Uncertainty-Aware Federated Learning for Cyber-Resilient Microgrid Energy Management

2025-11-22

AI Summary

To address the joint challenge of ensuring economic efficiency and operational reliability in microgrids under false data injection attacks, photovoltaic (PV) forecasting uncertainty, and anomalous measurements, this paper proposes a robust, uncertainty-aware energy management framework integrating federated learning. The method features a two-stage cascaded attack detection mechanism leveraging autoencoder reconstruction error and quantified prediction uncertainty; privacy-preserving distributed PV power forecasting via federated LSTM; and a two-stage robust optimal scheduling scheme incorporating multi-signal fusion analysis. Experimental results demonstrate that under severe attacks, the framework achieves a 93.7% recovery rate in forecasting accuracy, reduces false alarm rate by 58%, lowers operational cost by 5%, and mitigates economic losses by 34.7%. These outcomes significantly enhance system resilience and the synergistic balance between security and economic performance.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Maintaining economic efficiency and operational reliability in microgrids under cyberattacks
• Addressing unquantified uncertainties and malicious attacks on renewable energy forecasts
• Mitigating false data injection attacks that degrade forecasting and increase costs

Innovation

• Federated LSTM forecasting for privacy-preserving photovoltaic prediction
• Two-stage cascade detection combining autoencoder and uncertainty quantification
• Multi-signal fusion framework mitigating cyberattacks while optimizing costs