文章摘要
计及电池损耗的居民区电动汽车协调充放电优化策略
Coordinated charging and discharging optimization strategy for Electric Vehicles in residential areas considering battery degradation
投稿时间:2026-03-30  修订日期:2026-04-30
DOI:
中文关键词: 电动汽车  居民区  模型预测控制  电池退化  协调充放电
英文关键词: electric vehicles  residential area  model predictive control  battery degradation  coordinated charging and discharging
基金项目:上海市交通委科研项目
作者单位地址
毛玲* 上海电力大学 上海市杨浦区长阳路2588号
张泽成 上海电力大学 
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中文摘要:
      针对电动汽车参与电网互动过程中普遍存在的短期收益与电池寿命难以兼顾的问题,结合电居民区动汽车调度场景与电池老化特性,建立了计及电池退化的电动汽车协调充放电优化模型。首先,构建居民区内微电网系统模型,描述公共电网、可再生能源、储能系统及电动汽车停车场之间的能量交互关系;其次,将购售电成本、储能退化成本和电动汽车电池退化成本共同纳入优化目标;然后,采用基于模型预测控制的双层滚动优化方法,上层进行长时域能量规划,下层结合短时信息对充放电功率进行修正,并通过线性化处理提高求解效率。仿真结果表明,所提方法能够在满足车辆离站目标荷电状态要求的同时,有效降低系统综合运行成本,并在一定预测误差下保持较好的鲁棒性。与单层优化及基线方法相比,该方法在经济性、电池退化抑制和计算效率之间具有更好的综合性能。
英文摘要:
      In response to the common problem in EV–grid interactions of balancing short-term gains with battery life, combined with the residential-area EV dispatch scenario and battery aging characteristics, we establish a coordinating EV charging/discharging optimization model that accounts for battery degradation. Specifically, we proceed as follows: First, we construct a microgrid system model within a residential area to describe the energy interactions among the public grid, renewable energy sources, energy storage systems, and EV parking facilities; Second, we integrate purchase and sale costs, storage degradation costs, and EV battery degradation costs into the optimization objective; Third, we adopt a model-predictive-control–based two-layer rolling optimization approach, with the upper layer performing long-horizon energy planning and the lower layer adjusting charging/discharging powers using short-term information, with linearization applied to improve solution efficiency. Simulation results show that the proposed method can meet the target state of charge for vehicles at departure while effectively reducing the overall system operating cost and maintaining good robustness under certain forecast errors. Compared with single-layer optimization and baseline methods, this approach offers better overall performance in economics, battery degradation suppression, and computational efficiency.
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