Traffic-Constrained Multiobjective Planning of Electric-Vehicle Charging Stations

• Published in: IEEE transactions on power delivery Vol. 28; no. 4; pp. 2363 - 2372
• Main Authors: Wang, Guibin, Xu, Zhao, Wen, Fushuan, Wong, Kit Po
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Batteries; Charging station; cross-entropy; data-envelopment analysis; Direct energy conversion and energy accumulation; distribution systems; electric vehicle (EV); Electric vehicles; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Exact sciences and technology; Ground, air and sea transportation, marine construction; locating and sizing; Miscellaneous; Optimization; Power distribution; Power networks and lines; Road transportation and traffic; Smart grids; Studies; Traffic flow; Performance evaluation; traffic flow; Multiobjective programming; Charging station; cross-entropy; Electric vehicle; Networked system; Flow models; Power losses; locating and sizing; Distribution network; Voltage distribution; data-envelopment analysis; Secondary cell; Smart grid; Dimensioning; Case study; distribution systems; Electrical network; Optimal solution; System simulation; electric vehicle (EV); Planning; Effectiveness factor; Data envelopment analysis
• ISSN: 0885-8977; 1937-4208
• DOI: 10.1109/TPWRD.2013.2269142

Smart-grid development calls for effective solutions, such as electric vehicles (EVs), to meet the energy and environmental challenges. To facilitate large-scale EV applications, optimal locating and sizing of charging stations in smart grids have become essential. This paper proposes a multiobjective EV charging station planning method which can ensure charging service while reducing power losses and voltage deviations of distribution systems. A battery capacity-constrained EV flow capturing location model is proposed to maximize the EV traffic flow that can be charged given a candidate construction plan of EV charging stations. The data-envelopment analysis method is employed to obtain the final optimal solution. Subsequently, the well-established cross-entropy method is utilized to solve the planning problem. The simulation results have demonstrated the effectiveness of the proposed method based on a case study consisting of a 33-node distribution system and a 25-node traffic network system.