Capacity Configuration Optimization of Rail Transit Wind-solar-storage System Based on Improved K-means Algorithm

WEI Tingsan; ZHOU Hang; ZHANG Yulong; GAO Bo; HOU Shaobin

doi:10.14044/j.1674-1757.pcrpc.2025.05.003

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Capacity Configuration Optimization of Rail Transit Wind-solar-storage System Based on Improved K-means Algorithm

Optimal Control Technology of Traction Power Supply System and New Power Distribution System | 更新时间：2025-10-30

- Capacity Configuration Optimization of Rail Transit Wind-solar-storage System Based on Improved K-means Algorithm
- Power Capacitor & Reactive Power Compensation Vol. 46, Issue 5, Pages: 28-36(2025)
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- DOI：10.14044/j.1674-1757.pcrpc.2025.05.003
  CLC：
- Received：26 September 2025，
  
  Published：25 October 2025
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WEI Tingsan, ZHOU Hang, ZHANG Yulong, et al. Capacity Configuration Optimization of Rail Transit Wind-solar-storage System Based on Improved K-means Algorithm[J]. Power Capacitor & Reactive Power Compensation, 2025, 46(5): 28-36.
DOI：

WEI Tingsan, ZHOU Hang, ZHANG Yulong, et al. Capacity Configuration Optimization of Rail Transit Wind-solar-storage System Based on Improved K-means Algorithm[J]. Power Capacitor & Reactive Power Compensation, 2025, 46(5): 28-36. DOI： 10.14044/j.1674-1757.pcrpc.2025.05.003.

摘要

针对轨道交通供电系统中大规模风光接入引发的出力波动剧烈与储能容量配置不合理等问题，本文提出一种基于改进K-means算法的风光储系统容量优化配置方法。该方法创新地结合轮廓系数与手肘法来联合判定聚类数，从而提取出具有代表性的典型日场景并赋予概率权重，这一设计兼顾了数据的典型性与全面性，避免了传统全年法计算复杂度过高与两周法代表性不足的缺陷。在此基础上，本文构建了以系统全寿命周期成本最小化与供电可靠性指标（LPSP、EWR）改善为双目标的优化模型，并采用粒子群优化算法实现风光储容量的全局寻优。基于轨道交通典型运行数据的仿真结果表明，在平日典型负荷工况下，所提模型相较传统方法成本节约率可达32.56%，可靠性指标改善率可达28.10%，验证了该方法的有效性与优越性。

Abstract

In view of such problems as violent output fluctuation and unreasonable energy storage capacity allocation due to large-scale wind-solar integration in power supply system of rail transit，in this paper a optimal capacity allocation method of wind-solar e

nergy storage system based on improved

-means clustering algorithm is proposed. The method innovatively combines the contour coefficient and elbow method to jointly determine the number of clusters，thus extracting the representative typical day scenes and gives probability weights，and this design takes into account the typicality and comprehensiveness of the data， and avoids the defects of high computational complexity of the traditional year-round method and insufficient representativeness of the two-week method. Furthermore，an optimization model with double objectives of minimizing the system life cycle cost and improving the power supply reliability index（LPSP，EWR）is constructed，and the particle swarm optimization algorithm is used to achieve the global optimization of the wind-solar energy storage capacity. The simulation results based on the typical operation data of rail transit show that under the typical daily load conditions，the proposed method can，compared with the traditional method，save 32.56% in cost and the the improvement rate of reliability index can be up to 28.10%，which varifies both effectiveness and superiority of the method.

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