基于供需两侧协同优化的电动汽车V2G充放电负荷时空分布预测研究

Research on Prediction of Time and Space Distribution of V2G Charge and Discharge Load of Electric Vehicle Based on Collaborative Optimization of Supply and Demand Side

为准确预测电动汽车的V2G充放电负荷,以调节电网负荷峰谷差,保证供电稳定性,提出了一种基于供需两侧协同优化的电动汽车V2G充放电负荷时空分布预测方法。构建供需两侧协同优化目标模型,利用鲸鱼优化算法迭代求解,得出最优充放电负荷曲线,据此明确最优充放电时段。采集不同空间区域最优充放电时段内的充放电负荷影响指标,并以此为输入,构建基于多元线性回归的预测模型,实现电动汽车V2G充放电负荷时空分布预测。试验结果表明,采用所提出的方法得到的负荷预测模型具有较大的决定系数,表明该方法的预测结果更接近实际负荷,具有较高的预测准确性。

In order to accurately predict the V2G charging and discharging load of electric vehicles,so as to regulate the peak to valley difference of power grid load and ensure power supply stability,this paper proposed a spatiotemporal distribution prediction method for V2G charging and discharging loads of electric vehicles based on collaborative optimization of supply and demand sides.A collaborative optimization objective model for both supply and demand sides was built,the Whale Optimization Algorithm was used for iterative solution to obtain the optimal charging and discharging load curve,and the optimal charging and discharging period was determined.The influencing indicators of charging and discharging loads within the optimal time periods in different spatial regions were collected,serving as inputs for constructing a prediction model based on multiple linear regression,thus achieving the prediction of spatial-temporal distribution of electric vehicle V2G charging and discharging loads.The experimental results show that the load prediction model obtained with the proposed method has a relatively large coefficient of determination,indicating that the prediction results of this research method are closer to the actual load,and have high prediction accuracy.