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论文提出 HXAI 框架，结合局部与分区两级模型，在分布式能源系统中实现隐私保护下的可解释 AI 分析，用于电网负荷管理，同时在差分隐私约束下保持解释的语义结构而非最小化数值误差。
This paper proposes FedPPO-PG, a federated multi-agent reinforcement learning framework for transient stability control in smart grids. The method achieves 100% stabilization in simulations, reduces stability time by 72.4%, and cuts control power by 7-14 times compared to centralized baselines.
This paper proposes a spectral graph reinforcement learning framework for outage detection and power restoration in self-healing smart grids, achieving near-optimal real-time performance on IEEE test systems.
This paper presents a scalable heterogeneous graph neural network workflow for data-driven optimal power flow surrogate modeling, using distributed training on supercomputers and demonstrating improvements via fine-tuning pretrained models.