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A Robust Low-Carbon Optimal Dispatching Method for Power System Distribution Based on LCA Carbon Emissions

Author

Listed:
  • Peng Xi

    (State Grid Hebei Economic Research Institute, Shijiazhuang 050023, China)

  • Chenguang Yang

    (State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050023, China)

  • Xiaobin Xu

    (State Grid Hebei Economic Research Institute, Shijiazhuang 050023, China)

  • Hangtian Li

    (State Grid Hebei Economic Research Institute, Shijiazhuang 050023, China)

  • Shiqiang Lu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Jinchao Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

Under the dual carbon goal, in order to promote the consumption of new energy and reduce carbon emissions in power systems, this paper proposes a new distributed robust low-carbon optimization scheduling method for power systems based on LCA carbon emissions. Firstly, the carbon emissions of energy consumption in the power system are calculated based on the LCA method; secondly, a distributed robust optimization scheduling model is established with the goal of minimizing carbon emissions and economic costs. The model is linearly solvable through the description of uncertain parameters and the transformation of the model. Finally, the optimization results of the example scenario indicate that the distributed robust low-carbon optimization scheduling method based on LCA carbon emissions can effectively reduce carbon emissions and costs compared to traditional methods, providing theoretical support for the new low-carbon optimization strategy of power systems.

Suggested Citation

  • Peng Xi & Chenguang Yang & Xiaobin Xu & Hangtian Li & Shiqiang Lu & Jinchao Li, 2025. "A Robust Low-Carbon Optimal Dispatching Method for Power System Distribution Based on LCA Carbon Emissions," Energies, MDPI, vol. 18(13), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3522-:d:1694230
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