<abstract xmlns="http://www.w3.org/1999/xhtml">

As the current carbon measurement method makes it difficult to reflect the time-space variability of carbon emission factors, it leads to the inability to clarify the carbon emission responsibility of grid-side losses. In this paper, starting from the internal power system, based on the power system carbon emission flow analysis theory and knowledge graph technology, a trend-tracking analytical algorithm based on the node conductivity matrix operation is proposed to solve the complex power allocation relationship between each generating unit and each node load, and between each generating unit and the line network loss. On the basis of this algorithm, the carbon flow tracking model of the whole power system is established according to the conversion relationship between the current and the carbon flow, and the analytical expressions of the carbon emission distribution among the power generation side, the load side and the line loss are obtained by combining with matrix operation, so as to measure the carbon emission measurement results of the whole power system and to provide data references for the responsibility sharing of carbon emission. Finally, simulation verification is carried out based on the actual system operation and load data for C city. The total amount of carbon emissions from electricity consumption is 98.8 million tCO2 when C city is used as the minimum spatial scale, while the total amount of carbon emissions from electricity consumption is 67.9 million tCO2, 14.0 million tCO2, 16.9 million tCO2, and the total amount of the three regions is 98.8 million tCO2, which is consistent with that calculated when C city is used as the minimum spatial scale. The calculation results are consistent in real-time. The carbon measurement method proposed in this paper can obtain higher spatial resolution carbon measurement results for electricity consumption.
</abstract>

As the current carbon measurement method makes it difficult to reflect the time-space variability of carbon emission factors, it leads to the inability to clarify the carbon emission responsibility of grid-side losses. In this paper, starting from the internal power system, based on the power system carbon emission flow analysis theory and knowledge graph technology, a trend-tracking analytical algorithm based on the node conductivity matrix operation is proposed to solve the complex power allocation relationship between each generating unit and each node load, and between each generating unit and the line network loss. On the basis of this algorithm, the carbon flow tracking model of the whole power system is established according to the conversion relationship between the current and the carbon flow, and the analytical expressions of the carbon emission distribution among the power generation side, the load side and the line loss are obtained by combining with matrix operation, so as to measure the carbon emission measurement results of the whole power system and to provide data references for the responsibility sharing of carbon emission. Finally, simulation verification is carried out based on the actual system operation and load data for C city. The total amount of carbon emissions from electricity consumption is 98.8 million tCO2 when C city is used as the minimum spatial scale, while the total amount of carbon emissions from electricity consumption is 67.9 million tCO2, 14.0 million tCO2, 16.9 million tCO2, and the total amount of the three regions is 98.8 million tCO2, which is consistent with that calculated when C city is used as the minimum spatial scale. The calculation results are consistent in real-time. The carbon measurement method proposed in this paper can obtain higher spatial resolution carbon measurement results for electricity consumption.

A Whole-Segment Carbon Measurement Approach for Energy Systems Incorporating Knowledge Mapping Techniques

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

As the current carbon measurement method makes it difficult to reflect the time-space variability of carbon emission factors, it leads to the inability to...

A Whole-Segment Carbon Measurement Approach for Energy Systems Incorporating Knowledge Mapping Techniques

Published Online: Dec 18, 2023

Page range: -

Received: Mar 21, 2023

Accepted: Jun 28, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01537

Keywords
Power system, Whole-link carbon measurement, Knowledge graph, Trend tracking algorithm, Node conductivity matrix

© 2023 Qiang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Whole-Segment Carbon Measurement Approach for Energy Systems Incorporating Knowledge Mapping Techniques

Published Online: Dec 18, 2023

Page range: -

Received: Mar 21, 2023

Accepted: Jun 28, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01537

KeywordsPower system, Whole-link carbon measurement, Knowledge graph, Trend tracking algorithm, Node conductivity matrix

© 2023 Qiang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Power system, Whole-link carbon measurement, Knowledge graph, Trend tracking algorithm, Node conductivity matrix