A spatiotemporal optimization strategy for electric vehicle charging loads guided by node-differentiated charging prices

ZHANG Liang; LONG Yanliang; QI Jiajin; WANG Dianbin; HUANG Nantian; JIANG Liyang

doi:10.19783/j.cnki.pspc.256007

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A spatiotemporal optimization strategy for electric vehicle charging loads guided by node-differentiated charging prices

更新时间：2026-07-19

- A spatiotemporal optimization strategy for electric vehicle charging loads guided by node-differentiated charging prices
- Power System Protection and Control Vol. 54, Issue 14, (2026)
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- DOI：10.19783/j.cnki.pspc.256007
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- Published：2026
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ZHANG Liang, LONG Yanliang, QI Jiajin, et al. A spatiotemporal optimization strategy for electric vehicle charging loads guided by node-differentiated charging prices[J]. Power System Protection and Control, 2026, 54(14).
DOI：

ZHANG Liang, LONG Yanliang, QI Jiajin, et al. A spatiotemporal optimization strategy for electric vehicle charging loads guided by node-differentiated charging prices[J]. Power System Protection and Control, 2026, 54(14). DOI： 10.19783/j.cnki.pspc.256007.

摘要

针对大规模电动汽车接入配电网带来的负荷分配不均与电能质量下降问题，提出一种融合多因素用户响应特性与电网运行状态自适应调节的节点差异化充电价格机制及充电负荷空间优化策略。首先，该策略综合考虑充电站节点的安全性、经济性与可用性指标，构成差异化充电价格制定的核心依据。然后，依据配电网实时运行状态动态调整各指标权重，进而制定节点电价机制，引导电动汽车用户选择最优充电站点，实现负荷在空间上的合理再分配。最后，在IEEE33节点与69节点系统中进行仿真验证。结果表明，所提方法有助于提升充电站运行效率，缓解电网负荷压力，并为电动汽车与电网的协同发展提供技术支撑。

Abstract

To address the problems of uneven load distribution and deteriorated power quality caused by the large-scale electric vehicle integration into distribution networks

a node-differentiated charging pricing mechanism and a charging load spatial optimization strategy integrating multi-factor user response characteristics and adaptive adjustment of grid operation states are proposed. First

the proposed strategy comprehensively considers the safety

economy

and availability of charging station nodes

forming the core basis for differentiated charging price formulation. Then

it dynamically adjusts the weights of these indicators according to the real-time operation states of the distribution network

thereby formulating a node electricity pricing mechanism to guide electric vehicle users to select optimal charging stations and achieve a more balanced spatial load redistribution. Finally

simulation verification is conducted on the IEEE 33-bus and 69-bus systems. The results show that the proposed method effectively improves charging station operation efficiency

alleviates grid load pressure

and provides technical support for the coordinated development of electric vehicles and power grids.

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references

XU Biao, ZHANG Guiyuan, LI Ke, et al. Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles[J]. Protection and Control of Modern Power Systems, 2022, 7(4): 1-13.

HU Wei, WANG Shuo, DU Puliang. An optimized distribution model for energy system in virtual power plants integrating electric vehicles based on TD3 and DQN[J]. Protection and Control of Modern Power Systems, 2025, 10(6): 31-48.

ZHAO Zhonghao, LEE C K M. Dynamic pricing for EV charging stations: a deep reinforcement learning approach[J]. IEEE Transactions on Transportation Electrification, 2022, 8(2): 2456-2468.

LAI Shuying, QIU Jing, TAO Yuechuan, et al. Pricing for electric vehicle charging stations based on the responsiveness of demand[J]. IEEE Transactions on Smart Grid, 2023, 14(1): 530-544.

KUANG H, ZHANG X, QU H, et al. Unraveling the effect of electricity price on electric vehicle charging behavior: a case study in Shenzhen, China[J]. Sustainable Cities and Society, 2024, 115.

CUI Y, HU Z, DUAN X. Optimal pricing of public electric vehicle charging stations considering operations of coupled transportation and power systems[J]. IEEE Transactions on Smart Grid, 2021, 12(4): 3278-3288.

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