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Research on Carbon Emission Prediction Techniques for Intelligent Parks in New-Generation Power Systems

Published: 31 July 2024 Publication History

Abstract

The intelligent parks can promote overall economic development towards low-carbon and high efficiency through exemplary utility in monitoring, deduction, and services. The park's carbon emission trend deduction technology not only provides networked, digitized, and intelligent means but also empowers the construction of a clean, low-carbon, safe, and efficient energy system. Moreover, it can indirectly contribute to the overall reduction of societal energy consumption. In the future, park carbon emission trend deduction technology will be integrated into various stages of China's carbon inventory investigation, prediction of the peak and neutrality of carbon emissions, emission reduction path planning, and the promotion of each stage of emission reduction path implementation. Therefore, accurately deducing the carbon emission trend of the intelligent parks is the technical problem that this thesis aims to address.

References

[1]
D. J. Olsen, N. Zhang, C. Kang, M. A. Ortega-Vazquez and D. S. Kirschen, "Planning Low-Carbon Campus Energy Hubs," in IEEE Transactions on Power Systems, vol. 34, no. 3, pp. 1895-1907, May, 2019.
[2]
Z. Zhuo, N. Zhang, Q. Hou, E. Du and C. Kang, "Backcasting Technical and Policy Targets for Constructing Low-Carbon Power Systems," in IEEE Transactions on Power Systems, vol. 37, no. 6, pp.4896-4911, Nov. 2022.
[3]
Y. Wu, S. Lou and S. Lu, "A Model for Power System Interconnection Planning Under Low-Carbon Economy With CO2 Emission Constraints," in IEEE Transactions on Sustainable Energy, vol. 2, no. 3, pp. 205-214, July, 2011.
[4]
M. G,L. P,F. J C, A review on buildings energy information: Trends, end-uses, fuels and drivers [J]. Energy Reports, 2022, 8626-637.
[5]
Asam A, Tianshu G, Jinqing P, Assessment of the renewable energy generation towards net-zero energy buildings: A review[J]. Energy Buildings, 2022, 256.
[6]
Xinxin Z, Kaili X. Statistical data-based prediction of carbon dioxide emission factors of China's power generation at carbon peak in 2030 [J]. Case Studies in Thermal Engineering, 2023, 51.
[7]
Emmanouil G, Perikles P, C. H L, Electricity demand and carbon emission in power generation under high penetration of electric vehicles. A European Union perspective [J]. Energy Reports, 2020, 6(S6): 475-486.
[8]
YA. Knirel, Q. Sun, S.N. Senchenkova, Simultaneous capacity optimization of distributed generation and storage in medium voltage micro-grids [J]. International Journal of Electrical Power&Energy Systems, 2015, 67(7):101-113.
[9]
K. Jiang, N. Liu, X. Yan, Modeling Strategic Behaviors for GenCo With Joint Consideration on Electricity and Carbon Markets [J]. IEEE Transactions on Power Systems, 2023, 38 (5): 4724-4738.
[10]
Z. Zhuo, N. Zhang, Q. Hou, Backcasting Technical and Policy Targets for Constructing Low-Carbon Power Systems [J]. IEEE Transactions on Power Systems, 2022, 37 (6): 4896-4911.

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  1. Research on Carbon Emission Prediction Techniques for Intelligent Parks in New-Generation Power Systems

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 31 July 2024

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