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随着电动汽车在大型社区的渗透率不断提高，以及充电功率等级、车主出行行为和充电模式出现差异，充电负荷预测愈发复杂。针对此问题，提出一种多功率等级的电动汽车充电负荷预测方法。首先，利用具有短期高预测精度特性的灰色GM(1,1)预测模型预测近两年的城市电动汽车保有量以扩充样本数据，进而建立基于遗传算法和Bass模型的电动汽车保有量中长期预测方法；其次，结合所收集的城市充电桩数据，将典型日划分为工作日、双休日和大型节假日，建立符合中国城市居民出行特征的充电行为概率模型；最后，构建兼顾电动汽车电池容量与停车时长的不同充电功率选择模型，提出基于蒙特卡洛模拟法的典型日不同功率等级的电动汽车充电概率模型。通过天津市某大型社区的算例分析，验证了该方法能够准确预测不同功率等级电动汽车的充电负荷变化趋势，可为未来社区充电设施的规划和建设提供数据支持。
Abstract：With the increasing penetration rate of electric vehicles(EVs)in large communities,as well as the differences in charging power levels,vehicle owners' travel behaviors and charging patterns,the prediction of charging load is becoming more complex. To solve this problem,a charging load prediction method for EVs with multiple power levels is proposed. First,a grey GM(1,1)prediction model with short-term high prediction accuracy is used to predict the urban EV ownership for the past two years to expand the sample data,and then a middle and long-term prediction method for EV ownership based on the genetic algorithm and Bass model is established. Second,combined with the collected urban charging pile data,the typical days are divided into working days,weekends and major holidays,and a charging behavior probability model that conforms to the travel characteristics of urban residents in China is established. Finally,a model for selecting different charging power levels is constructed with the consideration of both the EV battery capacity and parking time,and a typical-day EV charging probability model of different power levels based on the Monte Carlo simulation method is put forward. Through the case analysis of a large community in Tianjin,it is verified that the proposed method can accurately predict the charging load trend of EVs with different power levels,providing data support for the planning and construction of charging facilities in communities in the future.
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基本信息:
DOI：10.19635/j.cnki.csu-epsa.001602
中图分类号:U491.8;TM910.6;TM715
引用信息:
[1]肖朝霞,刘翔宇,王璇,等.大型社区多功率等级的电动汽车充电负荷预测[J].电力系统及其自动化学报,2025,37(11):11-23.DOI:10.19635/j.cnki.csu-epsa.001602.
基金信息:
国家重点研发计划资助项目（2023YFE0198100）