影响因子：8.857
DOI码：10.1016/j.energy.2023.127806
发表刊物：Energy
关键字：Electric vehicle; Charging scheduling; Time-of-use price; Adaptive genetic algorithm
摘要：The impacts of large-scale electric vehicles (EVs) charging on the power grid and the lack of charging infrastructure may directly hinder the promotion of EVs. With the limited number of charging piles and maximum instantaneous power at the charging station, how to effectively charge scheduling for EVs and reduce the charging cost for users becomes an important issue. To address this problem, we propose an EV charging scheduling strategy in response to time-of-use price. Here, the least cost of charging is set as the objective function and the limitations of charging piles number and instantaneous power of the stations are constraints. EV charging behavior characteristic is simulated using the Monte Carlo method based on 876,012 sets of historical charging data. Then, after solving the optimization problem by the adaptive genetic algorithm, each EV is assigned a specific charging pile that can meet its charging demand. The experimental results show that the proposed method can achieve better results than the comparative methods while ensuring the safe operation of charging stations. The effect of peak and valley reduction on the grid side is also realized.
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：278
页面范围：127806
是否译文：否
发表时间：2023-05-22
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0360544223012008