| 引用本文: | 王 睿,高 欣,李军良,等.基于聚类分析的电动汽车充电负荷预测方法[J].电力系统保护与控制,2020,48(16):37-44.[点击复制] |
| WANG Rui,GAO Xin,LI Junliang,et al.Electric vehicle charging demand forecasting method based on clustering analysis[J].Power System Protection and Control,2020,48(16):37-44[点击复制] |
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| 摘要: |
| 准确预测电动汽车充电负荷是研究大规模电动汽车接入对电网影响的基础，现有充电负荷预测方法缺乏考虑路况拥堵因素对电动汽车荷电量的影响。提出了一种基于聚类分析的电动汽车充电负荷预测方法，在分析常规充电负荷影响因素并初步建立概率分布模型的基础上，对每段行程的行驶里程和行驶时间构成的二维出行特征数据进行聚类分析。挖掘常规统计数据无法得到的道路拥堵因素，考虑不同路况条件下道路拥堵因素对电动汽车荷电状态的影响并叠加该变量到负荷预测模型中。以北京市为例分别预测并比较分析了工作日、周末、夏季、冬季电动汽车日充电负荷曲线。计算结果表明该方法可在一定程度上提高充电负荷预测的精确度。 |
| 关键词: 电动汽车 负荷建模 充电负荷预测 聚类分析 路况拥堵因素 |
| DOI：DOI: 10.19783/j.cnki.pspc.191223 |
| 投稿时间：2019-10-08修订日期：2020-03-04 |
| 基金项目:国家电网公司科技项目资助(52110417001G) |
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| Electric vehicle charging demand forecasting method based on clustering analysis |
| WANG Rui,GAO Xin,LI Junliang,XU Jianhang,Guanqun,JING Xiao |
| (1. School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. NARI Group Corporation/State Grid Electric Power Research Institute, Beijing 100192, China) |
| Abstract: |
| Accurately forecasting electric vehicle charging demand can be a foundation for research on large scale electric vehicle access to the grid. Existing methods lack consideration of the impact of the congestion factor. An electric vehicle charging demand forecasting method based on cluster analysis is proposed. On the basis of analyzing the influence factors and establishing a probability distribution model, a cluster analysis of two-dimensional travel feature data composed of mileage and travel time of each trip is made. It mines a congestion factor that is not available by statistics, considers the influence of congestion on the state of charge under different conditions and superimposes the variable into the forecasting model. Taking Beijing as an example, daily charging load curves of weekdays, weekends, summers and winters are predicted and comparative analysis is made. Results show that the method can improve the accuracy of charging demand forecasting to some extent.
This work is supported by Science and Technology Project of State Grid Corporation of China (No. 52110417001G). |
| Key words: electric vehicle forecasting model charging demand forecast clustering analysis traffic congestion factor |
