Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2025 , Vol. 59 >Issue 7: 995 - 1006

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.448

New Type Power System and the Integrated Energy

Feature Extraction and Anomaly Identification Method for Power Customer Price in Power Market Enviroment

  • ZHU Feng ,
  • SHAN Chao ,
  • WU Ning ,
  • CAI Qixin ,
  • ZHU Yunan ,
  • LIU Yunpeng ,
  • ZUO Qiang
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  • 1. Marketing Service Center of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China
    2. State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China

Received date: 2023-09-06

  Revised date: 2024-03-10

  Accepted date: 2024-03-11

  Online published: 2024-03-29

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Abstract

Identifying electricity price anomalies and exploring the underlying reasons in such a complex market environment, especially with incomplete data, is a key issue for promoting the orderly operation of power market and ensuring the reasonable interests of power customers. Therefore, a method is established for feature extraction and anomaly identification of electricity prices for power customers. First, an electricity price feature vector is constructed, and its dimensionality is reduced using a spectral clustering algorithm. Then, typical electricity price characteristics are extracted as the basic standard for determining price anomalies. Next, the similarity between each power customer and typical electricity price characteristics is calculated. Finally, electricity price anomalies are identified in two stages. The causes of anomalies are initially and rapidly identified based on electricity consumption and trading behavior, and then further identified in-depth. Case analysis shows that this method can quickly and effectively extract typical electricity price features and identify anomalies. The reasons behind these anomalies are further analyzed from both electricity consumption and trading behaviors, and corresponding improvement measures are proposed.

Key words： power market; electricity price; spectral clustering; feature extraction; anomaly identification

Cite this article

ZHU Feng , SHAN Chao , WU Ning , CAI Qixin , ZHU Yunan , LIU Yunpeng , ZUO Qiang . Feature Extraction and Anomaly Identification Method for Power Customer Price in Power Market Enviroment[J]. Journal of Shanghai Jiaotong University, 2025 , 59(7) : 995 -1006 . DOI: 10.16183/j.cnki.jsjtu.2023.448

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