电动汽车（EV）充电需求的快速增长正给配电网（DPN）带来日益加剧的压力，而DPN的承载能力通常有限且在空间上分布不均。本文不仅验证了协调控制的有效性，更回答了一个对规划者至关重要的开放性问题：在区域尺度上，EV负荷灵活性所能实现的最大效益——即最大限度减少因过载引发的配电网升级改造需求——究竟有多大？确立这一理论上限在计算上极具挑战性，因其需求解并验证具有数百万变量、且存在时空耦合结构的大规模群体优化问题的近优解。为此，本文提出MAC（Mobility-Aware Coordinated EV charging，面向移动性感知的协调式电动汽车充电）框架，用于量化在不干扰驾驶员出行需求前提下，利用EV负荷灵活性缓解DPN过载风险的最大潜力。（i）MAC通过在整个移动性时间窗内耦合充电决策来扩展可行调度空间：它不强制要求单次充电会话必须补足全部耗电量，而仅需确保电动汽车的荷电状态（SOC）足以支撑后续行程；（ii）MAC采用基于交替方向乘子法（ADMM）的分解策略，并配备定制化子问题求解器，从而实现计算可扩展性；该框架亦具备去中心化解释性，其中对偶变量可被诠释为具有时空定位特征的价格信号，使社会最优解得以作为竞争均衡实现。基于旧金山湾区高分辨率移动轨迹数据与馈线承载能力数据，在面向未来的30%电动汽车渗透率情景下，本文表明MAC相较于无序充电可显著降低由过载驱动的升级需求。本研究展示了轨迹耦合型灵活性与可扩展、可验证的优化方法如何共同提供具有实操价值的最优基准。

Rapid growth in electric-vehicle (EV) charging demand is placing increasing stress on distribution power networks (DPNs), whose hosting capacity is often limited and spatially uneven. Beyond demonstrating that coordination can help, this paper answers an open question that is central for planners: what is the maximal achievable benefit of EV demand flexibility in reducing overload-driven distribution upgrades at a regional scale? Establishing such an upper bound is computationally challenging, as it entails solving and certifying near-optimal solutions to population-scale optimization problems with millions of variables and both spatial and temporal coupling. We introduce MAC (Mobility-Aware Coordinated EV charging), a framework that quantifies the maximum potential of leveraging EV demand flexibility to mitigate DPN overloading risk without interrupting drivers' travel needs. (i) MAC expands feasible scheduling by coupling charging decisions over a full mobility horizon: instead of enforcing per-session energy recovery, it only requires the EV state-of-charge (SOC) to remain sufficient for upcoming trips. (ii) MAC is computationally scalable via an ADMM-based decomposition with custom subproblem solvers, and admits a decentralized interpretation in which dual variables act as locational-temporal price signals that implement the social optimum as a competitive equilibrium. Using high-resolution mobility trajectories and feeder hosting-capacity data in a future-oriented 30% EV adoption scenario for the San Francisco Bay Area, we show that MAC can dramatically reduce overload-driven upgrade requirements relative to unmanaged charging. This paper illustrates how trajectory-coupled flexibility and scalable, certifiable optimization can provide actionable best-case benchmarks for DPN planning and operations.