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<p>As the largest contributor to carbon emissions in China’s industrial sector, the power industry accounts for over 40% of the nation’s total carbon emissions. Establishing an accurate carbon measurement system has become a critical prerequisite for achieving emission reduction targets in electricity systems. Current carbon measurement methodologies exhibit notable limitations, particularly in addressing the spatiotemporal heterogeneity of carbon emission factors. To address these challenges, this study proposes an integrated framework combining carbon emission flow theory with a low carbon demand response mechanism, accompanied by corresponding computational algorithms. Utilizing operational data from the Shenzhen power grid, we conducted a comprehensive empirical study featuring three key innovations. First, developing dynamic carbon accounting models reflecting temporal and spatial variations. Second, proposing demand-side management strategies for emission mitigation. Third, quantifying potential emission reductions through scenario simulations. The results demonstrate that the carbon emission flow methodology effectively captures the spatial-temporal disparities in carbon intensity across different districts and load periods within the Shenzhen power grid. Furthermore, the low carbon emission response mechanism is shown to offer operational guidance for grid decarbonization while quantifying emission reduction benefits. This research provides both methodological advancements and practical insights for implementing precision carbon management in urban power systems.</p>

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Carbon reduction analysis of power system based on carbon emission flow theory: a case study of Shenzhen power grid

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月1日

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  • Yingjie Li,
  • Ningrui Zhou,
  • Zeqi Zhang

摘要

As the largest contributor to carbon emissions in China’s industrial sector, the power industry accounts for over 40% of the nation’s total carbon emissions. Establishing an accurate carbon measurement system has become a critical prerequisite for achieving emission reduction targets in electricity systems. Current carbon measurement methodologies exhibit notable limitations, particularly in addressing the spatiotemporal heterogeneity of carbon emission factors. To address these challenges, this study proposes an integrated framework combining carbon emission flow theory with a low carbon demand response mechanism, accompanied by corresponding computational algorithms. Utilizing operational data from the Shenzhen power grid, we conducted a comprehensive empirical study featuring three key innovations. First, developing dynamic carbon accounting models reflecting temporal and spatial variations. Second, proposing demand-side management strategies for emission mitigation. Third, quantifying potential emission reductions through scenario simulations. The results demonstrate that the carbon emission flow methodology effectively captures the spatial-temporal disparities in carbon intensity across different districts and load periods within the Shenzhen power grid. Furthermore, the low carbon emission response mechanism is shown to offer operational guidance for grid decarbonization while quantifying emission reduction benefits. This research provides both methodological advancements and practical insights for implementing precision carbon management in urban power systems.