The rapid adoption of electric vehicles (EVs) introduces complex spatiotemporal demand management challenges for charging station operators (CSOs), exacerbated by demand imbalances, behavioral heterogeneity, and system uncertainty. Traditional dynamic pricing models, often relying on deterministic EV-CS pairings and network equilibrium assumptions, frequently oversimplify user behavior and lack scalability. This study proposes a stochastic, behaviorally heterogeneous dynamic pricing framework formulated as a bi-level Stackelberg game. The upper level optimizes time-varying pricing to maximize system-wide utility, while the lower level models decentralized EV users via a multinomial logit (MNL) choice model incorporating price sensitivity, battery aging, risk attitudes, and network travel costs. Crucially, the model avoids network equilibrium constraints to enhance scalability, with congestion effects represented via queuing-theoretic approximations. To efficiently solve the resulting large-scale optimization problem, a rolling-horizon approach combining the Dynamic Probabilistic Sensitivity Analysis-guided Cross-Entropy Method (PSA-CEM) with the Method of Successive Averages (MSA) is implemented. A real-world case study in Clayton, Melbourne, validates the framework using 22 charging stations. Simulation results demonstrate that the proposed mechanism substantially reduces queuing penalties and improves user utility compared to fixed and time-of-use pricing. The framework provides a robust, scalable tool for strategic EV charging management, balancing realism with computational efficiency.

翻译：电动汽车的快速普及给充电站运营商带来了复杂的时空需求管理挑战，需求失衡、行为异质性和系统不确定性加剧了这一挑战。传统的动态定价模型通常依赖于确定性的电动汽车-充电站配对和网络均衡假设，往往过度简化用户行为且缺乏可扩展性。本研究提出了一种随机、行为异质的动态定价框架，该框架被构建为一个双层Stackelberg博弈。上层优化时变定价以最大化系统整体效用，下层通过多项式Logit选择模型对分散的电动汽车用户进行建模，该模型综合考虑了价格敏感性、电池老化、风险态度和网络出行成本。关键在于，该模型避免了网络均衡约束以增强可扩展性，拥堵效应通过排队论近似表示。为高效求解由此产生的大规模优化问题，我们实施了一种滚动时域方法，该方法将动态概率敏感性分析引导的交叉熵法与连续平均法相结合。在墨尔本克莱顿地区进行的真实案例研究使用22个充电站验证了该框架。仿真结果表明，与固定电价和分时电价相比，所提出的机制显著减少了排队惩罚并提高了用户效用。该框架为战略性电动汽车充电管理提供了一个稳健、可扩展的工具，在现实性与计算效率之间取得了平衡。