A method for detecting abnormal electricity prices at charging stations based on two rounds of clustering

XUE Xiaohui; ZHANG Wen; ZHANG Jing; CHEN Yan; ZHOU Chun; CHEN Liang; CAO Zengwei

doi:10.11959/j.issn.1000-0801.2025004

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A method for detecting abnormal electricity prices at charging stations based on two rounds of clustering

Engineering and Application | 更新时间：2025-02-07

- A method for detecting abnormal electricity prices at charging stations based on two rounds of clustering
- Telecommunications Science Vol. 41, Issue 1, Pages: 184-190(2025)
- 作者机构：
  
  1.国网青海省电力公司，青海西宁 810008
  2.北京中电普华信息技术有限公司，北京 100192
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(72071070)
- DOI：10.11959/j.issn.1000-0801.2025004
  CLC： TM76
- Received：16 October 2024，
  
  Revised：2024-12-09，
  
  Published：20 January 2025
- 稿件说明：
移动端阅览
薛晓慧,张文,张静等.基于二次聚类的充电桩执行电价异常检测方法[J].电信科学,2025,41(01):184-190.

XUE Xiaohui,ZHANG Wen,ZHANG Jing,et al.A method for detecting abnormal electricity prices at charging stations based on two rounds of clustering[J].Telecommunications Science,2025,41(01):184-190.
薛晓慧,张文,张静等.基于二次聚类的充电桩执行电价异常检测方法[J].电信科学,2025,41(01):184-190. DOI： 10.11959/j.issn.1000-0801.2025004.

XUE Xiaohui,ZHANG Wen,ZHANG Jing,et al.A method for detecting abnormal electricity prices at charging stations based on two rounds of clustering[J].Telecommunications Science,2025,41(01):184-190. DOI： 10.11959/j.issn.1000-0801.2025004.

摘要

由于电价政策复杂，执行环节多，监管难度大，电价执行错误现象时有发生，这不仅损害电力市场的公平性和效率，也影响电力企业的经济效益和用户的用电成

本。提出了一种基于二次聚类的充电桩执行电价异常检测方法，首先进行电价执行异常分类及用电特征分析，其次通过

-means聚类算法剥离出电瓶车用户，进而在第二次聚类中采用含噪声应用的基于密度的空间聚类（density-based spatial clustering of applications with noise，DBSCAN）算法精确识别高价低接等更为复杂的违约情况。所提方法通过两次聚类分析，提高电价执行的准确性和效率，具有一定的理论意义和应用价值。

Abstract

Due to the complexity of electricity pricing policies

multiple implementation steps

and difficulty in regulation

errors in electricity pricing implementation occur from time to time. This not only damages the fairness and efficiency of the electricity market but also affects the economic benefits of power enterprises and the electricity costs of users. A method was proposed for detecting electricity price anomalies at charging stations based on secondary clustering. Firstly

the electricity price anomalies were classified and the electricity consumption characteristics were analyzed. Secondly

the

-means clustering algorithm was used to extract electric vehicle users. Then

in the second clustering

the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to accurately identify more complex default situations

such as high price and low connection. The proposed method improves the accuracy and efficiency of electricity price implementation through two rounds of cluster analysis

and has certain theoretical significance and application value.

关键词

Keywords

references

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