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Spatio-temporal Guidance Strategy for Electric Vehicle Loads with Energy Recovery under Dynamic Pricing
- Vol. 58, Issue 11, Pages: 122-134(2025)
- 作者机构：
1. 华北电力大学 经济与管理学院,北京,102206
2. 国网智慧车联网技术有限公司,北京,100052
3. 国网重庆市电力公司营销服务中心,重庆,400014
- 作者简介：
- 基金信息：
DOI：10.11930/j.issn.1004-9649.202501053
CLC：-
Published Online：01 December 2025，
Published：01 December 2025
- 稿件说明：
移动端阅览
杨新桥, 加鹤萍, 李培军, 李顺, 龙羿, 刘敦楠, 黄辉. 动态电价下计及能量回收的电动汽车负荷时空引导策略[J]. 中国电力, 2025, 58(11): 122-134.
YANG Xinqiao, JIA Heping, LI Peijun, et al. Spatio-temporal Guidance Strategy for Electric Vehicle Loads with Energy Recovery under Dynamic Pricing[J]. 2025, 58(11): 122-134.
杨新桥, 加鹤萍, 李培军, 李顺, 龙羿, 刘敦楠, 黄辉. 动态电价下计及能量回收的电动汽车负荷时空引导策略[J]. 中国电力, 2025, 58(11): 122-134. DOI： 10.11930/j.issn.1004-9649.202501053.
YANG Xinqiao, JIA Heping, LI Peijun, et al. Spatio-temporal Guidance Strategy for Electric Vehicle Loads with Energy Recovery under Dynamic Pricing[J]. 2025, 58(11): 122-134. DOI： 10.11930/j.issn.1004-9649.202501053.
摘要
随着电动汽车的规模化发展，高不确定性的电动汽车无序接入电网会加剧电网峰谷差，有必要通过动态电价的引导，提高电动汽车灵活调节能力。此外，广泛应用于电动汽车的能量回收技术有助于减少能量损耗，降低碳排放。为此，提出动态电价下考虑能量回收的电动汽车负荷时空引导策略。首先，基于城市交通网络拓扑图，建立考虑城市功能区的电动汽车出行链模型。然后，考虑电动汽车制动能量回收，基于蒙特卡洛模拟方法构建电动汽车负荷时空分布模型，并引入考虑用户连续充电行为的充电效用函数，建立考虑配电网负荷波动最小和电动汽车用户充电成本最小的主从博弈模型，实现动态电价机制下的计及能量回收的电动汽车负荷时空引导。最后，通过算例验证该方法对电动汽车负荷时空分布引导的有效性。
Abstract
With the large-scale development of electric vehicles
the disorderly access of high-uncertainty electric vehicles to the grid will exacerbate the peak-valley difference of the grid
and it is necessary to improve the flexible adjustment ability of electric vehicles through the guidance of dynamic pricing. In addition
the energy recovery technology widely used in electric vehicles helps to reduce energy loss and carbon emission. This paper proposes a spatio-temporal guidance strategy for electric vehicle loads considering energy recovery under dynamic pricing. Firstly
based on the topological map of urban transportation network
an electric vehicle travel chain model considering urban functional areas is established. Secondly
based on Monte Carlo simulation method
a spatio-temporal distribution model of electric vehicle load considering electric vehicle braking energy recovery is constructed; and by introducing a charging utility function that considers users' sequential charging behavior
a Stackelberg game model is established to minimize distribution grid load fluctuations and electric vehicle user charging costs
which achieves the spatio-temporal guidance for electric vehicle loads considering energy recovery under a dynamic pricing mechanism. Finally
a case study is used to verify the effectiveness of the proposed method in guiding the spatio-temporal distribution of electric vehicle loads.
关键词
Keywords
references
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