Summary of Federated Learning Forecasting For Strengthening Grid Reliability and Enabling Markets For Resilience, by Lucas Pereira et al.
Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience
by Lucas Pereira, Vineet Jagadeesan Nair, Bruno Dias, Hugo Morais, Anuradha Annaswamy
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Optimization and Control (math.OC)
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| 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 proposes a comprehensive approach to improve the reliability and resilience of future power grids, which are increasingly dependent on distributed energy resources. The authors combine federated learning-based attack detection with local electricity market-based attack mitigation methods, demonstrating the feasibility of this scheme by applying it to a real-world distribution grid rich in solar photovoltaic (PV) energy. The simulation results show that the approach can successfully mitigate the impacts of cyber-physical attacks on the grid. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a future where power grids are more reliable and less vulnerable to attacks. This paper takes a big step towards making that happen by combining two important ideas: detecting cyber-attacks using artificial intelligence, and mitigating those attacks with a special kind of electricity market. The authors tested their approach on real data from a power grid with lots of solar energy and showed it can help prevent problems caused by these attacks. |
Keywords
» Artificial intelligence » Federated learning
