1. Hunan Provincial Engineering Research Center for Electric Transportation and Smart Distribution Network, School of Electrical and Information Engineering, Changsha University of Science and technology, Changsha 410114, China; 2. Xinghua Power Supply Company of State Grid Jiangsu Electric Power Company, Xinghua 225700, China; 3. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; 4. Department of Electrical and Electronic Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei; 5. The Commonwealth Scientific and Industrial Research Organisation(CSIRO), ACT 2600, Australia; 6. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518100, China
A route selection and charging navigation optimization model for electric vehicles(EVs)is presented for reducing traveling costs of EV users and improving the security of the distribution system concerned. With the aid of crowd sensing, a traffic information matrix acquisition method based on the matrix factorization technique is first addressed. The formulated objective of the presented model is to minimize the sum of driving and waiting costs, fast and regular EV charging costs based on the time of use(TOU)price mechanism, subject to a variety of technical constraints such as path selections, time, battery capacities, and charging or discharging state mutual exclusion constraints. A sample system is built by connecting four EV charging stations to the IEEE 33-bus distribution system, and the four EV charging stations are within a city center within a 25 km×25 km zone, and served for demonstrating the essential feature of the presented method. With respect to the situations with and without real-time traffic information, the impacts of the EV quantities participating in crowd sensing on the travel route and charging/discharging of EVs, as well as on the distribution system are also analyzed.
[1] | YANG Hongming, LI Ming, WEN Fushuan, et al. Route Selection and Charging Navigation Strategy for Electric Vehicles Employing Real-time Traffic Information Perception[J]. Automation of Electric Power Systems,2017,41(11):106-113. DOI:10.7500/AEPS20160821005 |