计及天气因素影响的V2G有序充放电控制策略研究

Research on Control Strategy of V2G Orderly Charging and Discharge Considering Influence of Weather Factors

为了解决V2G充放电系统参与电网互动,但缺乏有序管理的问题,提出一种计及天气因素影响的有序充放电控制策略。所提策略以天气数据的欧式距离为依据,改进了BP神经网络的训练样本选择方法,训练后的网络模型,能可靠预测充放电机的有功或无功功率调节值。MATLAB的仿真结果表明,控制策略在充电或放电模式下,都能及时进行有功或无功支撑。最后,基于该控制策略的60 kW充放电机,在虚拟电厂中的示范应用数据证明,充放电机能够结合当前电网状态,达到功率调度的预期目标,可有效避免电动汽车充放电能量的无序流动,增强电网应对指标异常的能力。

In order to solve the problem that V2 G charging and discharging system participates in the interaction of power grid,but lacks orderly management,an orderly charging and discharge control strategy considering the weather factors is proposed.The strategy improves the training sample selection method of BP neural network based on the Euclidean distance of weather data.Then,the trained network model can reliably predict the active or reactive power regulation value of the charging and discharging motor.The simulation results in MATLAB show that the strategy can support the active or reactive power in time under the mode of charging or discharging.Finally,one V2 G prototype with power of 60 kW is demonstrated and applied in virtual power plant.The measured data shows that the prototype can achieve the expected goal of power dispatching by combinating the current state of power grid.The proposed strategy can effectively avoid the disorderly flow of charging and discharging energy,and enhance the ability of the power grid to deal with abnormal indicators.