| Issue |
MATEC Web Conf.
Volume 336, 2021
2020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|
|
|---|---|---|
| Article Number | 09028 | |
| Number of page(s) | 8 | |
| Section | Computer-Aided Advanced System and Management | |
| DOI | https://doi.org/10.1051/matecconf/202133609028 | |
| Published online | 15 February 2021 | |
Identification of abuse of market power by power generation enterprises
1 State Grid Shanghai Electric Power Company, Shanghai, China
2 State key laboratory of the new energy power system (North China Electricity Power University), Baoding, Hebei province, China
* Corresponding author: taohy@163.com
At present, the reform of the power market is progressing steadily. To ensure the efficient and healthy operation of the power market, there is an urgent need to strengthen the credit supervision of the electricity market entities. Identifying violations of power generation companies' abuse of market power is a key task in the credit supervision of power market entities. Traditional power generation companies' abuse of market power identification mainly relies on expert decision-making. However, with the increase in market transaction volume, expert decision-making cannot meet the needs of work, and an intelligent identification method suitable for computer analysis must be proposed. This paper first proposes a quantitative definition of abuse of market power, and then takes into account the specific data characteristics of the electricity market, and proposes a method of identifying violations of power generation companies based on improved cost-sensitive support vector machines. Finally, the power market simulation experiment data set labeled by the definition method is used for training and testing. The test results show that the abuse of market power by power generation companies can be quickly and accurately identified, which verifies the effectiveness of the proposed method.
© The Authors, published by EDP Sciences, 2021
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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