Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience
<|reference_start|>Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience: 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.<|reference_end|>
@article{pereira2024federated,
    title={Federated Learning Forecasting for Strengthening Grid Reliability and
  Enabling Markets for Resilience},
    author={Lucas Pereira, Vineet Jagadeesan Nair, Bruno Dias, Hugo Morais,
  Anuradha Annaswamy},
    journal={arXiv preprint arXiv:2407.11571},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11571},
    primaryClass={cs.LG cs.SY eess.SY math.OC}
}