| 李灏翀,宁龙飞,王德林,胡晨旭,王蓓蓓.基于大数据与K-means聚类的省间电力现货市场购售双侧报价行为特征分析[J].电力需求侧管理,2025,27(4):111-118 |
| 基于大数据与K-means聚类的省间电力现货市场购售双侧报价行为特征分析 |
| Analysis of bidding behavior characteristics in inter-provincial electricity spot markets using big data and K-means clustering |
| 投稿时间：2025-04-12 修订日期：2025-05-17 |
| DOI：10. 3969 / j. issn. 1009-1831. 2025. 04. 017 |
| 中文关键词: 省间电力现货市场 新能源消纳 大数据分析 K-means算法 报价行为分类 |
| 英文关键词: inter-provincial electricity spot market renewable energy consumption big data analysis K-means algorithm bidding behavior classification |
| 基金项目:国家重点研发计划项目（2022YFB2403400）；国家电网有限公司科技项目（5108-202355444A-3-2-ZN） |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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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