计及电动汽车保有量增长需求的充电负荷预测OACSTPCD
Charging load forecasting considering growth demand for electric vehicle ownership
当前对电动汽车充电负荷的研究大多集中在短期演变,对长时间尺度下的发展情况并未有较多研究.文中提出一种电动汽车保有量增长需求的充电负荷预测模型.首先采用萤火虫算法优化电动汽车保有量灰色预测模型的相关参数,对某地区2023-2033年电动汽车保有量进行预测;其次,综合考虑保有量预测结果、用户出行链、行驶里程及充电起始时间,结合在不同温度下的电动汽车电池容量和充电效率搭建充电负荷预测模型;最后,对江苏省某地区2023-2033年电动汽车充电负荷进行仿真预测.仿真结果有效地预测了电动汽车在未来10年中保有量发展趋势以及考虑保有量增长需求的充电负荷.
Most of the current studies on EV charging load focus on short-term evolution,and there is not much research on the development under long time scale.A charging load forecasting model for the growth demand of electric vehicle ownership is proposed.The firefly algorithm is used to optimize the relevant parameters of the gray fore-casting model of electric vehicle ownership and forecast the electric vehicle ownership in a region from 2023 to 2033.The charging load prediction model is built by considering the prediction results of the ownership,users′ travel chain,driving mileage and charging start time,and combining the battery capacity and charging efficiency of EVs under different temperatures.The simulation prediction for the electric vehicle charging load from 2023 to 2033 in a region of Jiangsu Province is conducted.The simulation results can effectively predict the development trend of electric vehicle ownership in the next decade and the charging load considering the in-creasing demand of electric vehicle ownership.
于梦桐;高辉;杨凤坤
南京邮电大学 自动化学院、人工智能学院, 江苏 南京 210023国电南瑞科技股份有限公司, 江苏 南京 211106
电子信息工程
充电负荷预测电动汽车保有量萤火虫算法灰色预测模型用户出行链电池容量
charging load predictionelectric vehicle ownershipfirefly algorithmgrey prediction modeluser travel chainbattery capacity
《现代电子技术》 2024 (006)
55-62 / 8
国家自然科学基金资助项目(52077107)
评论