A Data-Driven Method for Deriving the Dynamic Characteristics of Marginal Carbon Emissions for Power Systems

Bing Fang, Jiayi Zhang, Shuangyin Chen (), Li Li, Shanli Wang and Mingzhe Wen
Additional contact information
Bing Fang: Hainan Power Grid Co., Ltd., Haikou 570203, China
Jiayi Zhang: Hainan Power Grid Co., Ltd., Haikou 570203, China
Shuangyin Chen: Institute of New Energy, Wuhan 430206, China
Li Li: Institute of New Energy, Wuhan 430206, China
Shanli Wang: Hainan Power Grid Co., Ltd., Haikou 570203, China
Mingzhe Wen: Hainan Power Grid Co., Ltd., Haikou 570203, China

Energies, 2025, vol. 18, issue 13, 1-18

Abstract: Understanding the dynamic carbon emission status is vital for turning a power system into a low-carbon system. However, the existing research has normally considered the average carbon emissions as the indicator for the operation and planning of power systems. Detailed carbon emission responsibility is not well allocated to different demands within power systems, leading to inefficient emission control. To address this problem, this paper develops a data-driven method for accurately finding the characteristics of the nodal marginal emission factor without the requirement of real-time optimal power flow (OPF) simulation. First, the nodal marginal emission factor system is derived based on actual data covering a timespan of one year on top of the IEEE 118 system. Then, a Graphical Neural Network (GNN) is adopted to map both the spatial and temporal relationship between nodal marginal emission and other features, thereby identifying the marginal emission characteristics for different nodes of power transmission systems. Through case studies, fine-tuned GNNs estimate all nodal marginal emission factor (NMEF) values for power systems without the requirement of OPF simulation and achieve a 5.75% Normalized Root Mean Squared Error (nRMSE) and 2.52% Normalized Mean Absolute Error (nMAE). Last but not least, this paper brings a new finding: a strong inclination to reduce marginal emission rates would compromise economic operation for power systems.

Keywords: power system; data driven; marginal carbon emission; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/13/3297/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/13/3297/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:13:p:3297-:d:1685991

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().