在全球能源低碳转型中，综合能源系统（integrated energy system，IES）作为实现“双碳”目标的关键，面临两大核心挑战：传统碳核算方法的局限性和多能流耦合系统的动态碳管理。传统碳核算方法因静态碳排放因子难以捕捉新能源出力波动、负荷时变特性对碳流变化的影响，导致碳排放责任分摊不公平；多能流耦合系统的动态碳管理面临跨能源碳排放追踪困难，动态模型复杂度高等问题。针对上述挑战，提出了动态碳流传播理论框架，核心内容包括：1）构建了多能流耦合矩阵与时变节点碳强度（time-varying node carbon intensity，TV-NCI），构建了动态碳指标，分析了新能源出力波动、负荷时变特性对碳流变化的影响，为多能流耦合系统跨能源碳排放追踪提供了新的解决方案。2）引入碳流介数中心性（carbon flow betweenness centrality，CFBC）指标，分析了网架结构对碳流路径的影响，识别了影响碳传播的关键节点。通过IEEE 33节点扩展系统实验验证所提方法有效降低了碳排放和新能源弃电率，并揭示了网络拓扑对碳传播路径的影响。研究成果为IES低碳规划与运行提供了理论支撑。

In the global low-carbon energy transition, the integrated energy system （IES）, as the key to achieving the "dual carbon" goal, faces two core challenges: the limitations of traditional carbon accounting methods and dynamic carbon management of multi energy flow coupled systems. Traditional carbon accounting methods are difficult to capture the impact of fluctuations in new energy output and time-varying load characteristics on carbon flow due to static carbon emission factors, resulting in unfair allocation of carbon emission responsibilities. The dynamic carbon management of multi-energy flow coupled systems faces difficulties in tracking cross energy carbon emissions and high complexity of dynamic models. In response to the above challenges, a dynamic carbon flow propagation theoretical framework is proposed, which includes two aspects: 1） A multi-energy flow coupled matrix and time-varying node carbon intensity （TV-NCI） is constructed, and a dynamic carbon index is constructed, and the impact of new energy output fluctuations and time-varying load characteristics on carbon flow changes is analyzed, a new solution is provided for cross energy carbon emission tracking in multi-energy flow coupled systems. 2） By introducing the carbon flow betweenness centrality （CFBC） index, the impact of grid structure on carbon flow paths is analyzed and the key nodes that affect carbon transmission is identified. Through experimental verification of the IEEE 33-node extended system, the proposed method effectively reduces carbon emissions and new energy curtailment rates, and reveals the impact of network topology on carbon propagation paths. The research results provide theoretical support for the low-carbon planning and operation of IES.