期刊
IEEE ACCESS
卷 7, 期 -, 页码 86184-86193出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2925559
关键词
Electric vehicle; charging management; real-time strategy; grey wolf optimizer
资金
- National Natural Science Foundation of China [61202369, 61401269]
The problem of electric vehicle (EV) charging scheduling in commercial parking lots has become a meaningful study in recent years, especially for the parking lots near the workplace that serve fixed users. This paper focuses on the optimization of the EV charging in the parking lot integrating energy storage system (ESS) and photovoltaic (PV) system. A smart charging management system is first established. The charging optimization problem is formulated as a cost minimization problem. Then, grey wolf optimizer (GWO) is introduced as a method to find the optimal solution. Considering the constraint conditions in the optimization problem, an improved binary grey wolf optimizer (IBGWO) is proposed, which can improve the convergence speed and optimization accuracy. Finally, a real-time EV charging scheduling strategy based on short-term PV power prediction and IBGWO is proposed. Several cases are simulated to analyze the performance of the proposed strategy. The experimental results show that the proposed IBGWO is superior in solving the proposed charging scheduling problem compared with other meta-heuristic algorithms. Moreover, the proposed strategy can effectively improve the utilization rate of the PV power and reduce the electricity cost of operators.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据