The Study on Electricity Traceability Based on Regional Power Grid Flow Topology Analysis
With the vigorous development of new power systems, the proportion of zero-carbon electricity in regional power grids with abundant renewable energy resources is continuously increasing. To further reasonably evaluate the transmission flow of green electricity in high-voltage transmission and distribution networks and accurately analyse the proportion of green electricity in user consumption, thereby providing robust data support for corporate electricity carbon emission verification, this paper proposes a research method for regional renewable energy transmission flow based on power grid flow topology analysis. This method simplifies the regional power grid model into a power flow topology network model. Based on real-time power flow directions, it generates a unit injection matrix, node flow matrix, and node load matrix. Furthermore, relying on a depth-first search algorithm, it traces all power transmission paths from green power sources to users and calculates the proportion of green electricity from green power sources to each user. This method has been validated for effectiveness and speed through the IEEE example system and actual regional grid frameworks. It can serve regional power grid companies and electricity users by precisely tracking the consumption of green power delivered to various types of loads, providing reliable data evidence for the usage of green electricity in product production.
Authors
- Rui-Lin Zhao (ORCID: https://orcid.org/0000-0001-8129-9339)
- Hailin Zhao (ORCID: https://orcid.org/0000-0002-6298-1217)
- Huijuan Wang (ORCID: https://orcid.org/0000-0003-2684-4407)
- Yizhou Wang (ORCID: https://orcid.org/0009-0005-5042-1836)
- Fei Ding (ORCID: https://orcid.org/0000-0002-1960-618X)
- Yujun Wu (ORCID: https://orcid.org/0009-0001-6796-4934)
Institutions
- Shanghai Electric (China) (CN)
Publication Details
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1109/icips64173.2024.10900158
- Primary Topic
- Smart Grid and Power Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- State Grid Jiangsu Electric Power