Abstract：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.
刘灵恺, 雷霞, 李竹, 黄贵鸿, 雷海. 电动汽车换电站可用电池组数动态调度策略[J]. 电工技术学报, 2017, 32(22): 242-250.
Liu Lingkai, Lei Xia, Li Zhu, Huang Guihong, Lei Hai. Dynamic Scheduling Strategy for Available Battery Number of Electric Vehicle in Battery-Swap Station. Transactions of China Electrotechnical Society, 2017, 32(22): 242-250.
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2017, Vol. 32 
