Rapid calculation approach of carbon emission flow in power system based on spatiotemporal graph neural network
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| Title: | Rapid calculation approach of carbon emission flow in power system based on spatiotemporal graph neural network |
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| Authors: | CHEN Juan, WANG Yang, WANG Gang, GONG Yun, WENG Tongyang |
| Source: | Diance yu yibiao, Vol 62, Iss 9, Pp 26-36 (2025) |
| Publisher Information: | Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd., 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Instruments and machines LCC:Electrical engineering. Electronics. Nuclear engineering LCC:Science |
| Subject Terms: | carbon emission flow, deep learning, spatiotemporal graph neural network, power system, data-driven, carbon emission factor, branch carbon flow, carbon loss, Instruments and machines, QA71-90, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Science |
| Description: | To address the inefficiencies and inaccuracies in carbon emission calculations in power system, this paper proposes a data-driven approach based on spatiotemporal graph neural networks (ST-GNN), which aims to efficiently compute node carbon emission factors, branch carbon flows, and carbon flow losses. The paper first analyzes 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 determining 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, ensuring 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | Chinese |
| ISSN: | 1001-1390 |
| Relation: | http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20250225002&flag=1&journal_id=dcyyben&year_id=2025; https://doaj.org/toc/1001-1390 |
| DOI: | 10.19753/j.issn1001-1390.2025.09.004 |
| Access URL: | https://doaj.org/article/fc7ba55ba35c47109f1e12e62c8c3753 |
| Accession Number: | edsdoj.fc7ba55ba35c47109f1e12e62c8c3753 |
| Database: | Directory of Open Access Journals |
Nájsť tento článok vo Web of Science | Abstract: | To address the inefficiencies and inaccuracies in carbon emission calculations in power system, this paper proposes a data-driven approach based on spatiotemporal graph neural networks (ST-GNN), which aims to efficiently compute node carbon emission factors, branch carbon flows, and carbon flow losses. The paper first analyzes 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 determining 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, ensuring 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. |
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| ISSN: | 10011390 |
| DOI: | 10.19753/j.issn1001-1390.2025.09.004 |