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.

翻译：在遭受网络攻击的条件下，维持微电网能量管理系统的经济效率与运行可靠性仍具挑战性。多数现有方法假设测量数据无异常，以未量化的不确定性进行预测，且未缓解针对可再生能源预测的恶意攻击对能量管理优化的影响。本文提出一种综合性的网络弹性框架，将基于联邦长短期记忆网络的光伏预测与一种新颖的两级级联虚假数据注入攻击检测及能量管理系统优化相结合。该方法融合自编码器重构误差与预测不确定性量化，在保障数据隐私的同时实现抗攻击的储能调度。研究分析了极端虚假数据攻击条件，该攻击导致预测性能下降58%、运行成本增加16.9%。所提出的集成框架将误报检测降低70%，恢复93.7%的预测性能损失，实现5%的运行成本节约，缓解了34.7%的攻击所致经济损失。结果表明，采用多信号融合的精准级联检测优于单信号方法，验证了去中心化微电网安全与性能的协同效应。