1.China Electric Power Research Institute；2.Changzhou Power Supply Branch of State Grid Jiangsu Electric Power Company
Abstract： In response to challenges in power-grid emergency supply assurance such as dispersed information, heterogeneous states, missing knowledge, and tightly coupled decision-making, this paper proposes an integrated hydrogen knowledge graph and collaborative decision-making framework for grid emergency supply assurance. Centered on the main line of “using hydrogen as the means and grid supply assurance as the objective”, the framework constructs a three-domain coupled knowledge representation architecture covering the hydrogen side, the grid side, and the dispatch side. At the knowledge completion layer, a hybrid completion strategy is developed by integrating R-GCN with symbolic rule reasoning. At the state perception layer, time-series sensor streams, equipment drawings, and operational data are fused to build a dynamically updatable multimodal knowledge graph. At the evolution layer, an entropy-based uncertainty active learning mechanism is introduced to support continuous knowledge iteration. Experimental results show that the proposed method improves NER F1 to 92.0%, RE F1 to 88.9%, and cross-sentence RE F1 to 65.4%, while achieving 58.2% Hits@10 in knowledge completion. It also outperforms multiple baseline methods in critical-load restoration rate, restoration time, and decision latency. The proposed framework provides a technical pathway that jointly supports knowledge representation, reasoning, and collaborative decision-making for hydrogen-enabled grid resilience enhancement and emergency power supply dispatch.
Key words : power grid emergency supply assurance；hydrogen knowledge graph；large language model；multimodal fusion；active learning；multi-agent collaborative dispatch
