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

TL;DR

This paper introduces a federated learning-based approach combined with local electricity markets to detect and mitigate cyber-physical attacks, enhancing the reliability and resilience of power grids with distributed energy resources.

Contribution

It presents a novel integrated scheme that combines federated learning attack detection with market-based mitigation for resilient power grid management.

Findings

Feasible approach validated on real-world distribution grid.

Successfully mitigates impacts of cyber-physical attacks.

Enhances grid reliability and resilience.

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.