Centralized Scheduling Approach to Manage Smart Charging of Electric Vehicles in Smart Cities

被引:2
|
作者
Graber, Giuseppe [1 ]
Galdi, Vincenzo [1 ]
Calderaro, Vito [1 ]
Lamberti, Francesco [1 ]
Piccolo, Antonio [1 ]
机构
[1] Univ Salerno, Dept Ind Engn, Fisciano, SA, Italy
关键词
Charging Scheduling; Data Clustering; Electric Vehicles; Genetic Algorithms; Smart Charging; DEMAND; IMPACT;
D O I
10.5220/0006761002380245
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Electric vehicles (EVs) are emerging as the future of individual mobility systems in smart cities since they reduce greenhouse gas emissions and fossil fuel dependence. However, the deepening penetration of battery EVs forecasted for the incoming years could cause significant stress on distribution networks (DNs), as well as the need to address the growing energy demand. In order to limit the negative drawbacks associated with EVs charging demand, the paper proposes a centralized approach for the EVs smart charging, and its performance are compared with the uncontrolled charging approach. An optimization framework is formulated in order to reduce both the overall peak power demand and the EVs charging cost according to the electricity prices during the day. Finally, several Monte Carlo simulations are carried out to evaluate the benefits introduced by the proposed scheduling strategy on a real case study, in terms of charging cost for EVs' users, satisfaction of EVs charging needs, and flattening of the load profile.
引用
收藏
页码:238 / 245
页数:8
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