基于虚拟电价的电动汽车充放电优化策略

为了实现负荷侧的"削峰填谷",在用户侧降低充电费用和电池损耗成本,提出了一种基于虚拟电价理论的电动汽车充放电优化策略。利用虚拟电价建立负荷侧的电价模型;利用动态分时电价建立用户侧的充电电价模型,将两个电价模型进行匹配整合,形成完整的电动汽车充放电的优化调度模型。为优化上述模型,确保整个调度的可实施性,在模型中使用BP神经网络和遗传算法进行预测与优化,同时使用小波分析和模糊聚类方法对充放电负荷进行去噪,并划分不同的电价时段,最后使用MATLAB软件对该模型进行了仿真验证。

Electric Vehicles Charging and Discharging Based on Virtual Time-of-Use Tariffs

In order to achieve the "peak shaving" on the load side and to reduce the charging and the battery consumption cost on the users'' side,virtual time-of-use tariffs are used to establish the electricity price model on the load side,and the dynamic time-of-use tariffs are used to establish the model of charging price on the users'' side.Then the two tariff models are matched and integrated to form a complete optimal scheduling model.In order to optimize this model and ensure the enforceability of the whole scheduling,using the BP neural network and the genetic algorithm to predict and optimize.The wavelet analysis and the fuzzy cluster are used to de-noise the charging load and divide the time frame of tariffs.Finally,Matlab software is used to analyze the simulation model.The results illustrate the basic characteristics of the proposed model.