面向新型配电系统的光伏充电站负荷预测方法研究

庞欢; 何思源; 郑子东; 王雯靓; 曹楠; 王宇蛟

庞欢,何思源,郑子东,王雯靓,曹楠,王宇蛟.面向新型配电系统的光伏充电站负荷预测方法研究[J].电测与仪表,2026,63(4):152-162.
PANG Huan,HE Siyuan,ZHENG Zidong,WANG Wenliang,CAO Nan,WANG Yujiao.Research on load forecasting method of photovoltaic charging station for novel distribution system[J].Electrical Measurement & Instrumentation,2026,63(4):152-162.

面向新型配电系统的光伏充电站负荷预测方法研究

Research on load forecasting method of photovoltaic charging station for novel distribution system

DOI：10.19753/j.issn1001-1390.2026.04.016

中文关键词: 新型配电系统光伏充电站分位数回归多层极限学习机区间预测

英文关键词:novel distribution system, PV charging station, quantile regression, multi-layer extreme learning machine, interval prediction

基金项目:国家电网公司科技资助项目（2023YF-138）

作者中文名	作者英文名	单位
庞欢	PANG Huan	国网辽宁省电力有限公司阜新供电公司，辽宁阜新 123000
何思源	HE Siyuan	国网辽宁省电力有限公司阜新供电公司，辽宁阜新 123000
郑子东	ZHENG Zidong	国网辽宁省电力有限公司阜新供电公司，辽宁阜新 123000
王雯靓	WANG Wenliang	国网辽宁省电力有限公司阜新供电公司，辽宁阜新 123000
曹楠	CAO Nan	国网辽宁省电力有限公司阜新供电公司，辽宁阜新 123000
王宇蛟	WANG Yujiao	沈阳工程学院，沈阳 110136

摘要点击次数: 635

中文摘要:

在新型配电系统中，光伏充电站作为一种典型分布式资源聚合形态备受关注。由于分布式光伏发电与充电负荷都具有随机性、波动性等特点，光伏充电站负荷预测任务显得尤为复杂。考虑到光伏充电站接入对地区负荷曲线的动态影响，提出了一种基于多层极限学习机与分位数回归理论的光伏充电站负荷预测方法。文章对影响光伏充电站负荷的因素进行特征提取，提取关键特征量，结合分位数回归算法，构建多层核极限学习机深度神经网络模型，实现不同置信度水平下的光伏充电站负荷区间预测，并采用改进麻雀优化算法进行参数寻优，选择最优的模型进行负荷预测。选取北方某地某光伏充电站的负荷数据进行算例分析，结果表明提出的面向新型配电系统的光伏充电站负荷预测方法有较好的预测效果，可以更加精准地掌握负荷的预测信息。

英文摘要:

In the novel power distribution system, photovoltaic (PV) charging stations have attracted much attention as a typical distributed resource aggregation form. Since both distributed PV generation and charging loads are characterized by randomness and volatility, the load forecasting task of PV charging stations is particularly complex. In this paper, considering the dynamic impact of PV charging station access on the regional load profile, a PV charging station load forecasting method based on multi-layer limit learning machine and quantile regression theory is proposed. Firstly, the factors affecting the load of PV charging station are feature extracted and key feature quantities are extracted. Secondly, combined with the quantile regression algorithm, a multi-layer kernel limit learning machine deep neural network model is constructed to realize the load interval prediction of PV charging station under different confidence levels, and the improved sparrow optimization algorithm is used for the parameter optimization and the optimal model is selected for the load prediction. Finally, the load data of a photovoltaic charging station in a northern region of China is selected for example analysis. The results demonstrate that the proposed PV charging station load prediction method oriented to the novel distribution system achieves satisfactory forecasting performance and enables more accurate acquisition of load forecasting information.

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