Analysis of bidding behavior characteristics in inter-provincial electricity spot markets using big data and K-means clustering

李灏翀; 宁龙飞; 王德林; 胡晨旭; 王蓓蓓

doi:10.3969/j.issn.1009-1831.2025.04.017

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Analysis of bidding behavior characteristics in inter-provincial electricity spot markets using big data and K-means clustering

更新时间：2026-03-05

- Analysis of bidding behavior characteristics in inter-provincial electricity spot markets using big data and K-means clustering
- Vol. 27, Issue 4, (2025)
- 作者机构：
  
  1. 东南大学网络空间安全学院,南京,211189
  2. 东南大学电气工程学院,南京,210096
  3. 国家电力调度控制中心,北京,100031
  4. 中国电力科学研究院有限公司,南京,210003
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2025.04.017
  CLC：
- Published：2025
- 稿件说明：
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李灏翀, 宁龙飞, 王德林, et al. Analysis of bidding behavior characteristics in inter-provincial electricity spot markets using big data and K-means clustering[J]. 2025, 27(4).
DOI：

李灏翀, 宁龙飞, 王德林, et al. Analysis of bidding behavior characteristics in inter-provincial electricity spot markets using big data and K-means clustering[J]. 2025, 27(4). DOI： 10.3969/j.issn.1009-1831.2025.04.017.

摘要

我国能源资源与需求的空间错配叠加清洁能源快速发展

导致弃风、弃光、弃水问题突出

需通过电力市场化改革优化资源配置。省间电力现货市场通过“统一市场、两级运作”体系

促进新能源消纳与电力余缺互济。但当前静态限价机制难以平衡购售双侧动态与“保供稳价”目标

发电厂和购电侧报价行为分析不足。基于2022年省间现货市场试运行数据

采用大数据驱动方法

分析火电、水电、风电、光电及对端省份购电侧的报价特征与出清结果

揭示其时段性和季节性规律。使用K-means算法分类多类型机组报价行为

提取持续时间、报量、报价的关键差异

以分析影响因素。研究结果为市场机制优化提供数据支持

促进新能源消纳和双碳目标实现。

Abstract

The spatial mismatch between China’s energy resources and demand

alongside rapid clean energy growth

has caused significant wind

solar

and hydropower curtailment

necessitating power market reforms for resource optimization. The inter-provincial power spot market enables renewable energy integration and power balancing via a“unified market

two-level operation”system. Yet

static price caps struggle to balance supply-demand dynamics and supply-price stability goals

with limited analysis of bidding behaviors of power plants and electricity purchasers. Using 2022 trial data

a big data-driven approach examines bidding characteristics and clearing outcomes of thermal

hydro

wind

solar power

and counterpart provinces’purchasers

uncovering temporal and seasonal patterns. The KMeans algorithm classifies bidding behaviors of various units

identifying differences in duration

bid volume

and price to analyze influencing factors. Findings support market mechanism optimization

enhancing renewable energy integration and dual-carbon goals.

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