AI 中文总结
本文提出结合光伏自发自用的混合整数线性规划框架，对电动汽车与电动公交车充电基础设施进行鲁棒联合规划，通过50节点案例验证了相关策略的有效性。
AI 中文摘要
本文提出了一种混合整数线性规划（MILP）框架，用于结合光伏（PV）自发自用的电动汽车（EV）与电动公交车（eBus）充电基础设施的联合规划。该模型协同优化充电桩的选址、规模、技术选型、eBus到场站的分配以及小时级充电调度。首先将确定性MILP公式化为名义基准，随后扩展为考虑车辆能源需求不确定性的基于场景的鲁棒极小极大公式。该鲁棒模型采用共享的第一阶段基础设施决策和特定场景的运营决策，并通过上境图重构最小化基础设施成本与最坏场景运营成本之和。软可行性惩罚量化了压力场景下未满足的充电能量、终端荷电状态（SOC）不足以及容量违规情况。一项50节点案例研究表明，路段级eBus需求和PV权重会改变充电桩部署、技术组合和电网购电量，而车辆到电网（V2G）技术在惩罚权重足够大时可实现硬可行的鲁棒运行。这些结果强调，有效的长期基础设施规划必须同时考虑不同的车辆车队、适配不同场景的运营策略以及节点级充电容量约束。
英文摘要
This paper presents a mixed-integer linear programming (MILP) framework for joint electric vehicle (EV) and electric bus (eBus) charging-infrastructure planning with photovoltaic (PV) self-consumption. The model co-optimizes charger siting, sizing, technology selection, eBus-to-depot assignment, and hourly charging schedules. A deterministic MILP is first formulated as a nominal benchmark and then extended to a scenario-based robust min--max formulation under vehicle energy-demand uncertainty. The robust model uses shared first-stage infrastructure decisions and scenario-specific operating decisions, and minimizes infrastructure cost plus the worst-case scenario operating cost through an epigraph reformulation. Soft-feasibility penalties quantify unmet charging energy, terminal state-of-charge (SOC) shortfall, and capacity violations under stressed scenarios. A 50-node case study shows that route-segment eBus demand and PV weighting alter charger deployment, technology mix, and grid import, while V2G enables hard-feasible robust operation at sufficiently large penalty weights. These results highlight that effective long-term infrastructure planning must simultaneously account for diverse vehicle fleets, operational strategies that adapt to different scenarios, and node-level constraints on charging capacity.
CommentsThis manuscript has been submitted for peer review
