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Article type: Research Article
Authors: Yang, Wei* | Zhou, Chunlei | Song, Jinwei | Xuan, Donghai | Yuan, Qiheng | Zhu, Guangxin | Zhang, Pengyu
Affiliations: Big Data Center of State Grid Corporation of China, Beijing, China
Correspondence: [*] Corresponding author: Wei Yang, Big Data Center of State Grid Corporation of China, Beijing, China. E-mail: [email protected].
Abstract: In order to solve the problem of large-scale power grid, complex connection relationship, and large demand for electricity carbon factor calculation resources, this paper proposes an algorithm that is easy to model the power grid topology and has high computational efficiency to implement the application of “One Electricity Carbon Chart”. In this paper, the graph computing component is used to realize the graph modelling, distributed storage, and high-performance calculation of the electric carbon factor for the data representing the topology of the power grid, such as the power grid table, AC line segment table, and AC line end table. This paper realizes the rapid grid topology graph modelling of EMS dispatching system data, so that the grid structure and active power flow can be intuitively displayed and dynamically studied. This paper also realizes the distributed parallel calculation of electric carbon factor in the case of a ring network, which solves the problem of difficult analysis and calculation of large-scale power grid topology. We conduct experiments on real datasets, and the experimental results demonstrate the effectiveness of the proposed method. In addition, the research on the “One Electricity Carbon Chart” makes the carbon emission of power plants depend on the power flow of the power grid and transfers the accounting method, realizing the calculation of the carbon emission factor of the power grid at the plant level, and provides a new perspective for the sharing of carbon emission responsibilities in the power system.
Keywords: Electric carbon emission control, data-driven method, grid topology
DOI: 10.3233/JCM-247574
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 4-5, pp. 3245-3259, 2024
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