一种用于集成请求感知充电调度和服务分配的分层随机模型预测控制框架
A Hierarchical Stochastic Model Predictive Control Framework for Integrated Request-aware Charge Scheduling and Service Allocation
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中文总结 AI 辅助
研究针对按需出行电动汽车在A到B租赁模式下的充电与服务分配问题，提出分层随机模型预测控制框架，通过分段线性模型、混合逻辑切换机制及分布式方法，有效应对计算挑战，降低充电成本与电池容量退化。
中文摘要 AI 辅助
本研究为按需出行电动汽车（MoD-EV）在灵活的A到B租赁模式下的集成充电和服务分配引入了一种统一控制机制。MoD-EV的充电操作受随机客户租赁请求和时变电价影响。该框架捕捉非线性电池动态，针对动态电价优化充电操作，以满足随机客户需求，同时优先考虑电池健康和成本效率。为应对非线性电池动态带来的计算挑战，框架采用分段线性模型集成到多目标、机会约束混合整数线性规划（MILP）模型预测控制（MPC）公式中。利用混合逻辑切换机制确定最优充电序列。此外，与集中式方法相比，实施分布式方法以确保计算可扩展性。使用具有不同置信水平的随机电动汽车租赁请求和时变电价的先进商业求解器对该方法进行评估，结果表明集成机制设计具有显著优势，与现行的照常营业（BAU）方法和基于松弛的充电（LC）方法相比，可降低充电成本和电池容量退化。
英文摘要
This study introduces a unified control mechanism for integrated charging and service allocation of Mobility-on-Demand Electric Vehicles (MoD-EVs) operating under a flexible A-to-B rental model. The charging operation of MoD-EVs is influenced by stochastic customer rental requests and time-varying electricity prices. The framework captures the nonlinear battery dynamics and optimizes the charging operations against dynamic electricity prices and to meet stochastic customer demand while prioritizing battery health and cost efficiency. To address the computational challenges posed by the nonlinear battery dynamics, the framework employs a piece-wise linear model integrated into a multi-objective, chance-constrained mixed-integer linear programming (MILP) Model Predictive Control (MPC) formulation. A mixed logical switching mechanism is utilized to determine optimal charging sequences. Furthermore, a distributed approach is implemented to ensure computational scalability compared to centralized alternatives. Evaluation of this approach using a state-of-the-art commercial solver with stochastic EV rental requests under different confidence levels and time-varying electricity prices demonstrates significant benefits of the integrated mechanism design, including a reduction in charging costs and battery capacity degradation compared to the prevailing business-as-usual (BAU) approach and state of the art Laxity-based charging (LC) approach.
