arXiv:2605.21665cs.MAcs.AI2026-05综述

提出三层次框架,揭示充电系统设计中的真实权衡难题。

Planning, Scheduling, and Behavior in EV Charging Systems: A Critical Survey and Trilemma Framework

论文配图:Planning, Scheduling, and Behavior in EV Charging Systems: A Critical Survey and Trilemma Framework
图 1 · 摘自论文原文
  • 构建规划-调度-行为三层框架,统一电动车充电研究视角。
  • 发现三者融合时必然牺牲某层保真度,形成计算与真实性的权衡。
  • 适合政策制定者、城市规划者及智能电网研究人员参考。

电动汽车的快速发展正将交通电气化的瓶颈从车辆普及转向充电基础设施的部署与运营。充电网络设计需在三个相互关联的层面做决策:规划层决定建设地点与规模;调度层管理充电分配、定价及电网互动;行为层反映用户选择站点、充电时段与时长的模式。现有研究虽在各层面取得进展,但文献分散,跨层交互常依赖简化假设。本文提出规划-调度-行为(PSB)框架,按决策周期、主体目标与耦合结构组织研究。进一步识别出‘保真度-可计算性’权衡,即为每层均具计算难度,跨层真实整合通常需降低至少一层的细节。回顾三对组合研究(规划-调度、调度-行为、规划-行为),发现被忽略的第三层通常外生设定或以静态聚合模型替代。此类简化虽提升可计算性,却可能掩盖长期投资反馈、时间维度电网与排放动态,以及用户异质响应和公平性后果。基于此诊断,本文指出新兴充电技术、行为激励机制、公平性度量与城市尺度学习方法等关键开放问题。

原文摘要 · Abstract (English)

The rapid growth of electric vehicles is shifting the main constraint on transport electrification from vehicle adoption to the deployment and operation of charging infrastructure. Charging-network design requires decisions across three interdependent layers: Planning, which determines where and how much infrastructure to build; Scheduling, which governs charging dispatch, pricing, and grid interaction; and Behavior, which captures how users choose stations, charging times, and charging durations. Existing studies have advanced each layer substantially, but the literature remains fragmented, and cross-layer interactions are often treated through simplifying assumptions. This survey develops a three-layer Planning-Scheduling-Behavior (PSB) framework to organize EV charging research according to decision horizon, actor objective, and coupling structure. We further identify a fidelity-tractability tradeoff, termed the PSB trilemma: each layer is computationally difficult in isolation, and realistic integration across layers generally requires reducing the fidelity of at least one layer. Reviewing the three pairwise-coupling literatures - Planning-Scheduling, Scheduling-Behavior, and Planning-Behavior - we show that the omitted third layer is typically fixed exogenously or represented by a static aggregate surrogate. These simplifications enable tractability but impose distinct costs: they can obscure long-term investment feedback, temporal grid and emissions dynamics, or heterogeneous user response and equity outcomes. Building on this diagnosis, we identify open challenges in emerging charging technologies, behavioral incentives, equity metrics, and city-scale learning-based methods that balance fidelity, interpretability, and policy relevance.

电动车充电三层次框架政策分析系统权衡

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