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<p>This work evaluates<!--Query ID="Q1" Text="Please check if article title was captured correctly." Resolved="yes"--> the systemic costs of carbon emission reduction in the Mengdong power grid and proposes strategies to support renewable energy integration and sustainable development. Using data from the global power plant database, it first analyzes the power grid structure, generation capacity, and emissions. A carbon locking-based cost measurement model is then developed to quantify emission reduction costs. The filtering curve method is introduced to assess the relationship between renewable energy<!--Query ID="Q2" Text="Please confirm if the author names are presented accurately." Resolved="yes"--> accommodation and system costs. The work proposes a guidance mechanism combining market design, policy support, grid upgrades, and scheduling improvements. Results show the model achieves high prediction accuracy, wi<!--Query ID="Q3" Text="Please check if affiliations were captured and presented correctly. Otherwise, kindly amend if necessary." Resolved="yes"-->th errors within 2%, and mean square error and mean absolute error values of 0.025 and 0.153, respectively. The resulting carbon-locking curve indicates a decelerating emission trend, consistent with the model’s forecasts. The carbon locking curve reflects a slowing growth trend in emissions, consistent with model forecasts. The model captures cost increases under high-emission scenarios and accurately evaluates transition costs under low-carbon pathways. Different from previous static or linear cost assessment models, this work innovatively combines the carbon locking theory with the data-driven method based on the global power plant database. It constructs a comprehensive cost assessment and prediction framework that integrates the dynamics of renewable energy consumption, system operation costs, and institutional path dependence. The work provides replicable methodological support for the low-carbon transformation of power systems in high-carbon locking are<!--Query ID="Q4" Text="Please check abbreviation section if the provided description of the corresponding terms are captured correctly." Resolved="yes"-->as.</p>

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AI-driven framework for assessing carbon-reduction costs in power grids: a case study of the Mengdong system

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月30日

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  • Xia Miao,
  • Yonghui Sun,
  • Hongtao Zhu,
  • Yan Liu

摘要

This work evaluates the systemic costs of carbon emission reduction in the Mengdong power grid and proposes strategies to support renewable energy integration and sustainable development. Using data from the global power plant database, it first analyzes the power grid structure, generation capacity, and emissions. A carbon locking-based cost measurement model is then developed to quantify emission reduction costs. The filtering curve method is introduced to assess the relationship between renewable energy accommodation and system costs. The work proposes a guidance mechanism combining market design, policy support, grid upgrades, and scheduling improvements. Results show the model achieves high prediction accuracy, with errors within 2%, and mean square error and mean absolute error values of 0.025 and 0.153, respectively. The resulting carbon-locking curve indicates a decelerating emission trend, consistent with the model’s forecasts. The carbon locking curve reflects a slowing growth trend in emissions, consistent with model forecasts. The model captures cost increases under high-emission scenarios and accurately evaluates transition costs under low-carbon pathways. Different from previous static or linear cost assessment models, this work innovatively combines the carbon locking theory with the data-driven method based on the global power plant database. It constructs a comprehensive cost assessment and prediction framework that integrates the dynamics of renewable energy consumption, system operation costs, and institutional path dependence. The work provides replicable methodological support for the low-carbon transformation of power systems in high-carbon locking areas.