A coordinated charging scheduling method for electric vehicles considering different charging demands. (15th December 2020)

• Record Type: Journal Article
• Authors: Zhou, Kaile; Cheng, Lexin; Wen, Lulu; Lu, Xinhui; Ding, Tao
• Is Part Of: Energy. Volume 213(2020)
• Publication Date: 2020-12-15
• Subjects: Electric vehicles -- Coordinated charging -- Optimal load scheduling -- Charging demand; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118882
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The uncoordinated charging of large amounts of electric vehicles (EVs) can lead to a substantial surge of peak loads, which will further influence the operation of power system. Therefore, this study proposed a coordinated charging scheduling method for EVs in microgrid to shift load demand from peak period to valley period. In the proposed method, the charging mode of EVs was selected based on a charging urgency indicator, which can reflect different charging demand. Then, a coordinated charging scheduling optimization model was established to minimize the overall peak-valley load difference. Various constraints were considered for slow-charging EVs, fast-charging EVs, and microgrid operation. Furthermore, Monte Carlo Simulation (MCS) was used to simulate the randomness of EVs. The results have shed light on both the charging modes selection for EV owners and peak shaving and valley filling for microgrid operation. As a result, this model can support more friendly power supply-demand interaction to accommodate the increasing penetration of EVs and the rapid development of flexible microgrid. Highlights: A coordinated charging scheduling method for electric vehicles (EVs) is proposed. The urgency of charging demand for EV users is estimated in the proposed method. The optimal charging mode is chosen for EV based on urgency of charging demand. The proposed model can achieve peak shaving and valley filling for microgrid. Performance of the model is evaluated in different charging patterns and EV scales.