AI 中文总结
本文提出含DLinear、SVGD及两阶段随机MPC的框架，解决预测不确定性下换电站运营问题，120天评估显示其成本更低、服务缺口更少。
AI 中文摘要
换电站（BSS）可通过集中管理的电池库存作为灵活的并网储能，缩短电动汽车补能时间。要实现这两个优势，换电站需在未来客户需求和电价未知的情况下，安排充电、电网放电及换电服务。本文针对该问题开发了一种感知预测的滚动时域运营框架：轻量型DLinear模型预测24小时电价和需求轨迹，斯坦变分梯度下降（SVGD）通过代表性场景量化其不确定性，两阶段随机模型预测控制器将这些场景转换为换电站决策。该控制器考虑服务缺口、终端就绪状态、受保护的服务缓冲及电化学衰减，且不假设未来信息完全可知。应用层面的贡献是开发了可实施的控制器，协调换电站的移动服务与储能角色；方法层面的贡献是构建了模块化的预测-控制接口，将均值预测准确性的运营价值与不确定性表征的价值分离。在120天的闭环评估中，DLinear-SVGD SMPC在可实施的控制器中实现了最低成本，相较于确定性DLinear MPC，其最终成本降低1.2%，服务缺口时长减少80.7%，评估时段中99.10%无服务缺口。
英文摘要
Battery-swapping stations (BSSs) can shorten electric-vehicle energy replenishment while using centrally managed battery inventories as flexible grid-connected storage. Realizing both benefits requires the station to schedule charging, grid discharge, and swapping service before future customer demand and electricity prices are known. This paper develops a forecast-aware rolling-horizon operating framework for this problem. A lightweight DLinear model predicts 24-hour price and demand trajectories, Stein variational gradient descent quantifies their uncertainty through representative scenarios, and a two-stage stochastic model predictive controller converts those scenarios into station decisions. The controller accounts for service shortfall, terminal readiness, a protected service buffer, and electrochemical degradation without assuming perfect future information. The application contribution is an implementable controller that coordinates the station's mobility-service and energy-storage roles. The methodological contribution is a modular forecast-to-control interface that separates the operational value of mean-forecast accuracy from that of uncertainty representation. In a 120-day closed-loop evaluation, DLinear-SVGD SMPC achieves the lowest cost among the implementable controllers. Relative to deterministic DLinear MPC, it reduces final cost by 1.2\% and service-shortfall hours by 80.7\%, with 99.10\% of the evaluated hours free of shortfall.
Comments8 pages, 7 figures
