Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

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计及电池放电损耗的电动汽车充放电优化调度策略

来源:电工电气发布时间:2019-10-17 10:17 浏览次数:52
计及电池放电损耗的电动汽车充放电优化调度策略
 
戴越繁,杨伟
(南京理工大学 自动化学院,江苏 南京 210094)
 
    摘 要:针对现有电动汽车充放电策略对电池放电损耗考虑不够深入的问题,提出一种计及电池放电损耗的充放电优化调度策略。该策略以峰谷分时电价为背景,根据电池损耗模型计算电池放电损耗并计入用户用电成本,建立以电网负荷波动和用户用电成本为目标函数,以充放电功率、电池可用容量、不可用时段和出行SOC要求为约束条件的多目标优化调度模型,采用基于杂交的混合粒子群算法求解。利用Matlab进行算例仿真,针对不同电池损耗模型、不同调度车辆数以及不同分时电价下调度策略的优化效果进行了对比分析,验证了该策略的可行性和有效性。
    关键词:电动汽车;充放电调度;分时电价;电池损耗;多目标优化
    中图分类号:TM734;U469.72     文献标识码:A     文章编号:1007-3175(2019)10-0001-08
 
An Optimal Dispatching Strategy for Charging and Discharging of Electric Vehicles Accounting Battery Discharging Loss
 
DAI Yue-fan, YANG Wei
(College of Automation, Nanjing University of Science and Technology, Nanjing 210094, China)
 
    Abstract: Aiming at the problem that the existent charging and discharging strategies of electric vehicles seldom consider the battery discharging loss in depth, this paper proposed an optimal dispatching strategy for charging and discharging considering battery discharging loss. This strategy took the peak-valley time-of-use price as the background, calculated the battery discharging loss based on battery loss model and took the user's electricity cost into account. In the optimal dispatching model, the load fluctuation in power grid and user cost were set as the multiple objective function to be optimized. Charging and discharging power, available battery capacity, unavailable period and travel SOC requirements were set as the constraint of multi-objective optimization model. The model was solved by hybrid particle swarm optimization. With the Matlab example simulation, in allusion to the different battery loss models, different dispatching vehicles number and different time-of-use price on the dispatching strategy were compared and analyzed, which proves the feasibility and effectiveness of the proposed strategy.
    Key words: electric vehicles; charging and discharging dispatching; time-of-use price; battery loss; multi-objective optimization
 
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