电测与仪表2025，Vol.62Issue(9)：26-36,11.DOI:10.19753/j.issn1001-1390.2025.09.004
基于时空图神经网络的电力系统碳排放流快速计算方法
Rapid calculation approach of carbon emission flow in power system based on spatiotemporal graph neural network
摘要
Abstract
To address the inefficiencies and inaccuracies in carbon emission calculations in power system,this pa-per proposes a data-driven approach based on spatiotemporal graph neural networks(ST-GNN),which aims to effi-ciently compute node carbon emission factors,branch carbon flows,and carbon flow losses.The paper first analy-zes the complexity of carbon flow calculations in power system and the limitations of traditional methods.An ST-GNN model is then developed using active and reactive power(PQ),active power and voltage(PV),and slack node characteristics as inputs to directly compute carbon emission factors and branch carbon flows,while determi-ning carbon flow losses.The characteristics of the PQ node include active power and reactive power,which are sourced from the operational data of power system.The active power and voltage of the PV node are derived from the operating characteristics of the generators.The inputs of the slack node consist of voltage and phase angle,en-suring the power balance of the system.Experiments conducted on IEEE 9-bus,IEEE 57-bus and IEEE 118-bus systems validate the effectiveness of the proposed method.Results demonstrate that the ST-GNN model significantly outperforms traditional methods,such as linear regression,decision trees,long short-term memory(LSTM),and multilayer perception(MLP)in terms of calculation accuracy for carbon emission factors,branch carbon flows,and carbon flow losses,particularly in complex power networks.This study provides a precise and efficient technical support for the monitoring and optimization of carbon emission in power system.关键词
碳排放流/深度学习/时空图神经网络/电力系统/数据驱动/碳排放因子/支路碳流/碳损耗Key words
carbon emission flow/deep learning/spatiotemporal graph neural network/power system/data-driv-en/carbon emission factor/branch carbon flow/carbon loss分类
信息技术与安全科学引用本文复制引用
陈娟,汪洋,汪钢,龚赟,翁同洋..基于时空图神经网络的电力系统碳排放流快速计算方法[J].电测与仪表,2025,62(9):26-36,11.基金项目
国网安徽省电力有限公司项目(B312N024000J) （B312N024000J）
