Impact Factor:8.857

DOI number:10.1016/j.energy.2023.127806

Teaching and Research Group:Wu, J., Su, H., Meng, J., & Lin, M.

Journal:Energy

Key Words:Electric vehicle; Charging scheduling; Time-of-use price; Adaptive genetic algorithm

Abstract: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.

Indexed by:Journal paper

Discipline:Engineering

Document Type:J

Volume:278

Page Number:127806

Translation or Not:no

Date of Publication:2023-09-01

Included Journals:SCI

Links to published journals:https://www.sciencedirect.com/science/article/pii/S0360544223012008