greedy iterations hardly increases the execution time. They believe that the research community needs to further investigate TT, especially when combined with DRL.

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