Node-aware spatiotemporal carbon tracing and carbon-responsive control of electrified railways for emission reduction and tertiary reserves

The decarbonization of electrified transportation reshapes the interaction between power systems and mobile energy loads. As a key component of this sector, electrified railways draw large, geographically distributed traction loads whose emissions are governed by the power system’s spatiotemporally varying carbon intensity (CI). However, existing carbon tracing methods typically use spatially uniform emission factors, obscuring nodal carbon intensity (CI) differences. Consequently, long-distance trains cannot perceive or exploit spatial carbon heterogeneity when allocating traction power along their routes. To address this challenge, this paper proposes a unified framework that transforms heterogeneous railway fleets into active, carbon-responsive resources within the power system. First, a node-aware spatiotemporal carbon tracing method integrates Power Transfer Distribution Factors (PTDF) with dynamic train-node mapping, enabling real-time quantification of the nodal carbon intensity (NCI) experienced by mobile loads. Building upon this, a two-stage hierarchical coordinated control strategy is formulated, embedding dynamic NCI signals into an online receding-horizon predictive controller to achieve joint carbon minimization and tertiary reserve. The controller autonomously executes spatiotemporal load shifting by dynamically regulating traction and braking power to avoid high-carbon zones while maintaining schedule adherence. Case studies on high-speed and freight railways demonstrate that traditional system-average carbon accounting systematically obscures local low-carbon zones, overestimating the fleet’s true carbon footprint by more than four times under severe spatiotemporal mismatch. By actively exploiting these spatial zero-carbon opportunities, the proposed NCI-aware control achieves substantial carbon reductions, of 37.14% for high-speed trains and 34.74% for freight trains, while maintaining schedule adherence. Crucially, the Logarithmic Mean Divisia Index (LMDI) decomposition reveals that these emission mitigations originate predominantly from the strategic utilization of low-carbon nodes, rather than mere energy conservation. These results demonstrate a promising framework for integrated power–transport system decarbonization, transforming electrified railways from passive loads into active carbon-aware grid participants.