AI 中文总结
该研究针对电动公交停车场充电的成本、电网约束与鲁棒性问题，提出凸鲁棒优化模型，经瑞士4个停车场验证可大幅降本、减电网容量，为实际应用提供指导。
AI 中文摘要
电动公交车队的停车场充电必须最小化用电成本、遵守电网容量限制，且在出行能量需求不确定时仍可行。尽管成本最优充电已得到充分研究，但不同电价、电网连接容量下的成本最优值，以及鲁棒性的经济成本仍缺乏量化。我们提出一种凸鲁棒优化模型，通过最坏情况的荷电状态约束来界定需求不确定性。该模型针对4个瑞士停车场（含7至35辆公交）的实际运营时刻表，与到站即充策略进行对比评估。研究的停车场中，小型停车场从优化中获得的相对收益最大，总用电成本降低超50%，因为优化可缓解对成本影响显著的充电峰值。对于所有规模的停车场，优化带来的节约随电价波动增大而提升。优化还可降低可行运营所需的电网容量超40%，不过严格的电网限制会削弱削峰潜力。对10%的能量需求偏差提供保护，仅使总用电成本增加不足0.1%。生成的充电功率曲线呈现可解释的价格阈值和削峰行为，为实际应用提供了实用指导。
英文摘要
Depot charging of electric bus fleets must minimize electricity costs, respect grid limits, and remain feasible despite uncertain trip energy demand. While cost-optimal charging is well studied, its value under different electricity prices and grid connection capacities, as well as the economic cost of robustness, remain poorly quantified. We address these gaps with a convex robust formulation in which bounded demand uncertainty is enforced through worst-case state-of-energy constraints. The formulation is evaluated against charge-on-arrival using realistic service schedules for four Swiss depots containing 7-35 buses. In the depots studied, smaller depots achieve the greatest relative benefit from optimization, with total electricity cost reductions exceeding 50%, because optimization mitigates charging peaks that strongly affect their costs. For all depot sizes, the savings from optimization increase with electricity price volatility. Optimization can also lower the grid capacity required for feasible operation by over 40%, although tight limits reduce peak shaving potential. Protection against energy-demand deviations of 10% increases total electricity cost by less than 0.1%. The resulting charging power profiles exhibit interpretable price-threshold and peak-shaping behavior, providing practical guidance for real-world implementations.
CommentsPreprint submitted to the 26th International Conference on Control, Automation, and Systems (ICCAS 2026)
