CARBON EMISSION REDUCTION PATH OF POWER SYSTEM BASED ON IMPROVED CARBON EMISSION FLOW ALGORITHM: LOW CARBON DEMAND RESPONSE

Tianchun Xiang, Yang Wang, Jiang Bian, Yi Gao, Shangze Li, Shuai Luo

References

  1. [1] L. Wachs, C. McMillan, and S.B. Reese, Reviewing flexibilityin industrial electrification: Focusing on green ammonia andsteel in the United States, Energy Research & Social Science,103, 2023, 103202.
  2. [2] S. Kumar, and R. Sen, Are larger or denser cities more emissionefficient? Exploring the nexus between urban household carbonemission, population size and density, Applied Energy, 377,2025, 124500.
  3. [3] R. Koh´ut, M. Klauˇco, and M. Kvasnica, Unified carbonemissions and market prices forecasts of the power grid, AppliedEnergy, 377, 2025, 124527.
  4. [4] T. Akan, Forecasting the future of carbon emissions by businessconfidence, Applied Energy, 382, 2025, 125146.
  5. [5] S. Lee, P. Prabawa, and D.-H. Choi, Joint peak powerand carbon emission shaving in active distribution systemsusing carbon emission flow-based deep reinforcement learning,Applied Energy, 379, 2025, 124944.
  6. [6] G. Colucci, D. Lerede, M. Nicoli, and L. Savoldi, A dynamicaccounting method for CO2 emissions to assess the penetrationof low-carbon fuels: Application to the TEMOA-Italy energysystem optimization model, Applied Energy, 352, 2023, 121951.
  7. [7] W. Xia, Y. Ma, Y. Gao, Y. Huo, and X. Su, Spatial-temporalpattern and spatial convergence of carbon emission intensityof rural energy consumption in China, Environmental Scienceand Pollution Research International, 31(5), 2024, 7751-7774.
  8. [8] S. Khan, S.E. Awan, Y. Muhammad, I. Jadoon, and M.A.Z.Raja, Novel polynomial Abet data augmentation algorithmwith GRU paradigm for nuclear power prediction, Annals ofNuclear Energy, 201, 2024, 110441.
  9. [9] C. Gao, C. Wang, Y. Chen, R. Qu, K. Niu, and W. Chen, Novellow-carbon optimal operation method for flexible distributionnetwork based on carbon emission flow, Energy Engineering,122(2), 2025, 785-803.
  10. [10] E. Karapidakis, I. Mozakis, M. Nikologiannis, and A. Tsikalakis,Zero carbon emissions due to ultra-high RES penetration ininterconnected island, Applied Sciences, 14(11), 2024, 4668.
  11. [11] J. Hu, K. Qin, R. Ma, W. Liu, J. Zhang, L. Pang, and J.Zhang, A study on carbon emission flow tracking for new typepower systems, International Journal of Electrical Power &Energy Systems, 165, 2025, 110455.
  12. [12] M. Zhou, Z. Zhu, F. Hu, K. Bian, and W. Lai, An industrial loadclassification method based on a two-stage feature selectionstrategy and an improved MPA-KELM classifier: A chinesecement plant case, Electronics, 12(15), 2023, 3356.
  13. [13] J.S. Yu, Low-carbon economic dispatch of integratedenergy system considering demand response (in Chinese),Anhui: Anhui Polytechnic University, China, 2022.https://doi.org/10.27763/d.cnki.gahgc.2022.000281
  14. [14] Y. Xia, G. Sun, Y. Wang, Q. Yang, Q. Wang, and S. Ba, A novelcarbon emission estimation method based on electricity-carbonnexus and non-intrusive load monitoring, Applied Energy, 360,2024, 122773.
  15. [15] Z. Liu, T. Sun, Y. Yu, P. Ke, Z. Deng, C. Lu, D. Huo, and X.Ding, Near-real-time carbon emission accounting technologytoward carbon neutrality, Engineering, 14, 2022, 44-51.
  16. [16] S. Zhang, H. Chen, B. Wang, R. Yu, Z. Ma, andY. Liao, Carbon emission monitoring analysis based on‘electricity-carbon’ relationship of cement enterprises (inChinese), China Environmental Science, 43(7), 2023, 3787-3795. https://doi.org/10.3969/j.issn.1000-6923.2023.07.054
  17. [17] M.F. Tahir, K. Mehmood, C. Haoyong, A. Iqbal, A. Saleem, andS. Shaheen, Multi-objective combined economic and emissiondispatch by fully informed particle swarm optimization,International Journal of Power and Energy Systems, 42(10),2022.
  18. [18] V. Aryai, and M. Goldsworthy, Day ahead carbon emissionforecasting of the regional National Electricity Marketusing machine learning methods, Engineering Applications ofArtificial Intelligence, 123, 2023, 106314.
  19. [19] L. Jin, and Q. Sun, A low carbon economic optimal dispatchingmodel for comprehensive energy system based on improvedwhale algorithm, 1-12., International Journal of Power andEnergy Systems, 44(10), 2024.
  20. [20] S. Sarwar, G. Aziz, and A. Kumar Tiwari, Implication ofmachine learning techniques to forecast the electricity priceand carbon emission: Evidence from a hot region, GeoscienceFrontiers, 15(3), 2024, 101647.
  21. [21] L. Chen, and A.P. Wemhoff, Predicting embodiedcarbon emissions from purchased electricity for UnitedStates counties, Applied Energy, 292, 2021, 116898.https://doi.org/10.1016/j.apenergy.2021.116898
  22. [22] H. Chen, R. Wang, X. Liu, Y. Du, and Y. Yang, Monitoring theenterprise carbon emissions using electricity big data: A casestudy of Beijing, Journal of Cleaner Production, 396, 2023,136427.

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