电动汽车换电站可用电池组数动态调度策略  

Dynamic Scheduling Strategy for Available Battery Number of Electric Vehicle in Battery-Swap Station

作　　者：刘灵恺 雷霞[1] 李竹[1] 黄贵鸿 雷海[1] Liu Lingkai;Lei Xia;Li Zhu;Huang Guihong;Lei Hai(Key Laboratory of Fluid and Power Machinery Ministry of Education Xihua University Chengdu 610039 China)

机构地区：[1]流体及动力机械教育部重点实验室(西华大学),成都610039

出　　处：《电工技术学报》2017年第22期242-250,共9页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金(51677020);教育部春晖计划(Z2016145)资助项目

摘　　要：电动汽车换电站由于其换电过程耗时短、便于统一管理等优点,成为了电动汽车电能补充的重要方式。但由于电动汽车用户的换电需求具有随机性,目前的预测方法不能很准确地对其进行预测,因此对换电站精确地制定充放电调度计划有较大难度。针对这一问题,建立换电站日前调度与实时调度模型,并通过粒子群算法在Matlab中完成仿真计算。在日前调度模型中通过对用户换电需求的预测制定日前调度计划,在满足各时段需求的前提下优化换电站各时段充放电功率;在实时调度模型中根据各时段需求预测的误差,来动态调整后续时段的调度计划。通过实时调度与日前调度的协调,使换电站抑制了用户实际需求波动影响,同时合理兼顾用户利益、换电站收益与电网的优化运行。The battery-swap station （BSS） has become an important mode to supply electricity to electric vehicles for its rapidity of battery replacement and convenience of battery management. However, since the present prediction methods cannot precisely deal with the large-scale demand of battery-swapping due to the randomness, it is difficult to carry out the charge and discharge schedule. Based on the analysis on the demand forecast and battery number, the models of day-ahead and real-time scheduling for the BSS are set up, and the particle swarm optimization （PSO） is used to perform the simulation in Matlab. The day-ahead scheduling strategy is established on the forecast data of battery replacement demand to optimize the charge-discharge power of each time slot under the premise of meeting the demand. The real-time scheduling strategy of the following time slots is adjusted by dynamic dispatching according to the forecast error. By means of the coordination between two models above, BSS can suppress users’ actual demand fluctuation, while considering user benefits, BSS profit and optimal operation of power grid.

关 键 词：电动汽车 换电站 预测误差 动态调度 

分 类 号：TM734[电气工程—电力系统及自动化]