Data driven planning and optimization of high penetration electric vehicle charging

doi:10.11930/j.issn.1004-9649.202504013

Abstract

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

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.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202504013

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

Figures/Tables 12

Fig.1 Hourly load variation in a day

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

Fig.3 Cumulative distribution of wind speeds at the site

Fig.4 Probability densities function of solar energy resource

Fig.5 Cumulative distribution of solar energy resource

Fig.6 Architecture of the renewable energy integrated distribution network

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

Fig.7 EV charging and discharging scheduling algorithm flow

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。

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

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

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

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