Open Access

ARTICLE

Carbon Emission Factors Prediction of Power Grid by Using Graph Attention Network

Xin Shen¹, Jiahao Li¹, Yujun Yin¹, Jianlin Tang^2,3,*, Weibin Lin^2,3, Mi Zhou^2,3

1 Measurement Center, Yunnan Power Grid Co., Ltd., Kunming, 650000, China
2 Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou, 510530, China
3 Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou, 510530, China

* Corresponding Author: Jianlin Tang. Email:

Energy Engineering 2024, 121(7), 1945-1961. https://doi.org/10.32604/ee.2024.048388

Received 06 December 2023; Accepted 29 February 2024; Issue published 11 June 2024

Abstract

Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice, which is of immense importance in mobilizing the entire society to reduce carbon emissions. The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid. Therefore, it cannot provide carbon factor information beforehand. To address this issue, a prediction model based on the graph attention network is proposed. The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised network using the loads of the grid nodes and the corresponding carbon factor data. The network extracts features and transmits information more suitable for the power system and can flexibly adjust the equivalent topology, thereby increasing the diversity of the structure. Its input and output data are simple, without the power grid parameters. We demonstrated its effect by testing IEEE-39 bus and IEEE-118 bus systems with average error rates of 2.46% and 2.51%.

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Keywords

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