Uncertainty-Aware Federated Learning for Cyber-Resilient Microgrid Energy Management
This addresses cyber-resilient energy management for decentralized microgrids, though it appears incremental as it combines existing techniques like federated learning and autoencoders with novel integration.
The paper tackles the problem of maintaining economic efficiency and operational reliability in microgrid energy management under cyberattacks, which previously caused 58% forecast degradation and 16.9% operational cost increases. The proposed framework reduced false positive detections by 70%, recovered 93.7% of forecasting performance losses, and achieved 5% operational cost savings, mitigating 34.7% of attack-induced economic losses.
Maintaining economic efficiency and operational reliability in microgrid energy management systems under cyberattack conditions remains challenging. Most approaches assume non-anomalous measurements, make predictions with unquantified uncertainties, and do not mitigate malicious attacks on renewable forecasts for energy management optimization. This paper presents a comprehensive cyber-resilient framework integrating federated Long Short-Term Memory-based photovoltaic forecasting with a novel two-stage cascade false data injection attack detection and energy management system optimization. The approach combines autoencoder reconstruction error with prediction uncertainty quantification to enable attack-resilient energy storage scheduling while preserving data privacy. Extreme false data attack conditions were studied that caused 58% forecast degradation and 16.9\% operational cost increases. The proposed integrated framework reduced false positive detections by 70%, recovered 93.7% of forecasting performance losses, and achieved 5\% operational cost savings, mitigating 34.7% of attack-induced economic losses. Results demonstrate that precision-focused cascade detection with multi-signal fusion outperforms single-signal approaches, validating security-performance synergy for decentralized microgrids.