信息-物理-社会耦合视角下电动汽车集群调频控制综述

李俊; 侯慧; 吴细秀; 姚磊; 崔培强; 余谦

doi:10.19783/j.cnki.pspc.256009

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信息-物理-社会耦合视角下电动汽车集群调频控制综述

电动汽车充换电与驱动系统专题 | 更新时间：2026-07-19

- 信息-物理-社会耦合视角下电动汽车集群调频控制综述
- A review of electric vehicle cluster frequency regulation control from the perspective of cyber-physical-social coupling
- 电力系统保护与控制 2026年54卷第14期
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- DOI：10.19783/j.cnki.pspc.256009
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- 纸质出版：2026
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李俊, 侯慧, 吴细秀, 等. 信息-物理-社会耦合视角下电动汽车集群调频控制综述[J]. 电力系统保护与控制, 2026,54(14).

LI Jun, HOU Hui, WU Xixiu, et al. A review of electric vehicle cluster frequency regulation control from the perspective of cyber-physical-social coupling[J]. Power System Protection and Control, 2026, 54(14).
李俊, 侯慧, 吴细秀, 等. 信息-物理-社会耦合视角下电动汽车集群调频控制综述[J]. 电力系统保护与控制, 2026,54(14). DOI： 10.19783/j.cnki.pspc.256009.

LI Jun, HOU Hui, WU Xixiu, et al. A review of electric vehicle cluster frequency regulation control from the perspective of cyber-physical-social coupling[J]. Power System Protection and Control, 2026, 54(14). DOI： 10.19783/j.cnki.pspc.256009.

摘要

高比例新能源并网加剧新型电力系统调频压力，规模化电动汽车作为移动储能资源，通过车网互动提供时空灵活调节能力，然而，其规模化调频能力因信息物理社会多网络深度耦合而受到多重约束与不确定性的影响。基于“控制架构到聚合建模，再到指令分配”三维一体的分析框架，可逐层深入地解析电动汽车调频控制研究中的关键问题、研究进展与内在逻辑联系。首先，系统梳理了分散式、集中式与分层式控制架构的特点与适用场景，明确了分层式控制作为主流技术路径的优势。其次，围绕精确量化电动汽车集群动态可调潜力这一目标，提炼概述了行为推演驱动型、约束聚合驱动型及推演约束耦合型的集群聚合建模方法。然后，将调频指令分配方法归纳为预设规则驱动、优化模型驱动与人工智能赋能3类技术路径，并对比其核心思想、优势与局限。最后，从跨域协同理论、智能融合技术及公平可信互动机制3个方向对未来研究进行展望。

Abstract

The high penetration of renewable energy integration has significantly increased the frequency regulation requirements of modern power systems. As mobile energy storage resources

large-scale electric vehicle (EV) fleets can provide spatiotemporally flexible regulation capability through vehicle-to-grid interactions. However

their large-scale frequency regulation potential is constrained by multiple uncertainties arising from the deep coupling of cyber-physical-social networks. Based on a three-dimensional analytical framework encompassing control architecture

aggregation modeling

and regulation command allocation

key issues

research progress

and intrinsic logical connections in EV frequency regulation control are analyzed layer by layer. First

the characteristics and applicable scenarios of decentralized

centralized

and hierarchical control architectures are systematically reviewed

highlighting the advantages of hierarchical control as the prevailing technical pathway. Second

with the objective of accurately quantifying the dynamic regulation potential of EV clusters

existing aggregation modeling methods are classified into three categories: behavior-driven

constraint-driven

and behavior-constraint coupled methods. Subsequently

frequency regulation command allocation methods are categorized into three technical pathways: preset rule-driven

optimization model-driven

and artificial intelligence-enabled approaches

and their fundamental principles

strengths

and limitations are comparatively analyzed. Finally

future research directions are discussed from three perspectives: cross-domain collaboration theories

intelligent fusion technologies

and fair-trust interaction mechanisms.

关键词

Keywords

references

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