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arXiv · 2407.11571

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

Lucas Pereira·Vineet Jagadeesan Nair·Bruno Dias·Hugo Morais·Anuradha Annaswamy
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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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Lucas Pereira, Vineet Jagadeesan Nair, Bruno Dias, Hugo Morais, Anuradha Annaswamy. 2024-07-16. Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience. https://arxiv.org/abs/2407.11571

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