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

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:移動体負荷の時空間炭素トレーシングと制御の統合手法を提示し、炭素会計の空間解像度向上に貢献する。

🏢実務担当者:鉄道事業者や電力系統運用者は、ノード別炭素強度を活用した運行・充電戦略で排出削減と系統安定化を両立できる可能性がある。

🏛政策担当者:運輸部門の脱炭素政策において、地域別炭素強度を考慮したインセンティブ設計や系統連系ルールの重要性を示唆する。

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.

• openalex https://doi.org/10.1016/j.apenergy.2026.128985first seen 2026-10-16 05:02:37