Research on optimal allocation of power system based on achieving carbon peak: take Shandong province as an example

Yongcheng Guo; Changzheng Gao; Chao Han

doi:10.1117/12.2684877

19 October 2023 Research on optimal allocation of power system based on achieving carbon peak: take Shandong province as an example

Yongcheng Guo, Changzheng Gao, Chao Han

Author Affiliations +

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270929 (2023) https://doi.org/10.1117/12.2684877
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

The electric power industry is the key field of carbon emission reduction, and promoting the carbon peak of the electric power industry in advance is of overall significance to the realization of the “double carbon” goal across the country. This paper proposes a power system optimal allocation method that couples the power CO₂ emissions with the traditional power system operation simulation and aims at minimizing the total CO₂ emissions, and takes the power system of Shandong province as an example for empirical analysis. Taking the current power status data of Shandong province as the input parameter, a power system production simulation model that minimizes the total carbon emissions while achieving the goal of reaching the carbon peak is constructed, and the carbon emission trajectory and the optimal allocation results of various power sources and flexible resources are obtained; At the same time, the Mann-Kendall trend test method is used to verify and analyze the carbon emission trajectory, which proves that the carbon emission trajectory has a significant downward trend, which also verifies the correctness and effectiveness of the method proposed in this paper.

Citation Download Citation

Yongcheng Guo, Changzheng Gao, and Chao Han "Research on optimal allocation of power system based on achieving carbon peak: take Shandong province as an example", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270929 (19 October 2023); https://doi.org/10.1117/12.2684877

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