| Issue |
SHS Web Conf.
Volume 170, 2023
2023 International Conference on Digital Economy and Management Science (CDEMS 2023)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 7 | |
| Section | Artificial Intelligence and Digital Economy | |
| DOI | https://doi.org/10.1051/shsconf/202317001005 | |
| Published online | 14 June 2023 | |
Analyzing congestion surplus distribution based on social welfare optimization
1 Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co., LTD., Hangzhou 310002, China
2 School of Economics and Management, Changsha University of Science & Technology, Changsha 410076, China
* E-mail address: 285964995@qq.com
The distribution of congestion surplus affects not only the short-term economic interests of stakeholders in the power spot market, but the improvement of social welfare in the long run. At present, the congestion surplus in mature electricity spot markets of developed countries is usually returned to the power users or power generation enterprises according to established proportions. Although this method helps achieve rational distribution of congestion surplus, it does not offer a solution to the congestion problem and cannot achieve maximized social welfare. In light of the principles of welfare economics, we propose in this paper injecting the congestion surplus into the capital pool of the power grid as a transmission line expansion fund to fundamentally solve the power transmission congestion problem. Modeling results and case studies presented in our work verified that our proposed distribution model could achieve Pareto optimization. Finally, combining the power transmission and distribution (PTD) pricing and supervision method, this paper puts forward the method for use and supervision of congestion surplus on the power spot market to reach optimal allocation of resources.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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