电力建设2024，Vol.45Issue(6)：10-26,17.DOI:10.12204/j.issn.1000-7229.2024.06.002
考虑出行需求和引导策略的电动汽车充电负荷预测
Forecasting of Electric-Vehicle Charging Load Considering Travel Demand and Guidance Strategy
摘要
Abstract
With electric vehicles (EVs) gradually replacing fueled vehicles,the impact of their charging load on the power grid is increasing. Therefore,this study proposes a spatial-temporal distribution prediction method for the charging load of EVs that considers travel demand and a guidance strategy. First,a semi-dynamic traffic network model that divides functional areas was developed based on a road travel time model. Furthermore,an energy-consumption model of EVs was established,and the charging demand,semi-dynamic transportation network model,energy consumption model,and traditional travel chain were revised according to the influence of the electricity price,climate,and season on the travel demand of vehicle owners. Considering the limited rationality of vehicle owners based on the influence of external factors,a charging load prediction method for private cars and taxis based on a guidance strategy is proposed. Finally,the modified trip chain and OD matrix were used to simulate the travel behavior of private cars and taxis,respectively,in the semi-dynamic traffic network model during the study period,and the validity of the proposed prediction method was verified through a simulation experiment of the semi-dynamic traffic network in the divided regions. The results show that the spatial-temporal distribution of the charging load for EVs is consistent with the analysis of external influencing factors,and the proposed guidance strategy can improve the satisfaction of vehicle owners.关键词
电动汽车/出行需求/累积前景/充电负荷/时空分布/半动态交通网Key words
electric vehicle/travel demand/cumulative prospect/charging load/spatial and temporal distribution/semi-dynamic traffic network分类
信息技术与安全科学引用本文复制引用
丁乐言,柯松,张帆,林晓明,吴梦维,张杰明,杨军..考虑出行需求和引导策略的电动汽车充电负荷预测[J].电力建设,2024,45(6):10-26,17.基金项目
国家自然科学基金项目(51977154) （51977154）
中国南方电网有限责任公司科技项目(031200KK52222008)This work is supported by National Natural Science Foundation of China(No.51977154)and Technology Program of China Southern Power Grid Company Limited(No.031200KK52222008). （031200KK52222008）
