Spatio-temporal Guidance Strategy for Electric Vehicle Loads with Energy Recovery under Dynamic Pricing

doi:10.11930/j.issn.1004-9649.202501053

Abstract

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.

Key words: electric vehicles, spatio-temporal distribution, dynamic electricity pricing, energy recovery, stackelberg game model

YANG Xinqiao, JIA Heping, LI Peijun, LI Shun, LONG Yi, LIU Dunnan, HUANG Hui. Spatio-temporal Guidance Strategy for Electric Vehicle Loads with Energy Recovery under Dynamic Pricing[J]. Electric Power, 2025, 58(11): 122-134.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202501053

https://www.electricpower.com.cn/EN/Y2025/V58/I11/122

Figures/Tables 17

Table 1 Percentage of travel chain types

出行链类型		占比
居住区-工作区-居住区		0.47
居住区-其他区-居住区		0.15
居住区-工作区-其他区-居住区		0.12
居住区-其他区-工作区-居住区		0.13
其他区-居住区/工作区-其他区		0.03
工作区-居住区/其他区-工作区		0.03

Fig.1 Monte Carlo simulation flow

Fig.2 Analysis of user demand for charging under tariff guide

Fig.3 Stackelberg game modeling framework

Fig.4 Stackelberg game model solution process

Table 2 PSO parameters

参数		取值
种群数量/个		10
迭代次数		200
学习因子		2.0
例子速度限制		[–2, 2]
惯性权重		0.8

Fig.5 City network structure and functional area delineation

Fig.6 Computational flow of spatio-temporal guidance modeling of electric vehicle loads

Fig.7 24-hour curves of total electricity loads under three scenarios

Table 3 Peak-valley differences and total charging cost under three scenarios

场景	配电网负荷峰谷差/kW	用户充电总成本/元
1	19638.25	65907.52
2	9272.95	50029.13
3	7714.96	45315.26

Fig.8 Dynamic pricing optimization results for different functional areas

Fig.9 Spatial-temporal distribution of charging loads under scenario 3

Table 4 Change in charging load with and without the energy recovery at a typical node

	峰谷差/kW	平均负荷值/kW
未考虑能量回收	110.37	48.88
考虑能量回收	98.84	41.68

Fig.10 Spatial-temporal shift of charging loads in scenario 1 after dynamic pricing guidance

Fig.11 Spatial-temporal shift of charging loads in scenario 2 after dynamic pricing guidance

Fig.12 Comparison with and without the charging utility function

Fig.13 Comparison with and without the brake energy recovery

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