基于数据驱动的高渗透率电动汽车充电规划与优化

doi:10.11930/j.issn.1004-9649.202504013

中国电力 ›› 2026, Vol. 59 ›› Issue (2): 104-113.DOI: 10.11930/j.issn.1004-9649.202504013

基于数据驱动的高渗透率电动汽车充电规划与优化

戚成飞¹(), 王亚超¹(), 李文文¹(), 张炜¹(), 赵鹏²

1. 国网冀北电力有限公司计量中心，北京　100052
2. 输配电装备及系统安全与新技术国家重点实验室（重庆大学），重庆　400042）

收稿日期:2025-04-07 修回日期:2025-12-11 发布日期:2026-03-04 出版日期:2026-02-28
作者简介:
戚成飞（1995），男，通信作者，硕士，工程师，从事用电信息采集技术研究，E-mail：qi_chengfei@163.com
王亚超（1984），男，博士，高级工程师，从事车网互动、虚拟电厂研究，E-mail：wangyachaoa@126.com
李文文（1989），女，硕士，高级工程师，从事电能表动态计量、可靠性评估研究，E-mail：821328633@qq.com
张炜（1998），男，硕士，助理工程师，从事软件可靠性评估研究，E-mail：zw_tju016@163.com
基金资助:
国家自然科学基金资助项目（52077012）。

Data driven planning and optimization of high penetration electric vehicle charging

QI Chengfei¹(), WANG Yachao¹(), LI Wenwen¹(), ZHANG Wei¹(), ZHAO Peng²

1. State Grid Jibei Electric Power Co., Ltd. Measurement Center, Beijing 100052, China
2. State Key Laboratory of Transmission and Distribution Equipment and System Safety and New Technology (Chongqing University), Chongqing 400042, China

Received:2025-04-07 Revised:2025-12-11 Online:2026-03-04 Published:2026-02-28
Supported by:
This work is supported by National Natural Science Foundation of China (No.52077012).

摘要/Abstract

摘要：

在可再生能源和电动汽车高渗透率“双高”背景下，电网供需不确定性显著上升，亟须新的规划与调度策略以保障运行稳定。为此提出一种基于数据驱动的多源融合方法，构建充电需求预测模型，实现充电设施布局与动态充放电策略的联合优化。以Open配电系统仿真器（Open distribution system simulator，OpenDSS）平台为载体，对一个典型配电网络进行建模与仿真。研究结果表明，所提方法能够有效降低电网峰谷差，提升电网运行稳定性及充电设施利用率，并降低用户充电等待时间。

关键词: 电动汽车, 高渗透率, 充电需求预测

Abstract:

In the context of "double high" penetration of renewable energy and electric vehicles, the uncertainty of power grid supply and demand has significantly increased, urgently demanding planning and scheduling strategies to ensure stable operation. To address this, a data-driven multi-source fusion method is proposed to construct a charging demand prediction model, achieving joint optimization of facility layout and dynamic charging and discharging strategies. The Open Distribution System Simulator (OpenDSS) platform is used as a carrier to model and simulate a typical distribution network. results show that the proposed method can effectively reduce the peak-valley difference of the power grid, enhance the stability of power grid operation and the utilization rate of charging facilities, reduce user charging waiting time.

Key words: electric vehicles, high penetration rate, charging demand forecast

戚成飞, 王亚超, 李文文, 张炜, 赵鹏. 基于数据驱动的高渗透率电动汽车充电规划与优化[J]. 中国电力, 2026, 59(2): 104-113.

QI Chengfei, WANG Yachao, LI Wenwen, ZHANG Wei, ZHAO Peng. Data driven planning and optimization of high penetration electric vehicle charging[J]. Electric Power, 2026, 59(2): 104-113.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202504013

https://www.electricpower.com.cn/CN/Y2026/V59/I2/104

图/表 12

图 1 典型日负荷曲线

Fig.1 Hourly load variation in a day

图 2 场址风速的概率密度分布

Fig.2 Probability densities function of wind speeds at the site

图 3 场址风速的累积分布

Fig.3 Cumulative distribution of wind speeds at the site

图 4 场址风速的累积分布

Fig.4 Probability densities function of solar energy resource

图 5 场址风速的累积分布

Fig.5 Cumulative distribution of solar energy resource

图 6 可再生能源综合配电网架构

Fig.6 Architecture of the renewable energy integrated distribution network

表 1 优化中可再生能源系统的关键参数

Table 1 Key parameters for optimizing renewable energy systems

设备参数	技术规格
风力发电机容量/MW	2.3
风机运行风速范围/(m·s^–1)	切入3.5，额定15，切出25
风机性能模型	制造商功率曲线
光伏系统	单晶硅组件
光伏系统效率/%	19.4
逆变器/整流器效率/%	95

图 7 电动汽车充放电调度算法流程

Fig.7 EV charging and discharging scheduling algorithm flow

表 2 工作日电动汽车的连接/断开模式

Table 2 Connection/disconnection pattern of EVs during a workday

编号	时间	条件
1	00:00—06:00	任意时间步长保持78辆电动汽车连接，随机断开2辆模拟车辆紧急使用场景。
2	06:00—09:00	任意时间步长内保持60辆电动汽车连接，20辆处于断开状态。
3	09:00—18:00	任意时间步长内仅5辆电动汽车连接，其余75辆因使用中保持断开。
4	18:00—21:00	任意时间步长内保持40辆电动汽车连接，40辆随机断开。
5	21:00—00:00	任意时间步长保持78辆电动汽车连接，随机断开2辆模拟车辆紧急使用场景。

图 8 充电状态限制为90%的电动汽车每小时充放电率

Fig.8 Hourly charge-discharge rate of EVs with 90% SOC restriction

图 9 充电状态限制为75%的电动汽车每小时充放电率

Fig.9 Hourly charge-discharge rate of EVs with 75% SOC restriction

图 10 充电状态限制为60%的电动汽车每小时充放电率

Fig.10 Hourly charge-discharge rate of EVs with 60% SOC restriction

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基于数据驱动的高渗透率电动汽车充电规划与优化

Data driven planning and optimization of high penetration electric vehicle charging

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基于数据驱动的高渗透率电动汽车充电规划与优化

Data driven planning and optimization of high penetration electric vehicle charging

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PDF (PC)

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参考文献 35

相关文章 15

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