Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience

Lucas Pereira, Vineet Jagadeesan Nair, Bruno Dias, Hugo Morais, Anuradha Annaswamy
7/16/2024
cs.LGeess.SYmath.OC

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Abstract

We propose a comprehensive approach to increase the reliability and resilience of future power grids rich in distributed energy resources. Our distributed scheme combines federated learning-based attack detection with a local electricity market-based attack mitigation method. We validate the scheme by applying it to a real-world distribution grid rich in solar PV. Simulation results demonstrate that the approach is feasible and can successfully mitigate the grid impacts of cyber-physical attacks.

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Cite this paper

@article{pereira2024federated,
  title  = {Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience},
  author = {Lucas Pereira and Vineet Jagadeesan Nair and Bruno Dias and Hugo Morais and Anuradha Annaswamy},
  year   = {2024},
  eprint = {2407.11571},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.11571v1}
}

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