湖南电力 ›› 2026, Vol. 46 ›› Issue (4): 96-105.doi: 10.3969/j.issn.1008-0198.2026.04.013

• 配电网与用能技术 • 上一篇    下一篇

基于用户引导与多主体利益的电动汽车有序充电策略

宁志毫, 王小源, 朱吉然, 夏天, 张志丹   

  1. 国网湖南省电力有限公司电力科学研究院,湖南 长沙 410208
  • 收稿日期:2026-01-16 修回日期:2026-03-26 出版日期:2026-08-25 发布日期:2026-09-11
  • 作者简介:宁志毫(1983),男,河南洛阳人,博士,正高级工程师,研究方向为车网互动与有序充电、电能质量分析及治理、新能源并网评价等。
  • 基金资助:
    国网湖南省电力有限公司科技项目(重大专项)(5216A5240006)

Research on Orderly EV Charging Strategy Based on User Guidance and Multi-Stakeholder Interests

NING Zhihao, WANG Xiaoyuan, ZHU Jiran, XIA Tian, ZHANG Zhidan   

  1. State Grid Hunan Electric Power Company Limited Research Institute, Changsha 410208, China
  • Received:2026-01-16 Revised:2026-03-26 Online:2026-08-25 Published:2026-09-11

摘要: 为应对高比例电动汽车无序接入对配电网运行带来的峰谷失衡与安全风险,提出一种融合用户心理感知量化与多主体利益协同的电动汽车有序充电策略。首先,针对传统需求价格弹性模型难以刻画用户多维感知的局限,引入模糊逻辑理论与韦伯-费希纳定律,构建以充电价格和电流倍率为核心输入的用户引导难度量化模型,实现对用户响应意愿的精细化表征;其次,在电网、运营商与用户三方异质利益诉求的基础上,构建电网(上层)、运营商、用户(下层)双层斯塔克尔伯格(Stackelberg)博弈模型,以各方收益最大化为目标,并采用极大极小法求解,生成动态最优电价。算例分析结果表明,所提策略能有效平滑负荷曲线,降低38.9%的峰谷差,实现电网、运营商与用户收益的协同增长,提升电网运行安全性与经济性。

关键词: 有序充电, 用户引导, 模糊逻辑, 双层博弈, 动态电价

Abstract: To address the peak-valley imbalance and safety risks posed to distribution network operations by the disorderly integration of a high proportion of electric vehicles, an orderly charging strategy is proposed for electric vehicles that integrates the quantification of user psychological perceptions with the coordination of multi-stakeholder interests. Firstly, addressing the limitations of traditional demand price elasticity models in capturing users' multidimensional perceptions, fuzzy logic theory and the Weber-Fechner law are introduced to construct a quantitative model for user guidance difficulty,centred on charging price and current multiplier as core inputs, thereby achieving a refined representation of user response willingness. Secondly, based on the heterogeneous interests of the grid, operators, and users, a two-layer Stackelberg game model‘grid (upper layer)-operator-user (lower layer)’is constructed. Aiming to maximise benefits for all parties, the model employs the method of extremes to generate dynamically optimal electricity prices. Finally, case study analysis demonstrates that the proposed strategy effectively smooths load curves, reducing peak-to-off-peak differences by 38.9%. This achieves synergistic growth in grid, operator, and user revenues while enhancing grid operational security and economic efficiency.

Key words: orderly charging, user guidance, fuzzy logic, Stackelberg game, dynamic pricing

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