基于滚动时域方法的随机性下电池电动公交车充电调度
Scheduling Battery-Electric Bus Charging under Stochasticity using a Receding-Horizon Approach
AI总结：
本文针对电池电动公交车充电调度问题，提出一种基于混合整数线性规划的分层滚动时域规划方法，结合分时电价与需量成本及非线性部分充电模型，在随机环境下实现最高52%的成本节省并提升鲁棒性。
摘要：
在车队中采用电池电动公交车面临的一个重大挑战在于充电调度，而这一问题又因路线所施加的时间约束、较长的充电时间、充电桩数量有限以及公用事业成本结构等因素而变得更加复杂。本工作建立在以往基于网络流的充电调度方法之上，同时纳入需量电费和分时电价费用，并考虑同一电表上的不可控负载。此外，还开发了一种与混合整数线性规划（MILP）兼容的可变速率非线性部分充电模型，以提高充电建模保真度。为了在不确定环境中响应反馈，所得到的MILP被改造成一种分层滚动时域规划器，该规划器以一天的静态计划作为参考进行跟踪，同时定期对随机性做出反应。该滚动时域规划器与另外两种可能的规划方法一起通过蒙特卡洛技术进行了分析。结果发现，与不考虑分时电价的方法相比，它最多可节省52%的成本，并且与最优开环方法相比具有显著的鲁棒性优势。
英文摘要：
A significant challenge of adopting battery electric buses into fleets lies in scheduling the charging, which in turn is complicated by considerations such as timing constraints imposed by routes, long charging times, limited numbers of chargers, and utility cost structures. This work builds on previous network-flow-based charge scheduling approaches and includes both consumption and demand time-of-use costs while accounting for uncontrolled loads on the same meter. Additionally, a variable-rate, non-linear partial charging model compatible with the mixed-integer linear program (MILP) is developed for increased charging fidelity. To respond to feedback in an uncertain environment, the resulting MILP is adapted to a hierarchical receding horizon planner that utilizes a static plan for the day as a reference to follow while reacting to stochasticity on a regular basis. This receding horizon planner is analyzed with Monte-Carlo techniques alongside two other possible planning methods. It is found to provide up to 52\% cost savings compared to a non-time-of-use aware method and significant robustness benefits compared to an optimal open-loop method.
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