Equilibrium charging strategy for electric vehicles in residential areas based on distributed gradient projection

doi:10.11930/j.issn.1004-9649.202512027

Abstract

Abstract:

Residential charging load management is a key means to mitigate the adverse impacts of large-scale electric vehicle (EV) integration on the power grid. However, centralized management compromises users' privacy, and the autonomous response of users under time-of-use electricity prices tends to induce new load peaks. Firstly, a dynamic electricity pricing mechanism is introduced in residential communities, and an aggregative game model is constructed to guide users to spontaneously adjust their charging behaviors and avoid peak-valley reversal. Then, a distributed gradient projection algorithm is proposed to rapidly achieve the Nash equilibrium of the aggregate game, which fully protects the privacy of vehicle owners. Finally, simulation analysis is carried out based on the electricity consumption data of a residential community in southern China. The results show that the proposed method can effectively smooth the peak load caused by EV charging, and the effect is enhanced with the increase in the number of EVs. Compared with the time-of-use pricing mechanism, it can effectively avoid the emergence of new load peaks and meet the requirements of charging load management under the growing penetration of electric vehicles.

Key words: electric vehicles, demand response, aggregative game, Nash equilibrium, dynamic price

CAO Wangzhang, LU Yonghao, JIN Fengyuan, YANG Hao, JIN Xin, WANG Zongyi, ZHAO Boyang. Equilibrium charging strategy for electric vehicles in residential areas based on distributed gradient projection[J]. Electric Power, 2026, 59(4): 140-149.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202512027

https://www.electricpower.com.cn/EN/Y2026/V59/I4/140

Figures/Tables 10

References 39

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算法和准则组合	电动汽车规模
算法和准则组合	100辆	200辆	500辆	1000辆	1500辆
最优响应+普通收敛准则	2	—	—	—	—
最优响应+优化收敛准则	2	5	16	—	—
本文算法+普通收敛准则	5	9	—	—	—
本文算法+优化收敛准则	4	6	5	8	11

算法和准则组合	电动汽车规模
算法和准则组合	100辆	200辆	500辆	1000辆	1500辆
最优响应+普通收敛准则	2	—	—	—	—
最优响应+优化收敛准则	2	5	16	—	—
本文算法+普通收敛准则	5	9	—	—	—
本文算法+优化收敛准则	4	6	5	8	11

算法和准则组合	电动汽车规模
算法和准则组合	100辆	200辆	500辆	1000辆	1500辆
最优响应+普通收敛准则	1.4	—	—	—	—
最优响应+优化收敛准则	1.4	6.5	50.9	—	—
本文算法+普通收敛准则	1.2	6.9	—	—	—
本文算法+优化收敛准则	1.2	2.3	2.3	9.6	10.7

算法和准则组合	电动汽车规模
算法和准则组合	100辆	200辆	500辆	1000辆	1500辆
最优响应+普通收敛准则	1.4	—	—	—	—
最优响应+优化收敛准则	1.4	6.5	50.9	—	—
本文算法+普通收敛准则	1.2	6.9	—	—	—
本文算法+优化收敛准则	1.2	2.3	2.3	9.6	10.7

指标	动态电价机制	分时电价机制
22:00—次日06:00时段最低负荷水平/kW	27192	21708
22:00—次日06:00时段最高负荷水平/kW	27393	35596
22:00—次日06:00时段负荷标准差/kW	67	4487
全日峰谷差/kW	22808	28292