优化电动与柴油公交混队充电和调度，降低运营成本。
Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit
- 构建混合整数线性规划模型，统一优化充电与行车任务。
- 实测可降低混合车队运营成本，显著响应电价波动。
- 适合城市公交公司、交通规划者参考应用。
城市人口快速增长及可持续交通需求推动公共交通系统向电动公交车转型。然而，电动与柴油公交混合作业面临重大运营挑战，尤其在动态电价下充电成本随时间变化。公交机构需在考虑座位容量等约束的前提下，优化充电安排以应对电价波动。本文提出一种综合混合整数线性规划（MILP）模型，联合优化混队（电动与柴油）公交的充电计划与行程分配，兼顾动态电价、车辆载客量及路线约束。针对该模型可能因小规模车队仍出现计算不可行的问题，采用基于车队构成的分层求解策略。基于美国田纳西州查塔努加市真实数据验证，本方法能显著降低混合公交车队的运营成本。
原文摘要 · Abstract (English)
The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. However, the effective management of mixed fleets consisting of both electric and diesel buses poses significant operational challenges. One major challenge is coping with dynamic electricity pricing, where charging costs vary throughout the day. Transit agencies must optimize charging assignments in response to such dynamism while accounting for secondary considerations such as seating constraints. This paper presents a comprehensive mixed-integer linear programming (MILP) model to address these challenges by jointly optimizing charging schedules and trip assignments for mixed (electric and diesel bus) fleets while considering factors such as dynamic electricity pricing, vehicle capacity, and route constraints. We address the potential computational intractability of the MILP formulation, which can arise even with relatively small fleets, by employing a hierarchical approach tailored to the fleet composition. By using real-world data from the city of Chattanooga, Tennessee, USA, we show that our approach can result in significant savings in the operating costs of the mixed transit fleets.
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