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Article type: Research Article
Authors: Wang, Songcena; b | Chen, Hongyina; b; * | Li, Dezhia; b | Li, Jianfenga; b | Liu, Kaichenga; b | Zhong, Minga; b | Jia, Xiaoqianga; b | Jin, Lua; b
Affiliations: [a] National Key Laboratory of Power Grid Safety, Beijing, China | [b] China Electric Power Research Institute, Beijing, China
Correspondence: [*] Corresponding author: Hongyin Chen, National Key Laboratory of Power Grid Safety, Beijing 100192, China. E-mail: sysu_ag@126.com.
Abstract: With the development of the economy, people’s demand for green energy has increased significantly. However, the traditional single fossil energy supply system cannot meet the needs of low-carbon. Therefore, this study employs energy hub to establish a multi-energy flow network that enables the integration of carbon flow within the network. Additionally, by utilizing the multi-energy flow trend, a carbon flow tracking method is adopted to achieve real-time carbon flow calculation. Results show that this network calculates the electricity cost of 20043 yuan, gas cost of 67253 yuan, and carbon emission cost of 3152 yuan. Compared with the traditional energy flow system, gas cost is reduced by 4.3% and 1.7%, electricity cost by 21.3% and 15.0%, and carbon emission cost by 8.7% and 6.6%. The two-way sharing carbon flow calculation model calculates that the user side and power supply side of the node each bear half of the network loss, proving two-way sharing effectiveness. Test results on IEEE5 machine 14-node system show that the calculation method can accurately find high-emission and low-emission areas, making the carbon emission allocation between power generation and user more fair and reasonable. This research can effectively reduce emissions cost, accurately calculate emissions flow in real time, and facilitate reasonable emission reduction planning.
Keywords: Energy supply, energy hub, carbon flow, tidal current, green and environmentally friendly energy, multi energy flow power flow network
DOI: 10.3233/JCM-247175
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 1, pp. 3-18, 2024
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