Summary of End-to-end Reinforcement Learning Of Curative Curtailment with Partial Measurement Availability, by Hinrikus Wolf et al.
End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability
by Hinrikus Wolf, Luis Böttcher, Sarra Bouchkati, Philipp Lutat, Jens Breitung, Bastian Jung, Tina Möllemann, Viktor Todosijević, Jan Schiefelbein-Lach, Oliver Pohl, Andreas Ulbig, Martin Grohe
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel end-to-end approach using deep reinforcement learning to resolve congestion in distribution grids, enabling congestion-free grid operation. The proposed architecture learns to curtail power and set reactive power to determine a non-congested and feasible grid state. Unlike traditional methods like optimal power flow (OPF), which require detailed measurements of every bus, this method can make decisions under sparse information with just some buses observable in the grid. This is particularly useful for low-voltage grids that are not yet fully digitized and observable. The proposed approach demonstrates promising results on a real low-voltage grid, resolving 100% of voltage band violations and 98.8% of asset overloads. The paper’s contribution lies in providing a scalable method that can be used for decision-making on the majority of low-voltage grids, ultimately supporting the energy transition by enabling congestion-free grid operation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to help manage the power grid as more people switch to renewable energy sources like solar panels and electric cars. The problem is that these changes can cause congestion in the power grid, which can lead to power outages and other issues. To fix this, researchers developed a new way to use deep learning to determine how to reduce power usage and prevent congestion. This approach doesn’t require collecting data from every part of the grid, which makes it more practical for smaller grids that aren’t fully digitalized yet. The results show that this approach can effectively manage power usage on real grids, reducing the risk of power outages and ensuring a stable supply of electricity. |
Keywords
» Artificial intelligence » Deep learning » Reinforcement learning
