汽车技术2024,Issue(6) :17-23.DOI:10.19620/j.cnki.1000-3703.20230832

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

彭伟伦 马力 刘琦颖 于洋
汽车技术2024,Issue(6) :17-23.DOI:10.19620/j.cnki.1000-3703.20230832

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

彭伟伦 1马力 1刘琦颖 1于洋2
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作者信息

  • 1. 广东电网有限责任公司广州供电局,广州 510630
  • 2. 烟台海颐软件股份有限公司,烟台 264000
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摘要

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.

Abstract

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.

关键词

协同优化/电动汽车/V2G充放电负荷/时空分布预测

Key words

Collaborative optimization/Electric vehicle/V2G charging and discharging load/Time-space distribution prediction

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基金项目

中国南方电网有限责任公司科技项目(GZHKJXM20210055)

出版年

2024
汽车技术
中国汽车工程学会 长春汽车研究所

汽车技术

CSTPCD北大核心
影响因子:0.522
ISSN:1000-3703
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